romo-leon2014-using remote sensing tools to assess land use transitions in unsustainable arid...

8112019 Romo-Leon2014-Using Remote Sensing Tools to Assess Land Use Transitions in Unsustainable Arid Agro-ecosystems

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Using remote sensing tools to assess land use transitions inunsustainable arid agro-ecosystems

Jose Raul Romo-Leon a Willem JD van Leeuwen ab Alejandro Castellanos-Villegas ac

a School of Natural Resources and the Environment Arizona Remote Sensing Center 1955 E Sixth Street The University of Arizona Tucson AZ 85721 USAb School of Geography and Development The University of Arizona Tucson AZ 85721 USAc Departamento de Investigaciones Cienti 1047297cas y Tecnologicas Universidad de Sonora Luis Donaldo Colosio sn entre Sahuaripa y Reforma Colonia Centro

CP 83000 Hermosillo Sonora Mexico

a r t i c l e i n f o

Article history

Received 22 August 2012

Received in revised form

1 March 2014

Accepted 6 March 2014

Available online 12 April 2014

Keywords

Desert succession

Land-cover change

Remote sensing

Sonoran Desert

Sustainable management policies

a b s t r a c t

This research investigates the human impact on land-cover dynamics in arid agro-ecosystems Our study

area was La Costa de Hermosillo (northwestern Mexico) where the unregulated use of water resources

has resulted in the abandonment of irrigated agricultural 1047297elds and a shift to new economic activities

Using remote sensing and ancillary datasets combined with classi1047297cation and regression tree (CART)

models we mapped land-cover class distributions over 22 years (1988e2009) to characterize agricul-

tural changes following management decisions Our land-cover classi1047297cation maps had an overall ac-

curacy of over 80 Using these maps we were able to show the decrease in agriculture from

approximately 115066 to 66044 ha between 1988 and 2009 and the conversion to alternative economic

activities with aquaculture increasing from 0 to 10083 ha during the same period Our analyses also

show the temporalespatial dynamics of land-use management practices which suggest that imple-

mentation of the remote sensing methods developed in this manuscript may contribute to bridging the

gap of knowledge between ecological effects and unsustainable management practices and decrease the

time required to inform and make policy decisions in arid agro-ecosystems

2014 Elsevier Ltd All rights reserved

1 Introduction

11 Agricultural land use in arid ecosystems

Increasing demands and needs from a growing human popu-

lation have resulted in the widespread use and rapid conversion of

most natural land-cover types to agriculture This conversion trend

only slowed down in recent decades because of the intensi1047297cation

of agricultural practices resulting in increased crop yields from

fertilizer and pesticide use (Tilman et al 2002) However other

limiting factors (water sunlight or temperature) for each particularagricultural region also affect productivity (Rabbinge 1993)

When agricultural practicestake place in aridenvironments large

supplies of fresh water are required for crop irrigation (Ewel 1999)

Therefore as itis fornaturalecosystemswater isoftenone of themost

important limiting factors for agriculture in arid lands In many arid

regions around the world irrigation agriculture leads to unsustain-

able land uses which has resulted in therapid abandonment of large

agricultural areas in recent years (Halvorson et al 2003)

An example of such an abandonment occurred in northwestern

Mexico where irrigation agriculture had a boom and bust cycle of

approximately 1047297fty years (Halvorson et al 2003 Moreno-Vaacutezquez

2006) Initially higher volumes of ground-water were required to

irrigate crops because of increasedland used for agriculture and the

use of crops with a high-water requirement Increased water

extraction resulted in marine water intrusions to the aquifer andincreased salt levels in many agricultural 1047297elds which had major

economic and environmental impacts (Andrews 1981 Halvorson

et al 2003 Rangel Medina et al 2002) To mitigate further

degradation of the underground aquifers governmental regula-

tions were enacted to reduce and control water extraction for crop

irrigation however considerable damage had already been done It

is recognized that methodologies to quantify the success of these

efforts are not documented or well developed

The unsustainable use of ground-water in desert regions has

been documented and a required shortening of the cycle between

scienti1047297c discovery management proposals to policy makers and

Corresponding author Current address Luis Donaldo Colosio sn entre

Sahuaripa y Reforma Colonia Centro CP 83000 Hermosillo Sonora Mexico

Tel thorn52 662 259 2169 fax thorn52 662 259 2195

E-mail addresses joser2emailarizonaedu (JR Romo-Leon) leeuwemail

arizonaedu (WJD van Leeuwen) acastellguaymasusonmx (A Castellanos-

Villegas)

Contents lists available at ScienceDirect

Journal of Arid Environments

j o u r n a l h o m e p a g e w w w e l s e v i e r c om l o c a t e j a r i d e n v

httpdxdoiorg101016jjaridenv201403002

0140-1963

2014 Elsevier Ltd All rights reserved

Journal of Arid Environments 106 (2014) 27e35



the implementation of proper regulations has been highlighted

(MacKay 2006) Land-use analysis to understand ecosystem func-

tions has been recognized as a necessary tool to increase stake-

holder participation and involvement in sustainable management

practices (Koumlnig et al 2012) We propose that ef 1047297cient and accurate

monitoring protocols to evaluate land use and management prac-

tices in the desert can lead to the development of better policies for

sustainable agriculture We strongly believe that because of the

high rate of conversion of land cover in agricultural areas it is

crucial to understand and monitor changes across time to effec-

tively assess management practices (from government and stake-

holders) and mitigate potentially adverse ecological impacts

12 Remote sensing and land cover dynamics

Land-cover changes modify landscapes and ecosystem pro-

cesses all over the world (Lambin et al 2001) To measure and

detect such change numerous approaches that use remote sensing

tools are available (Coppin et al 2004 Lu et al 2004 Mas 1999)

In this study we plan to use the post-classi1047297cation comparison as a

change detection approach (Lu et al 2004 Singh 1989) to assess

changes in the distribution of agricultural lands at 1047297ve dates overdifferent years in our study area

The main goal of this research is to design and apply remote

sensing approaches with the potential of improving monitoring

and assessment of rapid land-cover change phenomena and eval-

uate land-use trends for arid agroecosytems These improvements

could lead to more rapid development of informed and sustainable

management policies To test such tools we reconstruct land-use

and land-cover dynamics in an arid agricultural region in north-

western Mexico to assess how land cover has changed over the last

22 years Using this case study we evaluate and discuss those

changes with respect to the multiple drivers (economic political

and ecological) that impact the use of arid lands

2 Materials and methods

21 Study area

This research tests remote sensing tools tailored for arid lands to

assess regional land-cover change over time with respect to agri-

culture to develop informed input for decision making policies We

selected the area of La Costa de Hermosillo in Sonora Mexico as the

study area This arid agro-ecosystem is located in northwestern

Mexico west of the city of Hermosillo and approximately between

the coordinates 28 140Ne28 570N latitude and 111 150We111

450W longitude La Costa de Hermosillo is located within an exoreic

watershed and its surface waters run to the Gulf of California

(Rangel Medina et al 2002) The 833 km2 irrigation district of La

Costa de Hermosillo (Castellanos et al 2005) is located within theplains and the central gulf subdivision of the Sonoran Desert

