landsat near-infrared (nir) band and elm-fates sensitivity to … · 2020. 12. 9. · al., 2017;...

Biogeosciences 17 6185ndash6205 2020httpsdoiorg105194bg-17-6185-2020copy Author(s) 2020 This work is distributed underthe Creative Commons Attribution 40 License

Landsat near-infrared (NIR) band and ELM-FATES sensitivity toforest disturbances and regrowth in the Central AmazonRobinson I Negroacuten-Juaacuterez1 Jennifer A Holm1 Boris Faybishenko1 Daniel Magnabosco-Marra23Rosie A Fisher45 Jacquelyn K Shuman4 Alessandro C de Araujo6 William J Riley1 and Jeffrey Q Chambers1

1Climate Sciences Department Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley CA 94720 USA2Max Planck Institute for Biogeochemistry Hans-Knoell Str 10 07745 Jena Germany3National Institute of Amazonian Research (INPA) Av Andreacute Arauacutejo 2936 690060-001 Manaus Brazil4National Center for Atmospheric Research (NCAR) 1850 Table Mesa Dr Boulder CO 80305 USA5Centre Europeacuteen de Recherche et de Formation Avenceacutee en Calcul Scientifique (CERFACS) Toulouse France6Embrapa Amazonia Oriental Tv Dr Eneacuteas Piheiro sn Marco CEP 66095-903 Caixa postal 48 Belem-Para Brazil

Correspondence Robinson I Negroacuten-Juaacuterez (robinsoninjlblgov)

Received 18 November 2019 ndash Discussion started 10 December 2019Revised 29 June 2020 ndash Accepted 23 October 2020 ndash Published 9 December 2020

Abstract Forest disturbance and regrowth are key pro-cesses in forest dynamics but detailed information on theseprocesses is difficult to obtain in remote forests such asthe Amazon We used chronosequences of Landsat satelliteimagery (Landsat 5 Thematic Mapper and Landsat 7 En-hanced Thematic Mapper Plus) to determine the sensitivityof surface reflectance from all spectral bands to windthrowclear-cut and clear-cut and burned (cut+ burn) and theirsuccessional pathways of forest regrowth in the CentralAmazon We also assessed whether the forest demographymodel Functionally Assembled Terrestrial Ecosystem Sim-ulator (FATES) implemented in the Energy Exascale EarthSystem Model (E3SM) Land Model (ELM) ELM-FATESaccurately represents the changes for windthrow and clear-cut The results show that all spectral bands from the Land-sat satellites were sensitive to the disturbances but after 3to 6 years only the near-infrared (NIR) band had significantchanges associated with the successional pathways of for-est regrowth for all the disturbances considered In generalthe NIR values decreased immediately after disturbance in-creased to maximum values with the establishment of pio-neers and early successional tree species and then decreasedslowly and almost linearly to pre-disturbance conditions withthe dynamics of forest succession Statistical methods pre-dict that NIR values will return to pre-disturbance valuesin about 39 36 and 56 years for windthrow clear-cut andcut+ burn disturbances respectively The NIR band cap-

tured the observed and different successional pathways offorest regrowth after windthrow clear-cut and cut+ burnConsistent with inferences from the NIR observations ELM-FATES predicted higher peaks of biomass and stem densityafter clear-cuts than after windthrows ELM-FATES also pre-dicted recovery of forest structure and canopy coverage backto pre-disturbance conditions in 38 years after windthrowsand 41 years after clear-cut The similarity of ELM-FATESpredictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can beused to benchmark forest regrowth in ecosystem models Ourresults show the potential of Landsat imagery data for map-ping forest regrowth from different types of disturbancesbenchmarking and the improvement of forest regrowth mod-els

1 Introduction

Old-growth tropical forests are declining in extent at accel-erated rates due to deforestation (Keenan et al 2015) andthey currently occupy an area of about 50 of their origi-nal coverage (FAO 2010) This decline affects the carbonwater and nutrient cycles of the ecosystems and acceler-ates the loss of ecosystem goods and services (Foley et al2007 Nobre et al 2016) Furthermore natural and anthro-pogenic disturbances may act synergistically to exacerbate

Published by Copernicus Publications on behalf of the European Geosciences Union

6186 R I Negroacuten-Juaacuterez et al Landsat NIR band and ELM-FATES sensitivity

forest degradation (Silverio et al 2018 Schwartz et al2017) Under natural conditions disturbed forests recoverto their pre-disturbance conditions through complex inter-actions that vary across spatial and temporal scales (Chaz-don 2014) In general it is known that forest pathways ofregrowth (ie pattern of regrowth) are initiated with fast-growing and shade-intolerant species (pioneers) that are es-tablished from seeds and dominate a few years after dis-turbance followed by the recruitment and establishment ofshade-tolerant species and finally the establishment of aclosed-canopy old-growth forest (Chazdon 2014 Denslow1980 Mesquita et al 2001 Swaine and Whitmore 1988)Understanding of the dynamics of forest regrowth followingnatural and anthropogenic disturbances in the Amazon how-ever has so far been limited by lack of long-term observa-tional data showing different stages of forest regrowth

Remote sensing data can be used to assess forest regrowthvia changes in spectral characteristics (Frolking et al 2009Roberts et al 2004 Schroeder et al 2011 DeVries et al2015 Lucas et al 2002 McDowell et al 2015) Landsatsatellite imagery is appropriate for examining land surfacechanges due to its long-term record availability and spa-tial resolution of 30 m (Loveland and Dwyer 2012 Wul-der et al 2012 Alcantara et al 2011 Woodcock et al2008 Cohen and Goward 2004 Hansen et al 2013) Land-sat imagery has been used to detect forest disturbance andpathways of regrowth in temperate and boreal forests in theUnited States and Canada (Kennedy et al 2007 2010 2012Pickell et al 2016 Schroeder et al 2011 Dolan et al2009 2017) and for the detection of forest disturbance andregrowth of biomass in the Amazon (Vieira et al 2003 De-Vries et al 2015 Lucas et al 2002 Powell et al 2010Lu and Batistella 2005 Steininger 2000 Shimabukuro etal 2019) These studies suggest that Landsat may be sen-sitive to different types of disturbances and their subsequentpathways of forest regrowth in the Amazon but these appli-cations have not yet been assessed

The ability to forecast future trajectories of forests de-pends upon the fidelity with which disturbance and regrowthprocesses are represented within terrestrial biosphere mod-els These models capture processes operating between theleaf and landscape scales and can represent regrowth changesover large regions (Fisk 2015) long time periods (Holm etal 2017 Putz et al 2014) a range of disturbance intensities(Powell et al 2013) and interactions between multiple dis-turbance types and disturbance histories (Hurtt et al 2006)But how well these models simulate and capture the diversearray of successional pathways of forest regrowth after an-thropogenic or natural disturbances needs to be more thor-oughly evaluated given observed increases in disturbancerates (Lewis et al 2015) The few modeling studies analyz-ing tropical disturbances have focused on the effects of frag-mented edges or the regrowth of specific tree species (Dantasde Paula et al 2015 Kammesheidt et al 2002)

Cohort-based dynamic vegetation demographic models(VDMs) are particularly suitable tools for expanding uponthese studies (Fisher et al 2018) In contrast to traditionalland surface models VDMs include ecological demographicprocesses such as discretized vegetation height with differ-ent plant types competing for light within the same verti-cal profile and heterogeneity in light availability along dis-turbance and recovery trajectories all of which facilitatedirect simulation of regrowth dynamics during successionThis structured demography in VDMs allows for simula-tion of canopy gap formation competitive exclusion and co-existence of vegetation thus producing variability in foreststand age and composition (Fisher et al 2010 Moorcroft etal 2001 Longo et al 2019) VDMs are designed for vege-tation to dynamically respond to variation in traits (Fyllas etal 2014) leading to differences in plant mortality growthand recruitment rates (Shugart and West 1980) These at-tributes influence the ecosystem fluxes of carbon energyand water (Bonan 2008) Despite their potential for simu-lating regrowth processes there has been limited VDM test-ing of regrowth following tropical forest disturbances Im-portantly projections of future climate using Earth systemmodels (ESMs) are strongly influenced by the terrestrial car-bon cycle in the tropics (Arora et al 2013 Friedlingstein etal 2014) which is strongly regulated by disturbance and re-growth (Chazdon et al 2016 Trumbore et al 2015 Magna-bosco Marra et al 2018)

Observational studies have shown that Amazon forests fol-low a range of successional regrowth pathways after clear-cutting and burning (Mesquita et al 2001 2015) Thus thetype of disturbance and the pre-disturbance ecosystem stateare important determinants of the successional pathways offorest regrowth Nonetheless this information is difficult toobtain in remote forests of the Amazon In this study weaddressed this issue in the context of windthrow clear-cutand clear-cut and burned (cut+ burn) disturbances to analyze(i) the sensitivity of Landsat to detect and distinguish theserelevant disturbances and their pathways of forest regrowthand (ii) the time span of forest regrowth This understandingof forest regrowth was used to (iii) test the modeled forest re-growth of the Functionally Assembled Terrestrial EcosystemSimulator (FATES) model (Fisher et al 2015) implementedin the Energy Exascale Earth System Model (E3SM) LandModel (ELM) (Riley et al 2018 Zhu et al 2019) ELM-FATES This study provides insights into the use of remotesensing to identify drivers of forest disturbance in the Ama-zon and a better understanding of the pathways of forest re-growth provides insights into the resilience of these foreststo repeated disturbances and can help improve land models

Biogeosciences 17 6185ndash6205 2020 httpsdoiorg105194bg-17-6185-2020

R I Negroacuten-Juaacuterez et al Landsat NIR band and ELM-FATES sensitivity 6187

Figure 1 Location of disturbed forests (a) The disturbed areaswere located in Central Amazonia and comprised (b) a windthrowsite close to the village of Tumbira (c) a clear-cut site on the PortoAlegre farm and (d) a cut+ burn site on the Dimona farm Thesethree areas are encompassed in the Landsat scene Path 231 Row 062as shown in the inset in (a) For the spectral characteristics beforeand after disturbances we used cells of 3times 3 pixels (blue squares)over disturbed and undisturbed areas For the pathways of forest re-growth after clear-cutting and burning sites we analyzed areas withdifferent distances from the disturbance edge (A1 A2 and A3 inyellow) The background image in (a) is from copy Google Earth ProThe background images in (b) (c) and (d) are from Landsat 5 on12 July 1987 1 June 1984 and 12 July 1987 and were composed inRGB color using bands 5 band 4 and band 3 respectively

2 Study area and methods

21 Study area and sites

Forests in the Central Amazon affected by windthrow clear-cut and cut+ burn were addressed in this study Windthrows(Mitchell 2013) in the Amazon are caused by strong de-scending winds that uproot or break trees (Garstang et al1998 Negroacuten-Juaacuterez et al 2018 Nelson et al 1994) Inclear-cut areas forests are cut and cleared and in cut+ burnareas forests are cleared and burned (Mesquita et al 20012015 Lovejoy and Bierregaard 1990) The windthrow clear-cut and cut+ burn sites used in this study were selectedbased on the following conditions (a) prior to disturbancethey were upland (no flooding) old-growth forest and locatedin the same region with similar climatic edaphic and floris-tic composition (b) long-term records of satellite imageryand corresponding field data before and after disturbance areavailable and (c) no subsequent disturbance has occurred

The three windthrow clear-cut and cut+ burn sites an-alyzed in this study are located near the city of ManausCentral Amazon (Fig 1a) The windthrow (centered at 3 S6075W Fig 1b) was located near the village of Tumbiraabout 80 km southwest of Manaus occurred in 1987 andcovered an area of sim 75 ha At this site data on forest re-growth including forest structure and species compositionfor trees ge 10 cm DBH (diameter at breast height of 13 m)have been collected since 2011 covering disturbed and undis-turbed areas and found that the genus Cecropia is one ofthe dominant species in the most disturbed areas (Magna-bosco Marra et al 2018) The clear-cut and cut+ burn siteswere experimentally created within the Biological Dynam-ics of Forest Fragments Project (BDFFP) which encom-passes an area of sim 1000 km2 (centered at 25 S 60W) lo-cated 80 km north of the city of Manaus Brazil The BDFFPwas established and managed in the early 1980s by BrazilrsquosNational Institute of Amazonian Research (INPA) and theSmithsonian Institution and is the longest-running experi-ment of forest fragmentation in the tropics (Bierregaard etal 1992 Lovejoy et al 1986 Laurance et al 2011 2018Tollefson 2013) Further details of the BDFFP are in Bier-regaard et al (2001) The selected clear-cut site is locatedon the Porto Alegre farm (centered at 235 S 5994WFig 1c) This site was clear-cut in 1982 without subse-quent use and was dominated by the pioneer tree genus Ce-cropia 6ndash10 years after abandonment (Mesquita et al 19992001) The cut+ burn site is located on the Dimona farm(centered at 233 S 6011W Fig 1d) which was clear-cut and burned in September 1984 maintained as pasturefor 2ndash3 years and then abandoned By 1993 this site was6 years old and dominated by the pioneer tree genus Vismia(Mesquita et al 1999 2001)

