Monitoring and Analysing Land Use/Cover Changes in an Arid Region Based on Multi-Satellite Data: the Kashgar Region, Northwest China

land

Article Monitoring and Analysing Land Use/Cover Changes in an Arid Region Based on Multi-Satellite Data: The Kashgar Region, Northwest China

Ayisulitan Maimaitiaili 1,*, Xiaokaiti Aji 2, Akbar Matniyaz 1 and Akihiko Kondoh 3

1 Geosystem and Biological Sciences Division, Graduate School of Science, Chiba University, Chiba 263-8522, Japan; [email protected] 2 Paciﬁc Consultants Co., Ltd., Chiyoda 101-8462, Japan; [email protected] 3 Center environmental remote sensing, Chiba University, Chiba 263-8522, Japan; [email protected] * Correspondence: [email protected]

Received: 3 November 2017; Accepted: 9 January 2018; Published: 12 January 2018

Abstract: In arid regions, oases ecosystems are fragile and sensitive to climate change, and water is the major limiting factor for environmental and socio-economic developments. Understanding the drivers of land use/cover change (LUCC) in arid regions is important for the development of management strategies to improve or prevent environmental deterioration and loss of natural resources. The Kashgar Region is the key research area in this study; it is a typical mountain-alluvial plain-oasis-desert ecosystem in an arid region, and is one of the largest oases in Xinjiang Uyghur Autonomous Region, China. In addition, the Kashgar Region is an important cotton and grain production area. This study’s main objectives are to quantify predominant LUCCs and identify their driving forces, based on the integration of multiple remote sensors and applications of environmental and socio-economic data. Results showed that LUCCs have been signiﬁcant in the Kashgar Region during the last 42 years. Cultivated land and urban/built-up lands were the most changed land cover (LC), by 3.6% and 0.4% from 1972 to 10.2% and 3% in 2014, respectively. By contrast, water and forest areas declined. Grassland and snow-covered areas have ﬂuctuated along with climate and human activities. Bare land was changed slightly from 1972 to 2014. According to the land use transfer matrix, cultivated land replaced grass- and forestland. Urban/built-up land mainly expanded over cultivated and bare land. LUCCs were triggered by the interplay of natural and social drivers. Increasing runoff, caused by regional climate changes in seasonal variation, and snow melt water, have provided water resources for LC changes. In the same way, population growth, changes in land tenure, and socio-economic development also induced LUCCs. However, expansion of cultivated land and urban/built-up land led to increased water consumption and stressed fragile water systems during on-going climate changes. Therefore, the selection of adaption strategies relating to climate change and oasis development is very important for sustainable development in the Kashgar Region.

Keywords: arid region; LUCC; driving forces; snow index; SPOT VGT; Kashgar Region

1. Introduction LUCC studies have emerged in research on global environmental changes via their interactions with climate, ecosystem processes, biogeochemical cycles, biodiversity, and human activities [1]. Therefore, for the last several years, numerous researchers have improved measurement of LUCCs by integrating environmental, human, and remote sensing/GIS science to answer various questions about LUCC and its driving forces [2]. Scholars believe the most common driving forces of LUCC have included population growth [3], human settlement and land tenure policy [4,5], changes in technology [6], culture [7], and political and economic changes [8]. Apart from that, climate-related

Land 2018, 7, 6; doi:10.3390/land7010006 www.mdpi.com/journal/land Land 2018, 7, 6 2 of 18 changes, such as rainfall variability [9], drought [10], and fire [11] were recognized as major LUCC drivers. While those research efforts have suggested that LUCC drivers differ depending on location, drivers remain contentious issues, and clearly their dynamics still requires analysis [12]. Located in the southwestern Xinjiang Uyghur Autonomous Region, China, the Kashgar Region is characterized by high mountains with snow/ice and an arid basin. Mountain uplift, westerly circulation, and the East Asian monsoon have determined its climate, creating a unique geographical unit—a mountain-alluvial plain-oasis-desert system. During the last half-century, rapid population growth and economic development have led to the modification of natural ecological processes. Increasing number of reservoirs and over-irrigation for cultivated land leads decreasing water flow in rivers by 1.5 × 109 m3 year−1. Consequently, a 240 km distance in the Kashgar and Yarkant Rivers has dried up, leading to the desert area’s expansion towards the north [13]. Over-cutting of desert vegetation and trees along rivers resulted in the activation of 80% sand, which is moving towards the oasis at a rate of 5–10 m/year [14], and the salinization area has expanded into 59.17% of arable land (3.38 × 105 ha; [15]). Water resources in the Kashgar Region depend mainly on snow melt from the mountain glacier, snow, and ice, which are major supply sources of surface runoff, ground water storage, and drinking water for the large population in this area. In the Kashgar Region, therefore, water is a major limiting resource, not only as a precious natural resource, but also as an important social factor for economic development [16]. Besides human activities, climate changes have also affected the hydrological cycle in the Kashgar Region. Global warming accelerates the process of atmospheric circulation and the hydrological cycle, subsequently affecting spatial and temporal distribution of water resources and exacerbating water shortages, especially in arid regions [17]. The occurrence of surface water in the Kashgar Region is strongly related to climatic, hydrological, and geomorphological factors. In recent decades, increases in the Kashgar Region’s social and economic activities and environmental changes have posed a great challenge to its sustainable development strategy, and extensive research has been conducted regarding this region. Anwaer et al. (2010) [18] analyzed total water uses in the Kashgar Region from 2001 to 2007. Results showed that agricultural water consumption increased most rapidly, from 76.78 × 108 m3 to 91.06 × 108 m3 during the six-year period. Anwaer et al. (2011) [19] also noted a significantly high correlation between urbanization and water utilization. Mansuer et al. (2011) [20] analyzed spatio–temporal dynamic changes of groundwater in the Kashgar Region, finding that cultivated land had been increasing continually, resulting in decreased groundwater levels midstream and downstream of the Kashagar and Yarkant Rivers, respectively. Rapid urbanization, socio-economic development, expansion of agricultural areas, and other anthropogenic activities played an important role in LUCCs. The Kashgar Region’s arid environmental landscape has undergone tremendous LUCCs. However, the region’s changes and drivers are not understood clearly. Most studies on the Kashgar Region have focused on individual factors, such as water stress [18,19], groundwater [20], and climate change [21]. However, few studies have shown that changes in climate and human activities have interacted over time to influence LUCC patterns in the region on different spatial and temporal scales. Therefore, this study’s main objectives are to quantify LUCCs and to identify these changes’ main driving forces. To achieve these goals, first LC maps are produced to provide a broad, synoptic perspective of land use measurements and mapping of environmental change. Then, investigation results of snow cover changes can strengthen knowledge of this region’s water resources. Accumulation of snow cover and snow melt water are the main sources for rivers, groundwater recharges, and agriculture in the Kashgar Region. Finally, driving forces are identified based on integrated LC maps, snow cover monitoring, and environmental (temperature, precipitation, evaporation, and runoff) and socio-economic datasets (population, GDP, primary industry, and policy changes). This study’s findings form a basis for a better understanding of the Kashgar Region’s LUCCs and provide information to assist policymakers’ decisions on future strategic management and sustainable development. Land 2018, 7, 6 3 of 18

