Land-Use Change and Landscape

sustainability Article

Assessment of Land-Cover/Land-Use Change and Landscape Patterns in the Two National Nature Reserves of Ebinur Lake Watershed, Xinjiang, China Fei Zhang 1,2, *, Hsiang-te Kung 3 and Verner Carl Johnson 4 1 2 3 4

*

College of Resources and Environmental Sciences, Xinjiang University, Urumqi 830046, China Ministry of Education of Key Laboratory of Oasis Ecology at Xinjiang University, Urumqi 830046, China Department of Earth Sciences, the University of Memphis, Memphis, TN 38152, USA; [email protected] Department of Physical and Environmental Sciences, Colorado Mesa University, Grand Junction, CO 81501, USA; [email protected] Correspondence: [email protected]; Tel.: +86-991-8581-281

Academic Editor: Audrey L. Mayer Received: 16 March 2017; Accepted: 27 April 2017; Published: 2 May 2017

Abstract: Land-cover and land-use change (LCLUC) alters landscape patterns and affects regional ecosystems. The objective of this study was to examine LCLUC and landscape patterns in Ebinur Lake Wetland National Nature Reserve (ELWNNR) and Ganjia Lake Haloxylon Forest National Nature Reserve (GLHFNNR), two biodiversity-rich national nature reserves in the Ebinur Lake Watershed (ELW), Xinjiang, China. Landsat satellite images from 1972, 1998, 2007 and 2013 were used to calculate the dynamics of a land-cover and land-use (LCLU) transition matrix and landscape pattern index using ENVI 5.1 and FRAGSTATS 3.3. The results showed drastic land use modifications have occurred in ELWNNR during the past four decades. Between 1972 and 1998, 1998 and 2007, and 2007 and 2013, approximately 251.50 km2 (7.93%), 122.70 km2 (3.87%), and 195.40 km2 (6.16%) of wetland were turned into salinized land. In GLHFNNR both low and medium density Haloxylon forest area declined while high density Haloxylon forest area increased. This contribution presents a method for characterizing LCLUC using one or more cross-tabulation matrices based on Sankey diagrams, demonstrating the depiction of flows of energy or materials through ecosystem network. The ecological landscape index displayed that a unique landscape patches have shrunk in size, scattered, and fragmented. It becomes a more diverse landscape. Human activities like farming were negatively correlated with the landscape diversity of wetlands. Furthermore, evidence of degraded wetlands caused by air temperature and annual precipitation, was also observed. We conclude that national and regional policies related to agriculture and water use have significantly contributed to the extensive changes; the ELWNNR and GLHFNNR are highly susceptible to LCLUC in the surrounding Ebinur Lake Watershed. Keywords: land-cover and land-use change; landscape pattern; landscape index; remote sensing

1. Introduction Land-cover and land-use (LCLU) has been a traditional and important research topic in both local and global environmental studies [1,2]. It is widely acknowledged that LCLUC is a primary cause of the current global biodiversity crisis, mostly through its effects on habitat quality. Numerous studies have reported that LCLUC is the main cause for species extinction worldwide, and also results in species replacement and biotic homogenization or differentiation [3,4]. For example, habitat fragmentation has become one of the main threats to biodiversity at local, regional and global scales and is causing LCLUC [5,6], as well as causing increasing rarity of species and driving many species