In the Manaus region the mean annual temperature is27 C (with higher temperatures from August to Novem-ber and a peak in October) and the mean annual rainfall is2365 mm with the dry season (rainfall lt 100 mm per monthSombroek 2001) from July to September (Negroacuten-Juaacuterez etal 2017) The topography is relatively flat with landformsranging from 50ndash105 m asl (Laurance et al 2007 2011Renno et al 2008) and the mean canopy height is sim 30 mwith emergent trees reaching 55 m (Laurance et al 2011Lima et al 2007 Da Silva 2007) The soil in this regioncomprises Ferrosols (Quesada et al 2011 Bierregaard etal 2001 Ferraz et al 1998) following the Food and Agri-culture Organization (FAO) classification and with similarfloristic composition (Bierregaard et al 2001 Carneiro etal 2005 Vieira et al 2004 Higuchi et al 2004) In theBDFFP and for old-growth forest trees with DBH ge 10 cmthere are 261plusmn18 species haminus1 the stem density is 608plusmn52stems haminus1 and the basal area is 28 m2 haminus1 (Laurance etal 2010) which is representative of the region (da Silva etal 2002 Vieira et al 2004 Carneiro et al 2005 Magna-bosco Marra et al 2014 2016 2018) In this region 93 of stems are between 10 and 40 cm in DBH (Higuchi et al

httpsdoiorg105194bg-17-6185-2020 Biogeosciences 17 6185ndash6205 2020


2012) and the annual tree mortality is 87 trees haminus1 for treesge 10 cm in DBH (Higuchi et al 1997)

22 Landsat satellite data and procedures

The Landsat Ecosystem Disturbance Adaptive ProcessingSystem (LEDAPS) (Schmidt et al 2013 Masek et al 20062008 2013) surface reflectance (SR) from the Landsat 5Thematic Mapper (TM) was used in this study to charac-terize the type of disturbance and the subsequent pathwaysof forest regrowth over our study areas LEDAPS was devel-oped to ensure that spectral changes in Landsat are associatedwith regrowth dynamics (Masek et al 2012 Schmidt et al2013) and to facilitate robust studies of land surface changesat different temporal and spatial scales in tropical forests(Kim et al 2014 Valencia et al 2016 Alonzo et al 2016)LEDAPS SR Landsat 5 TM (L5 hereinafter) is generated bythe United States Geological Survey (USGS) using the Sec-ond Simulation of the Satellite Signal in the Solar Spectrum(6S) that corrects for the influences of among others wa-ter vapor ozone aerosol optical thickness and digital eleva-tion on spectral bands (USGS 2017 Vermote et al 1997)L5 bands are derived using per-pixel solar illumination an-gles and generated at a 30 m spatial resolution on a Univer-sal Transverse Mercator (UTM) mapping grid (USGS 2017)LEDAPS in the Landsat 7 Enhanced Thematic Mapper Plus(ETM+) sensor (L7 hereinafter) was also used to corrobo-rate our predictions (described below) Though L5 and L7use the same wavelength bands they are different sensorsand differences in surface reflectance may exist especially intropical forests due to large atmospheric effects (Claverie etal 2015) Landsat 8 was not used since comparison betweenLandsat 8 and both L5 and L7 is not straightforward due todifferences in the spectral bandwidth of these sensors Weused LEDAPS since a long time series of data is availablewith high spectral performance (Claverie et al 2015) and itis suitable for ecological studies in the Amazon (van Don-inck and Tuomisto 2018 Valencia et al 2016) L5 and L7are available in Google Earth Engine (Gorelick et al 2017)which we used to retrieve and analyze these data

The L5 and L7 spectral bands used in this study were blue(045ndash052 microm) green (052ndash062 microm) red (063ndash069 microm)near infrared (NIR) (076ndash090 microm) shortwave infrared 1(SWIR1) (155ndash175 microm) and shortwave infrared 2 (SWIR2)(208ndash235 microm) L5 and L7 measurements provide the frac-tion of energy reflected by the surface and range from 0 (0 )to 10 000 (100 ) Only scenes from June July and Augustwere used since these dry-season months present less cloudcover over our study area (Negroacuten-Juaacuterez et al 2017) Thisprocedure also reduces effects associated with illuminationor phenology since images correspond to the same periodeach year Only images that were cloud free cloud shadowfree and haze free over our disturbed areas were used toeliminate errors associated with these elements For this pro-cedure visual inspection of visible bands and quality infor-

mation from L5 and L7 were used No further correctionswere applied due to the robustness of L5 imagery over theAmazon (Valencia et al 2016) All of the disturbances arein the Landsat scene Path 231 Row 062 The dates of L5images used (Landsat 5 operational imaging ended in 2011)were 1 June 1984 (except for the windthrow) 6 July 198512 July 1987 2 August 1989 20 July 1990 8 August 199131 July 1994 21 June 1997 26 July 1998 13 July 199924 July 2003 4 August 2007 6 August 2008 27 July 2010and 31 August 2011 The dates of L7 images used were 7 Au-gust 2011 22 June 2012 12 June 2014 2 August 2015 and7 August 2017

The spectral characteristics of old-growth forests and theirchanges after disturbances were investigated using 19 cellsof 3times 3 pixels (Fig 1b c d) The average of each cellwas used in our analysis Spectral characteristics for old-growth forests for each site were determined from cells lo-cated in the same position as the disturbance but previousto the disturbance andor from adjacent areas Five cells ofold-growth forests were located from 1 to 2 km away fromthe windthrow site Though closer distances may also repre-sent old-growth forests we were conservative since Landsatis not sensitive to clusters of downed trees comprising fewerthan eight trees (Negroacuten-Juaacuterez et al 2011) For the clear-cutand cut+ burn sites the spectral characteristics of their re-spective old-growth forests (control) were studied from threecells per site located 500 to 800 m away from the edge ofthe disturbance to minimize edge effects that are relevantin the first 100 m (Lovejoy et al 1986 Laurance et al2007 Mesquita et al 1999) The spectral characteristics forthe windthrow were acquired from two cells containing thehighest level of SWIR1 values in 1987 which is associatedwith the maximum disturbance (Negroacuten-Juaacuterez et al 2011Magnabosco Marra et al 2018 Nelson et al 1994) For theclear-cut site three cells were located 400ndash500 m from theedge and for the cut+ burn site three other cells were lo-cated at a distance of 100ndash300 m with respect to the edge Forthe clear-cut site we also selected four areas A1 A2 A3 andAT (AT = A1+A2+A3) shown in Fig 1c For the cut+ burnsite we selected three areasA1A2 andAT (AT = A1+A2)shown in Fig 1d

L5 data for the windthrow clear-cut and cut+ burn sitesencompass a period of 28 years with 13 years of missingdata due to cloud cover or lack of image In order to assessthe forest regrowth to spectral levels similar to old-growthforests (control) we applied a gap-filling method (Gerber2018) for time series to obtain estimates for missing years us-ing the R package ldquozoordquo (Zeileis et al 2018) The gap-filleddatasets were analyzed using the smoothing-spline technique(R package 2017) To determine whether L5 bands were sen-sitive to regrowth we analyzed changes in the slope (β) ofthe bands across our chronosequence A t test on the slopecoefficient was used to test the null hypothesis that β is zero(H0 β = 0) against the alternative hypothesis (H1 β 6= 0)at a 5 significance level (α = 005) If the computed test



statistic (t stat) was inside the critical values then H0 wasnot rejected The critical values (plusmnt1minusα2 nminus 2 n is thenumber of data points) were obtained from statistical tables(Neter et al 1988) Forests in the Manaus region affectedby windthrows were dominated by tree species from the gen-era Cecropia and Pourouma in about 3ndash5 years (MagnaboscoMarra et al 2018 Nelson and Amaral 1994) and the clear-cut and cut+ burn sites were dominated by Cecropia andVismia about 6 years after the disturbances (Mesquita et al1999 2001) The slopes of the time series were determinedafter these periods ie 1991 1987 and 1990 for windthrowclear-cut and cut+ burn sites respectively

A comparison of successional pathways of forest regrowthamong studied disturbances was conducted which was feasi-ble due to the similar conditions of climate soils and struc-ture and composition of the old-growth forests Time seriesof L5 bands were analyzed using the statistical nonparamet-ric function (univariate fit) with the smoothing spline and theGaussian regression ANOVA (analysis of variance) modelCalculations were conducted on the R 352 software plat-form (R package 2017) using the package ldquogssrdquo (GeneralSmoothing Splines) (Gu 2018) We calculated the smoothspline (using the cubic fit algorithm) of observed data andthe associated standard errors from which we calculatedBayesian 95 confidence intervals Predictions of the timeafter disturbance needed to reach old-growth forests valuesare based on these data using the function ldquossanovardquo (FittingSmoothing Spline ANOVA Models) of the R package gss(General Smoothing Splines) version 21-9 The predictionswere compared with published field observations (Sect 21)where data were available and L7 images were used to as-sess the reliability of our predictions

23 Forest regrowth simulation in ELM-FATES

Time series of L5 bands sensitive to disturbances and thepathways of forest regrowth were compared with modelingresults from ELM-FATES (Fisher et al 2010 2015 Holmet al 2020) The underlying model structure and concepts inFATES are based on the Ecosystem Demography (ED) con-cept (Moorcroft et al 2001) and are described in detail athttpsgithubcomNGEETfates last access 1 July 2019 Amajor development is the modularization of the model struc-ture in FATES so that boundary conditions and vegetationcan be coupled with ESM land models FATES is integratedinto the E3SM Land Model (ELM) (Riley et al 2018 Zhuet al 2019) and the Community Land Model (CLM) (Fisheret al 2019 Lawrence et al 2019) coupled to the Com-munity Earth System Model (Hurrell et al 2013) In thisstudy we used ELM-FATES ELM-FATES simulates vege-tation that varies in terms of successional age and size plantcompetition and dynamic rates of plant mortality growthand recruitment all on landscapes partitioned by areas ofdisturbance The main updates and modifications in ELM-FATES compared with ED include changes to carbon alloca-

tion and allometry and the introduction of the perfect plas-ticity approximation (PPA) (Purves et al 2008 Fisher et al2010) used for the accounting of crown spatial arrangementsthroughout the canopy and organizing cohorts into discretecanopy layers Photosynthesis and gas exchange physiologyin ELM-FATES follows the physics within the CLM v45(Bonan et al 2011) and unlike ED uses the original Ar-rhenius equation from Farquhar et al (1980) ELM-FATEStropical forest simulations conducted here were based on pa-rameter and demography sensitivity analysis at a site 40 kmfrom the BDFFP (Holm et al 2020) at the ZF2 researchstation (Magnabosco Marra et al 2014) Holm et al (2017)found that with the improved parameterization ELM-FATESclosely matched observed values of basal area leaf area in-dex (LAI) and mortality rates but underestimated stem den-sity for a Central Amazon old-growth forest near the BDFFP

Model simulations were driven by climate-forcing data de-rived from measurements collected between the years 2000and 2008 at the K34 flux tower located at 26 S 602W(de Araujo et al 2002) about 40 km from the BDFPPat the ZF2 research station ELM-FATES (using the gitcommit ldquo4a5d626rdquo and the version corresponding to tagldquosci100_api100rdquo) was run and spun up for 400 years untila stable biomass equilibrium was reached within the modeledforest We then simulated a one-time logging treatment of anear-complete mortality of all trees (98 ldquoclear-cutrdquo) with aremaining 2 consisting of only small trees gt 5 cm DBH toaid in recruitment (Mesquita et al 2015) Second we sim-ulated a one-time windthrow disturbance that killed 70 oftrees (ldquowindthrowrdquo) as was reported in a recent observationalstudy on windthrows in the same region (Magnabosco Marraet al 2018) All dead trees were ldquoremovedrdquo and thereforedid not enter modeled soil pools The fire module in ELM-FATES is currently under final development and testing andtherefore burned simulations are not included in this studyThe old-growth forest simulated by ELM-FATES used asa pre-disturbance metric was based on previously validatedtropical parameterization and sensitivity testing in the sameregion (Holm et al 2020) see supplementary material ofFisher et al (2015) for a description of plant-functional-type specific carbon allocation and allometry schemes andupdates from the ED model framework Simulations of dis-turbance and subsequent vegetation regrowth were initiatedfrom this old-growth forest state The model design used hereonly allows for simulating intact forests with natural dis-turbances (eg gap dynamics or windthrows) or harvestedforests and not both at the same time or in adjacent patchesAccounting for distance to intact forests was excluded dueto the current limited understanding of seed dispersal mech-anisms (ie spatial variability dispersal limitation etc) intropical forests (Terborgh et al 2019) We use a more gen-eral form of seed production such that the individual cohortsin ELM-FATES use a targeted fraction of net primary pro-duction (NPP) during the carbon allocation process (afteraccounting for tissue turnover and storage demands) which



Figure 2 Imposed trait variation used in the parameterization ofELM-FATES tropical evergreen plant functional types (PFTs) forthe 20-ensemble simulations and the resulting average growth rateaverage over the 50-year simulation period Each simulation con-sisted of a single PFT varying by all four traits at once wooddensity Vcmax the canopy area allometric coefficient and leaf-clumping index for leaf self-occlusion Dark green points representfast-growing evergreen pioneer PFTs while light green points rep-resent slow-growing late successional PFTs

adds to the site-level seed pool for recruitment of new co-horts Field data were not used to simulate or calibrate themodeled forest regrowth post-disturbance

To account for uncertainty in the representation of plantphysiology within tropical evergreen forests we analyzed anensemble of 20 simulations varying in targeted plant func-tional traits We prescribed each ensemble with a single trop-ical evergreen plant functional type (PFT) that varied in wooddensity (044 to 106 g cmminus3) and maximum rate of carboxy-lation (Vcmax 42 to 55 micromol m2 sminus1) (Table 1) via randomsampling To evaluate changes in canopy coverage of theforest stand each PFT additionally varied by an allomet-ric coefficient (135 to 165) determining the crown-area-to-diameter ratio and a leaf-clumping index (059 to 10 out ofa 0ndash1 fraction) that determines how much leaf self-occlusionoccurs and decreases light interception and the direct anddiffuse extinction coefficients in the canopy radiation calcu-lations The default values for these parameters are basedon or derived from references given in Table 1 Each en-semble member represents a single PFT across the spectrumof fast-growing ldquopioneerrdquo PFTs and slow-growing ldquolate suc-cessionalrdquo PFTs to provide a reasonable spread across thetrait uncertainty when assessing regrowth from disturbanceWe characterized pioneer plants in our simulations as havinglow wood density (Baker et al 2004) and high Vcmax basedon the inverse relationship between these two plant traits aswell as a low crown area coefficient and low leaf-clumpingfactor ie a monolayer planophile distribution (Lucas etal 2002) These correlated relationships were applied to theensemble-selected traits (Fig 2) The opposite relationshipwas applied for slow-growing late successional PFTs (iehigh wood density low Vcmax high crown area coefficientand high leaf-clumping factor)