Land 2018, 7, 6 3 of 18 2. Materials and Methods 2. Materials and Methods 2.1. Study Area 2.1. Study Area The Kashgar Region, located in Northwest China, is in the southwest Xinjiang Uyghur AutonomousThe Kashgar Region Region, (Figure located1). The in study Northwest area’s administrativeChina, is in the boundary southwest lies withinXinjiang the Uyghur range ofAutonomous 71◦390–79◦52 Region0 E and (Figure 35◦280–40 1). ◦The180 N.study Its administrative area’s administrative divisions boundary include 11lies counties within andthe range one city of within71°39′–79°52 a 113,913′ E and km 235°28area.′–40°18 According′ N. Its to administrative 2014 census statistics, divisions the include total population 11 counties was and 4,683,114; one city thewithin non-agricultural a 113,913 km2 populationarea. According accounts to 2014 for census 22.23%, statistics, and the the agricultural total population population was 4,683,114; accounts forthe 77.66%non-agricultural [22]. Three population mountains accounts surround for 22.23%, the Kashgar and the Region:agricultural Tainshan population Mountain accounts to thefor 77.66% north, the[22]. Kunlun Three mountains Mountain tosurround the south, the andKashgar the Pamir Region peaks: Tainshan to the Mountain west, stretching to the tonorth, 4.25 the× 10 Kunlun4 km2. WaterMountain vapor to from the south, the Indian and Oceanthe Pamir and thepeaks Arctic to the Cold west, Front stretching has difﬁculty to 4.25 penetrating × 104 km the2. Water area duevapor to thefrom high the mountain Indian Ocean barriers. and Thus,the Arctic the studyCold areaFront has has a difficulty typical inner-continental penetrating the climate,area due with to the annual high precipitationmountain barriers. of less thanThus, 100 the mm study and evaporation area has ofa ty higherpical thaninner-continental 2000 mm annually climate, in the with plain, annual while permanentprecipitation snow of less and than glaciers 100 existmm atand higher evaporation altitudes. of The higher study than area 2000 consists mm mainlyannually of in the the Kashgar plain, Riverwhile andpermanent Yarkant snow River and Alluvial glaciers Plains. exist Theat higher water altitudes. supply along The study the Kashgar area consists and Yarkant mainly Rivers of the dependsKashgar mainlyRiver and on Yarkant glaciers River and snow Alluvial melt Plains. water The from water the mountains. supply along The the average Kashgar annual and Yarkant surface runoffRivers ofdepends the Kashgar mainly and on Yarkant glaciers Rivers and snow is 4.5 melt and 7.5water× 10 from9 m3 ,the respectively mountains. [23 The]. Water average from annual these riverssurface is runoff used mainly of the Kashgar for irrigation, and Yarkant groundwater Rivers recharge,is 4.5 and and7.5 × hydropower 109 m3, respectively generation. [23]. Water from these rivers is used mainly for irrigation, groundwater recharge, and hydropower generation.

Figure 1. Location map of study area. Figure 1. Location map of study area.

2.2. Data 2.2. Data In this study, we integrated the following different multi-spectral satellite images for LUCC In this study, we integrated the following different multi-spectral satellite images for LUCC analysis: Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper analysis: Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operation Land Imagery (OLI), and VGTATION images on board the SPOT-4. We Plus (ETM+), Operation Land Imagery (OLI), and VGTATION images on board the SPOT-4. We further further used natural and social statistical datasets to identify LUCCs’ driving forces. Table 1 used natural and social statistical datasets to identify LUCCs’ driving forces. Table1 summarizes the summarizes the characteristics of these datasets. characteristics of these datasets.

Land 2018, 7, 6 4 of 18

Table 1. Datasets used in this study.

Data Category Sensor Date Resolution Path/Row Sources of Data MSS 1972 (From June to October) 60 m P147R32~P149/35 Remote sensing TM 1990 (From June to September) 30 m P147R32~P149/35 USGS data ETM+ 2000 (From June to September) 30 m P147R32~P149/35 OLI 2014 (From June to September) 30 m P147R32~P149/35 71◦390~79◦520 E SPOT-4 1 January 1999–30 May 2014 1 km VITO 35◦280~40◦180 N Hydrological Karbal, Kirik, data (annual - 1964–2014 Station data - Kaqung Stations runoff) Meteorological data(annual Kashgar, Yarkant, temperature, - 1964–2014 Station data - Maralbeshi precipitation, Tashkorgan stations evaporation) Xinjiang Statistical Statistical data - 1984–2014 - Year Book 11 August 1972 Landsat 29 June 1973 Landsat 31 August 1990 Landsat 31 December 2000 Landsat Google 31 July 2013 Airbus P147R32~P149/35 Reference data Earth 6 August 2013 Airbus 25 May 2014 Airbus 10 June 2014 Digital Globe 19 September 2014 Digital Globe GDEM 17 October 2011 30 m P147R32~P149/35 USGS

2.3. Methodology

2.3.1. Land Cover Mapping Based on the study area’s morphology and landscape, the possible appearance of LC types was considered to be 21 classes, and a hierarchical classification system of 21 classes was grouped into seven aggregated classes of LC: urban/built-up area, water area, grassland, cultivated land, forestland, snow/ice (considered only in summertime), and bare land. Table2 provides detailed descriptions of each class. These classes’ information was extracted using the maximum likelihood classification method combined with image segmentation (Figure2). During summer seasons with cloud-free conditions, 48 Landsat images were acquired to map snow/ice and major LC types in the study area. Geometric correction was conducted by collecting for each image 40 well-conditioned ground control points, determined from topographic maps using ArcGIS. Root mean square (RMS) errors were less than 0.5 pixels for MSS (60 m), TM, ETM+, and OLI (30 m) images. MSS images were resampled to a 30 m cell size using the nearest-neighbor method. All images were geometrically corrected, with a reference system of a UTM map projection Zone 44 North on a WGS 1984 datum. In the initial step, we segmented band-stacked images into groups of pixels as objects. Homogenous pixel groups were created, which are called image objects, based on size, shape, and area. The multi-segmentation method utilized object-based classification because it generates objects very closely resembling ground features [24]. We implemented image segmentation using eCognition 9.0 to create objects that delineated LC classes. We applied two levels for the segmentation and classification process based on LC classes. Level 1 was applied for small and spread LC classes, such as grassland. The scale parameter was set at 5 [25]. Level 2 was applied for large LC classes, such as cultivated land, water, and bare land. The scale parameter was set at 10. Color and shape parameters were adjusted to 0.9 and 0.1, respectively [26]. After segmentation was completed, LC information was extracted with the maximum likelihood classification method [27]. Training sites were collected from original Landsat Land 2018, 7, 6 5 of 18 data based on segmentation results, their different spectral features, Google Earth high-resolution images,Land 2018 and, 7, topographic6 maps, respectively. Because of the study area’s high mountains’5 of 18 cloudy conditions during wintertime, collecting cloud-free Landsat images was difficult. Therefore, SPOT VGT high-resolution images, and topographic maps, respectively. Because of the study area’s high imagery, chosen for mapping snow/ice cover due to its high temporal resolution and its maximum mountains’ cloudy conditions during wintertime, collecting cloud-free Landsat images was difficult. value composite (MVC) approach, helped minimized the effect of cloud cover [28]. The next section Therefore, SPOT VGT imagery, chosen for mapping snow/ice cover due to its high temporal providesresolution a detailed and its descriptionmaximum value of the composite snow/ice (MVC) cover approach, mapping helped process. minimized the effect of cloud cover [28]. The next section provides a detailed description of the snow/ice cover mapping process. Table 2. Classification of land types in this study. Table 2. Classification of land types in this study. Classes Definition Classes Definition Urban/build-up area Urban area, villages, industrial and commercial areas, transportation, communication and utilities Urban/build-up area Urban area, villages, industrial and commercial areas, transportation, communication and utilities Cultivated land Crop fields, cotton fields, vegetable lands, irrigated land, non-irrigated vegetation CultivatedWater land All areas Crop of fields, open water, cotton rivers, fields, streams, vegetable channels, lands, lakes,irrigated reservoir land, andnon-irrigated ponds, bottomland, vegetation swampland GrasslandWater All areas of open water, rivers, streams, Dense grass, channels, moderate lakes, grass,reservoir sparse and grass,ponds, bottomland, swampland GrasslandForest Dense forest, Dense scrubland, grass, moderate Sparse forest, grass, orchards, sparse grass, groves, shrubland Snow/iceForest Dense forest, scrubland, Sparse Glacier, forest, snow, orchards, ice groves, shrubland Snow/iceBare land Desert, Gobi, salinized land, Glacier, area of snow, thin soil, ice rock, almost no vegetation cover Bare land Desert, Gobi, salinized land, area of thin soil, rock, almost no vegetation cover