(McGinnies 1981 Shreve and Wiggins 1964) However agricul-

tural practices expanded beyond the district boundaries to occupy

over 2000 km2 In an effort to characterize agricultural areas and

the landscape surrounding these developments our study area

extends to the coast line for a total area of 5090 km2 (Fig 1)

Elevation in the study area ranges from 0 to 455 m Mean annual

temperature for the region ranges between 18 and 22 C with

maximum temperatures of over 45 C during the summer and

minimum temperatures above 0 C in the winter Precipitation

varies from 100 to 300 mm-yr1 in a bimodal summerwinter

regime with the wettest months being July and August Potential

evapotranspiration range is 2200e2500 mm-yr1 (Halvorson et al

2003)

Currently numerous agricultural developments are taking place

in the study area Two can be considered the most important

because of the large extent of land used for agriculture We delin-

eated these areas based on available aerial photography from the

late 1970s and Landsat images from the late 1980s These areas of

study are referred to as northern and southern agricultural de-

velopments and their areal extents are 3533 and 16632 km2

respectively (Fig 2)

Fig 1 Map of Sonora the Hermosillo municipality and the selected study area The

study area is based on the location of the La Costa de Hermosillo irrigation district and

subsequent agricultural expansion

Fig 2 Map of the study area showing a Landsat-based Normalized Difference Vege-

tation Index (NDVI) from 1988 and the delineation of the southern and northern

agricultural developments

JR Romo-Leon et al Journal of Arid Environments 106 (2014) 27 e 3528



22 Datasets and derived variables used for land usecover

classi 1047297cation

In the case of arid ecosystems precipitation measurements

show a high correlation with land cover and seasonality (Beatley

1974 Loik et al 2004 Young and Nobel 1986) At La Costa de

Hermosillo the majority of precipitation occurs during the summer

monsoon therefore our remotely sensed data must be selected

before and after this season to catch the phenological differences

between vegetation types By using two images capturing two

different phenological stages (before and during the growing sea-

son) we inform the land-cover classi1047297cation algorithm by depict-

ing how much change the different classes undergo from one stage

to another this becomes a new differentiation factor when we

conduct the thematic classi1047297cation

We selected 1047297ve years (1988 1993 1998 2004 and 2009) from

which we chose two Landsat TM scenes one pre- and another post-

monsoon season Each of the images used for the analysis was

processed with the cosine of the zenith angle (COST) model to

minimize atmospheric in1047298uences on the re1047298ectance signal

(Chander et al 2007 Chavez 1996) The Instituto Nacional de

Estadistica en Informacion Geogra1047297ca (INEGI) also supplied us with

a digital elevation model (DEM) dataset that was resampled to thesame spatial resolution and projection as the Landsat scenes

The atmospherically corrected multispectral and DEM data were

used to derive a set of remote sensing and topographic variables

respectively for use in our classi1047297cation (Table 1)

23 Land-cover classi 1047297cation scheme

To derive the land-cover classes in our study area we devised a

hybrid approach using the land-cover classi1047297cation scheme pro-

posed by Anderson et al (1976) and the vegetation-communities

scheme proposed by CONAFOR in the Mexican national forest in-

ventory (SARH 1994) In this study we used Level I classes that

Anderson et al (1976) recommended for Landsat type sensorsLevel I classes attempt to describe the general land use represented

by the sensor signal (eg water agriculture urban area etc)

without going into further detail on the classes (eg urban-

residential urban-commercial and services etc) Further subdivi-

sion of certain classes was achieved by using the vegetation

schemes proposed by CONAFOR which further describes vegeta-

tion communities according to plant physiognomic characteristics

(height shape and form) 1047298oristic composition and ecological

distribution (Table 2)

Because of the high degree of confusion introduced into our

automated classi1047297cation process by the class denoting urban areas

we decided to manually assign this class to corresponding land-

marks that were well-identi1047297ed in the landscape We used aerial

photos high resolution images and the Landsat dataset for this

process An automated supervised classi1047297cation approach was

conducted for the remaining land usecover classes (Table 2)

24 Classi 1047297cation model training datasets and accuracy

assessment

241 CART Model

We used the classi1047297cation and regression tree (CART) modeling

approach (Breiman et al 1984 Dersquoath and Fabricius 2000) for the

classi1047297cation of land-cover types This approach has been widelyused to achieve land-cover classi1047297cations of landscapes at different

resolutions (De Fries et al 1998 Friedl et al 2002 Lowry et al

2007 Rogan et al 2002) and has been documented to outper-

form other classi1047297cation techniques (Hansen et al 1996 Pal and

Mather 2003) A CART model classi1047297cation was generated for

each year using the combined variables of the two Landsat images

collected per period and the topography products derived from the

DEM A similar approach is described by Villarreal et al (2012) and

was successfully applied to classify and analyze land-cover change

in semi-arid regions in Arizona

242 Classi 1047297cation training

To achieve high accuracy in our classi1047297ed thematic maps we

decided to use the supervised classi1047297cation approach by training

our classi1047297er with samples from each of the classes (Tso and

Mather 2009) Each classi1047297cation required the sampling of

training points that were speci1047297cally identi1047297ed for the periods we

Table 1

Variables used in the land cover classi1047297cation derived from Landsat spectral

re1047298ectance data (each year) and a DEM These variables were processed to a com-

mon resolution (30 m)

No of layers Variable Reference

1 Enhanced Vegetation

Index 2 (EVI2)

( Jiang et al 2008)


Index (EVI)

(Huete et al 2002

van Leeuwen et al 1999)

1 Modi1047297ed Soil Adjusted

Vegetation Index MSAVI

(Qi et al 1994)

1 Normalized Difference

Vegetation Index (NDVI)

(Tucker 1979)

1 Soil Adjusted Total

Vegetation Index (SATVI)

(Marsett et al 2006)

1 Soil Adjusted

Vegetation Index (SAVI)

(Huete 1988)

6 Re1047298ectance

6 Tasseled Cap (Crist and Cicone 1984)

6 Texture (Alhaddad et al 2007

Asner et al 2002)