In order to evaluate ELM-FATES performance during for-est regrowth we compared the NIR band the most sensitiveband to regrowth (see results) with ELM-FATES outputs ofaboveground biomass (AGB Mg haminus1) total stem density oftrees ge 10 cm DBH (stems haminus1) leaf area index (LAI one-sided green leaf area per unit ground surface area m2 mminus2)and total live crown area (m2 mminus2) since these variables di-rectly influence the surface reflectance (Ganguly et al 2012Lu 2005 Masek et al 2006 Powell et al 2010 Ruiz etal 2005) We suggest that testing an array of modeled forestvariables (eg biomass structure density coverage of vege-tation and proportion of the tree crown that has live foliage)provides a robust comparison to NIR values due to multipleforests characteristics contributing to and affecting NIR re-flectance (Ollinger 2011) and reduces model unknowns andbiases that can arise when using only one model variableThe usage of different stand structure and canopy processescan be helpful when evaluating ELM-FATES during differ-ent phases of forest regrowth In addition we averaged mod-eled outputs of the crown area stem density and LAI sinceeach of these variables influences the reflectance of forestsand defined this average as the modeled ldquocanopy coveragerdquoMeasurements of forest canopy cover have been used to ana-lyze plant growth and survival and it is an important ecologi-cal parameter related to many vegetation patterns (Ganey andBlock 1994 Jennings et al 1999 Paletto and Tosi 2009)Modeled diameter growth rates (cm yrminus1) for trees with DBHge 10 cm are also shown to provide information on the suc-cessional dynamics within ELM-FATES

3 Results

31 L5 bands and disturbances

All L5 bands showed an increase in surface reflectance im-mediately after windthrow clear-cut and cut+ burn sites ex-cept the NIR values which decreased (with higher decreaseafter burning) (Fig 3a b and c) This decrease in NIR valueswas due to exposed woody material and dry leaves typicalafter windthrow (Negroacuten-Juaacuterez et al 2010a Negroacuten-Juaacuterezet al 2011) and clear-cutting (Sect 7 in Adams and Gille-spie 2006) or the dark surface following burning (Pereira etal 1997) For windthrows these effects last about 1 yearafter which vegetation regrowth covers the ground surface(Negroacuten-Juaacuterez et al 2010a 2011) The spectral character-istics of old growth and disturbances are shown in Fig 3dndashfwith the error bands representing the standard deviation ofall pixels from respective cells About 1 year after the dis-turbance the bands that experienced increases in surface re-flectance showed a decrease in surface reflectance (the oppo-site to the NIR band) due to the increases in vegetation coverA similar response is expected for the clear-cut that occurredin 1982 and therefore before the beginning of our availabledata (L5 imagery is available from 1984 Fig 3e) The simi-



Table 1 The range (minimum to maximum) of four key model input parameters used in the 20-ensemble ELM-FATES simulations for bothwindthrow and clear-cut simulations to account for uncertainty in the representation of plant traits along with the default value used inthe ELM-FATES model Wood density value from Moorcroft et al (2001) Vcmax based on Oleson et al (2013) and Walker et al (2014)crown area DBH derived from Farrior et al (2016) and adjusted based on site-specific sensitivity tests and the leaf-clumping index basedon radiation transfer theory of Norman (1979)

Variations in ensemble parameters in ELM-FATES

Default Minimum Maximum Range

Wood density (g cmminus3) 07 044 106 062Vcmax (micromol m2 sminus1) 50 42 55 13Crown area DBH (unitless) 15 135 165 030Leaf clumping (0ndash1) 085 059 100 041

larity of spectral signatures for the control forests previous tothe disturbances suggests comparable structure and floristiccomposition

32 Pathways of forest regrowth

About 6 years after the disturbance NIR values reacheda maximum and then decreased slowly with time showinga significant negative trend (Table 2) SWIR1 values alsoshowed a significant negative trend with time but only forthe clear-cut site (Table 2) In general the green blue redSWIR1 and SWIR2 band values returned to pre-disturbancevalues (control) about 6 years after the disturbance (Fig 3de f and Table 2) Therefore we used the NIR band (whichremained higher than pre-disturbance values throughout thetime series and is potentially sensitive to ecosystem proper-ties of regrowing forest) to investigate the regrowth dynamicsin comparison to our control forests

We used the relationships presented in Figs 4 5 and 6to determine the time taken for NIR values from the distur-bance sites to become similar to control NIR values The av-erage control NIR value was 28plusmn 1 For the windthrowsite the NIR values became similar to control levels afterabout 39 years (range 32 to 57 years) For the clear-cut andcut+ burn sites this period was estimated to be 36 years(range 31 to 42 years) and 56 years (range 42 to 93 years)respectively From Figs 4ndash6 it is evident that the type of dis-turbance has a clear effect on the pathways of NIR recoveryL7 data in general are within the 95 CI of predictions

During the first 12 years following the windthrowthe spline curve fitted to the NIR data decreased bysim 013 yrminus1 after which the rate of decrease doubled(026 yrminus1 Fig 4) For clear-cutting NIR values de-creased faster iesim 04 yrminus1 The decrease in NIR valuesfor the clear-cut site appears to be independent of the distancefrom the edge of the disturbance since the changes in NIRvalues of all selected areas (A1 A2 A3 and AT ) are similar(Fig 5) For the cut+ burn site the rate of change in NIRvalues to reach values similar to the control forests was theslowest among all disturbances considered (sim 015 yrminus1)

(Fig 6) The cut+ burn site showed differences with respectto the border of the disturbance (areas A1 A2 and AT )which may be related to the spatial heterogeneity of burningsand forest responses

33 FATES model and regrowth from forestdisturbance

To address our goal of improving the connection between re-mote sensing model benchmarking and the fidelity of fu-ture predictions of forest regrowth processes we examinethe representation of such processes within ELM-FATESThe average of the ELM-FATES 20-member ensemble pre-dicted a continuous and almost linear regrowth of biomass(Fig 7a) after clear-cut and windthrows The modeled re-covering biomass returned to modeled old-growth forest val-ues more quickly for windthrows (37 years range 21 to83 years) compared to clear-cuts (42 years range 27 to80 years) However the annual rate of change in biomassregrowth over 50 years was faster in the clear-cut simula-tion (25 Mg haminus1 yrminus1) than in the windthrow simulations(20 Mg haminus1 yrminus1) which was due to the clear-cut site re-covering from initial biomass of near zero and a proportion-ally greater contribution of fast-growing pioneer species

The model simulation of stem density LAI and crownarea are shown in Fig 7bndashd respectively For stem den-sity ELM-FATES (black line) predicted an average of up to8 years before any new stems reachedge10 cm DBH (a stand-developing period Fig 7b) for the clear-cut The simulatedstem density for old-growth forests (Fig 7b green line) wassim 200 stems haminus1 (ge 10 cm at 13 m) sim 408 trees lower thanobservations The model ensembles with typical early suc-cessional traits predicted a forest with many fast-growingsmall-diameter stems lt 10 cm with maximum early succes-sional stem densities reaching 1560 and 1414 stems haminus1

for clear-cut and windthrows respectively Once the canopycloses and self-thinning dominates (average of 15 years af-ter disturbance) there are declines in stem density as treesgain biomass and canopy closure forces some trees intothe understory where they die at faster rates due to shad-



Figure 3 L5 (LEDAPS SR Landsat 5) spectral characteristics for (a) windthrow (12 July 1987) (b) clear-cut (1 June 1984) and (c) cut+ burn(12 July 1987) (in red) and control (old-growth) forests (in green) sites Time series of each L5 spectral band for (d) windthrow (e) clear-cutand (f) cut+ burn sites The bars represent the standard deviation from all pixels from all 3times 3 cells comprising the respective disturbancesshown in Fig 1 Vertical dashed line in (d) (e) and (f) represents the year of the disturbance

Table 2 Test of the significance for the slopes of the time series of six bands from L5 (LEDAPS SR Landsat 5) for the windthrow (period1991ndash2011) clear-cut (period 1987ndash2011) and cut+ burn (period 1990ndash2011) cases in Central Amazonia shown in Fig 3dndashf The criticalvalues (t09758 and t097512) for the t distribution were obtained from statistical tables Bold represents H1

Windthrow Clear-cut Cut+ burnt097512 = 2179 t09758 = 2306 t09758 = 2306

β t stat β t stat β t stat

Blue minus151 minus110 minus063 minus025 minus392 minus115Green minus003 minus002 minus072 minus031 minus303 minus078Red minus012 minus068 minus018 minus008 minus319 minus093NIR minus1236 minus407 minus351 minus1017 minus1172 minus283SWIR1 087 070 minus483 minus452 minus225 minus198SWIR2 095 145 017 022 minus048 036

ing The modeled forests returned to old-growth stem den-sity conditions 39 and 41 years after windthrows and clear-cut respectively (Table 3) Though the two disturbance typeshave very similar times of return to pre-disturbance condi-tions they differ in the speed of recovery ELM-FATES pre-dicts faster diameter growth increments (13 cm yrminus1) andcanopy closure for a forest composed of all pioneer typePFTs and slower (05 cm yrminus1) diameter growth and moreopen canopies for the late successional forest type (Fig 8)Diameter growth is an emergent model feature of dynamicplant competition for light and stand structure and is con-sistent with observational studies of secondary forests grow-ing through succession (Brown and Lugo 1990 Winter andLovelock 1999 Chapin et al 2003)

The LAI of the modeled old-growth forest (40 mminus2 mminus2)prior to disturbances was close to the observed LAI(47 mminus2 mminus2) measured near our study sites (Chamberset al 2004) Due to disturbance the initial modeled LAI(Fig 7c) and total crown area (Fig 7d) decreased as ex-pected During regrowth from disturbance both the LAI andtotal crown area rapidly recovered and the LAI even sur-passed pre-disturbance values This pattern resembles theinitial NIR spike due to fast-growing PFTs These twocanopy coverage attributes reached maximum values after 3to 6 years depending on the disturbance and response of for-est attributes (Table 3) To evaluate model results against re-mote sensing observations we compared the initial period af-ter the disturbance of the spikes in NIR values to the canopy-coverage metric (combination of LAI stem density total



Figure 4 Changes in NIR values for windthrows and prediction(based on extrapolation of fitted spline curve) of NIR values reach-ing pre-disturbance values The plots show the SR data (circles) thefit (solid lines) and the 95 confidence interval (CI dashed lines)Grey bar represents the control (old-growth forests) NIR value of28plusmn 1 and the horizontal dashed black line is 28

Figure 5 Changes in NIR values after clear-cuts for areas A1 A2A3 and AT = A1+A2+A3 (shown in Fig 1c) and prediction ofNIR values reaching pre-disturbance values The plots show thedata (circles) the fit (solid line) and the 95 confidence inter-val (CI dashed lines) Grey bar represents the control (old-growthforests) NIR value of 28plusmn 1 and the horizontal dashed black lineis 28

crown area) over the same modeled period ELM-FATESpredicted that after a windthrow the forest took 57 yearsto reach maximum values of canopy coverage which wassooner than the clear-cut simulation (7 years) While themodeled time span for this initial period was similar to thatinferred from NIR values there was disagreement betweenwhich disturbance recovery occurred fastest (windthrow inELM-FATES vs clear-cut in the NIR band) similar to dis-agreement in the recovery of AGB (Table 3)

ELM-FATES provided a prediction of the values in eachforest variable when the stand reached its production limitand full canopy closure at which point there was a shift toa declining trend and decreases in forest attributes that out-paced any gains (Table 3) At the maximum peak recovery

Figure 6 Changes in NIR values for cut+ burn site in areas A1A2 andAT = A1+A2 (shown in Fig 1) and prediction of NIR val-ues reaching pre-disturbance values The linear fit (solid line) andthe 95 CI (dashed line) are shown Grey bar represents the con-trol (old-growth forests) NIR value of 28plusmn 1 and the horizontaldashed black line is 28

and carrying capacity limit the highest forest values occurredin the clear-cut simulation matching the higher NIR valuesfrom clear-cut a few years after the disturbance (Figs 4ndash6)Over the longer self-thinning period the modeled LAI de-creased and returned to modeled old-growth values 26 yearsafter clear-cut and gradually over 53 years for windthrows(Table 3) LAI was the only variable that had a noticeablefaster recovery in the clear-cut simulations After both dis-turbances the total crown area permanently remained high(099 m2 mminus2) and slightly higher than the crown area of thesimulated old-growth forests (098 m2 mminus2) suggesting thatdisturbances can generate a denser canopy as discussed be-low

4 Discussion

Our results show that Landsat reflectance observations weresensitive to the initial changes in vegetation followingwindthrows clear-cut and cut+ burn three common distur-bances in the Amazon Specifically a decrease in NIR val-ues and an increase in SWIR1 values were the predominantspectral changes immediately (within a few years) follow-ing disturbances The increase in SWIR1 values was dif-ferent among the disturbances with the maximum increaseobserved in the cut+ burn followed by clear-cut and thenthe windthrow site The highest increase in SWIR1 valuesin cut+ burn sites may be related to the high thermal emis-sion of burned vegetation (Riebeek 2014) Likewise therelatively higher moisture content of woody material in thewindthrow site decreases the reflection of the SWIR2 bandOn the other hand in our control (old-growth) forests weobserved typically high NIR reflectance due to the cellular