Pre-processing MSS SPOT VGT NDSI

Layer stacking Resampling TM MSS (30 m) NDSI= Geometric Red-SWIR/Red+SWIR ETM+ (MSS) Image registration enhancement Testing Threshold

OLI Annual snow cover Segmentation MSC

Rule set Seasonal snow cover Accuracy Classification assessment Water area

Cultivated land Natural driving

Overall, kappa LC maps Urban/built-up forces accuracy Forest land Social Snow/ice driving forces Transfer matrix Grassland Driving forces Bare land anaylsis

Figure 2. Flow chart of methodology. Figure 2. Flow chart of methodology. 2.3.2. Snow Cover Changes 2.3.2. SnowInvestigation Cover Changes of snow cover changes was completed in two steps, including the selection of criticalInvestigation threshold of values snow for cover Normalized changes Differen was completedce Snow Index in (NDSI) two steps, and threshold including values the selectionfrom of the NDSI index for extracting snow and ice information. VGT data have four spectral bands: B0 (blue, critical threshold values for Normalized Difference Snow Index (NDSI) and threshold values from 430–470 nm), B2 (red, 610–680 nm), B3 (near infrared, 780–890 nm), and SWIR (short-wave infrared, the NDSI index for extracting snow and ice information. VGT data have four spectral bands: B0 1580–1750 nm). Three standard VGT available products are VGT-P, VGT-S1, and VGT-S10. We (blue,applied 430–470 the VGT-S10 nm), B2 (10-day (red, 610–680 synthesis nm), product) B3 (near data for infrared, snow cover 780–890 mapping nm), in and this SWIRstudy. (short-waveThere infrared,are three 1580–1750 10-day composites nm). Three for standard 1 month: days VGT 1–10, available 11–20, products and day 21 are to VGT-P,the last VGT-S1,day of the and month VGT-S10. We appliedby MVC the[29]. VGT-S10 The NDSI (10-day takes advantage synthesis of product) difference data in reflectance for snow of cover snow-covered mapping and in this snow-free study. There are threeareas in 10-day near infrared composites and short-wave for 1 month: infrared days band 1–10,s [30,31]. 11–20, andThe NDSI day 21 was to calculated the last day as follows: of the month by Land 2018, 7, 6 6 of 18

MVC [29]. The NDSI takes advantage of difference in reflectance of snow-covered and snow-free areas Land 2018, 7, 6 6 of 18 in near infrared and short-wave infrared bands [30,31]. The NDSI was calculated as follows: are three 10-day composites for 1 month: days 1–10, 11–20, and day 21 to the last day of the month by MVC [29]. The NDSI takesNDSI advantage = (Red of differen− SWIR)/(Redce in reflectance + SWIR), of snow-covered and snow-free (1) areas in near infrared and short-wave infrared bands [30,31]. The NDSI was calculated as follows: where Red and SWIR are band 2 and band 4 of digital numbers of SPOT VGT, respectively [32]. NDSI = (Red − SWIR)/(Red + SWIR), (1) To determine whether a pixel is covered by snow or not, an observed pixel must meet a certain NDSI thresholdwhere value.Red and A SWIR threshold are band value 2 and of 0.4 band has 4 beenof digital used numbers to distinguish of SPOT snow VGT, fromrespectively bright soils,[32]. To rocks, anddetermine clouds [32 whether]. Kondoh a pixel and is Suzuki covered [33 by] insnow 2005 or modified not, an observed the 0.4 ofpixel NDSI must threshold meet a certain using NDSI an NDVI adjustmentthreshold to value. take A into threshold account value forest of 0.4 effects has been on snow used detectionto distinguish inNorthern snow from Eurasia. bright soils, In thisrocks, study, the originaland clouds threshold [32]. Kondoh value and of 0.4Suzuki underestimated [33] in 2005 modified snow-covered the 0.4 of areas. NDSI Therefore,threshold using several an NDVI threshold adjustment to take into account forest effects on snow detection in Northern Eurasia. In this study, values of 0.4, 0.3, and 0.2 were tested for NDSI, and the best agreement was found with a slightly lower the original threshold value of 0.4 underestimated snow-covered areas. Therefore, several threshold threshold value of 0.2 (Figure3). The best agreement for snow and ice pixels extraction using VGT values of 0.4, 0.3, and 0.2 were tested for NDSI, and the best agreement was found with a slightly datalower should threshold correspond valueto of broad0.2 (Figure snow-cover 3). The best characteristics agreement for of thesnow local and area. ice pixels Thus, extraction a lower thresholdusing valueVGT of 0.2data was should adopted correspond for VGT to broad snow snow-cover and ice detection characteristics in this of study the local area. area. Then, Thus, 567 a stageslower for the entirethreshold duration value of of 0.2 time-series was adopted VGT for data VGT were snow collected and ice todetection investigate in this snow study cover area. changesThen, 567 from 1 Januarystages 1999for the to entire 30 May duration 2014. Fromof time-series first year VG ofT September data were tocollected the next to yearinvestigate of August, snow 36cover images werechanges composited, from 1 representingJanuary 1999 to the 30 annual May 2014. maximum From first snow year coverof September area. Then, to the we next averaged year of August, the annual areas36 toimages determine were composited, the annual representing average extent the annu of snowal maximum cover insnow square cover kilometers. area. Then, we Seasonal averaged snow coverthe variation annual areas was to calculated determine the quarterly: annual average spring exte (March,nt of snow April, cover May), in square summer kilometers. (June, July, Seasonal August), fall (September,snow cover variation October, was November), calculated and quarterly: winter (December,spring (March, January, April, November). May), summer (June, July, August), fall (September, October, November), and winter (December, January, November).

FigureFigure 3. Test 3. Test results results of of different different threshold threshold valuevalue for the Normalized Normalized Difference Difference Snow Snow Index Index (NDSI). (NDSI).

2.3.3.2.3.3. Accuracy Accuracy Assessment Assessment In this study, the accuracy assessment of image classification used the stratified random In this study, the accuracy assessment of image classification used the stratified random sampling sampling design. Reference points were created randomly from classified maps of the study area for design. Reference points were created randomly from classified maps of the study area for 1972, 1990, 1972, 1990, 2000, and 2014. A total of 700 points were selected randomly (100 points for each class). 2000,The and original 2014. Landsat A total ofand 700 Google points Earth were images selected were randomly used as reference (100 points sources for to each classify class). the The selected original Landsatpoints. and The Google reference Earth points images were confirmed were used by asvisual reference interpretation sources of to the classify reference the source, selected which points. Thewere reference the only points available were reference confirmed data by for visual evaluati interpretationng the accuracy of theassessment. reference Overall source, accuracy, which wereuser the onlyaccuracy, available and reference the Kappa data coefficient for evaluating were computed the accuracy from assessment.error matrices. Overall accuracy, user accuracy, and the Kappa coefficient were computed from error matrices. Land 2018, 7, 6 7 of 18

2.3.4. Trend Analysis with the Mann-Kendall Test for Hydro-Climate Variables Characteristics of hydro-climatic changes in the Kashgar Region were analyzed based on data collected at four meteorological stations and three hydrological stations for the period of 1964–2014. The Mann-Kendall (MK) test can assess trends in a time series without requiring normality or linearity [34,35]; it is highly recommended for use by the World Meteorological Organization [36]. Therefore, the MK trend test has been widely used to test for randomness against trends in hydrology and climatology [37]. This study also used the MK trend test method, adapted by Xu et al. (2013) [38] to analyze trends in precipitation, temperature, evaporation, and annual runoff.

3. Results

3.1. Land Cover Mapping Results of overall accuracy ranged from 88% to 91% (Table3), and standard overall accuracy for LC maps has been established at 85% [39]. Kappa coefﬁcient values ranged from 0.86 to 0.90; users and producers’ accuracy for individual classes ranged from 100% to 65%. The grassland and forest’s lowest accuracy could be explained by the area’s aridity and sparse vegetation, which led to confusion between these LC types. However, the visual comparison method between the original satellite images and LC maps showed robust classiﬁcation accuracy.

Table 3. Accuracy assessment of land cover (LC) map for 1972, 1990, 2000, and 2014.