12 Multitemporal

Kauth Thomas (MKT)

(Collins and Woodcock 1996)

12 Principal Components (Collins and Woodcock 1996

Fung and LeDrew 1987)

1 Elevation

1 Aspect

1 Slope

Table 2

Land-cover classi1047297cation scheme used for La Costa de Hermosillo region

C la ss ID La nd -cover c la ss D escr ip ti on

1 Active agriculturea Row crops orchard and pastures

2 Fallow 1047297elds Active agricultural 1047297elds not planted

at the particular time the imagery

was acquired

3 Aquaculture Pools destined for the farming of 1047297sh

and shrimp

4 Barrenscarce

vegetation

Rock barren soil or less than 10 cover

of vegetation

5 Desert shru bla nd s M ostl y fol ia ge shru bs fou nd in the

driest regions of Mexico

6 Succ ulent shr ub lan ds Plan ts with thic k 1047298eshy stems located

on rocky shallow soils and found on

hills and hill slopes Succulent shrubs

are also found in these communities

7 Estuarywater Water ponds and estuaries

8 Halophytecoastal

vegetation

Scrub and herbaceous vegetation that

can grow in soil with high salt content

9 Mesquite shrublands County dominated principally by

mesquite and other trees

10 Urban area Area covered by structures impervious

surfaces vegetation and dirt

a This class collapsed all types of active agriculture to observe if we managed to

obtain high accuracy with our classi1047297

cation algorithms

JR Romo-Leon et al Journal of Arid Environments 106 (2014) 27 e 35 29



classi1047297ed or were in close proximity in time (where no visually

perceptible changes occurred)

For each of the classes the number of training samples varied

between 35 and 130 depending on class distribution and area

occupied by the class across the landscape The classes that occupy

less area were less intensively sampled (eg estuarywater) We

intended to obtain the highest accuracy possible for the classes

related to agricultural activities and aquaculture Therefore these

areas were sampled more intensively even if the land-cover type

was not extensively distributed in our study area

The training samples were obtained using various data sources

that allowed us to recognize the land-cover types present during

different time ranges These datasets included active 1047297eld sampling

previous 1047297eld datasets available for the area the use of remotely

sensed high resolution imagery (Quickbird and IKONOS) and aerial

photography

243 Assessing classi 1047297cation accuracy

We conducted accuracy assessment analysis using the error

matrix approach (Congalton 1991 Foody 2002 Lu et al 2004)

which consistsof a simple array of rows andcolumns that represent

the number of units sampled assigned to particular categories or

classes in contrast with the actual category measured on theground The columns represent reference data and the rows

represent the classi1047297cation estimates generated via remote sensing

(Congalton 1991 Story 1986) The major diagonal in the matrix

represents the classi1047297ed and observed ldquotrue valuesrdquo in the 1047297eld

with the overall accuracy obtained by summing the entries in the

entire diagonal then dividing the sum by the total number of

samples taken (Story 1986) Several statistical measurements were

derived from the error matrix the producer and user accuracies

and the widely used Kappa statistic (Congalton 1991 Story 1986)

The classi1047297cation accuracies for 2009 2004 and 1998 were

assessed by generating a total of 315 random strati1047297ed points

Because no in-situ datasets existed to assess the distribution of the

classes on the ground we veri1047297ed the accuracy of these classes by

using aerial photography and high-resolution satellite imageryprovided by web services and Mexican agencies

The quality of the datasets used for accuracy assessment for the

2004 and 2009 classi1047297cation were very similar because the loca-

tions were obtained from the historical products provided by the

Google Earth web service (httpwwwgooglecomearthindex

html) The accuracy points for 1998 were derived from panchro-

matic orthophotos that were provided by the Mexican agency

INEGI and several of the samples used in the classi1047297cation of 2004

that showed changes in spectral response (ie water)

25 Agricultural land dynamics at La Costa de Hermosillo

251 Change detection analysis

From the classi1047297ed land-cover maps obtained for La Costa deHermosillo we proceeded to extract the northern and southern

agricultural developments from each year For all land-cover maps

we grouped all classes in a single ldquoclassrdquo except for fallow agri-

cultural 1047297elds and active agricultural 1047297elds We then calculated the

amount of area dedicated to agriculture to assess the increase or

decrease of these particular land-cover classes through time

Using the thematic maps derived for the northern and southern

agricultural developments at La Costa de Hermosillo we created

change-detection matrices and change maps between different

years (Coppin et al 2004 Lu et al 2004 Shalaby and Tateishi

2007 Singh 1989) To obtain these results we used two classi1047297-

cations at a time to assess where the change occurred (spatial

representation of the change) and what had changed In total four

change-detection maps and matrices were generated for the

following pairs of years 1988e1993 1993e1998 1998e2004 and

2004e2009

Using the change-detection maps we derived a series of land-

scape metrics per change class (area occupied by each change type

and change trend) to help us understand how these landscapes

have changed and what the classes were that changed the most

through time

3 Results and discussion

31 Classi 1047297cation accuracy

The overall accuracy of our classi1047297cations was higher than 84

for all the years The user and producer accuracy ( Story 1986)

obtained for different vegetation cover classes in each of our the-

matic maps varied between 70e100 and 57e100 respectively

(Table 3) We focused on user accuracy for the purpose of this study

because it signi1047297es how well the thematic map represents land

cover Some of the confusion among several of the land-cover

classes is likely because of similarities in their spectral

characteristics

Mesquite shrublands were sometimes confused with desertshrubland because of overlapping species and similarities in

response to monsoon rains Mesquite shrublands were sometimes

confused with active agricultural 1047297elds especially 1047297elds with

irregular orchards (observed during our accuracy assessment) that

might possibly have a similar spectral response as the mesquite

trees (according to the classi1047297er) Most other land-cover classes

were classi1047297ed correctly primarily because of their unique

phenological biophysical and topographic characteristics that

easily differentiated them from other land-cover classes For

example the succulent-stemmed shrublands were identi1047297ed by

unique components related to elevation and species composition

and active agricultural 1047297elds were accurate because the variables

captured phenological change this was expected because irrigated

crops are not dependent on precipitation to conduct biologicalactivity

Because of the lack of high resolution imagery the vegetation

classi1047297cation products from 1993 to 1988 were derived from

training datasets extracted from later or earlier images For some of

the classes training points from 1998 were utilized when limited

spectral variation was observed among the images during and after

Table 3

Summary of the user producer Kappa and overall accuracies for the CART-based

vegetation classi1047297cation for 1998 2004 and 2009

Class 1998 2004 2009 1998e2009

User Producer User Producer User Producer Average

Active a gr ic ultur e 1 00 0 9 2 0 86 0 77 0 89 0 7 9 0 8 7Fallow 1047297elds 086 081 077 087 083 091 084