Figure 7 Simulated regrowth of the Central Amazon forest after a clear-cut (98 tree mortality black line) and windthrow (70 treemortality orange line) event using 20 simulations of the demographic model ELM-FATES compared to the modeled old-growth valuesprior to disturbance (dashed green line) and against field data (solid green line) from close sites except for crown area data that weretaken from lidar data in Acre Brazil (Figueiredo et al 2016) The shaded grey and orange areas represent the spread across the ensemblesshowing minimum and maximum values of each forest attribute over its regrowth (a) Regrowth of aboveground biomass (AGB Mg C haminus1)(b) Regrowth of stem density (stems haminus1) of stems gt 10 cm DBH and years of returning to modeled old-growth values (c) Regrowth of leafarea index (mminus2 mminus2) and (d) regrowth of total crown area

Table 3 Summary of different time spans of regrowth (years) to old-growth forest status after two disturbance types windthrows and clear-cuts from ELM-FATES model results and remote sensing Additionally the time (years) it takes forest attributes to reach maximum valuesduring regrowth and the corresponding value at this maximum peak AGB (Mg C haminus1) stem density (stems haminus1) LAI and crown area(m2 mminus2) refer to simulation results as compared against NIR remote sensing The average of AGB and stem density is characterized asmodeled ldquoforest structurerdquo The averages of crown area stem density and LAI are characterized as modeled canopy coverage in this studyand are additionally compared against NIR values

Regrowth to old growth (years)

Disturbance ELM-FATES ELM-FATES ELM-FATES Model average of NIRtype AGB stem density LAI forest structure

Windthrow 37 39 53 380 39Clear-cut 42 41 26 415 361

Time to reach maximum values of regrowth (years)

Disturbance ELM-FATES ELM-FATES ELM-FATES Model average of NIRtype crown area stem density LAI canopy coverage


Values at maximum peak of regrowth

Disturbance ELM-FATES ELM-FATES ELM-FATES NIRtype crown area stem density LAI

Windthrow 099 1414 49 355Clear-cut 099 1560 75 420



structure of leaves (Sect 7 in Adams and Gillespie 2006)absorption of red radiation by chlorophyll (Tucker 1979)and absorption of the SWIR1 band by the water content inleaves (Sect 7 in Adams and Gillespie 2006)

While the SWIR1 band is frequently used to identifyexposed woody biomass immediately after disturbances(Negroacuten-Juaacuterez et al 2010b 2008) we found that the NIRband was more sensitive to the successional pathways of re-growth for all the disturbances considered The NIR bandhas also been associated with succession (Lu and Batistella2005) and regrowth (Roberts et al 1998) in naturally and an-thropogenically disturbed tropical forests (Laurance 2002Chazdon 2014 Magnabosco Marra 2016 Laurance et al2011) Previous studies have shown that changes in NIR val-ues are related to leaf structure and surface characteristics(Roberts et al 1998 Xiao et al 2014) with the youngestleaves having higher NIR values with respect to fully formedand older leaves (Roberts et al 1998) Maximum values ofthe NIR band were observed about 6 years after clear cutwhich is the time pioneers form a closed canopy (Mesquitaet al 1999 2001 2015) and characterized by a relativelyuniform distribution of tree diameter and heights (Vieira etal 2003) This maximum in the NIR band was higher inthe clear-cut site dominated by species from the genera Ce-cropia and Pourouma (Mesquita et al 2015 Massoca et al2012) than the site affected by cut+ burn dominated by Vis-mia species (Mesquita et al 2015 Laurance et al 2018)The higher NIR values in Cecropia and Pourouma are dueto the generarsquos monolayer planophile distribution of largeleaves that produces high reflectance compared to Vismiaspecies of which have rougher and denser canopies that trapmore NIR reflectance (Lucas et al 2002) The high values ofthe NIR band might be related to the low leaf wax (Chavana-Bryant et al 2017) from new trees andor scattering relatedto leaf and canopy water (Asner 2008) NIR values decreasewith the dynamics of succession due to an increase in thecanopy roughness (Hallik et al 2019)

After the establishment of pioneers the NIR values de-crease with time but with different rates depending on thetype of disturbance In windthrown areas tree mortality andsubsequent recruitment may continue for several decadespromoting changes in functional composition and canopy ar-chitecture (Magnabosco Marra et al 2018) Cecropia andPourouma trees grow relatively quickly and after closing thecanopy they limit light penetration due to their large leavescreating a dark cooler and wetter understory (Mesquita etal 2001 Jakovac et al 2014) As a result light levels in theunderstory decline faster with time and thus allow the recruit-ment and establishment of shade-tolerant species The co-hort of Cecropia and Pourouma species has a relatively shortlifespan and sim 20 years after disturbance secondary andold-growth forest species start to be established (Mesquita etal 2015) With the self-thinning of Cecropia and Pouroumathe growing understory traps more light and consequentlyalbedo decreases (Roberts et al 2004) This pattern is con-

Figure 8 Change in predicted diameter increment growth rate(cm yrminus1) for one simulation from the 20 ensembles that repre-sents a fast-growing pioneer forest stand and a slow-growing latesuccessional forest stand from a clear-cut disturbance (black andgrey) and a windthrow disturbance (orange) Variations in the di-ameter increment are a result of differences in the following traitswood density Vcmax crown area and a leaf-clumping index

sistent with the decline in NIR values and observed changesin canopy architecture (Mesquita et al 2015) photosyn-thesis and the LAI (Saldarriaga and Luxmoore 1991) Incontrast the architecture of Vismia species that dominatecut+ burn areas allows higher light levels in the under-story and subsequent recruitment of Vismia or other generawith similar light requirements As a consequence speciesturnover and structural changes are slower than in clear-cutareas (as found by Jakovac et al 2014 in a study conducted afew kilometers from the BDFFP) and windthrows consistentwith changes in NIR values In the course of succession Vis-mia tends to be replaced by Bellucia which is a genus with asimilar leaf and canopy structure to Vismia (Mesquita et al2015) This pattern favors the penetration of light through thecanopy (Longworth et al 2014) for several decades beforea more shaded understory allows the germination and estab-lishment of old-growth species (Williamson et al 2014)

For the windthrow we estimate that the NIR valuesshould become similar to pre-disturbance conditions in about39 years This value agrees with the 40 years of biomass re-growth found using ground-based data in the Central Ama-zon (Magnabosco Marra et al 2018) This result also corrob-orates previous studies that the NIR band operates in the bestspectral region to distinguish vegetation biomass (Tucker1979 1980) and photosynthesis (Badgley et al 2017) Forclear-cutting and cut+ burn the regrowth time was about36 and 56 years respectively but no ground-based estimateswere available for comparison Still NIR values showed thatthe pathways of regrowth from clear-cut and cut+ burn aredivergent with time (Figs 5 and 6) which is consistent withobservational studies (Mesquita et al 2015)



Figure 9 Total stem density (stems haminus1) separated into six diam-eter (cm) size classes from Central Amazon field data located at thenearby ZF2 site (green bars) averaged from 1996ndash2011 and pre-dicted by ELM-FATES (grey bars) The percentages represent theproportion of stems in each size class relative to the total stem den-sity

In general we found that NIR values may be used asa proxy in modeling studies aimed at addressing forest re-growth after disturbances Though NIR values are useful todistinguish successional stages up to decades after the distur-bance they may not represent all the successional processesAs soon as the forest canopy becomes structurally similarto that of the mature forest the NIR band will no longerbe sensitive to changes in vegetation attributes (Lucas et al2002) Though L5 NIR data may be complemented with cur-rent Landsat measurements (the L5 NIR band has a compa-rable performance to the Landsat 8 NIR Operational LandImager (OLI) algorithm Vermote et al 2016) it is impor-tant to emphasize that our estimates of recovered reflectanceand biomass in disturbed areas do not capture the full re-covery of diversity in floristic attributes and species compo-sition that can take centuries (Rozendaal et al 2019) Thepredominance of Cecropia after clear-cut and Vismia aftercut+ burn have also been found in the western (Gorchov etal 1993 Saldarriaga et al 1986) and the southern (Rocha etal 2016) Amazon suggesting that our findings are applicableto other regions However an Amazon-wide study is beyondthe scope of our work which is to explore the sensitivity ofLandsat to different disturbance types

Our analysis demonstrates that this version of ELM-FATES has the capacity to reproduce the initial responseto disturbance and regrowth patterns after the clear-cut andwindthrow that occurred over similar time ranges com-pared to NIR values The strongest agreement occurred whenELM-FATES predicted higher peaks of the post-disturbancestem density and LAI in clear-cuts than in windthrows whichcan be used for future benchmarking consistent with thehigher peak of NIR values from clear-cuts (Fig 5 vs Fig 4)This effect may be due to ELM-FATES having more ho-

mogeneous canopies after clear-cuts as well as more opendisturbed area for fast-growing plants which is also an ob-served trend In addition the average regrowth times to pre-disturbance values were close between ELM-FATES andNIR results (Table 3) showing that pathways of forest re-growth in ELM-FATES are comparable to observed pat-terns in tropical forests ELM-FATES predicted a contin-uous and almost linear regrowth of biomass for the first50 years of simulation after both clear-cut and windthrows(Fig 7a) consistent with NIR results and observational stud-ies (Mesquita et al 2015 Saldarriaga et al 1988 Jako-vac et al 2014 Magnabosco Marra et al 2018) In addi-tion the changes in biomass rates predicted by ELM-FATESwere similar to biomass observations recorded after clear-cut(23 vs 26 Mg haminus1 yrminus1) (Mazzei et al 2010) as well asthere being a faster rate of AGB accumulation after clear-cutcompared to windthrow similar to a study reporting higherregrowth rates in more highly disturbed sites (MagnaboscoMarra et al 2018)

Landsat showed a faster recovery of NIR valuesto pre-disturbance conditions in clear-cuts compared towindthrows Faster growth is characteristic of anthropogeni-cally driven secondary forests that reflect rapid coloniza-tion and monodominance of adapted species and genera inchanged environmental conditions eg high growth rateslow self-competition high leaf area index low herbivoryrates (Poorter et al 2016 Mesquita et al 2015 Rozen-daal and Chazdon 2015) ELM-FATES predicted a fasterrecovery of structural AGB and canopy coverage to pre-disturbance conditions for windthrows (70 tree mortality)compared to clear-cuts (98 tree mortality) A major con-tributing factor to this pattern resulted from larger modeleddiameter increments after windthrows (092 cm yrminus1) com-pared to clear-cuts (082 cm yrminus1) in the first 20 years afterthe disturbance setting the trajectory for faster regrowth topre-disturbance after windthrows Only the LAI had a fasterrecovery to pre-disturbance values after clear-cuts which isexpected due to the newly developed forest having a sim-plified forest structure and the canopy being more homoge-neous after a clear-cut (Rosenvald and Lohmus 2008) ELM-FATES predicted the timing of peak canopy coverage wasmarginally sooner after windthrows compared to clear-cutsopposite to the NIR pattern This discrepancy may be relatedto more biomass loss and open canopy coverage followed bya lack of rapid colonization in the modeled clear-cut Due tothe higher stand disturbance that naturally occurs from clear-cuts and the diverse complexities in tropical forest composi-tion we emphasize that the dynamics of different competingPFTs in ELM-FATES requires further investigation Emerg-ing modeling studies that include plant trait trade-offs forexample in the leaf and stem economic spectrum or fast-growth vs slow-growth strategies may help to better cap-ture the drivers of forest productivity and demography en-abling improved modeled responses to global change sce-narios (Fauset et al 2019 Sakschewski et al 2015) Here



we test the basic representation of biomass demographicsprior to the more challenging aspects of representing inter-acting functional diversity in recovering systems (Fisher etal 2015 Powell et al 2018)

ELM-FATES predicts the total stem density for a closed-canopy forest to be very low compared to observations(200 simulated vs 600 stems haminus1) but modeled total AGBwas close to that reported for the same region (110 sim-ulated vs sim 150 Mg C haminus1 Chambers et al 2013) Thisdiscrepancy is due to ELM-FATES predicting a dispropor-tionately high number of large trees (Fig 9 with 8 ofstems gt 60 cm and 45 of stems gt 100 cm) resulting ina crowded canopy which out-compete smaller understorytrees Higuchi et al (2012) report 93 of trees ge 10 cmDBH in the study site to be below 40 cm DBH while ELM-FATES predicted noticeably fewer trees below 40 cm DBH(Fig 9) Low stem density could be attributed to multiplemodel assumptions such as high density-dependent mortal-ity and self-thinning due to the marginal carbon economics ofunderstory trees low branch-fall turnover a need for greaterlimitation of maximum crown area than currently modeledand increases in mortality rates with tree size (Johnson et al2018) Our findings here will guide future ELM-FATES andecosystem modeling development efforts towards improvingthe representation of forests comprising dense canopies andhow they shift during regrowth

Land surface models do not typically simulate spectralleaf reflectance but there is potential to include such outputwithin radiative transfer schemes as is currently done in theCLM That addition would greatly assist our ability to com-pare with Earth observation datasets In lieu of this develop-ment we show that with successional aging modeled foreststructure returns to pre-disturbed values (through canopy clo-sure) with a similar recovery time to that inferred from NIRdata occurring with the process of canopy closure all whichcan be compared against remote sensing vegetation indices(see Supplement Fig S1) and metrics Which vegetation in-dex (eg normalized difference vegetation index ndash Rouse etal 1973 enhanced vegetation index ndash Huete et al 2002) ormetric properly represents the successional pathways follow-ing disturbances remains an important area of study

5 Conclusions

We tested the sensitivity of Landsat surface reflectance towindthrow clear-cut and cut+ burn forest in the CentralAmazon The NIR band was more responsive to the suc-cessional pathways of forest regrowth years after the distur-bance NIR values showed that pathways of forest regrowthwere different among the disturbances with cut+ burn be-ing the most different in terms of spatial heterogeneity andregrowth time to old-growth status in agreement with obser-vational studies Our results indicate that after disturbancesthe NIR values will reach old-growth forest values in about