1972 1990 2000 2014 Land Cover Classes Users’ Producers’ Users’ Producers’ Users’ Producers’ Users’ Producers’ Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Urban 86% 67% 100% 95% 100% 91% 100% 68% Water 100% 78% 97% 97% 100% 88% 100% 90% Grassland 91% 91% 88% 65% 100% 69% 100% 96% Cultivated land 80% 97% 89% 97% 94% 100% 91% 100% Forestland 90% 60% 76% 73% 98% 91% 87% 81% Snow/ice 100% 100% 100% 100% 100% 94% 100% 97% Bare land 87% 100% 92% 100% 68% 100% 73% 100% Overall accuracy 88% 93% 93% 91% Kappa statistic 0.86 0.92 0.91 0.90

Each LC type’s area changed signiﬁcantly during the 42-year study period, as shown in Figure4 and Table4. Results indicate that cultivated land increased remarkably from 1972 to 2014, with its area reaching 11,677.5 km2. This cultivated area results were compared with the sown area of XJSYB of Kashgar Region—as Figure5 showed, the correlation was 0.9. Increased cultivated land led to increased water consumption and utilization of water resources. Water area has consistently decreased from 2762.9 km2 (2.4%) in 1972 to 1062.4 km2 (0.9%) in 2014. The urban/built-up area increased from 40.2 km2 (0.4%) in 1972 to 392.8 km2 (3%) in 2014. Increased population is associated with urban and built-up land expansion. The urbanization level (as the proportion of total population residing in the state’s urban areas) increased from 19.63% in 2000 to 22.76% in 2014 [22]. Mainly distributed from the southern mountain to the northern plain in the study area, grasslands decreased from 4779.1 km2 (4.1%) in 1972 to 2680.6 km2 (2.3%) in 2000. In general, rainfall variability is a key driver of grassland changes in arid regions. Therefore, monthly precipitation data was observed in 2000 and 2014, respectively (Figure6). In contrast, conversion of grassland to cultivated land and over-grazing are other reasons for grasslands’ decreasing on the plain (Table5;[ 40]). However, by 2014, grassland had increased to 3302.5 km2 (2.9%). This trend was related to increasing precipitation in 2014 and the grassland restoration program launched in 2003. Figure6 showed high precipitation in 2014, compared with 2000. In addition, the grassland restoration program mainly focused on improving the richness of local plant species in grasslands and controlling the stock density. Therefore, grasslands increased after in 2000. Land 2018, 7, 6 8 of 18 Land 2018, 7, 6 8 of 18

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Figure 4. LC map of study area in 1972, 1990, 2000 and 2014. Figure 4. LC map of study area in 1972, 1990, 2000 and 2014. Table 4. Results of land use/cover change (LUCC) statistics for 1972, 1990, 2000, and 2014. TableTable 4. 4.Results Results of of land land use/cover use/cover change (LUCC) (LUCC) statistics statistics for for 1972, 1972, 1990, 1990, 2000, 2000, and and 2014. 2014. Year 1972 1990 2000 2014 Year 1972 1990 2000 2014 Year 1972 19902 2000 2014 LandLand Cover Cover Class Class Total AreaArea (km (km2) )Percentage Percentage (%) (%) Land Cover Class Total Area (km2) Percentage (%) WaterWater area area 2762.9 2762.9 2.4 1925.6 1.61.6 1291.21291.2 1.11.1 1062.41062.4 0.9 0.9 CultivatedCultivatedWater area land land 4148.6 2762.9 4148.6 3.6 2.43.6 5991.41925.6 5.2 1.65.2 6572.5 1291.26572.5 5.7 1.15.7 11,677.511,677.5 1062.4 10.2 0.910.2 Cultivated land 4148.6 3.6 5991.4 5.2 6572.5 5.7 11,677.5 10.2 Urban/BuiltUrban/Built up up 40.2 40.2 0.4 96.396.3 0.80.8 169.2169.2 1.51.5 392.8392.8 3 3 Urban/Built up 40.2 0.4 96.3 0.8 169.2 1.5 392.8 3 Grassland 4779.1 4.1 3701.3 3.2 2680.6 2.3 3302.5 2.9 GrasslandGrassland 4779.1 4779.1 4.1 3701.3 3.2 3.2 2680.6 2680.6 2.3 2.3 3302.5 3302.5 2.9 2.9 ForestForestForest land landland 3584.5 3584.5 3584.5 3.1 3.1 2991.1 2.6 2.62.6 1545.7 1545.71545.7 1.3 1.31.3 1344.5 1344.51344.5 1.2 1.2 1.2 Snow/iceSnow/iceSnow/ice 10,838.8 10,838.810,838.8 10 10,789.5 9.4 9.49.4 8987.8 8987.88987.8 7.8 7.87.8 10,001.2 10,001.210,001.2 8.7 8.7 8.7 BareBareBare land landland 87,558.4 87,558.4 87,558.4 77 88,417.0 77 7777 92,665.5 92,665.592,665.5 80 8080 86,131.7 86,131.786,131.7 74 74 74 TotalTotal area areaarea 113,912.5 113,912.5 113,912.5 100 100 113,912.5 113,912.5 100 100100 113,912.5 113,912.5113,912.5 100 100100 113,912.5 113,912.5113,912.5 100 100 100

Figure 5. Sown area and remotely sensed (RS) estimated cultivated area comparison for Kashgar Region (sown area source [22]).

Conversely, from 1972 to 2014, forestland decreased from 3584.5 km2 (3.1%) to 1344.5 km2 (1.2%). FigureFigure 5. 5.Sown Sown area area and and remotelyremotely sensedsensed (RS) estimated estimated cultivated cultivated area area comparison comparison for forKashgar Kashgar AgriculturalRegionRegion (sown expansion(sown area area source source was [ 22[22]).a ]).key factor for forestland’s reduction. Forest area has mainly been distributed around the study area’s riverbanks, and it depends on groundwater resources, that is, groundwaterConversely, seeping from from 1972 ato river. 2014, forestlandTherefore, decreased forestland from was 3584.5 easily km converted2 (3.1%) to 1344.5into cultivated km2 (1.2%). land Conversely, from 1972 to 2014, forestland decreased from 3584.5 km2 (3.1%) to 1344.5 km2 (1.2%). dueAgricultural to abundant expansion water resource was a skey for factoragricultural for forest activity.land’s reduction. Forest area has mainly been Agricultural expansion was a key factor for forestland’s reduction. Forest area has mainly been distributedFrom 1972 around to 2000, the studythe area area’s of snow/iceriverbanks, cover and itdecreased depends onslightly groundwater from 10,838.8 resources, km 2that (10%) is, to distributed around the study area’s riverbanks, and it depends on groundwater resources, that is, 8987.8groundwater km2 (7.8%), seeping but then from increased a river. Therefore, to 10,001.2 forest km2 land(8.7%) was in 2014.easily Overall, converted however, into cultivated snow/ice land cover groundwater seeping from a river. Therefore, forestland was easily converted into cultivated land due showsdue toa decreasingabundant water trend. resource Based son for the agricultural First and activity.Second Glacier Inventory of China, the glacier area to abundant water resources for agricultural activity. 2 From 1972 to 2000, the area of snow/ice2 cover decreased slightly from 10,838.8 km (10%) to in the Kashgar Region has shrunk by 927 km due to global warming [41]. 2 8987.8From km 19722 (7.8%), to 2000, but then the increased area of snow/ice to 10,001.2 cover km2 (8.7%) decreased in 2014. slightly Overall, from however, 10,838.8 snow/ice km cover(10%) to Bare 2land was the Kashgar Region’s most domi2 nant LC, accounting for 77%–74% of its total 8987.8shows km a decreasing(7.8%), but trend. then increasedBased on the to 10,001.2First and km Second(8.7%) Glacier in 2014. Inventory Overall, of however,China, the snow/ice glacier area cover overall area. Due to decreased of snow/ice, bare land remained at 92,665.5 km2, or 80% of its 1990– showsin the a Kashgar decreasing Region trend. has Based shrunk on by the 927 First km2 and due Secondto global Glacier warming Inventory [41]. of China, the glacier area 2000 measurement. Bare land is characterized by sand, desert, and rock, so it is difficult to reclaim, in the KashgarBare land Region was the has Kashgar shrunk Region’s by 927 km most2 due domi to globalnant LC, warming accounting [41]. for 77%–74% of its total as summarized in Table 2. overall area. Due to decreased of snow/ice, bare land remained at 92,665.5 km2, or 80% of its 1990– Furthermore, several observations can be made from time-series LC maps. Cultivated land: (i) expanded generally from west to east (the Kashgar River side); (ii) expanded along the banks of the Land 2018, 7, 6 9 of 18