Aquaculture 094 100 094 097 100 095 097

Barrenscarce

vegetation

074 079 077 087 080 085 080

Desert shrubland 083 063 080 061 077 057 070

Succulent

Shrublands

094 100 089 094 094 097 095

Estuarywater 091 094 094 097 100 100 096

Halophyte

coastal

vegetation

080 090 074 068 077 093 081

Mesquite

shrublands

074 084 071 086 074 096 081

Urban areas 094 097 097 097 100 095 097

Overall accuracy 8714 84 8743

Kappa 086 082 086




the monsoon for those years For this reason we expect them to

have similar accuracies to the ones achieved in the rest of our

classi1047297cations

32 Landscape changes (1988e 2009)

As expected the most signi1047297cant events regarding land-cover

change and distribution found at La Costa de Hermosillo tookplace in the two main agricultural areas (northern and southern

developments) where high rates of human modi1047297cation to the

landscape occurred

Based on our newly derived thematic maps we were able to

differentiate clear trends indicating that between 1988 and 2009

the active and fallow agricultural areas have been decreasing and

barren soil and desert shrublands have been increasing (Fig 3)

which is expected after agricultural abandonment (Castellanos

et al 2005) Land-cover transitions from agricultural areas to

other land-cover types are most likely related to wasteful water use

by stakeholders a lack of water regulation policies (Halvorson et al

2003) and salinization of soils (Henderson1965) that results in the

reduction of suitable areas for crop cultivation

Another important change occurring at La Costa de Hermosilloduring this 22 year period was the establishment of aquaculture

farms for the production of shrimp oysters and other pro1047297table

species (Martinez-Cordova and Martinez-Porchas 2006 Paacuteez-

Osuna et al 2003) Although the actual sustainability of these

practices has been discussed in the past (Paacuteez-Osuna et al 2003)

they have increased rapidly in Sonora since the late 1980s and early

1990s (Cruz-Torres 2000) with large coastal areas converted from

native vegetation to aquaculture farms The main land usecover

change regarding these practices is the transition from halophytic

vegetation (salt resistant) to aquaculture farms

We found that land-cover classes such as barren soil and desert

shrubland have increased with the decrease of agricultural areas

while other classes such as succulent-stemmed and mesquite

shrublands were more stable through time (Fig 3) Considering the

most important land-cover changes identi1047297ed and the evidence

collected from the literature and expert sources (Castellanos et al

2005 Halvorson et al 2003) we found that the changes portrayed

by the maps re1047298ect the progression of land-cover dynamics of the

region Because our goal was to improve our ability to differentiate

and increase our understanding regarding land-cover changes

introduced by human practices we consequently focused the rest

of our analyses on the main trends in the actively changing agri-

cultural areas

33 Change in agriculture at La Costa de Hermosillo

331 Accuracy assessment northern and southern agricultural

developmentsUsing the thematic maps we created new maps by grouping

together all classes except active agriculture and fallow 1047297elds

which result in three total classes to conduct accuracyassessments

Our results yielded similar accuracies for the northern and south-

ern agricultural developments (KAPPA statistic ranging from 08 to

09 for all years) when compared with the results obtained for the

previous landscape classi1047297cations for the entire La Costa de Her-

mosillo area In all our land-cover maps the class denominated

ldquoOtherrdquo was an aggregation of desert shrubland vegetation and

barren soil classes

332 Test for changes in active agriculture areas

Our classi1047297cation results had high levels of accuracy by ac-

counting for phenological changes in the different land-covertypes We proceeded to generate a post-classi1047297cation change-

detection analysis for abandonment trends at La Costa de Hermo-

sillo To assess overall changes in the most representatively active

agricultural areas we used the thematic maps extracted from the

northern and southern agricultural 1047297eld developments (Fig 4) For

both agricultural developments our results showed a sharp

decrease between 1988 and 2009 in the area occupied by active

agriculture The reductions in active agriculture were from 12004

to 3512 ha in the northern area and from 96730 to 52466 ha in the

southern development (Fig 5) Each of the generated products

contained nine potential changes from one land-cover type to

another which allowed us to determine where changes occurred

between active agriculture fallow and other land-cover types and

what actually changed We assumed that increases in the ldquootherrdquocategory represent the area that was abandoned during the period

of time between each classi1047297cation

Because the imagery used for the classi1047297cations was taken from

similar periods in each of the years and the errors of miss-

registration were low (Carmel et al 2001) the sources of error in

the change detection were mostly because of misclassi1047297cations

Fig 3 Total areas for all land-cover types for 1988 1993 1998 2004 and 2009 at La Costa de Hermosillo This 1047297

gure shows the variation in land-cover types through time




These Type I (erroneous classi1047297cation as lsquono changersquo) and Type II

(erroneous classi1047297cation as lsquochangersquo) errors are common in post-

classi1047297cation techniques (Hall et al 1991 Villarreal et al 2012)

It is generally accepted that the accuracy of the change maps is

equal to the product of the two accuracies of the classi1047297cations used

for the years in question

Thematic spatial representations of land-cover change charac-

terized the highly dynamic transformations with regard to agri-

cultural vegetation cover and its changes through time in the

northern agricultural site (Fig 6a) These spatial representations

showed the location and types of changes occurring in speci1047297c

areas When analyzing net agricultural change at La Costa de Her-

mosillo (new or re-opened areas for agriculture less the areas

converted from agriculture to other land-cover types) we found

that there has been a general decrease in agriculture in everyperiod analyzed (Fig 6b) Changes in agriculture present a

continuing decrease (when compared to the 1988e1993 period) in

areas that were being transformed to other land-cover types

Table 4 provides an overview of the changes between 1988 1993

1998 2004 and 2009 for active agriculture fallow and other land-

cover types for both agricultural developments

We were able to capture differences in rates of change between

periods and sites For the northern agricultural development

higher rates of change were found among the land-cover types

during the 1047297rst two periods analyzed This was coincidental and

apparently related to the enforcement of water-use regulation

policies that were implemented by the Mexican government in the

mid-1970s (Halvorson et al 2003) The decrease in crop production

is also attributed to the salinization of soils (Castellanos et al 2005Rangel Medina et al 2002) During the 1980s larger amounts of

land dedicated to agriculture were converted to non-agriculture

cover types compared with more recent years Additionally it is

important to note that even if new areas were opened for agricul-

ture in every period agricultural land transitions to other non-

agricultural cover types always represented a larger area

Change detection thematic maps for the southern agricultural

development depicted the locations and types of change occurring

at particular time intervals in this study (Fig 7a) Similar to the

northern agricultural development the general trajectory of the

southern (and main) agricultural development indicates a decrease

in the amount of area used for active agriculture However the time

when the most agricultural change (abandonment) occurred is

different in this area (Fig 7b) with the greatest amount of change

Fig 4 Example maps of the simpli1047297ed land-cover class distributions from 1988 to 2009 including the northern (left) and southern (right) agriculture developments A general

decrease in fallow and active agriculture is observed for the last two decades (see also Figs 3 and 5)