39 years following windthrows (in agreement with observedbiomass regrowth) 36 years for clear-cuts and 56 years forcut+ burn These results were then compared with simula-tions of regrowth after windthrows and clear-cut from ELM-FATES The simulated forest structure and the remote sens-ing NIR values from the windthrow and clear-cut have simi-lar return times to those of old-growth forest conditions Fu-ture studies applying ELM-FATES should focus on improv-ing stem density predictions which were underestimatedand on enhancing the capacity to compare with remote sens-ing observations through representation of canopy-spectral-reflectance characteristics

Code and data availability The Landsat data used in this studyare freely available through the Google Earth Engine platformELM-FATES is available at httpsgithubcomNGEETfates Ob-servational data used to compare remote sensing and modeling re-sults have been previously published and references provided (egMesquita et al 2015 Jakovac et al 2014)

Supplement The supplement related to this article is available on-line at httpsdoiorg105194bg-17-6185-2020-supplement

Author contributions RINJ designed the study and performed theremote sensing analysis JAH performed the model simulations andanalysis RNJ and JAH formulated the research goals and wrote theinitial draft of the manuscript BF performed the statistical analy-sis of the remote sensing data DMM provided the field data forwindthrows and contributed to the discussion of biomass recov-ery RAF and JKS contributed with modeling improvements ACdAcontributed with the observational input data to initialize the modelWJR and JQC were responsible for project funding and administra-tion All authors contributed to the final writing and editing of themanuscript

Competing interests The authors declare that they have no conflictof interest

Acknowledgements This research was supported as part of theNext-Generation Ecosystem ExperimentsndashTropics (NGEE-Tropics)of the TES Program and the RUBISCO project under the Re-gional and Global Climate Modeling Program funded by theUS Department of Energy Office of Science Office of Bio-logical and Environmental Research under contract DE-AC02-05CH11231 Among others the NGEE-Tropics Program supportsa large part of the past and current model development for the ELMFunctionally Assembled Terrestrial Ecosystem Simulator (FATES)Daniel Magnabosco-Marra was supported as part of the ATTOProject Max Planck Society (MPG) and German Federal Ministryof Education and Research (BMBF) Rosie A Fisher was supportedby the National Center for Atmospheric Research which is spon-sored by the National Science Foundation under cooperative agree-ment no 1852977



Financial support This research has been supported as part of theNext Generation Ecosystem Experiments-Tropics funded by theUS Department of Energy Office of Science Office of Biolog-ical and Environmental Research under contract no DE-AC02-05CH11231 This research was also supported through the Reduc-ing Uncertainties in Biogeochemical Interactions through Synthe-sis and Computation Scientific Focus Area (RUBISCO SFA) undercontract to LBNL which is sponsored by the Regional and GlobalClimate Modeling (RGCM) Program in the Climate and Environ-mental Sciences Division (CESD) of the Office of Biological andEnvironmental Research (BER) in the US Department of EnergyOffice of Science

Review statement This paper was edited by Christopher Still andreviewed by two anonymous referees

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Chavana-Bryant C Malhi Y Wu J Asner G P Anasta-siou A Enquist B J Caravasi E G C Doughty C ESaleska S R Martin R E and Gerard F F Leaf ag-ing of Amazonian canopy trees as revealed by spectral andphysiochemical measurements New Phytol 214 1049ndash1063httpsdoiorg101111nph13853 2017

Chazdon R L Second Growth The promise of tropical forest re-generation in an age og deforestation The University of ChicagoPress Chicago USA 2014

Chazdon R L Broadbent E N Rozendaal D M A BongersF Zambrano A M A Aide T M Balvanera P Becknell JM Boukili V Brancalion P Craven D Almeida-Cortez J SCabral G A de Jong B Denslow J S Dent D H DeWaltS Dupuy J Duraacuten S Espiacuterito-Santo M Fandino M CeacutesarR Hall J S Hernaacutendez-Stefanoni J Jakovac C JunqueiraA Kennard D Letcher S G Lohbeck M Martiacutenez-RamosM Massoca P Meave J A Mesquita R Mora F Muntildeoz RMuscarella R Nunes Y Ochoa-Gaona S Orihuela-BelmonteE Pentildea-Claros M Peacuterez-Garciacutea E Piotto D Powers JRodriacuteguez-Velazquez J Romero-Peacuterez I Ruiacutez J SaldarriagaJ G Sanchez-Azofeifa A Schwartz N Steininger M Swen-son N G Uriarte M van Breugel M van der Wal H VelosoM Vester H Vieira I Bentos T Williamson G B andPoorter L Carbon sequestration potential of second-growth for-est regeneration in the Latin American tropics Sci Adv 2e1501639 httpsdoiorg101126sciadv1501639 2016



Claverie M Vermote E F Franch B and Masek J G Eval-uation of the Landsat-5 TM and Landsat-7 ETM + surfacereflectance products Remote Sens Environ 169 390ndash403httpsdoiorg101016jrse201508030 2015

Cohen W B and Goward S N Landsatrsquos rolein ecological applications of remote sensing Bio-science 54 535ndash545 httpsdoiorg1016410006-3568(2004)054[0535lrieao]20co2 2004

Dantas de Paula M Groeneveld J and Huth A Tropical for-est degradation and recovery in fragmented landscapes ndash Sim-ulating changes in tree community forest hydrology and car-bon balance Global Ecology and Conservation 3 664ndash677httpsdoiorg101016jgecco201503004 2015

Da Silva R P Allometry storage and biomass dynamics of pri-mary and secondary forests in the Manaus Region (AM) [in Por-tuguese] PhD thesis Universidade Federal do Amazonas Man-aus Brazil 135 pp 2007

da Silva R P dos Santos J Tribuzy E S Chambers J Q Naka-mura S and Higuchi N Diameter increment and growth pat-terns for individual tree growing in Central Amazon Brazil For-est Ecol Manag 166 295ndash301 2002

de Araujo A C Nobre A D Kruijt B Elbers J A DallarosaR Stefani P von Randow C Manzi A O Culf A D GashJ H C Valentini R and Kabat P Comparative measurementsof carbon dioxide fluxes from two nearby towers in a centralAmazonian rainforest The Manaus LBA site J Geophys Res-Atmos 107 D208090 httpsdoiorg1010292001jd0006762002

Denslow J S Patterns of plant-species diversity during succes-sion under different disturbancs regimes Oecologia 46 18ndash21httpsdoiorg101007bf00346960 1980

DeVries B Decuyper M Verbesselt J Zeileis A HeroldM and Joseph S Tracking disturbance-regrowth dynam-ics in tropical forests using structural change detection andLandsat time series Remote Sens Environ 169 320ndash334httpsdoiorg101016jrse201508020 2015

Dolan K Masek J G Huang C Q and Sun G Q Re-gional forest growth rates measured by combining ICESat GLASand Landsat data J Geophys Res-Biogeo 114 G00E05httpsdoiorg1010292008jg000893 2009

Dolan K A Hurtt G C Flanagan S A Fisk J P SahajpalR Huang C Q Le Page Y Dubayah R and Masek J GDisturbance Distance quantifying forestsrsquo vulnerability to dis-turbance under current and future conditions Environ Res Lett12 114015 httpsdoiorg1010881748-9326aa8ea9 2017

FAO Food and Agriculture Organization of the United NationsGlobal Forest Resources Assessment FAO 163 Rome ISBN978-92-5-106654-6 available at httpwwwfaoorg3a-i1757epdf (last access 2 December 2020) 2010

Farquhar G D Caemmerer S V and Berry J A A biochemical-model of photosynthetic CO2 assimilation in leaves of C3species Planta 149 78ndash90 httpsdoiorg101007bf003862311980

Farrior C E Bohlman S A Hubbell S and PacalaS W Dominance of the suppressed Power-law sizestructure in tropical forests Science 351 155ndash157httpsdoiorg101126scienceaad0592 2016

Fauset S Gloor M Fyllas N M Phillips O L Asner G PBaker T R Bentley L P Brienen R J W Christoffersen B

O del Aguila-Pasquel J Doughty C E Feldpausch T RGalbraith D R Goodman R C Girardin C A J Coron-ado E N H Monteagudo A Salinas N Shenkin A Silva-Espejo J E van der Heijden G Vasquez R Alvarez-DavilaE Arroyo L Barroso J G Brown F Castro W ValverdeF C Cardozo N D Di Fiore A Erwin T Huamantupa-Chuquimaco I Vargas P N Neill D Camacho N P Gutier-rez A P Peacock J Pitman N Prieto A Restrepo ZRudas A Quesada C A Silveira M Stropp J TerborghJ Vieira S A and Malhi Y Individual-Based Modelingof Amazon Forests Suggests That Climate Controls Productiv-ity While Traits Control Demography Front Earth Sci 7 83httpsdoiorg103389feart201900083 2019

Ferraz J Oht S and Salles P C Distribuiccedilatildeo dos solos ao longode dois transectos em floresta primaacuteria ao norte de Manaus (AM)in Pesquisas Florestais para a Conservaccedilatildeo da Floresta e Reabil-itaccedilatildeo de Aacutereas Degradadas da Amazocircnia edited by HiguchiN Campos M A A Sampaio P T B and Santos J INPAManaus brazil 111ndash143 1998

Figueiredo E O drsquoOliveira M V N Braz E M Papa D D andFearnside P M LIDAR-based estimation of bole biomass forprecision management of an Amazonian forest Comparisons ofground-based and remotely sensed estimates Remote Sens En-viron 187 281ndash293 httpsdoiorg101016jrse2016100262016

Fisher R A McDowell N Purves D Moorcroft P SitchS Cox P Huntingford C Meir P and Woodward FI Assessing uncertainties in a second-generation dynamicvegetation model caused by ecological scale limitationsNew Phytol 187 666ndash681 httpsdoiorg101111j1469-8137201003340x 2010

Fisher R A Muszala S Verteinstein M Lawrence P Xu CMcDowell N G Knox R G Koven C Holm J RogersB M Spessa A Lawrence D and Bonan G Taking off thetraining wheels the properties of a dynamic vegetation modelwithout climate envelopes CLM45(ED) Geosci Model Dev8 3593ndash3619 httpsdoiorg105194gmd-8-3593-2015 2015

Fisher R A Koven C D Anderegg W R L ChristoffersenB Dietze M C Farrior C E Holm J Hurtt G Knox RLawrence P J Lichstein J W Longo M Matheny A MMedvigy D Muller-Landau H C Powell T Serbin S PSato H Shuman J K Smith B Trugman A T Viskari TVerbeeck H Weng E Xu C Xu X Zhang T and Moor-croft P Vegetation demographics in Earth System Models Areview of progress and priorities Global Change Biol 24 35ndash54 httpsdoiorg101111gcb13910 2018

Fisher R A Wieder W R Sanderson B M Koven C OlesonK Xu C Fisher J B Shi M Walker A P and Lawrence DParametric Controls on Vegetation Responses to BiogeochemicalForcing in the CLM5 J Adv Model Earth Sy 11 2879ndash2895httpsdoiorg1010292019MS001609 2019

Fisk J Net effects of disturbance spatial temporal and societaldimensions of forest disturbance and recovery on terrestrial car-bon balance PhD thesis University of New Hampshire USA 74pp 2015

Foley J A Asner G P Costa M H Coe M T DeFries RGibbs H K Howard E A Olson S Patz J RamankuttyN and Snyder P Amazonia revealed forest degradation andloss of ecosystem goods and services in the Amazon Basin



Front Ecol Environ 5 25ndash32 httpsdoiorg1018901540-9295(2007)5[25arfdal]20co2 2007

Friedlingstein P Meinshausen M Arora V K Jones C DAnav A Liddicoat S K and Knutti R Uncertainties inCMIP5 Climate Projections due to Carbon Cycle Feedbacks JClimate 27 511ndash526 httpsdoiorg101175jcli-d-12-0057912014

Frolking S Palace M W Clark D B Chambers J QShugart H H and Hurtt G C Forest disturbance andrecovery A general review in the context of spaceborneremote sensing of impacts on aboveground biomass andcanopy structure J Geophys Res-Biogeo 114 GE00E02httpsdoiorg1010292008jg000911 2009

Fyllas N M Gloor E Mercado L M Sitch S Quesada C ADomingues T F Galbraith D R Torre-Lezama A VilanovaE Ramiacuterez-Angulo H Higuchi N Neill D A Silveira MFerreira L Aymard C G A Malhi Y Phillips O L andLloyd J Analysing Amazonian forest productivity using a newindividual and trait-based model (TFS v1) Geosci Model Dev7 1251ndash1269 httpsdoiorg105194gmd-7-1251-2014 2014

Ganey J and Block W Technical Note A Comparison of TwoTechniques for Measuring Canopy Closure West J Appl For9 21ndash23 httpsdoiorg101093wjaf9121 1994

Ganguly S Nemani R R Zhang G Hashimoto H Milesi CMichaelis A Wang W L Votava P Samanta A Melton FDungan J L Vermote E Gao F Knyazikhin Y and My-neni R B Generating global Leaf Area Index from Landsat Al-gorithm formulation and demonstration Remote Sens Environ122 185ndash202 httpsdoiorg101016jrse201110032 2012

Garstang M White S Shugart H H and Halverson J Con-vective cloud downdrafts as the cause of large blowdowns in theAmazon rainforest Meteorol Atmos Phys 67 199ndash212 1998

Gerber F Package lsquogapfillrsquo R A language and environmentfor statistical computing R Foundation for Statistical Com-puting Vienna Austria available at httpscranr-projectorgwebpackagesgapfillgapfillpdf (last access 2 December 2020)2018