Bare land was the Kashgar Region’s most dominant LC, accounting for 77%–74% of its total overall area. Due to decreased of snow/ice, bare land remained at 92,665.5 km2, or 80% of its 1990–2000 measurement. Bare land is characterized by sand, desert, and rock, so it is difficult to Land 2018, 7, 6 9 of 18 reclaim, as summarized in Table2. Furthermore,2000 measurement. several Bare observations land is characterized can be by made sand, desert, from and time-series rock, so it is LC difficult maps. to reclaim, Cultivated land: (i) expandedas summarized generally in fromTable 2. west to east (the Kashgar River side); (ii) expanded along the banks of Furthermore, several observations can be made from time-series LC maps. Cultivated land: (i) the Yarkantexpanded River; generally (iii) increased from west near to east the (the forest Kashgar area; River and side); (iv) (ii) expanded expanded muchalong the more banks near of the the southern borders ofYarkant the Yarkant River; (iii) River. increased These near observations the forest area; wereand (iv) mainly expanded related much tomore the near study the area’ssouthern topography and waterborders resources of the Yarkant because River. the These flat alluvialobservations plain were could mainly sustain related to increasing the study area’s expansion topography of cultivated land andand had water sufficient resources water because resources the flat alluvial for agricultural plain could sustain production. increasing expansion of cultivated land and had sufficient water resources for agricultural production.

(mm) 100 2000 2014

FigureFigure 6. Monthly 6. Monthly precipitation precipitation variationvariation ofof mountain mountain region region in 2000 in and 2000 2014. and 2014.

TableTable 5. Area 5. Area transition transition matrix matrix of Kashgar Kashgar Region Region during during 1972 to 1972 2014. to 2014. Land Cover Classes in 2014 (km2) Land Cover Classes Cultivated 2 Bare in 1972 (km2) Water Urban/Built-upLand Cover Grassland Classes Forestland in 2014 (kmSnow/Ice) Total Land Cover Classes Land Land Cultivated Bare in 1972 (kmWater2) Water954 151 Urban/Built-up 0.01 Grassland 157 70 Forestland 456 Snow/Ice974.8 2762.8 Total Land Land Cultivated land 27.1 2631 205.75 711 402 0.21 171.5 4148.5 WaterUrban/built-up 9540.23 151 5.9 28.990.01 0.1 157 0.02 70 0 456 5 974.8 40.2 2762.8 CultivatedGrassland land 27.10.12 26311913 205.75 13.6 2196 711 29.06 402316 0.21311.9 171.54779.6 4148.5 Urban/built-upForestland 0.230.1 5.92328 28.99 10.74 9.4 0.1 843 0.0252.4 0340.8 3584.4 5 40.2 GrasslandSnow/ice 0.121.45 19131.6 13.6 0.00 38.9 2196 0.19 29.068456 3162340.6 311.910,838.7 4779.6 ForestlandBare land 0.1 79.4 2328 4647 10.74 133.4 189.9 9.4 0.01 843 720.6 52.4 81,987 87,757.4340.8 3584.4 Snow/iceTotal 2014 1.45 1062.4 1.6 11,677.5 392.4 0.00 3302.5 38.9 1344.5 0.19 10,001.2 8456 86,131.6 113,911.88 2340.6 10,838.7 Bare land 79.4 4647 133.4 189.9 0.01 720.6 81,987 87,757.4 Total3.2. 2014 Snow Cover1062.4 Changes 11,677.5 392.4 3302.5 1344.5 10,001.2 86,131.6 113,911.88 This research’s second aim was to investigate snow cover changes in the Kashgar Region, and 3.2. SnowFigure Cover 7 Changesshows those changes from 1999 to 2014. In general, a maximum snow-covered area exists in winter and spring when most snowfall has occurred but melting has not begun. As the summer Thisseason research’s advances, second depletion aim of wassnow-covered to investigate area occurs snow and temperatures cover changes increase. in In the this Kashgar study Region, and Figureregion,7 shows annual those snow changes cover variation from 1999 was complicated to 2014. In and general, fluctuated a maximum greatly. Two snow-covered high snowfalls area exists occurred in the mountains of Kashgar and Yarkant in 2005 and 2009, respectively (Figure 7), most in winterlikely and caused spring by when an accumulation most snowfall of winter has and occurredspring snowfall but in melting those years. has In not contrast, begun. low Assnow the summer season advances,cover was recorded depletion in 2007, of snow-coveredand total snow cover area tended occurs to decrease, and temperatures especially in the increase. Yarkant River In this study region, annualMountain snow area, coverwhere variationit decreased wasremarkably complicated year by year. and The ﬂuctuated Mountains greatly. of Kashgar Two and high the snowfalls occurred inYarkant the mountains River have a of mean Kashgar elevation and above Yarkant 4000 in m, 2005 and andthe elevation 2009, respectively of some mountains (Figure areas7), most likely reaches 5000–6000 m—the terrain’s complexity results in great variations in distribution of caused bytemperature an accumulation and precipitation. of winter The mountain and spring area has snowfall only one meteorological in those years. station In (Tashkorgan, contrast, low snow cover was3097.7 recorded m). Annual in 2007, mean and precipitation total snow is 450 cover mm in tendedthe high-elevation to decrease, area (>5000 especially m) and in 100 the mm Yarkant in River Mountainthe area, lower where region (<3000 it decreased m). The long-term remarkably trend of year temperature by year. and Theprecipitation Mountains increased of at Kashgar a rate and the Yarkant Riverof 0.72°C/10 have ayear mean and elevation8.11 mm/10 aboveyear, respectively 4000 m, and. Indeed, the increasing elevation temperature of some mountains accelerated the areas reaches melting and hindered the formation of glaciers, snow, and ice. 5000–6000 m—the terrain’s complexity results in great variations in distribution of temperature and precipitation. The mountain area has only one meteorological station (Tashkorgan, 3097.7 m). Annual mean precipitation is 450 mm in the high-elevation area (>5000 m) and 100 mm in the lower region (<3000 m). The long-term trend of temperature and precipitation increased at a rate of 0.72◦C/10 year and 8.11 mm/10 year, respectively. Indeed, increasing temperature accelerated the melting and hindered the formation of glaciers, snow, and ice. Land 2018Land, 20187, 6 , 7, 6 10 of 1810 of 18

Figure 7. Annual and seasonal snow cover variation of Kashgar River (a) and Yarkant River (b). Figure 7. Annual and seasonal snow cover variation of Kashgar River (a) and Yarkant River (b).