Fig 5 Area extents for aggregated land-cover types from 1988 1993 1998 2004 and

2009 at the a) northern agricultural development (total area of 35664 ha) and b) the

southern agricultural development (total area of 166785 ha) A decrease in agricultural

areas can be observed at both developments




occurring during the 1047297rst (1988e1993) and the last (2004e2009)

periods The lowrates of change in the two middle periods could be

the result of efforts by land owners and the government to main-

tain agricultural productivity

We attribute La Costa de Hermosillo land-use change trends in

agricultural development to the salinization of water sources and

strict water regulations implemented by the Mexican government

Further social and economic analyses will be needed to fully un-

derstand such patterns however geographically explicit inputs

such as those developed in our study can be used as critical inputs

for expert decision making

4 Conclusions

The use of the CARTSee5-based vegetation type modeling

approach to classify Landsat TM datasets allowed us to generate

detailed thematic maps to analyze the landscape dynamics in an

arid agro-ecosystem For our study site in La Costa de Hermosillo

the remotely sensed observations and changes in the landscape

from one period to another re1047298ect the human impact that this agro-

ecosystem has been exposed to during the last 22 years As a

consequence of the regulation of water use soil degradation and

other economic factors such as the development of aquaculture La

Costa de Hermosillo underwent a rapid modi1047297cation in land cover

land use

The most conspicuous change that we were able to show for the

region was the progressive abandonment of agricultural 1047297elds

Therefore our study can help document the vulnerability of this

arid agro-ecosystem to anthropogenic change As expected thechange detection maps were able to show the high rates of change

from agriculture to other types of vegetation (Figs 6b and 7b)

Those changes can be interpreted as either the abandonment of

agricultural 1047297elds or a long period without human use of the

terrain

In our case study we were able to provide inputs to document

possible ecological pathways and decision support policies that

could intervene at the degraded sites The abandonment of agri-

cultural 1047297elds in this arid ecosystem opens the possibility to mul-

tiple scenarios for the use of the proposed techniques Restoration

policiesand efforts from government agencies andNGOrsquosas wellas

the resilience and succession rate in each particular 1047297eld will

dictate the pace of recovery and if the vegetation communities will

reach a new steady state (Briske et al 2005 Westoby et al 1989)Our analysis provides ecologically accurate and spatially explicit

support to such efforts and policies

Laws regulation monitoring and research will be needed from

governmental agencies and public institutions (universities

research centers and non-governmental organizations) to deter-

mine the best course of action to promote the recovery of arid agro-

ecosystems

By establishing the framework proposed in this research it is

possible to develop cost-effective monitoring analysis and assess-

ment of highly degraded landscapes Our land-cover change-

detection analysis demonstrates that the high level of accuracy

achieved by our classi1047297cations might be adequate for analyzing

land-cover trends at the landscape level in diverse agro-ecosystems

located in arid environmentsIn this work we propose a framework to describe the landscape

compositionbased on the mapping of features of interest However

more analysis will be required to assess the changes in actual

landscape con1047297guration (probability of adjacency and contagion)

dynamics (Turner et al 2001) and analyze the spatial arrangement

of land-cover types (OrsquoNeill et al 1988 Turner et al 2001) Further

analysis of accurate land-cover thematic maps using fragmentation

statistics to analyze con1047297guration dynamics will help to further

explain land-cover changes and community succession and aid in

the analysis of the progression (in time) of land-cover class

relationships

La Costa de Hermosillo even though it encompasses a highly

dynamic and impacted ecosystem is only one among many similar

locations worldwide that requires careful planning and monitoring

Fig 6 a) Map of the progressive land-cover change detection at the northern agri-

culture development Land-cover conversions and changes are visible for active agri-

culture (AA) fallow 1047297elds (FF) and other land-cover types (O) b) The net change in area

from agriculture to other land-cover types (abandonment minus new agricultural

areas) from one year to the next period approximately 5 years later

Table 4

Summary of land-cover changes during four time intervals at the northern and

southern agricultural developments at La Costa de Hermosillo The 1047298uctuation

among land-cover classes during the periods is given in hectares

Northern Agricultural Development

1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 558 249 123 206

Fallow to Active 780 251 187 149

Other to Fallow 1833 1493 1493 1226

Fallow to Other 4718 2803 2711 966

Other to Active 747 799 990 273

Active to Other 2365 1287 949 941

Southern Agricultural Development

1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 5214 5967 6237 2569

Fallow to Active 1240 4870 3709 3125

Other to Fallow 9604 16269 14180 10678

Fallow to Other 27699 15465 17185 22748

Other to Active 3602 5022 4933 3904

Active to Other 9078 8042 5652 6601




to avoid further degradation of the natural capital Implementationof spatially explicit models and monitoring practices to analyze

land-cover dynamics in deforested landscapes in arid agro-

ecosystems are necessary to determine the allocation of efforts

required to restore highly degraded environments to sustainability

Acknowledgments

The authors wish to thank the support forthis researchprovided

by the Arizona Remote Sensing Center University of Arizona Tuc-

son AZ USA JRRL wishes to acknowledge support from CONACYT

in the form of a PhD scholarship and to recognize the assistance

with the 1047297eld work provided by the CONAFOR-CONACYT grant

(10644) to AECV Landsat TM data were obtained through the on-

line USGSEarth Resources Observation and Science (EROS) EarthExplorer website httpedcsns17crusgsgovNewEarthExplorer

References

Alhaddad BI Burns MC Cladera JR 2007 Texture analysis for correcting anddetecting classi1047297cation structures in urban land uses ldquometropolitan area casestudy e Spainrdquo In Urban Remote Sensing Joint Event 2007 pp 1e6

Anderson JF Hardy EE Roach JT Witmer RE 1976 A Land Use and Land CoverClassi1047297cation System for Use with Remote Sensor Data US Geological SurveyWashington DC

Andrews RW 1981 Salt-water-intrusion in the Costa de Hermosillo Mexico e anumerical analysis of water management proposals Ground Water 19 635e647

