laboratory of forest geomatics wall-to-wall, spatial ... · study area extend of 48 657 km2 more...
TRANSCRIPT
Wall-to-wall, spatial prediction of growing stock volume based on ItalianNational Forest Inventory plots and remotely sensed data
Chirici, G., Giannetti, F., Travaglini, D.,
McRoberts, R., Maselli, F., Chiesi, M.,
Pecchi, M., Corona, P.
Laboratory of Forest Geomatics
htt
p:/
/ww
w.s
lu.s
e/s
kogs
kart
a/
htt
p:/
/ww
w.m
etla
.fi/
julk
aisu
t
Introduction
National Forest Inventory sampling data
Wall-to-wallspatial
predictionRemote Sensing data
Exam
ple
s
htt
ps:
//w
ww
.fia
.fs.
fed
.us/
+Small-area estimation
Bio
div
erit
y
Wo
od
p
rod
uct
ion
Soci
al b
enef
its
Car
bo
n S
tock
Imp
ort
ant
to m
on
ito
r, m
eas
ure
and
man
age
Mapping
• NORDIC EUROPEAN COUNTRIES: Sweden, Finland, Denmark, Norway (Næsset et al., 2004; Nord-Larsen and Schumacher, 2012; Tomppo et al., 2008)
• NORTH AMERICA CONTINENT: Canada (Boudreau et al., 2008; Matasci et al., 2018), USA (Blackardet al., 2008)
• CENTRAL EUROPEAN COUNTRIES: Austria (Hollaus et al., 2009) and Switzerland (Waser et al., 2017, 2015)
are nowadays integrating the classical design-based sampling-based inventories with forest mapping using remote sensing
Forest cover Growing Stock Volume Forest area Biomass
Multispectral satellite images
LiDAR Photogrammetric data
Introduction
VARIABLES TO BE PREDICTED PREDICTORS
And in Mediterranean forests?Tests carried out in study sites
ITALY:Chirici et al., 2008; Chirici et al., 2016; Mura et al., 2017SPAIN: Fernández-Landa et al., 2018GREECE: Crysafis et al., 2017
Introduction
Moderate Resolution National Growing Stock Map of Italy
NATIONAL FOREST INVENTORY PLOTS WERE NOT AVAILABLE
Local forest inventory plots Predictors: MODIS + GLAS
canopy height model and the CORINE land cover maps
Grid resolution 1 km x 1 km
Introduction
INFC 20056.782 plot230.875 measured trees
Italian National Forestry and carbon Inventory
(INFC) were became open-access and freely
available on-line in 2016 as a spatial database at https://www.inventarioforestale.org/ (Borghettiand Chirici, 2016)
Introduction
New Possibilities
Unalligned systematic sampling design
Aims
The current study aimed atproducing:• set up the best option for wall-to-wall
estimates of forest growing stock (GSV) inItaly
• to do so we tested several approachesin a large test area (i.e. 48 657 km2) in Italyby combining remotely sensed, ancillaryvariables and INFC plots
• GSV spatial estimation with a high spatial
resolution (23x23 m)
• small-area model-assisted estimation
Study area
extend of 48 657 km2
more than 16 248 km2 of forest and other
wood land (INFC, 2004), 1350 plots
Complex environment: precipitation ranging
between 3000 mm and 600 mm, mean annual temperature ranging between 6 and 16 °C
Complex species composition: downy oak,
pedunculated oak, Turkey oak, and sessile oak. With beech and chestnut. Coniferous species mainly from artificial plantations (maritime pine, black pine, white fir, douglas fir).
Complex structure: high forests and coppice (88%)
Study area
GSV distribution in INFC2005 plots
NUMBER OF PLOT 1350
Poor in GSV!