4. Discussion4. Discussion On aOn regional a regional scale, scale, the LUCCthe LUCC of oases of oases is key is key to environmentalto environmental change change and and has has a cumulativea cumulative effect in thiseffect arid in land. this arid LUCC land. in LUCC the arid in the region arid isregion mainly is mainly driven driven by unique by unique natural natural and and social social factors factors [42 ,43]. Therefore,[42,43]. research Therefore, on research LUCCs’ on driving LUCCs’ forces driving in forces the arid in the region arid region is conducted is conducted by social by social and and natural sciences,natural and sciences, requires and an requires interdisciplinary an interdisciplinary approach. ap Inproach. this respect,In this respect, research research on the on driving the driving forces in the Kashgarforces in Region the Kashgar focuses Region mainly focuses on mainly natural on and natural human and factors. human factors. 4.1. Natural Driving Forces of Land Use/Cover Change (LUCC) 4.1. Natural Driving Forces of LUCC The Kashgar Region covers a vast area; the region’s southwest part is high, and its northeast part The Kashgar Region covers a vast area; the region’s southwest part is high, and its northeast is low. Elevations in the study area divided into high and low mountains, plain, and desert areas. part isDue low. to differing Elevations characteristics in the study of temperature area divided an intod precipitation high and lowin these mountains, areas, we plain,selected and four desert areas.observation Due to differing stations characteristics for investigating of temperaturecharacteristics andof climate precipitation variables: in Kashgar these areas, (1288.7 we m, selected in the four observationnorth) mountain stations forarea, investigating Maralbishi (1116.5 characteristics m, in the of east), climate Yarkant variables: (1231 Kashgarm, in the (1288.7south), m,and in the north),Tashkorgan Maralbishi (3093.7 (1116.5 m, in m, the in mountains). the east), Yarkant As with (1231the results m, in of theMK south),trend tests, and all Tashkorgan variables showed (3093.7 m, in thesignificant mountains). increasing As with trends, the results except offor MK evaporation trend tests, on all the variables mountains showed (Table signiﬁcant6). These results increasing trends,indicated except that for evaporationduring the past on 50 the years, mountains the clim (Tableate exhibited6). These a warming results indicated trend. Other that scholars during have the past 50 years,observed the climate a similar exhibited trend in the a warmingnorthwest trend.Xinjiang Other Region scholars over the have last 30 observed years [44]. a similar trend in the In general, temperatures on the plain (Kashgar, Maralbeshi, and Yarkant) are higher than in the northwest Xinjiang Region over the last 30 years [44]. mountain ranges (Tashkorgan). On the one hand, increasing temperatures have favored agricultural Inproduction, general, temperaturesand cotton production on the plain average (Kashgar, yield per Maralbeshi, acre increase and 373.5 Yarkant) kg∙hm⁻ are² per higher decade than from in the mountain1990 to ranges 2013 [45]. (Tashkorgan). Cultivated land On increased the one hand, from 4148.6 increasing km2 in temperatures 1972 to 11,677.5 have km2 favoredin 2014 (Table agricultural 4). −2 production,On the other and cottonhand, increasing production temperatures average yield enha pernced acre transpiration increase 373.5and evaporation kg hm per of crop decade and from 1990 tosurface 2013 water. [45]. Cultivated Evaporation land on the increased plain ranged from 4148.6from 0.02 km to2 in0.04 1972 (Table to 11,677.56), indicating km2 ina significant 2014 (Table 4). On the other hand, increasing temperatures enhanced transpiration and evaporation of crop and surface water. Evaporation on the plain ranged from 0.02 to 0.04 (Table6), indicating a signiﬁcant increasing trend. Expansion of cultivated land led to increased water consumption, from 50 × 108 m3 to 155 × 108 m3 from 1950 to 2010 [22], and increasing utilization of surface water. LUCC results Land 2018, 7, 6 11 of 18 indicated that surface water decreased from 2762.9 km2 to 1062.4 km2 (Table4), possibly caused by increased temperature and agricultural water consumption.

Table 6. The results of the Mann-Kendall test for climatic-hydro variables.

Kashgar * Maralbishi * Yarkant * Tashkorgan * Kaqun + Karbel + Kirik + Zc β Zc β Zc β Zc β Zc β Zc β Zc β T 5.01 0.11 3.14 0.02 4.13 0.03 2.09 0.04 ------P 2.59 0.57 3.05 0.12 3.19 0.46 4.15 0.23 ------E 3.10 0.02 3.15 0.04 2.04 0.04 −0.54 −0.04 ------R ------0.64 0.11 1.2 0.1 1.19 0.36 Note: * meteorological station; + hydrological station; T-temperature, P-precipitation, E-evaporation, and R-runoff; Zc-standard normal random variable. β-Kendall slope, a positive β is rising trend, while a negative β means a decreasing trend.

In the mountainous areas, high-elevation glaciers and snow cover provide annual runoff and groundwater in the Kashgar Region. Seasonal runoff patterns were heavily dominated by winter snow accumulation and spring melt. Generally, snow cover is a seasonal phenomenon, and its variability is higher than that of glaciers. The air temperature is a major index for the snow and ice melt process, due to snow’s particular sensitivity to climate changes. An increase of 2 ◦C in air temperature had a much more important effect on snow cover than doubling the precipitation during the snowmelt period [46]. Figure8a displays the increased mountain temperatures over time. As temperatures rise, more precipitation falls as rain instead of snow, leading to smaller snowpack, decreased proportion of solid precipitation, and enhanced snowmelt. Figure7 displays the slightly decreasing trend in annual snow cover. Then, rising temperatures have caused changes in snow cover that contributed to increased natural runoff of the Kashgar and Yarkant Rivers. The MK test results in Table6 and Figure9 indicate that runoff has increased. Particularly during the observation period from 1999 to 2014, snow cover changes have had strong effects on runoff discharge, and runoff displays an increasing trend, with pronounced peaks having occurred in 2005 and 2009. The relationship among snow cover, runoff, precipitation, and temperature on short- and long-term scales have been analyzed based on the only available annual data (snow cover, precipitation, temperature, and runoff). The high snow cover accumulations recorded in 2005 and 2009 (Figure7) was attributed to annual precipitation in the mountain range in the same years (Figure8b). During the same time, temperatures of the study area recorded their higher peak in mountain (Figure8a). Increased temperatures might have accelerated snowmelt and contributed to the increased runoff in 2005 and 2009 (Figure9) on the short-term scale. Yan et al. (2007) [47] showed that regional runoff increased by 10–16% when temperature rose by 1 ◦C, based on the correlation between temperature and the Yarkant River’s monthly mean discharge. The magnitude of glacier contribution to natural runoff is much less than snow melt runoff in the short-term. However, Dong et al. (2009) [48] addressed the long-term effect of temperature and precipitation changes on physical characteristics of the area’s glaciers. With an increase in air temperature during the study period (1960–2000), glaciers in the Yarkant River basin retreated by 6.1% and overcame effects of increased precipitation on the glacier mass balance due to regional climate changes that enhanced glacier ablation, which provided melt water for a longer time. In addition, Chen et al. (2006) [49], Xu et al. (2009) [50], and Qiang et al. (2010) [51] have addressed hydro-meteorological processes by investigating changing characteristics of precipitation, temperature, and runoff. Their results indicated significant runoff increases due to climate changes of the Yarkant and Kashgar Rivers. As discussed above, snow and glacier contributions form a major part of total runoff in the Kashgar Region, and therefore changes in snow and glacier melt runoff due to climate change are reflected in changes of the total stream flow of those rivers. Natural runoff from melting snow and ice has played an important role in the recharge of groundwater infiltration and storage. The total natural recharge of groundwater is 89.3 × 108 m3/year in the Kashgar Region [52]. Land 2018, 7, 6 12 of 18

Land 2018, 7, 6 12 of 18 Replenishment by precipitation is not very signiﬁcant to groundwater in this study area’s arid climate. thisThe studyLand geographical 2018 area’s, 7, 6 arid distribution climate. The of runoffgeographical has changed distribution due to of increased runoff has human changed activities, due to which 12increased of 18 may humanhave affectedactivities, replenishment which may ofhave groundwater affected repl andenishment changes toof the groundwater changes of totaland streamchanges ﬂow to the of thosethis rivers. study area’s arid climate. The geographical distribution of runoff has changed due to increased changes of total stream flow of those rivers. human activities, which may have affected replenishment of groundwater and changes to the changes of total20.0 stream flowKashgar of those rivers.Yarkant Maralbeshi Tashkorgan

℃ (a) 20.0 Kashgar Yarkant Maralbeshi2005 2009Tashkorgan

15.0℃ (a) 15.0 10.0 10.0 5.0 5.0

Anual meantemperature / 0.0 Anual meantemperature / 0.0 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014

KashgarKashgar YarkantYarkant MaralbishiMaralbishi TashkorganTashkorgan 200.0200.0 (b)(b) 2005 150.0150.0 2009

100.0100.0

50.050.0

0.0 0.0precipitation/mm mean Anual Anual mean precipitation/mm mean Anual 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014

Figure 8. Annual mean temperature (a); and precipitation (b) trend of Kashgar Region. FigureFigure 8. 8.AnnualAnnual mean mean temperature temperature (a (a);); and and precipitation precipitation ( (bb)) trend trend of KashgarKashgar Region.Region.