Asner GP Keller M Pereira Jr R Zweede JC 2002 Remote sensing of selectivelogging in Amazonia assessing limitations based on detailed 1047297eld observations

Landsat ETMthorn

and textural analysis Remote Sens Environ 80 483e

496

Beatley JC 1974 Phenological events and their environmental triggers in MojaveDesert ecosystems Ecology 55 856e863

Breiman L Friedman JH Olshen RA Stone CG 1984 Classi1047297cation andRegression Trees Wadsworth International Group Belmont California USA

Briske DD Fuhlendorf SD Smeins FE 2005 State-and-transition modelsthresholds and rangeland health a synthesis of ecological concepts and per-spectives Rangel Ecol Manag 58 1e10

Carmel Y Denis J Flather H 2001 Cornbining location and classi1047297cation errorsources for estimating multi-temporal database accuracy Photogramm EngRemote Sens 67 865e872

Castellanos AE Martiacutenez MJ Llano JM Halvorson WL Espiricueta MEspejel I 2005 Successional trends in Sonoran Desert abandoned agricultural1047297elds in Northern Mexico J Arid Environ 60 437e455

Chander G Markham BL Barsi JA 2007 Revised Landsat-5 thematic mapperradiometric calibration Geosci Remote Sens Lett IEEE 4 490e494

Chavez Jr PS 1996 Image-based atmospheric corrections e revisited andImproved Photogramm Eng Remote Sens 62 1025e1036

Collins JB Woodcock CE1996 An assessment of several linear change detectiontechniques for mapping forest mortality using multitemporal Landsat TM dataRemote Sens Environ 56 66e77

Congalton RG 1991 A review of assessing the accuracy of classi1047297cations of remotely sensed data Remote Sens Environ 37 35e46

Coppin P Jonckheere I Nackaerts K Muys B Lambin E 2004 Review articledigital change detection methods in ecosystem monitoring a review Int JRemote Sens 25 1565e1596

Crist EP Cicone RC 1984 A physically-based transformation of thematicmapper data-the TM tasseled cap IEEE Trans Geosci Remote Sens 22 256e

263Cruz-Torres ML 2000 ldquoPink gold rushrdquo shrimp aquaculture sustainable devel-

opment and the environment in northwestern Mexico J Polit Ecol 7 63De Fries RS Hansen M Townshend JRG Sohlberg R 1998 Global land cover

classi1047297cations at 8 km spatial resolution the use of training data derivedfrom Landsat imagery in decision tree classi1047297ers Int J Remote Sens 193141e3168

Dersquoath G Fabricius KE 2000 Classi1047297cation and regression trees a powerful yetsimple technique for ecological data analysis Ecology 81 3178e3192

Ewel J 1999 Natural systems as models for the design of sustainable systems of land use Agrofor Syst 45 1e21

Foody GM 2002 Status of land cover classi1047297cation accuracy assessment RemoteSens Environ 80 185e201

Friedl MA McIver DK Hodges JCF et al 2002 Global land cover mapping fromMODIS algorithms and early results Remote Sens Environ 83 287e302

Fung T LeDrew E 1987 Application of principal components analysis to changedetection Photogramm Eng Remote Sens 53 1649e1658

HallFG Botkin DBStrebelDE Woods KD GoetzSJ 1991 Large-scale patternsof forest succession as determined by remote sensing Ecology 72 628e640

Halvorson WL Castellanos AE Murrieta-Saldivar J 2003 Sustainable land use

requires attention to ecological signals Environ Manag 32 551e

558Hansen M Dubayah R DeFries R 1996 Classi1047297cation trees an alternative totraditional land cover classi1047297ers Int J Remote Sens 17 1075e1081

Henderson DA 1965 Arid lands under agrarian reform in Northwest MexicoEcon Geogr 41 300e312

Huete A Didan K Miura T Rodriguez EP Gao X Ferreira LG 2002 Overviewof the radiometric and biophysical performance of the MODIS vegetationindices Remote Sens Environ 83 195e213

Huete AR 1988 A soil-adjusted vegetation index (SAVI) Remote Sens Environ 25295e309

Jiang Z Huete AR Didan K Miura T 2008 Development of a two-bandenhanced vegetation index without a blue band Remote Sens Environ 1123833e3845

Koumlnig HJ Sghaier M Schuler J et al 2012 Participatory impact assessment of soil and water conservation scenarios in Oum Zessar watershed Tunisia En-viron Manag 50 153e165

Lambin EF Turner BL Geist HJ et al 2001 The causes of land-use and land-cover change moving beyond the myths Glob Environ Change 11 261e269

Loik ME Breshears DD Lauenroth WK Belnap J 2004 A multi-scale

perspective of water pulses in dryland ecosystems climatology and ecohy-drology of the western USA Oecologia 141 269e281

Lowry J Ramsey RD Thomas K et al 2007 Mapping moderate-scale land-coverover very large geographic areas within a collaborative framework a case studyof the Southwest Regional Gap Analysis Project (SWReGAP) Remote Sens En-viron 108 59e73

Lu D Mausel P Brondizio E Moran E 2004 Change detection techniques Int JRemote Sens 25 2365e2407

MacKay H 2006 Protection and management of groundwater-dependent eco-systems emerging challenges and potential approaches for policy and man-agement Aust J Bot 54 231e237

Marsett RC Qi J Heilman P Sharon HB Watson MC Amer S Weltz MGoodrich D Marsett R 2006 Remote sensing for grassland management inthe arid southwest Rangel Ecol Manag 59 530e540

Martinez-Cordova LR Martinez-Porchas M 2006 Polyculture of Paci1047297c whiteshrimp Litopenaeus vannamei giant oyster Crassostrea gigas and black clamChione 1047298uctifraga in ponds in Sonora Mexico Aquaculture 258 321e326

Mas JF 1999 Monitoring land-cover changes a comparison of change detectiontechniques Int J Remote Sens 20 139e152

Fig 7 a) Map of the progressive land-cover change detection at the southern agri-

culture development Land-cover conversion and the spatial distribution of the

changes are visible for active agriculture (AA) fallow 1047297elds (FF) and other land-cover

types (O) b) The net change in area from agriculture to other land-cover types

(abandonment minus new agricultural areas) for each 5-year time step




McGinnies WG 1981 Discovering the Dessert The University of Arizona PressTucson

Moreno-Vaacutezquez JL 2006 Por Debajo del Agua Sobreexplotacioacuten y Agotamientodel Acuiacutefero de la Costa de Hermosillo 1945e2005 El Colegio de SonoraHermosillo