“Italy is rich of poor forests” (Ciancio, 2000)
Landsat 7 ETM+ vs. IMAGE2006 (IRS+SPOT)
CHM from LiDAR GLONASS-ICESAT
PALSAR JASA
DTM
Climate Variables
Soil Variables
Available Predictors
projectionFUSO_1 zone (Est=East, Ovest=West), Gauss-BoagaprojectionCODISTAT05 administrative region, Italian National Statistics codeNOMEDIST05 administrative region, nameCODCFOR forest category, codeLAND_B1: feature variable Landsat 7 ETM+ B1 from the single pixelLAND_B2: feature variable Landsat 7 ETM+ B2 from the single pixelLAND_B3: feature variable Landsat 7 ETM+ B3 from the single pixelLAND_B4: feature variable Landsat 7 ETM+ B4 from the single pixelLAND_B5: feature variable Landsat 7 ETM+ B5 from the single pixelLAND_B6: feature variable Landsat 7 ETM+ B6 from the single pixelLAND_B7: feature variable Landsat 7 ETM+ B7 from the single pixelLAND_B1_DF: feature variable Landsat 7 ETM+ B1 from 3x3 windowLAND_B2_DF: feature variable Landsat 7 ETM+ B2 from 3x3 windowLAND_B3_DF: feature variable Landsat 7 ETM+ B3 from 3x3 windowLAND_B4_DF: feature variable Landsat 7 ETM+ B4 from 3x3 windowLAND_B5_DF: feature variable Landsat 7 ETM+ B5 from 3x3 windowLAND_B6_DF: feature variable Landsat 7 ETM+ B6 from 3x3 windowLAND_B7_DF: feature variable Landsat 7 ETM+ B7 from 3x3 windowIMAGE2006_: feature variable IRS/SPOT B1 from the single pixelIMAGE20061: feature variable IRS/SPOT B2 from the single pixelIMAGE20062: feature variable IRS/SPOT B3 from the single pixelIMAGE20063: feature variable IRS/SPOT B4 from the single pixelIMAGE20064: feature variable IRS/SPOT B1 from 3x3 windowIMAGE20065: feature variable IRS/SPOT B2 from 3x3 windowIMAGE20066: feature variable IRS/SPOT B3 from 3x3 windowIMAGE20067: feature variable IRS/SPOT B4 from 3x3 windowDTM: feature variable elevationCOPERNICUS: feature variable percent crown coverradar_HV: feature variable radar PALSAR HV polarizationradar_HH: feature variable radar PALSAR HH polarizationSLOPE: feature variable slopeh_prec: feature variable annual total precipitation (average past 30 years)temp_med: feature variable mean annual temperature (average past 30 years)tmax_med: feature variable average of maximum temperatures (average past 30 years)tmin_med: feature variable average of minimum temperatures (average past 30 years)AWC_SUB_P: feature variable subsoil available water capacityAWC_TOP_P: feature variable topsoil available water capacityVS_P: feature variable volume of stonesDR_P Depth to rockCEC_SUB_P Subsoil cation exchange capacity.CEC_TOP_P Topsoil cation exchange capacity.DIMP_P
List
of
Pre
dic
tors
IMAGE2006IRS LISS IIISPOT HRG20 mcloud freenormalized
Landsat 7 ETM+200530 mcloud freenormalized
PALSAR ALOSHV, HH200625 m
4 Different predicting models:
+ k-NN+Random Forest
Non-parametrics
Parametrics
+ Multivariate Linear Regression+ Locally spatially weighted regression
Leave one out accuracy evaluation at plot levelR2, RMSE, RMSE%, BIAS, BIAS%
Most accurate for spatial estimation GSV
GSV INFC PLOTS Remote Sensing data
AREA BASED APPROCH
Accuracy assessment of GSV map. With independent dataset (stand level information derived by forest management plans)