Kizl River Gaz River Linear trend 60 (a) 2005 2009

) Kizl River Gaz River Linear trend 60 3 (a) 2005 2009 m 8 ) 3 40 m 8 40 20 20 0 Annual runoff (10 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014 0 Annual runoff (10 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014

FigureFigure 9. 9.Runoff Runoff variationvariation of Kashgar River River (a ();a); and and Yarkant Yarkant River River (b). (b ).

Figure 9. Runoff variation of Kashgar River (a); and Yarkant River (b).

Land 2018, 7, 6 13 of 18 Land 2018, 7, 6 13 of 18

4.2. Social Driving Forces of LUCC

4.2.1. Impact of increasing of population on LUCCs Population growth has been identiﬁedidentified as a majormajor drivingdriving forceforce forfor agriculturalagricultural expansionexpansion and waterwater consumptionconsumption byby Zhou et al. (2003) [[53],53], Zhang et al. (2003) [54], [54], Wang et al. (2008) [55], [55], and Til et al. (2015) [[56]56] for differentdifferent areasareas inin aridarid NorthwestNorthwest China.China. TheThe populationpopulation ofof thethe KashgarKashgar RegionRegion rose from 2,745,362 2,745,362 in in 1988 1988 to to 4,683,114 4,683,114 in in 2014 2014 [22] [22, an], an increase increase of approximately of approximately 70%. 70%. Generally, Generally, the thegrowing growing population population has has requested requested more more land land for for ba basicsic needs needs of of living, living, such such as as food food and and housing. housing. More than 78% ofof householdshouseholds in ruralrural areasareas dependdepend largelylargely onon landland resources,resources, andand agricultureagriculture contributes a large share of thethe grossgross domesticdomestic productproduct (GDP).(GDP). Crop andand cottoncotton productionproduction has increased, and the the total total agricultural agricultural area area has has expanded. expanded. Figure Figure 10 10 shows shows the the population population increase increase in inthe the Kashgar Kashgar Region. Region. A high A high positive positive correlation correlation was was found found between between population population and andsown sown area. area.The Themajority majority of grassland of grassland and andforestland forestland was wasoccupied occupied by cultivated by cultivated land land to satisfy to satisfy people’s people’s demand demand for 2 2 forfood food (Table (Table 6). 6As). Asa result, a result, grassland grassland decreased decreased from from 4779.1 4779.1 km km2 in 1972in 1972 to 2680.6 to 2680.6 km2 kmin 2000,in 2000, and 2 2 andforest forest decreased decreased from from 3584.5 3584.5 km2 kmin 1972in 1972to 1344.5 to 1344.5 km2 in km 2014.in 2014.Bare land Bare and land cultivated and cultivated land were land 2 2 wereconverted converted to urban/built-up to urban/built-up land, which land, whichincreased increased from 40.2 from km 40.22 in km 1972in to 1972 392.8 to 392.8km2 in km 2014.in 2014.Population Population growth growth resulted resulted in the in theexpansion expansion of ofcultivated cultivated land, land, with with reclamation reclamation and and evident shrinkage of grass andand forestforest cover.cover. Therefore,Therefore, populationpopulation isis aa majormajor factorfactor inin LUCCs.LUCCs.

Figure 10. Total population of study area (source: [22]). Figure 10. Total population of study area (source: [22]).

4.2.2. Impact of Policy-Induced Agricultural Land Development on LUCCs 4.2.2. Impact of Policy-Induced Agricultural Land Development on LUCCs Policy has contributed directly to the area’s economy because political and economic activities are Policy has contributed directly to the area’s economy because political and economic activities strongly interlinked with economic needs. Moreover, economic pressures were reflected in political are strongly interlinked with economic needs. Moreover, economic pressures were reflected in programs,political programs, and economic and economic instruments instruments were used were to implement used to implement political driving political forces. driving Socio-economic forces. Socio- developmentseconomic developments of the Kashgar of the Region Kashgar have Region been have observed been observed from 1984 from to 2014, 1984 during to 2014, which during time which the averagetime theannual average growth annual rate growth of the rate GDP of and theprimary GDP and industry primary increased industry by increased 138% and by 35%, 138% respectively and 35%, (Figurerespectively 11). Policy(Figure influences 11). Policy on influences LUCCs inon the LUCCs Kashgar in the Region Kashgar have Region been have demonstrated been demonstrated based on documentbased on analysis,document that analysis, is, international that is, scientific international literature, scientific national literature, studies, andnational government studies, reports. and Policygovernment influences reports. were Policy closely influences associated were with closely LUCC associated driving with factors LUCC observed driving in factors the study observed area. Forin the clarity, study we area. organized For clarity, these majorwe organized events into thes thee followingmajor events three into distinct the following historical periods.three distinct First period, 1979–1984 historical periods. : The land tenure system adopted an ‘open-door policy’. This policy led to theFirst distribution period, 1979–1984 of land: from The land proprietors tenure system to individual adopted households an ‘open-door according policy’. to This land policy tenure led [57 to]. Consequently,the distribution land of land tenure from policy proprietors has impacted to individual agricultural households production’s according efficiency to land and thetenure region’s [57]. economicConsequently, development. land tenure The policy primary has changeimpacted in LUCCagricultural was the production’s expansion ofefficiency cultivated and land the nearregion’s the alluvialeconomic plain development. (LC map for The 1972, primary Figure change4). Government in LUCC planswas the supported expansion this of expansion cultivated of land agriculture near the andalluvial provided plain (LC opportunities map for 1972, for expansionFigure 4). Governme of grain andnt cropplans cultivation, supported whichthis expansion has initially of agriculture supported theand population’s provided opportunities food and economic for expansion desires. of grain and crop cultivation, which has initially supported the population’s food and economic desires. Land 2018, 7, 6 14 of 18

Land 2018, 7, 6 14 of 18 Second period, 1984–1990s: This period includes the most signiﬁcant changes to land policy. Second period, 1984–1990s: This period includes the most significant changes to land policy. The The bureau of land administration was established and became responsible for land allocation, bureau of land administration was established and became responsible for land allocation, acquisition, monitoring, development, and for implementations of land law. Administrative law acquisition, monitoring, development, and for implementations of land law. Administrative law has has legalized individual access to owning land in an attempt to develop the land market in Xinjiang legalized individual access to owning land in an attempt to develop the land market in Xinjiang and and the inner cities of China. Individual ownership was implemented to promote socio-economic the inner cities of China. Individual ownership was implemented to promote socio-economic development and further improve people’s living standards. Agriculture attracted the attention of development and further improve people’s living standards. Agriculture attracted the attention of land businesspeople. Because of rising market prices, cotton cultivation brought fast economic growth. land businesspeople. Because of rising market prices, cotton cultivation brought fast economic China became a global leading textile and clothing exporter, with rising demand for cotton driving growth. China became a global leading textile and clothing exporter, with rising demand for cotton prices to historically high levels [58]. The Kashgar Region has accounted for 43% of Xinjiang cotton driving prices to historically high levels [58]. The Kashgar Region has accounted for 43% of Xinjiang production, and responses to cotton market expansion resulted in cultivating more land there. cotton production, and responses to cotton market expansion resulted in cultivating more land there.

(a) (b)

Figure 11. Primary industry (a); and gross domesticdomestic productproduct (GDP)(GDP) ((bb)) ofof studystudy areaarea (source:(source: [[22]).22]).