OrsquoNeill RV Krummel JR Gardner RH et al 1988 Indices of landscape patternLandsc Ecol 1 153e162

Paacuteez-Osuna F Gracia A Flores-Verdugo F Lyle-Fritch LP Alonso-Rodriacuteguez RRoque A Ruiz-Fernaacutendez AC 2003 Shrimp aquaculture development andthe environment in the Gulf of California ecoregion Mar Pollut Bull 46 806 e

815Pal M Mather PM 2003 An assessment of the effectiveness of decision tree

methods for land cover classi1047297cation Remote Sens Environ 86 554e565Qi J Chehbouni A Huete AR Kerr YH Sorooshian S 1994 A modi 1047297ed soil

adjusted vegetation index Remote Sens Environ 48 119e126Rabbinge R 1993 The ecological background of food-production Ciba Found

Symp 177 2e29Rangel Medina M Monreal Saavedra R Morales Montantildeo M Castillo Gurrola J

2002 Vulnerabilidad a la Intrusion Marina de Acuiferos Costeros en el Paci1047297coNorte Mexicano un caso el Acuifero Costa de Hermosillo Sonora Mexico RevLat-Am Hirdrogeol 31e51

Rogan J Franklin J Roberts DA 2002 A comparison of methods for monitoringmultitemporal vegetation change using thematic mapper imagery RemoteSens Environ 80 143e156

SARH 1994 Inventario Forestal Nacional Perioacutedico Meacutexico 94 Memoria NacionalSecretaria de Agricultura y Recursos Hidraacuteulicos Subsecretariacutea Forestal y deFauna Silvestre Meacutexico

Shalaby A Tateishi R 2007 Remote sensing and GIS for mapping and monitoringland cover and land-use changes in the northwestern coastal region of EgyptAppl Geogr 27 28e41

Shreve F Wiggins IL 1964 Vegetation and Flora of the Sonoran Desert StanfordUniversity Press CA

Singh A 1989 Review article digital change detection techniques using remotely-sensed data Int J Remote Sens 10 989e1003

Story MH 1986 Accuracy assessment a userrsquos perspective Photogramm EngRemote Sens 52 397e399

Tilman D Cassman KG Matson PA Naylor R Polasky S 2002 Agricultural

sustainability and intensive production practices Nature 418 671e

677Tso B Mather MP 2009 Classi1047297cation Methods for Remotely Sensed Data second

ed Taylor amp Francis Broken Sound Parkway NWTucker CJ 1979 Red and photographic infrared linear combinations for moni-

toring vegetation Remote Sens Environ 8 127e150Turner MG Gardner RH OrsquoNeill RV 2001 Landscape Ecology in Theory and

Practice Springer New YorkvanLeeuwenWJDHueteAR LaingTW1999MODIS vegetation indexcompositing

approach a prototype with AVHRR data Remote Sens Environ 69 264e280Villarreal ML van Leeuwen WJD Romo-Leon JR 2012 Mapping and moni-

toring riparian vegetation distribution structure and composition withregression tree models and post-classi1047297cation change metrics Int J RemoteSens 33 (13) 4266e4290

Westoby M Walker B Noy-Meir I 1989 Opportunistic management for range-lands not at equilibrium J Range Manag 42 266e274

Young DR Nobel PS 1986 Predictions of soil-water potentials in the north-western Sonoran Desert J Ecol 74 143e154




the implementation of proper regulations has been highlighted

(MacKay 2006) Land-use analysis to understand ecosystem func-

tions has been recognized as a necessary tool to increase stake-

holder participation and involvement in sustainable management

practices (Koumlnig et al 2012) We propose that ef 1047297cient and accurate

monitoring protocols to evaluate land use and management prac-

tices in the desert can lead to the development of better policies for

sustainable agriculture We strongly believe that because of the

high rate of conversion of land cover in agricultural areas it is

crucial to understand and monitor changes across time to effec-

tively assess management practices (from government and stake-

holders) and mitigate potentially adverse ecological impacts

12 Remote sensing and land cover dynamics

Land-cover changes modify landscapes and ecosystem pro-

cesses all over the world (Lambin et al 2001) To measure and

detect such change numerous approaches that use remote sensing

tools are available (Coppin et al 2004 Lu et al 2004 Mas 1999)

In this study we plan to use the post-classi1047297cation comparison as a

change detection approach (Lu et al 2004 Singh 1989) to assess

changes in the distribution of agricultural lands at 1047297ve dates overdifferent years in our study area

The main goal of this research is to design and apply remote

sensing approaches with the potential of improving monitoring

and assessment of rapid land-cover change phenomena and eval-

uate land-use trends for arid agroecosytems These improvements

could lead to more rapid development of informed and sustainable

management policies To test such tools we reconstruct land-use

and land-cover dynamics in an arid agricultural region in north-

western Mexico to assess how land cover has changed over the last

22 years Using this case study we evaluate and discuss those

changes with respect to the multiple drivers (economic political

and ecological) that impact the use of arid lands

2 Materials and methods

21 Study area

This research tests remote sensing tools tailored for arid lands to

assess regional land-cover change over time with respect to agri-

culture to develop informed input for decision making policies We

selected the area of La Costa de Hermosillo in Sonora Mexico as the

study area This arid agro-ecosystem is located in northwestern

Mexico west of the city of Hermosillo and approximately between

the coordinates 28 140Ne28 570N latitude and 111 150We111

450W longitude La Costa de Hermosillo is located within an exoreic

watershed and its surface waters run to the Gulf of California

(Rangel Medina et al 2002) The 833 km2 irrigation district of La

Costa de Hermosillo (Castellanos et al 2005) is located within theplains and the central gulf subdivision of the Sonoran Desert

(McGinnies 1981 Shreve and Wiggins 1964) However agricul-

tural practices expanded beyond the district boundaries to occupy

over 2000 km2 In an effort to characterize agricultural areas and

the landscape surrounding these developments our study area

extends to the coast line for a total area of 5090 km2 (Fig 1)

Elevation in the study area ranges from 0 to 455 m Mean annual

temperature for the region ranges between 18 and 22 C with

maximum temperatures of over 45 C during the summer and

minimum temperatures above 0 C in the winter Precipitation

varies from 100 to 300 mm-yr1 in a bimodal summerwinter

regime with the wettest months being July and August Potential

evapotranspiration range is 2200e2500 mm-yr1 (Halvorson et al

2003)

Currently numerous agricultural developments are taking place

in the study area Two can be considered the most important

because of the large extent of land used for agriculture We delin-

eated these areas based on available aerial photography from the

late 1970s and Landsat images from the late 1980s These areas of

study are referred to as northern and southern agricultural de-

velopments and their areal extents are 3533 and 16632 km2

respectively (Fig 2)