Gorchov D L Cornejo F Ascorra C and Jaramillo M Therole of seed dispersal in the natural regeneration of rain forestafter strip-cutting in the Peruvian Amazon Vegetatio 108 339ndash349 1993

Gorelick N Hancher M Dixon M Ilyushchenko S Thau Dand Moore R Google Earth Engine Planetary-scale geospa-tial analysis for everyone Remote Sens Environ 202 18ndash27httpsdoiorg101016jrse201706031 2017

Gu C General Smoothing Splines 61 available at httpscranr-projectorgwebpackagesgssgsspdf (last access 2 Decem-ber 2020) 2018

Hallik L Kuusk A Lang M and Kuusk J Reflectance Prop-erties of Hemiboreal Mixed Forest Canopies with Focus on RedEdge and Near Infrared Spectral Regions Remote Sens-Basel11 1ndash22 httpsdoiorg103390rs11141717 2019

Hansen M C Potapov P V Moore R Hancher M TurubanovaS A Tyukavina A Thau D Stehman S V Goetz S JLoveland T R Kommareddy A Egorov A Chini L Jus-tice C O and Townshend J R High-resolution global mapsof 21st-century forest cover change Science 342 850ndash853httpsdoiorg101126science1244693 2013

Higuchi F G Siqueira J D P Lima A J N Figueiredo Aand Higuchi N The effect of plot size on the precision of theWeibull distribution of diameters in the primary forest of the cen-tral Amazon Floresta 2 599ndash606 2012

Higuchi N Dos Santos J Ribeiro R J Freitas J V VieiraG and Cornic A Crescimento e Incremento de uma Flo-resta Amazocircnica de Terra-Firme Manejada ExperimentalmenteINPA Manaus Brazil 89ndash132 in MCT-INPA Biomassa enutrientes florestais- Bionte 345 pp available at httpsrepositorioinpagovbrhandle136085 (last access 2 Decem-ber 2020) 1997

Higuchi N Chambers J Q Santos J Ribeiro R J Pinto A CSilva R P Rocha R M and Tribuzy E S Carbon balance anddynamics of primary vegetation in the central Amazon Floresta34 295ndash304 2004

Holm J Knox R Zhu Q Fisher R Koven C Lima A JN Riley W Longo M Negroacuten Juaacuterez R Araujo A CKueppers L M Moorcroft P Higuchi N and Chambers JQ Modeling the Central Amazon forest carbon sink and forestdynamics under current and rising atmospheric carbon dioxideEcological Society of America (ESA) Meeting Portland Ore-gon USA 6ndash11 August 2017 COS 148-8 2017

Holm J Knox R Zhu Q Fisher R Koven C Lima A JN Riley W Longo M Negroacuten Juaacuterez R De Araujo A CKueppers L M Moorcroft P Higuchi N and Chambers JThe Central Amazon biomass sink under current and future at-mospheric CO2 Predictions from big-leaf and demographic veg-etation models J Geophys Res-Biogeo 125 e2019JG005500httpsdoiorg1010292019JG005500 2020

Huete A Didan K Miura T Rodriguez E P Gao X and Fer-reira L G Overview of the radiometric and biophysical perfor-mance of the MODIS vegetation indices Remote Sens Environ83 195ndash213 httpsdoiorg101016s0034-4257(02)00096-22002

Hurrell J W Holland M M Gent P R Ghan S Kay J EKushner P J Lamarque J F Large W G Lawrence DLindsay K Lipscomb W H Long M C Mahowald NMarsh D R Neale R B Rasch P Vavrus S VertensteinM Bader D Collins W D Hack J J Kiehl J and Mar-shall S The Community Earth System Model A Framework forCollaborative Research B Am Meteorol Soc 94 1339ndash1360httpsdoiorg101175bams-d-12-001211 2013

Hurtt G C Frolking S Fearon M G Moore B Shevli-akova E Malyshev S Pacala S W and Houghton R AThe underpinnings of land-use history three centuries of globalgridded land-use transitions wood-harvest activity and result-ing secondary lands Global Change Biol 12 1208ndash1229httpsdoiorg101111j1365-2486200601150x 2006

Jakovac A C C Bentos T V Mesquita R C G andWilliamson G B Age and light effects on seedlinggrowth in two alternative secondary successions incentral Amazonia Plant Ecol Divers 7 349ndash358httpsdoiorg101080175508742012716088 2014

Jennings S B Brown N D and Sheil D Assessingforest canopies and understorey illumination canopy clo-sure canopy cover and other measures Forestry 72 59ndash73httpsdoiorg101093forestry72159 1999

Johnson D J Needham J Xu C G Massoud E C Davies SJ Anderson-Teixeira K J Bunyavejchewin S Chambers J



Q Chang-Yang C H Chiang J M Chuyong G B ConditR Cordell S Fletcher C Giardina C P Giambelluca T WGunatilleke N Gunatilleke S Hsieh C F Hubbell S Inman-Narahari F Kassim A R Katabuchi M Kenfack D LittonC M Lum S Mohamad M Nasardin M Ong P S Os-tertag R Sack L Swenson N G Sun I F Tan S ThomasD W Thompson J Umana M N Uriarte M Valencia RYap S Zimmerman J McDowell N G and McMahon SM Climate sensitive size-dependent survival in tropical treesNat Ecol Evol 2 1436ndash1442 httpsdoiorg101038s41559-018-0626-z 2018

Kammesheidt L Kohler P and Huth A Simulating log-ging scenarios in secondary forest embedded in a fragmentedneotropical landscape Forest Ecol Manag 170 89ndash105httpsdoiorg101016s0378-1127(01)00783-6 2002

Keenan R J Reams G A Achard F de Freitas JV Grainger A and Lindquist E Dynamics of globalforest area Results from the FAO Global Forest Re-sources Assessment 2015 Forest Ecol Manag 352 9ndash20httpsdoiorg101016jforeco201506014 2015

Kennedy R E Cohen W B and Schroeder T A Trajectory-based change detection for automated characterization of for-est disturbance dynamics Remote Sens Environ 110 370ndash386httpsdoiorg101016jrse200703010 2007

Kennedy R E Yang Z and Cohen W B Detect-ing trends in forest disturbance and recovery using yearlyLandsat time series 1 LandTrendr ndash Temporal segmenta-tion algorithms Remote Sens Environ 114 2897ndash2910httpsdoiorg101016jrse201007008 2010

Kennedy R E Yang Z Q Cohen W B Pfaff E BraatenJ and Nelson P Spatial and temporal patterns of for-est disturbance and regrowth within the area of the North-west Forest Plan Remote Sens Environ 122 117ndash133httpsdoiorg101016jrse201109024 2012

Kim D H Sexton J O Noojipady P Huang C Q Anand AChannan S Feng M and Townshend J R Global Landsat-based forest-cover change from 1990 to 2000 Remote Sens En-viron 155 178ndash193 httpsdoiorg101016jrse2014080172014

Laurance S G W Laurance W F Andrade A FearnsideP M Harms K E Vicentini A and Luizao R C CInfluence of soils and topography on Amazonian tree di-versity a landscape-scale study J Veg Sci 21 96ndash106httpsdoiorg101111j1654-1103200901122x 2010

Laurance W F Hyperdynamism in fragmented habitatsJ Veg Sci 13 595ndash602 httpsdoiorg101111j1654-11032002tb02086x 2002

Laurance W F Nascimento H E M Laurance S G An-drade A Ewers R M Harms K E Luizao R C C andRibeiro J E Habitat Fragmentation Variable Edge Effectsand the Landscape-Divergence Hypothesis PloS one 2 e1017httpsdoiorg101371journalpone0001017 2007

Laurance W F Camargo J L C Luizao R C C LauranceS G Pimm S L Bruna E M Stouffer P C WilliamsonG B Benitez-Malvido J Vasconcelos H L Van HoutanK S Zartman C E Boyle S A Didham R K AndradeA and Lovejoy T E The fate of Amazonian forest frag-ments A 32-year investigation Biol Conserv 144 56ndash67httpsdoiorg101016jbiocon201009021 2011

Laurance W F Camargo J L C Fearnside P M Lovejoy TE Williamson G B Mesquita R C G Meyer C F J Bo-browiec P E D and Laurance S G W An Amazonian rain-forest and its fragments as a laboratory of global change BiolRev 93 223ndash247 httpsdoiorg101111brv12343 2018

Lawrence D M Fisher R A Koven C D Oleson K W Swen-son S C Bonan G Collier N Ghimire B van Kampen-hout L Kennedy D Kluzek E Lawrence P J Li F LiH Y Lombardozzi D Riley W J Sacks W J Shi M JVertenstein M Wieder W R Xu C G Ali A A BadgerA M Bisht G van den Broeke M Brunke M A BurnsS P Buzan J Clark M Craig A Dahlin K Drewniak BFisher J B Flanner M Fox A M Gentine P HoffmanF Keppel-Aleks G Knox R Kumar S Lenaerts J LeungL R Lipscomb W H Lu Y Q Pandey A Pelletier J DPerket J Randerson J T Ricciuto D M Sanderson B MSlater A Subin Z M Tang J Y Thomas R Q MartinM V and Zeng X B The Community Land Model Version5 Description of New Features Benchmarking and Impact ofForcing Uncertainty J Adv Model Earth Sy 11 4245ndash4287httpsdoiorg1010292018ms001583 2019

Lewis S L Edwards D P and Galbraith D Increasing hu-man dominance of tropical forests Science 349 827ndash832httpsdoiorg101126scienceaaa9932 2015

Lima A J N Teixeira L M Carneiro V M C Santos Jand Higuchi N Biomass stock and structural analysis of a sec-ondary forest in Manaus (AM) region ten years after clear cut-ting followed by fire Acta Amazon 37 49ndash54 2007

Longo M Knox R G Levine N M Swann A L S MedvigyD M Dietze M C Kim Y Zhang K Bonal D BurbanB Camargo P B Hayek M N Saleska S R da Silva RBras R L Wofsy S C and Moorcroft P R The biophysicsecology and biogeochemistry of functionally diverse verticallyand horizontally heterogeneous ecosystems the Ecosystem De-mography model version 22 ndash Part 2 Model evaluation fortropical South America Geosci Model Dev 12 4347ndash4374httpsdoiorg105194gmd-12-4347-2019 2019

Longworth J B Mesquita R C Bentos T V Moreira MP Massoca P E and Williamson G B Shifts in Domi-nance and Species Assemblages over Two Decades in Alterna-tive Successions in Central Amazonia Biotropica 46 529ndash537httpsdoiorg101111btp12143 2014

Lovejoy T E Bierregaard R Rylands A Malcolm J QuintelaC Harper L Brown K Powell A Powell G Schubart Hand Hays M Edge and other effects of isolation on Amazon for-est fragments in Conservation biology the science of scarcityand diversity edited by Soule M E Sinauer Associates Sun-derland Massachusetts USA Conserv Biol 257ndash285 1986

Lovejoy T E and Bierregaard R Central Amazonian forests andthe minimum critical size of ecosystem project in Four neotrop-ical rainforests edited by Gentry A Yale University Press NewHaven USA 60ndash71 1990

Loveland T R and Dwyer J L Landsat Buildinga strong future Remote Sens Environ 122 22ndash29httpsdoiorg101016jrse201109022 2012

Lu D Aboveground biomass estimation using Landsat TM datain the Brazilian Amazon Int J Remote Sens 26 2509ndash2525httpsdoiorg10108001431160500142145 2005



Lu D and Batistella M Exploring TM image texture and its rela-tionships with biomass estimation in Rondonia Brazilian Ama-zon Acta Amazon 35 249ndash257 2005

Lucas R M Honzak M Amaral I D Curran P J andFoody G M Forest regeneration on abandoned clearancesin central Amazonia Int J Remote Sens 23 965ndash988httpsdoiorg10108001431160110069791 2002

Magnabosco Marra D Chambers J Q Higuchi N TrumboreS E Ribeiro G H P M dos Santos J Negron-Juarez R IReu B and Wirth C Large-Scale Wind Disturbances PromoteTree Diversity in a Central Amazon Forest PloS one 9 e103711httpsdoiorg101371journalpone0103711 2014

Magnabosco Marra D Effects of windthrows on the interactionbetween tree species composition forest dynamics and carbonbalance in Central Amazon PhD thesis Institute of BiologyLeipzig University Leipzig Germany 210 pp 2016

Magnabosco Marra D Trumbore S E Higuchi N Ribeiro GH P M Negron-Juarez R I Holzwarth F Rifai S W DosSantos J Lima A J N Kinupp V F Chambers J Q andWirth C Windthrows control biomass patterns and functionalcomposition of Amazon forests Global Change Biol 24 5867ndash5881 httpsdoiorg101111gcb14457 2018

Masek J G Vermote E F Saleous N E Wolfe RHall F G Huemmrich K F Gao F Kutler J andLim T K A Landsat surface reflectance dataset for NorthAmerica 1990ndash2000 IEEE Geosci Remote S 3 68ndash72httpsdoiorg101109lgrs2005857030 2006

Masek J G Huang C Wolfe R Cohen W Hall F Kutler Jand Nelson P North American forest disturbance mapped froma decadal Landsat record Remote Sens Environ 112 2914ndash2926 httpsdoiorg101016jrse200802010 2008

Masek J G Vermote E Saleous N E Wolfe R Hall F GHuemmrich F Gao F Kutler J and Lim T K LEDAPSLandsat Calibration Reflectance Atmospheric Correction Pre-processing Code Model product Oak Ridge National Labora-tory Distributed Active Archive Center Oak Ridge TennesseeUSA httpsdoiorg103334ORNLDAAC1080 2012

Masek J G Goward S N Kennedy R E Cohen W B MoisenG G Schleeweis K and Huang C United States Forest Dis-turbance Trends Observed Using Landsat Time Series Ecosys-tems 16 1087ndash1104 httpsdoiorg101007s10021-013-9669-9 2013