The general pattern of agricultural expansion from 1984 to 1990 in the plains and along the rivers The general pattern of agricultural expansion from 1984 to 1990 in the plains and along the rivers has continued and led to intensified land use (LC map for 1990, Figure 4). The change in the has continued and led to intensiﬁed land use (LC map for 1990, Figure4). The change in the distribution distribution of land use, especially the expansion of cultivation around critical water resources, has of land use, especially the expansion of cultivation around critical water resources, has resulted in the resulted in the agricultural sector’s increased demand for water. Thus, the government has increased agricultural sector’s increased demand for water. Thus, the government has increased investment investment to establish irrigation channels, and the pattern of expansion along irrigation channels to establish irrigation channels, and the pattern of expansion along irrigation channels has led to has led to continually increasing agricultural production. The Kashgar Region’s increasing continually increasing agricultural production. The Kashgar Region’s increasing agricultural output agricultural output has also led to the construction of transport corridors—a long southern Xinjiang has also led to the construction of transport corridors—a long southern Xinjiang railway and a national railway and a national highway from Kashgar to other domestic markets. These corridors, in turn, highway from Kashgar to other domestic markets. These corridors, in turn, have led to increased have led to increased economic linkages between the Kashgar Region and surrounding cities [59]. economic linkages between the Kashgar Region and surrounding cities [59]. Current period, 2000s–2014: The government announced the abolition of the agricultural tax [60] Current period, 2000s–2014: The government announced the abolition of the agricultural tax [60] and an adjustment of crop and cotton production structures towards market requirements. In and an adjustment of crop and cotton production structures towards market requirements. In addition, addition, in 2001, China joined the World Trade Organization, which supports the agricultural sector in 2001, China joined the World Trade Organization, which supports the agricultural sector [61]. [61]. Since 2002, China has implemented direct agricultural income and agricultural machinery Since 2002, China has implemented direct agricultural income and agricultural machinery subsidies. subsidies. For these reasons, since 2005, the Kashgar Region’s primary industry has undergone For these reasons, since 2005, the Kashgar Region’s primary industry has undergone dramatic dramatic changes (LC map for 2000 and 2014, Figures 4). Moreover, the government launched special changes (LC map for 2000 and 2014, Figure4). Moreover, the government launched special economic economic development zones along the Kashgar River to attract domestic and foreign investment. development zones along the Kashgar River to attract domestic and foreign investment. The main The main goal of this economic zone is to boost economic development in the Kashar Region by goal of this economic zone is to boost economic development in the Kashar Region by providing providing favorable policies such as tax exemptions, energy supplies, and access to transportation in favorable policies such as tax exemptions, energy supplies, and access to transportation in 2011 [62]. 2011 [62]. Consequently, in 2013, China and several Central Asian countries cooperated to build the Consequently, in 2013, China and several Central Asian countries cooperated to build the ‘Silk Road ‘Silk Road Economic Belt’ for mutual commercial benefits [63]. These on-going political and new Economic Belt’ for mutual commercial beneﬁts [63]. These on-going political and new economic economic events have altered LUCCs (such as opened the new land for cultivation, Figure 12) by eventslocal- and have national-level altered LUCCs approach (such ases openedto the Kashgar the new Region’s land for development. cultivation, Figure These 12 external) by local- drivers, and national-levelcombined with approaches local situations, to the have Kashgar contributed Region’s todevelopment. LUCCs. These external drivers, combined with local situations, have contributed to LUCCs. Land 2018 7 Land 2018, 7, 6 , , 6 15 of 1815 of 18

FigureFigure 12. 12.Opened Opened new new cultivated cultivated land continually land from from 1990 1972 to 2014. to 2014.

5. Conclusions5. Conclusions In Kashgar Region, agricultural and economic development in oasis mainly depends on In Kashgar Region, agricultural and economic development in oasis mainly depends on melt- melt-water from glaciers and seasonal snow in the high mountain areas. Knowledge of quantifying water LUCCfrom glaciers and its driving and seasonal forces including snow in natural the high and mountain social drivers areas. determined Knowledge by anthropogenic of quantifying and LUCC and itsclimate driving change forces are including essential fornatural sustainable and social development drivers determined in this region. by We anthropogenic combined multi-scale and climate change(Landsat) are essential and multi-temporal for sustainable (SPOT development VGT) remotely in sensed this region. images We to investigate combined spatial multi-scale and temporal (Landsat) and multi-temporaldynamics of LUCCs, (SPOT including VGT) snow remotely cover changes, sensed during images the lastto 42investigate years (1972–2014). spatial Theand results temporal dynamicsshowed of LUCCs, that water, including forest, grassland,snow cover and changes, bare land during have exhibitedthe last 42 declining years (1972–2014). trends due toThe the results showedexpansion that water, of cultivated forest, andgrassland, urban/built-up and bare land land from have 3.6%, exhibited 0.4% to 10.2%, declining and 3%, trends respectively. due to the expansionSnow/ice of cultivated covered areas and have urban/built-up fluctuated, considering land from that 3.6%, only 0.4% snow to area 10.2%, in the summerand 3%, time respectively. first decreased and then increased, according to the Landsat snapshot. However, the total snow covered Snow/ice covered areas have fluctuated, considering that only snow area in the summer time first slightly decreased, as confirmed by the SPOT VGT time-series data analysis. decreased Theand increasing then increased, of temperature according and to precipitation the Landsat have snapshot. different However, effects on plainthe total and mountainsnow covered slightlyareas. decreased, In the plain as confirmed areas, increasing by th temperaturee SPOT VGT has time-series increased evaporation data analysis. and water consumption, Thebut inter-annualincreasing of changes temperature in precipitation and precipitation had less impact have on LUCCs.different Meanwhile, effects on in plain mountain and areas,mountain areas. bothIn the of plain them especiallyareas, increasing experienced temperature warming andhas decreasedincreased snowevaporation cover, which and water led to increasedconsumption, but inter-annualnatural runoff, changes providing in precip water resourcesitation had in theless plain. impact on LUCCs. Meanwhile, in mountain areas, both of themBesides especially natural experienced factors, demands warming from theand growing decreased population snow cover, for basic which needs led of to living increased naturaland runoff, economic providing development water wereresources the anthropogenic in the plain. drivers of LUCCs. Policy-related agricultural expansion shifted land use from small- to large-scale intensive farming. These drivers were the primary Besides natural factors, demands from the growing population for basic needs of living and drivers of LUCCs in the Kashgar Region. At the same time, changes to the temperature, snow cover, economicand runoffdevelopment was a facilitator were ofthe implementing anthropogenic anthropogenic drivers drivingof LUCCs. forces. Policy-related This study’s results agricultural are expansionvaluable shifted in the land Kashgar use Region from andsmall- other to arid larg regionse-scale with intensive similar geographic farming. conditions.These drivers were the primary drivers of LUCCs in the Kashgar Region. At the same time, changes to the temperature, Acknowledgments: Ayisulitan Maimaitiaili would like to express and most sincere gratitude go to the Kawashima snow Shojicover, Memorial and runoff Scholarship was Funda facilitator for financially of implem supportingenting during anthropogenic the study period driv of thising research. forces. This study’s results are valuable in the Kashgar Region and other arid regions with similar geographic conditions. Author Contributions: Main part of this study was performed by Ayisulitan Maimaitiaili and she wrote this paper; XiaoKaiti Aji and Akbar Matniyaz gave key suggestions, especially methodology and conclusions part Acknowledgments:during this study. Ayisulitan Akihiko Kondoh Maimaitiaili was her supervisorwould like during to thisexpr studyess and and providedmost sincere invaluable gratitude guidance go on to the technical aspects of this study, as well as improving this manuscript. Kawashima Shoji Memorial Scholarship Fund for financially supporting during the study period of this research. Conflicts of Interest: I declare that the authors of this paper no conflict of interest. Author Contributions: Main part of this study was performed by Ayisulitan Maimaitiaili and she wrote this paper; XiaoKaiti Aji and Akbar Matniyaz gave key suggestions, especially methodology and conclusions part during this study. Akihiko Kondoh was her supervisor during this study and provided invaluable guidance on technical aspects of this study, as well as improving this manuscript.

Conflicts of Interest: I declare that the authors of this paper no conflict of interest.

Land 2018, 7, 6 16 of 18

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