Fig 1 Map of Sonora the Hermosillo municipality and the selected study area The

study area is based on the location of the La Costa de Hermosillo irrigation district and

subsequent agricultural expansion

Fig 2 Map of the study area showing a Landsat-based Normalized Difference Vege-

tation Index (NDVI) from 1988 and the delineation of the southern and northern

agricultural developments




















































assessment

241 CART Model



















Table 1






Index 2 (EVI2)

( Jiang et al 2008)


Index (EVI)

(Huete et al 2002




(Qi et al 1994)



(Tucker 1979)




1 Soil Adjusted


(Huete 1988)

6 Re1047298ectance



Asner et al 2002)

12 Multitemporal

Kauth Thomas (MKT)




1 Elevation

1 Aspect

1 Slope

Table 2






was acquired


and shrimp

4 Barrenscarce

vegetation


of vegetation








8 Halophytecoastal

vegetation









cation algorithms



















photography













































2004e2009





through time











characteristics




















Table 3



Class 1998 2004 2009 1998e2009



Aquaculture 094 100 094 097 100 095 097

Barrenscarce

vegetation

074 079 077 087 080 085 080


Succulent

Shrublands

094 100 089 094 094 097 095

Estuarywater 091 094 094 097 100 100 096

Halophyte

coastal

vegetation

080 090 074 068 077 093 081

Mesquite

shrublands

074 084 071 086 074 096 081

Urban areas 094 097 097 097 100 095 097


Kappa 086 082 086











landscape occurred

































cultural areas












barren soil classes
























































































4 Conclusions











land use








terrain















ecosystems

















relationships









Table 4





1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)



Other to Fallow 1833 1493 1493 1226

Fallow to Other 4718 2803 2711 966


Active to Other 2365 1287 949 941


1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 5214 5967 6237 2569

Fallow to Active 1240 4870 3709 3125

Other to Fallow 9604 16269 14180 10678

Fallow to Other 27699 15465 17185 22748

Other to Active 3602 5022 4933 3904

Active to Other 9078 8042 5652 6601








Acknowledgments








References





Landsat ETMthorn


496


























































































































assessment

241 CART Model



















Table 1






Index 2 (EVI2)

( Jiang et al 2008)


Index (EVI)

(Huete et al 2002




(Qi et al 1994)



(Tucker 1979)




1 Soil Adjusted


(Huete 1988)

6 Re1047298ectance



Asner et al 2002)

12 Multitemporal

Kauth Thomas (MKT)




1 Elevation

1 Aspect

1 Slope

Table 2






was acquired


and shrimp

4 Barrenscarce

vegetation


of vegetation








8 Halophytecoastal

vegetation









cation algorithms



















photography













































2004e2009





through time











characteristics




















Table 3



Class 1998 2004 2009 1998e2009



Aquaculture 094 100 094 097 100 095 097

Barrenscarce

vegetation

074 079 077 087 080 085 080


Succulent

Shrublands

094 100 089 094 094 097 095

Estuarywater 091 094 094 097 100 100 096

Halophyte

coastal

vegetation

080 090 074 068 077 093 081

Mesquite

shrublands

074 084 071 086 074 096 081

Urban areas 094 097 097 097 100 095 097


Kappa 086 082 086











landscape occurred

































cultural areas












barren soil classes
























































































4 Conclusions











land use








terrain















ecosystems

















relationships









Table 4





1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)



Other to Fallow 1833 1493 1493 1226

Fallow to Other 4718 2803 2711 966


Active to Other 2365 1287 949 941


1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 5214 5967 6237 2569

Fallow to Active 1240 4870 3709 3125

Other to Fallow 9604 16269 14180 10678

Fallow to Other 27699 15465 17185 22748

Other to Active 3602 5022 4933 3904

Active to Other 9078 8042 5652 6601








Acknowledgments








References





Landsat ETMthorn


496

























































































photography













































2004e2009





through time











characteristics




















Table 3



Class 1998 2004 2009 1998e2009



Aquaculture 094 100 094 097 100 095 097

Barrenscarce

vegetation

074 079 077 087 080 085 080


Succulent

Shrublands

094 100 089 094 094 097 095

Estuarywater 091 094 094 097 100 100 096

Halophyte

coastal

vegetation

080 090 074 068 077 093 081

Mesquite

shrublands

074 084 071 086 074 096 081

Urban areas 094 097 097 097 100 095 097


Kappa 086 082 086











landscape occurred

































cultural areas












barren soil classes
























































































4 Conclusions











land use








terrain















ecosystems

















relationships









Table 4





1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)



Other to Fallow 1833 1493 1493 1226

Fallow to Other 4718 2803 2711 966


Active to Other 2365 1287 949 941


1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 5214 5967 6237 2569

Fallow to Active 1240 4870 3709 3125

Other to Fallow 9604 16269 14180 10678

Fallow to Other 27699 15465 17185 22748

Other to Active 3602 5022 4933 3904

Active to Other 9078 8042 5652 6601








Acknowledgments








References





Landsat ETMthorn


496

















































































landscape occurred

































cultural areas












barren soil classes
























































































4 Conclusions











land use








terrain















ecosystems

















relationships









Table 4





1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)



Other to Fallow 1833 1493 1493 1226

Fallow to Other 4718 2803 2711 966


Active to Other 2365 1287 949 941


1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 5214 5967 6237 2569

Fallow to Active 1240 4870 3709 3125

Other to Fallow 9604 16269 14180 10678

Fallow to Other 27699 15465 17185 22748

Other to Active 3602 5022 4933 3904

Active to Other 9078 8042 5652 6601








Acknowledgments








References





Landsat ETMthorn


496







































































































































4 Conclusions











land use








terrain















ecosystems

















relationships









Table 4





1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)



Other to Fallow 1833 1493 1493 1226

Fallow to Other 4718 2803 2711 966


Active to Other 2365 1287 949 941


1988-1993

(Ha)

1993-1998

(Ha)

1998-2004

(Ha)

2004-2009

(Ha)

Active to Fallow 5214 5967 6237 2569

Fallow to Active 1240 4870 3709 3125

Other to Fallow 9604 16269 14180 10678

Fallow to Other 27699 15465 17185 22748

Other to Active 3602 5022 4933 3904

Active to Other 9078 8042 5652 6601








Acknowledgments








References





Landsat ETMthorn


496














































































Acknowledgments








References





Landsat ETMthorn


496








































































romo-leon2014-using remote sensing tools to assess land use transitions in unsustainable arid...

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