Massoca P E Jakovac A C C Bentos T Williamson G B andMesquita R C Dynamics and trajectories of secondary succes-sion in Central Amazonia Bol Mus Para Emiacutelio Goeldi CiencNat 7 235ndash250 2012

Mazzei L Sist P Ruschel A Putz F E MarcoP Pena W and Ferreira J E R Above-groundbiomass dynamics after reduced-impact logging in theEastern Amazon Forest Ecol Manag 259 367ndash373httpsdoiorg101016jforeco200910031 2010

McDowell N G Coops N C Beck P S A Chambers JQ Gangodagamage C Hicke J A Huang C-Y KennedyR Krofcheck D J Litvak M Meddens A J H Muss JNegron-Juarez R Peng C Schwantes A M Swenson J JVernon L J Williams A P Xu C Zhao M Running SW and Allen C D Global satellite monitoring of climate-induced vegetation disturbances Trends Plant Sci 20 114ndash123httpsdoiorg101016jtplants201410008 2015

Mesquita R C G Delamonica P and Laurance W F Ef-fect of surrounding vegetation on edge-related tree mortalityin Amazonian forest fragments Biol Conserv 91 129ndash134httpsdoiorg101016s0006-3207(99)00086-5 1999

Mesquita R C G Ickes K Ganade G and Williamson G BAlternative successional pathways in the Amazon Basin J Ecol89 528ndash537 httpsdoiorg101046j1365-2745200100583x2001

Mesquita R D G Massoca P E D Jakovac C C BentosT V and Williamson G B Amazon Rain Forest SuccessionStochasticity or Land-Use Legacy Bioscience 65 849ndash861httpsdoiorg101093bioscibiv108 2015

Mitchell S J Wind as a natural disturbance agentin forests a synthesis Forestry 86 147ndash157httpsdoiorg101093forestrycps058 2013

Moorcroft P R Hurtt G C and Pacala S W Amethod for scaling vegetation dynamics The ecosys-tem demography model (ED) Ecological Mono-graphs 71 557ndash585 httpsdoiorg1018900012-9615(2001)071[0557amfsvd]20co2 2001

Negroacuten-Juaacuterez R I Chambers J Q Zeng H and BakerD B Hurricane driven changes in land cover create biogeo-physical climate feedbacks Geophys Res Lett 35 L23401httpsdoiorg1010292008gl035683 2008

Negroacuten-Juaacuterez R I Baker D B Zeng H Henkel T K andChambers J Q Assessing hurricane-induced tree mortality inUS Gulf Coast forest ecosystems J Geophys Res-Biogeo115 G04030 httpsdoiorg1010292009jg001221 2010a

Negroacuten-Juaacuterez R I Chambers J Q Guimaraes G ZengH Raupp C F M Magnabosco Marra D Ribeiro GH P M Saatchi S S Nelson B W and Higuchi NWidespread Amazon forest tree mortality from a single cross-basin squall line event Geophys Res Lett 37 L16701httpsdoiorg1010292010gl043733 2010b

Negroacuten-Juaacuterez R I Chambers J Q Magnabosco Marra DRibeiro G H P M Rifai S W Higuchi N and Roberts DDetection of subpixel treefall gaps with Landsat imagery in Cen-tral Amazon forests Remote Sens Environ 115 3322ndash3328httpsdoiorg101016jrse201107015 2011

Negroacuten-Juaacuterez R I Jenkins H S Raupp C F M Riley WJ Kueppers L M Magnabosco Marra D Ribeiro G Mon-teiro M T F Candido L A Chambers J Q and Higuchi NWindthrow Variability in Central Amazonia Atmosphere 8 28httpsdoiorg103390atmos8020028 2017

Negroacuten-Juaacuterez R I Holm J A Magnabosco Marra D Ri-fai S W Riley W J Chambers J Q Koven C D KnoxR G McGroddy M E Di Vittorio A Urquiza-MuntildeozJ D Tello-Espinoza R Alegria-Muntildeoz W Ribeiro G HP M and Higuchi N Vulnerability of Amazon forests tostorm-driven tree mortality Environ Res Lett 13 054021httpsdoiorg1010881748-9326aabe9f 2018

Nelson B W and Amaral I Destructive wind efects detected inTM images of the Amazon Basin Int Soc Photogramme 30339ndash343 1994

Nelson B W Kapos V Adams J B Oliveira W and Braun OForest disturbance by large blowdowns in the Brazilian AmazonEcology 75 853ndash858 httpsdoiorg1023071941742 1994

Neter J Wasserman W and Whitmore G A Applied Statistics3rd Edn Allyn and Bacon Press Boston 1988



Nobre C A Sampaio G Borma L S Castilla-Rubio J CSilva J S and Cardoso M Land-use and climate change risksin the Amazon and the need of a novel sustainable develop-ment paradigm P Natl Acad Sci USA 113 10759ndash10768httpsdoiorg101073pnas1605516113 2016

Norman J M Modeling the complete crop canopy in Modifica-tion of the aerial environment of plant edited by Barfield B Jand Gerber J F American Society of Agricultural EngineersSt Joseph USA 249ndash277 1979

Oleson K W Lawrence D Bonan G B Drewniak B HuangM Koven C D Levis S Li F Riley W J Subin Z Swen-son S C and Thornton P E Technical Description of ver-sion 45 of the Community Land Model (CLM) National Centerfor Atmospheric Research Boulder Colorado NCAR TechnicalNote 420 2013

Ollinger S V Sources of variability in canopy reflectance andthe convergent properties of plants New Phytol 189 375ndash394httpsdoiorg101111j1469-8137201003536x 2011

Paletto A and Tosi V Forest canopy cover and canopy closurecomparison of assessment techniques Eur J For Res 128265ndash272 httpsdoiorg101007s10342-009-0262-x 2009

Pereira J Chuvieco E Beaudoin A and Desbois N Remotesensing of burned areas A review in A review of remote sens-ing methods for the study of large wildland fires edited by Chu-vieco E Universidad de Alcala Alcalaacute de Henares Spain 127ndash184 1997

Pickell P D Hermosilla T Frazier R J Coops N C and Wul-der M A Forest recovery trends derived from Landsat timeseries for North American boreal forests Int J Remote Sens37 138ndash149 httpsdoiorg1010802150704x201511263752016

Poorter L Ongers F B Aide T M Almeyda Zambrano AM Balvanera P Becknell J M Boukili V Brancalion P HS Broadbent E N Chazdon R L Craven D de Almeida-Cortez J S Cabral G A L de Jong B H J Denslow JS Dent D H DeWalt S J Dupuy J M Duran S MEspirito-Santo M M Fandino M C Cesar R G Hall JS Hernandez-Stefanoni J L Jakovac C C Junqueira A BKennard D Letcher S G Licona J C Lohbeck M Marin-Spiotta E Martinez-Ramos M Massoca P Meave J AMesquita R Mora F Munoz R Muscarella R Nunes Y RF Ochoa-Gaona S de Oliveira A A Orihuela-Belmonte EPena-Claros M Perez-Garcia E A Piotto D Powers J SRodriguez-Velazquez J Romero-Perez I E Ruiz J Saldar-riaga J G Sanchez-Azofeifa A Schwartz N B SteiningerM K Swenson N G Toledo M Uriarte M van Breugel Mvan der Wal H Veloso M D M Vester H F M Vicentini AVieira I C G Bentos T V Williamson G B and RozendaalD M A Biomass resilience of Neotropical secondary forestsNature 530 211ndash214 101038nature16512 2016

Powell S L Cohen W B Healey S P Kennedy R E Moi-sen G G Pierce K B and Ohmann J L Quantification oflive aboveground forest biomass dynamics with Landsat time-series and field inventory data A comparison of empiricalmodeling approaches Remote Sens Environ 114 1053ndash1068httpsdoiorg101016jrse200912018 2010

Powell T L Galbraith D R Christoffersen B O Harper AImbuzeiro H M A Rowland L Almeida S Brando P MLola da Costa A C Costa M H Levine N M Malhi Y

Saleska S R Sotta E Williams M Meir P and MoorcroftP R Confronting model predictions of carbon fluxes with mea-surements of Amazon forests subjected to experimental droughtNew Phytol 200 350ndash364 httpsdoiorg101111nph123902013

Powell T L Koven C D Johnson D J Faybishenko BFisher R A Knox R G McDowell N G Condit RHubbell S P Wright S J Chambers J Q and KueppersL M Variation in hydroclimate sustains tropical forest biomassand promotes functional diversity New Phytol 219 932ndash946httpsdoiorg101111nph15271 2018

Purves D W Lichstein J W Strigul N and Pacala S WPredicting and understanding forest dynamics using a simpletractable model P Natl Acad Sci USA 105 17018ndash17022httpsdoiorg101073pnas0807754105 2008

Putz S Groeneveld J Henle K Knogge C MartensenA C Metz M Metzger J P Ribeiro M C dePaula M D and Huth A Long-term carbon loss infragmented Neotropical forests Nat Commun 5 5037httpsdoiorg101038ncomms6037 2014

Quesada C A Lloyd J Anderson L O Fyllas N M SchwarzM and Czimczik C I Soils of Amazonia with particular ref-erence to the RAINFOR sites Biogeosciences 8 1415ndash1440httpsdoiorg105194bg-8-1415-2011 2011

Renno C D Nobre A D Cuartas L A Soares J V Hod-nett M G Tomasella J and Waterloo M J HAND a newterrain descriptor using SRTM-DEM Mapping terra-firme rain-forest environments in Amazonia Remote Sens Environ 1123469ndash3481 httpsdoiorg101016jrse200803018 2008

Riebeek H Why is that Forest Red and that Cloud Blue Howto Interpret a False-Color Satellite Image availabe at httpsearthobservatorynasagovFeaturesFalseColorprintallphp (lastaccess 1 May 2020) 2014

Riley W J Zhu Q and Tang J Y Weaker land-climate feed-backs from nutrient uptake during photosynthesis-inactive peri-ods Nat Clim Change 8 1002 httpsdoiorg101038s41558-018-0325-4 2018

R package R A Language and Environment for Statistical Comput-ing R Foundation for Statistical Computing availabe at httpswwwR-projectorg (last access 1 May 2020) 2017

Roberts D A Nelson B W Adams J B and Palmer F Spec-tral changes with leaf aging in Amazon caatinga Trees-StructFunct 12 315ndash325 httpsdoiorg101007s0046800501571998

Roberts D A Ustin S L Ogunjemiyo S GreenbergJ Dobrowski S Z Chen J Q and Hinckley T MSpectral and structural measures of northwest forest vege-tation at leaf to landscape scales Ecosystems 7 545ndash562httpsdoiorg101007s10021-004-0144-5 2004

Rocha G P E Vieira D L M and Simon M F Fastnatural regeneration in abandoned pastures in south-ern Amazonia Forest Ecol Manag 370 93ndash101httpsdoiorg101016jforeco201603057 2016

Rosenvald R and Lohmus A For what when and whereis green-tree retention better than clear-cutting A reviewof the biodiversity aspects Forest Ecol Manag 255 1ndash15httpsdoiorg101016jforeco200709016 2008

Rouse J W Hass R H Schell J A and Deering D W Moni-toring vegetation systems in the great plains with ERTS Proceed-



ings of the 3rd Earth ResourceTechnology Satellite (ERTS) Sym-posium Washington DC USA 10ndash14 December 1973 309ndash317 1973

Rozendaal D M A and Chazdon R L Demographicdrivers of tree biomass change during secondary succes-sion in northeastern Costa Rica Ecol Appl 25 506ndash516httpsdoiorg10189014-00541 2015

Rozendaal D M A Bongers F Aide T M Alvarez-DavilaE Ascarrunz N Balvanera P Becknell J M Bentos TV Brancalion P H S Cabral G A L Calvo-Rodriguez SChave J Cesar R G Chazdon R L Condit R DallingaJ S de Almeida-Cortez J S de Jong B de Oliveira ADenslow J S Dent D H DeWalt S J Dupuy J M Du-ran S M Dutrieux L P Espirito-Santo M M Fandino MC Fernandes G W Finegan B Garcia H Gonzalez NMoser V G Hall J S Hernandez-Stefanoni J L HubbellS Jakovac C C Hernandez A J Junqueira A B Ken-nard D Larpin D Letcher S G Licona J C Lebrija-Trejos E Marin-Spiotta E Martinez-Ramos M Massoca PE S Meave J A Mesquita R C G Mora F Muller SC Munoz R Neto S N D Norden N Nunes Y R FOchoa-Gaona S Ortiz-Malavassi E Ostertag R Pena-ClarosM Perez-Garcia E A Piotto D Powers J S Aguilar-CanoJ Rodriguez-Buritica S Rodriguez-Velazquez J Romero-Romero M A Ruiz J Sanchez-Azofeifa A de AlmeidaA S Silver W L Schwartz N B Thomas W W ToledoM Uriarte M Sampaio E V D van Breugel M van derWal H Martins S V Veloso M D M Vester H F MVicentini A Vieira I C G Villa P Williamson G BZanini K J Zimmerman J and Poorter L Biodiversity re-covery of Neotropical secondary forests Sci Adv 5 eaau3114httpsdoiorg101126sciadvaau3114 2019

Ruiz J Fandino M C and Chazdon R L Vegetation structurecomposition and species richness across a 56-year chronose-quence of dry tropical forest on Providencia island Colom-bia Biotropica 37 520ndash530 httpsdoiorg101111j1744-7429200500070x 2005

Sakschewski B von Bloh W Boit A Rammig A Kattge JPoorter L Penuelas J and Thonicke K Leaf and stem eco-nomics spectra drive diversity of functional plant traits in a dy-namic global vegetation model Glob Change Biol 21 2711ndash2725 httpsdoiorg101111gcb12870 2015