Model assisted estimation (GREG). Small-scale estimation at province level of GSV
MET
HO
DS
Result – selected predictors by
different imputation
Results – different imputation methods
The performance of the different imputations
were evaluated using the leave-one-out
(LOO) cross validation. We calculate for each
model the coefficient of determination (R2)
between the measured and predicted values,
and the root main square error (RMSE) :
𝑅𝑀𝑆𝐸 = 𝑖=1
𝑛 (𝑦𝑖 − 𝑦𝑖)2
𝑛
Predicted vs observed Leave one out
Random Forest wall-to-wall prediction
The independent error was calculated on the basis of forest management data R2 = 0.62 and RMSE40 m3ha-1
Independent validation
LOO R2 = 0.61 and RMSE 52 m3ha-1
Random Forest wall-to-wall prediction
National application
848 Lansat 5 TM images: 312 2004, 282 2005, 254 2006Best Available Pixel in Google Earth Engine
Wall-to-wall spatial prediction as
a bridge between National Forest Inventory and local forest management
Small-area estimations: model-assisted generalized regression (GREG) estimators
The map-based estimate of GSV area was:
𝑆𝐸 𝜇𝐺𝑅𝐸𝐺 = 𝑉 𝑎𝑟 𝜇𝐺𝑅𝐸𝐺 =1
𝑛(𝑛−1) 𝑖=1
𝑛 (𝑒𝑖 − 𝑒)2 were 𝑒𝑖 = ( 𝑦𝑖−𝑦𝑖) and 𝑒 =1
𝑛 𝑖=1
𝑛 𝑒𝑖
Forest pixel
N= total number of population units
𝑦𝑖 = the model prediction of GSV for the j-th population unit
𝐵 𝑖𝑎𝑠( 𝜇𝑚𝑎𝑝) =1
𝑛
𝑖=1
𝑛
( 𝑦𝑖−𝑦𝑖)
𝑦𝑖 = the observed value of GSV for the i-th plot
n = number of population units observed
𝜇𝐺𝑅𝐸𝐺 = 𝜇𝑚𝑎𝑝 − 𝐵 𝑖𝑎𝑠( 𝜇𝑚𝑎𝑝)
Forest pixels with field observation
No Forest pixel 𝜇𝑚𝑎𝑝 =1
𝑁
𝑗=1
𝑁
𝑦𝑖
(Breidt and Opsomer, 2009; McRoberts et al., 2016)
23m
23
m
model-assisted, generalized regression (GREG) estimators
Region Province Province Area(Km2)
Total Forest Area (ha)(INFC, 2005)
SE Total Forest Area (%) (INFC, 2005)
ni GSV 𝜇𝐺𝑅𝐸𝐺
GSV𝑆𝐸 𝜇𝐺𝑅𝐸𝐺
Tuscany Arezzo 323300 179219 4.2 127 111.13 8.5Firenze 351369 178500 4.2 117 151.89 12.3Grosseto 450312 197961 4.0 116 98.35 8.6Livorno 121371 47364 8.6 23 108.88 16.7Lucca 177322 121044 5.2 64 198.86 13.1Massa Carrara 115468 86713 6.2 30 148.69 14.69Pisa 244472 95053 6.0 54 98.82 11.28Pistoia 96412 50640 8.3 32 214.30 43.91Prato 36572 23334 12.3 13 186.87 24.1Siena 38298 171710 4.3 115 85.81 6.09
Emilia-Romagna
Bologna 370232 100761 5.6 56 112.60 11.12Forlì-Cesena 237840 106621 5.5 70 86.08 15.24Modena 268802 68695 7.0 49 123.97 14.97Parma 344748 152542 4.4 85 170.81 9.94Piacenza 258586 84837 6.2 51 111.66 13.07Ravenna 185944 21332 13.0 19 80.55 12.39Reggio Emilia 229126 63518 7.3 58 126.75 11.26
Liguria La Spezia 88135 54229 7.6 46 144.55 16.34
SMALL AREA ESTIMATION
Conclusions The estimation of growing stock volume using INFC2005
plot and remote sensing auxiliary variables appears
feasible at country level also in complex
Mediterranean environments and without LiDAR data
The accuracy calculated at single pixel level is useful ONLY for in the optimization phase and to choose the estimation method
The results will be produced in aggregated form (Province, Municipality, forest parcels) -> SMALL AREA ESTIMATION
The Landsat data are confirmed the best type of
predictive variables from optical satellite for vast areas
Different methods achieve similar performance, limited commission values
Future predictions integrating LiDAR