Saldarriaga J G West D C and Tharp M L Forest succes-sion in the Upper Rio Negro of Colombia and Venezuela UnitedStates httpsdoiorg1021727109527 1986

Saldarriaga J G West D C Tharp M L and Uhl CLong-term chronosequence of forest succession in the upperrio Negro of Colombia and Venezuela J Ecol 76 938ndash958httpsdoiorg1023072260625 1988

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Schmidt G Jenkerson C Masek J G Vermote E and GaoF Landsat Ecosystem Disturbance Adaptive Processing Sys-tem (LEDAPS) Algorithm Description US Department of theInterior Reston Virginia Open-File Report 2013-1057 17pp available at httppubsusgsgovof20131057 (last access2 December 2020) 2013

Schroeder T A Wulder M A Healey S P and Moisen GG Mapping wildfire and clearcut harvest disturbances in bo-real forests with Landsat time series data Remote Sens Envi-ron 115 1421ndash1433 httpsdoiorg101016jrse2011010222011

Schwartz N B Uriarte M DeFries R Bedka K M FernandesK Gutierrez-Velez V and Pinedo-Vasquez M A Fragmenta-tion increases wind disturbance impacts on forest structure andcarbon stocks in a western Amazonian landscape Ecol Appl27 1901ndash1915 httpsdoiorg101002eap1576 2017

Shimabukuro Y E Arai E Duarte V Jorge A dos San-tos E G Gasparini K A C and Dutra A C Mon-itoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data inthe Brazilian Amazon Int JRemote Sens 40 5475ndash5496httpsdoiorg1010800143116120191579943 2019

Shugart H H and West D C Forest succesion models Bio-science 30 308ndash313 httpsdoiorg1023071307854 1980

Silverio D Brando P Bustamante M C Putz F E Magna-bosco Marra D Levick S R and Trumbore S Fire frag-mentation and windstorms A recipe for tropical forest degra-dation J Ecol 107 656ndash667 httpsdoiorg1011111365-274513076 2018

Sombroek W Spatial and temporal patterns of Amazonrainfall ndash Consequences for the planning of agricul-tural occupation and the protection of primary forestsAmbio 30 388ndash396 httpsdoiorg1016390044-7447(2001)030[0388satpoa]20co2 2001

Steininger M K Satellite estimation of tropical sec-ondary forest above-ground biomass data from Braziland Bolivia Int J Remote Sens 21 1139ndash1157httpsdoiorg101080014311600210119 2000

Swaine M D and Whitmore T C On the definition of ecologi-cal species groups in tropical rain forests Vegetatio 75 81ndash86httpsdoiorg101007bf00044629 1988

Terborgh J Zhu K Loayza P A and Valverde F C Seed limi-tation in an Amazonian floodplain forest Ecology 100 e02642httpsdoiorg101002ecy2642 2019

Tollefson J Forest ecology Splinters of the Amazon Nature 496286ndash289 2013

Trumbore S Brando P and Hartmann H Foresthealth and global change Science 349 814ndash818httpsdoiorg101126scienceaac6759 2015

Tucker C J Red and photographic infrared linear combinationsfor monitoring vegetation Remote Sens Environ 8 127ndash150httpsdoiorg1010160034-4257(79)90013-0 1979

Tucker C J A critical review of remote sensing andother methods for nondestructive estimation of standingcrop biomass Grass and Forage Science 35 177ndash182httpsdoiorg101111j1365-24941980tb01509x 1980

USGS US Geological Survey Surface Refelctance Produc Guideavailabe at httpswwwusgsgovmediafiles)(last access 17February 2017) 2017

Valencia G M Anaya J A and Caro-Lopera F J Imple-mentation and evaluation of the Landsat Ecosystem DisturbanceAdaptive Processing Systems (LEDAPS) model a case studyin the Colombian Andes Revista de Teledeteccion 46 83ndash101httpsdoiorg104995raet20163582 2016



van Doninck J and Tuomisto H A Landsat compositecovering all Amazonia for applications in ecology andconservation Remote Sens Ecol Conserv 4 197ndash210httpsdoiorg101002rse277 2018

Vermote E F Tanre D Deuze J L Herman M and MorcretteJ J Second Simulation of the Satellite Signal in the Solar Spec-trum 6S An Overview IEEE T Geosci Remote 35 675ndash6861997

Vermote E Justice C Claverie M and Franch B Prelimi-nary analysis of the performance of the Landsat 8OLI land sur-face reflectance product Remote Sens Environ 185 46ndash56httpsdoiorg101016jrse201604008 2016

Vieira I C G de Almeida A S Davidson E A Stone T Ade Carvalho C J R and Guerrero J B Classifying succes-sional forests using Landsat spectral properties and ecologicalcharacteristics in eastern Amazonia Remote Sens Environ 87470ndash481 httpsdoiorg101016jrse200209002 2003

Vieira S de Camargo P B Selhorst D da Silva R Hutyra LChambers J Q Brown I F Higuchi N dos Santos J WofsyS C Trumbore S E and Martinelli L A Forest structureand carbon dynamics in Amazonian tropical rain forests Oecolo-gia 140 468ndash479 httpsdoiorg101007s00442-004-1598-z2004

Walker A P Beckerman A P Gu L Kattge J Cernusak LA Domingues T F Scales J C Wohlfahrt G WullschlegerS D and Woodward F I The relationship of leaf photosyn-thetic traits ndash Vcmax and Jmax ndash to leaf nitrogen leaf phosphorusand specific leaf area a meta-analysis and modeling study EcolEvol 4 3218ndash3235 httpsdoiorg101002ece31173 2014

Williamson G B Bentos T V Longworth J B and MesquitaR C G Convergence and divergence in alternative successionalpathways in Central Amazonia Plant Ecol Divers 7 341ndash348httpsdoiorg101080175508742012735714 2014

Winter K and Lovelock C E Growth responses ofseedlings of early and late successional tropical foresttrees to elevated atmospheric CO2 Flora 194 221ndash227httpsdoiorg101016s0367-2530(17)30900-3 1999

Woodcock C E Allen R Anderson M Belward A Bind-schadler R Cohen W Gao F Goward S N Helder DHelmer E Nemani R Oreopoulos L Schott J ThenkabailP S Vermote E F Vogelmann J Wulder M A and WynneR Free access to Landsat imagery Science 320 1011ndash10112008

Wulder M A Masek J G Cohen W B LovelandT R and Woodcock C E Opening the archiveHow free data has enabled the science and monitoringpromise of Landsat Remote Sens Environ 122 2ndash10httpsdoiorg101016jrse201201010 2012

Xiao Y F Zhao W J Zhou D M and Gong H LSensitivity Analysis of Vegetation Reflectance to Biochem-ical and Biophysical Variables at Leaf Canopy and Re-gional Scales IEEE T Geosci Remote 52 4014ndash4024httpsdoiorg101109tgrs20132278838 2014

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Zhu Q Riley W J Tang J Y Randerson J R Collier NHoffman F M Yang X and Bisht G Representing nitrogencarbon and phosphorus interactions in the ELMv1-ECA LandModel Model development and global benchmarking J AdvModel Earth Sy httpsdoiorg1010292018MS001571 2019


Abstract

Introduction

Study area and methods

Study area and sites

Landsat satellite data and procedures

Forest regrowth simulation in ELM-FATES

Results

L5 bands and disturbances

Pathways of forest regrowth

FATES model and regrowth from forest disturbance

Discussion

Conclusions

Code and data availability

Supplement

Author contributions

Competing interests

Acknowledgements

Financial support

Review statement

References


forest degradation (Silverio et al 2018 Schwartz et al2017) Under natural conditions disturbed forests recoverto their pre-disturbance conditions through complex inter-actions that vary across spatial and temporal scales (Chaz-don 2014) In general it is known that forest pathways ofregrowth (ie pattern of regrowth) are initiated with fast-growing and shade-intolerant species (pioneers) that are es-tablished from seeds and dominate a few years after dis-turbance followed by the recruitment and establishment ofshade-tolerant species and finally the establishment of aclosed-canopy old-growth forest (Chazdon 2014 Denslow1980 Mesquita et al 2001 Swaine and Whitmore 1988)Understanding of the dynamics of forest regrowth followingnatural and anthropogenic disturbances in the Amazon how-ever has so far been limited by lack of long-term observa-tional data showing different stages of forest regrowth

Remote sensing data can be used to assess forest regrowthvia changes in spectral characteristics (Frolking et al 2009Roberts et al 2004 Schroeder et al 2011 DeVries et al2015 Lucas et al 2002 McDowell et al 2015) Landsatsatellite imagery is appropriate for examining land surfacechanges due to its long-term record availability and spa-tial resolution of 30 m (Loveland and Dwyer 2012 Wul-der et al 2012 Alcantara et al 2011 Woodcock et al2008 Cohen and Goward 2004 Hansen et al 2013) Land-sat imagery has been used to detect forest disturbance andpathways of regrowth in temperate and boreal forests in theUnited States and Canada (Kennedy et al 2007 2010 2012Pickell et al 2016 Schroeder et al 2011 Dolan et al2009 2017) and for the detection of forest disturbance andregrowth of biomass in the Amazon (Vieira et al 2003 De-Vries et al 2015 Lucas et al 2002 Powell et al 2010Lu and Batistella 2005 Steininger 2000 Shimabukuro etal 2019) These studies suggest that Landsat may be sen-sitive to different types of disturbances and their subsequentpathways of forest regrowth in the Amazon but these appli-cations have not yet been assessed

The ability to forecast future trajectories of forests de-pends upon the fidelity with which disturbance and regrowthprocesses are represented within terrestrial biosphere mod-els These models capture processes operating between theleaf and landscape scales and can represent regrowth changesover large regions (Fisk 2015) long time periods (Holm etal 2017 Putz et al 2014) a range of disturbance intensities(Powell et al 2013) and interactions between multiple dis-turbance types and disturbance histories (Hurtt et al 2006)But how well these models simulate and capture the diversearray of successional pathways of forest regrowth after an-thropogenic or natural disturbances needs to be more thor-oughly evaluated given observed increases in disturbancerates (Lewis et al 2015) The few modeling studies analyz-ing tropical disturbances have focused on the effects of frag-mented edges or the regrowth of specific tree species (Dantasde Paula et al 2015 Kammesheidt et al 2002)

Cohort-based dynamic vegetation demographic models(VDMs) are particularly suitable tools for expanding uponthese studies (Fisher et al 2018) In contrast to traditionalland surface models VDMs include ecological demographicprocesses such as discretized vegetation height with differ-ent plant types competing for light within the same verti-cal profile and heterogeneity in light availability along dis-turbance and recovery trajectories all of which facilitatedirect simulation of regrowth dynamics during successionThis structured demography in VDMs allows for simula-tion of canopy gap formation competitive exclusion and co-existence of vegetation thus producing variability in foreststand age and composition (Fisher et al 2010 Moorcroft etal 2001 Longo et al 2019) VDMs are designed for vege-tation to dynamically respond to variation in traits (Fyllas etal 2014) leading to differences in plant mortality growthand recruitment rates (Shugart and West 1980) These at-tributes influence the ecosystem fluxes of carbon energyand water (Bonan 2008) Despite their potential for simu-lating regrowth processes there has been limited VDM test-ing of regrowth following tropical forest disturbances Im-portantly projections of future climate using Earth systemmodels (ESMs) are strongly influenced by the terrestrial car-bon cycle in the tropics (Arora et al 2013 Friedlingstein etal 2014) which is strongly regulated by disturbance and re-growth (Chazdon et al 2016 Trumbore et al 2015 Magna-bosco Marra et al 2018)

Observational studies have shown that Amazon forests fol-low a range of successional regrowth pathways after clear-cutting and burning (Mesquita et al 2001 2015) Thus thetype of disturbance and the pre-disturbance ecosystem stateare important determinants of the successional pathways offorest regrowth Nonetheless this information is difficult toobtain in remote forests of the Amazon In this study weaddressed this issue in the context of windthrow clear-cutand clear-cut and burned (cut+ burn) disturbances to analyze(i) the sensitivity of Landsat to detect and distinguish theserelevant disturbances and their pathways of forest regrowthand (ii) the time span of forest regrowth This understandingof forest regrowth was used to (iii) test the modeled forest re-growth of the Functionally Assembled Terrestrial EcosystemSimulator (FATES) model (Fisher et al 2015) implementedin the Energy Exascale Earth System Model (E3SM) LandModel (ELM) (Riley et al 2018 Zhu et al 2019) ELM-FATES This study provides insights into the use of remotesensing to identify drivers of forest disturbance in the Ama-zon and a better understanding of the pathways of forest re-growth provides insights into the resilience of these foreststo repeated disturbances and can help improve land models
































3 Results




































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5 Conclusions












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Abstract

Introduction





Results




Discussion

Conclusions


Supplement


Competing interests

Acknowledgements

Financial support

Review statement

References































3 Results




































4 Discussion



































5 Conclusions












References






























































































































































































Abstract

Introduction





Results




Discussion

Conclusions


Supplement


Competing interests

Acknowledgements

Financial support

Review statement

References

































4 Discussion



































5 Conclusions












References






























































































































































































Abstract

Introduction





Results




Discussion

Conclusions


Supplement


Competing interests

Acknowledgements

Financial support

Review statement

References

































5 Conclusions












References






























































































































































































Abstract

Introduction





Results




Discussion

Conclusions


Supplement


Competing interests

Acknowledgements

Financial support

Review statement

References




References






























































































































































































Abstract

Introduction





Results




Discussion

Conclusions


Supplement


Competing interests

Acknowledgements

Financial support

Review statement

References










































































































































































Abstract

Introduction





Results




Discussion

Conclusions


Supplement


Competing interests

Acknowledgements

Financial support

Review statement

References

landsat near-infrared (nir) band and elm-fates sensitivity to … · 2020. 12. 9. · al., 2017;...

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