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toward extinction [3,7,8]. It is well known that human activities and natural processes often accelerate the speed of LCLUC [9,10]. The complex interaction of various social, economic and biophysical situations following agricultural diversification, advancement in technology coupled with alarming rate of population pressure result in LCLUC [11]. Therefore, human factors and natural factors are considered as major driving factors of LCLUC in current study. Wetlands are the only ecosystems formed as a result of land and water interactions [12]. Wetlands play an important role in many ecosystems by mitigating pollution, providing habitats for plants and wildlife, regulating climate, and preserving biodiversity [13–16]. The Ebinur Lake watershed (ELW), located in Xinjiang Uyghur Autonomous Region, China, is an arid to semi-arid region suffering from frequent droughts and water scarcity and has become the second most significant area of ecological degradation after the Tarim River Basin in Xinjiang. It contains the Ebinur Lake Wetland National Nature Reserve (ELWNNR) and Ganjia Lake Haloxylon Forest National Nature Reserve (GLHFNNR). In the last six decades, increased human population density has led to a dramatic expansion of agricultural areas over the ELW, resulting in significant changes in LCLU, i.e., shrinking wetlands. Although wetlands within the ELWNNR and GLHFNNR have been recognized as critical ecosystems and part of oasis ecology, these wetland areas within the ELW are rapidly disappearing, causing the natural wetland habitats and Haloxylon forest to shrink because of an increase in human activities in the study area. It is critical to providing essential ecological and environmental services in the study area. The development of the Watershed on the north slope of the economic belt of the Tianshan Mountains region and the Asia-Europe Continental Bridge are very important to the sustainable development of the social economy and oasis ecology in Xinjiang. Therefore, we conducted a comparative examination of LCLUC and landscape patterns in the study area using GIS spatial automatic overlay method and Landsat satellite images collected during 1972, 1998, 2007 and 2013. Among these available remotely sensed data, the Landsat TM/ETM+ data have been widely used in many case studies of LCLU worldwide, given the free open-access for data acquisition, the long time span, and the spatial coverage for most of the LCLU. Moreover, compared with the coarse resolution TIR data, such as AVHRR and MODIS, the recognition of LULC based on Landsat TM/ETM+ data can produce persuasive results with much greater accuracy [17]. Previous case studies have provided a wealth of useful information, which has allowed us to rethink the adverse consequences of LULC change and rapid urbanization and to therefore help the decision-makers develop and execute rational land use policies. However, studies using a combination of socio-economic analysis and time series Landsat TM/ETM+ data over a long time span were relatively scarce. The purposes of this study were (i) using remote sensing and geographic information system (GIS) analysis to monitor and analyze the dynamics of LCLUC in the ELWNNR and GLHFNNR, (ii) analyze the changes in landscape patterns using landscape metrics, i.e., number of patches (NP), largest patch index (LPI), landscape shape index (LSI), contagion index (CONTAG), Shannon diversity index (SHDI), Shannon evenness index (SHEI), interspersion and juxtaposition index (IJI), fragmentation index (FI) and aggregation index (AI) and (iii) explore natural and anthropogenic drivers of LCLUC in the study area. 2. Materials and Methods 2.1. Study Area Located in the center of Eurasia and in the northwestern part of Xinjiang Uyghur Autonomous Region, the Ebinur Lake watershed lies between 43◦ 380 –45◦ 520 N and 79◦ 530 –85◦ 020 E (Figure 1). Covering a total area of 5062 km2 , the Ebinur Lake watershed is characterized by mountains, plains and wetland/lake areas. The altitude and slope of Ebinur Lake watershed at the range of 190–5500 m and 3–10‰, respectively. Three major river systems including the Bortala, Jing and Kuitun rivers along with 12 of their tributaries once fed the Ebinur Lake watershed. The Ebinur Lake watershed is mainly


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recharged by alpine glacier meltwater and mountain precipitation, totaling 37.46 × 108 m3 /yr. Natural environmental changes and human activities (i.e., modern agricultural development in oases) have caused many rivers to gradually lose their hydraulic connections with Ebinur Lake. Currently, only Bortala and Jing rivers supply water to Ebinur Lake. The typical arid continental climate of the Ebinur Lake watershed features hot summers, chilly winters, rare precipitation events and strong evaporation. The mean annual temperature varies from 4.0 to 8.1 ◦ C, while the mean annual precipitation varies between 102.60 and 229.40 mm from the plain to the mountains, respectively [18]. Figure 1 shows location of the two biodiversity-rich national nature reserves, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) and Ganjia Lake Haloxylon forest National Nature Reserve (GLHFNNR). Ebinur Lake Wetland National Nature Reserve is the most representative temperate arid zone wetland ecosystem in China acting as the center of oasis and a region experiencing desertification on the northern slope of the Tianshan Conjugate. It can be characterized as a region of lake-wetlands, marshes and river-wetlands; this wetland area is located in an ecologically fragile district that is one of the few areas of desert species habitat and has critical implications for the environment of the Junggar Basin [19]. The GLHFNNR stands in the western margin of the Gurbantünggüt Desert and adjoins Ebinur Lake. This reserve serves a natural barrier buffering the region against the east wind that blows into the Ebinur Lake region. The GLHFNNR has the largest of area and preserve of intact white Haloxylon forest. The Haloxylon forest generally grows in the Gobi desert, salt desert and migratory dunes. Haloxylon forest also serves in sand stabilization, regulating climate, maintaining biodiversity and protecting landscape resources. This reserve is not only part of China’s “Three North” forest protection system (a forest restoration program started in 1978, designed to be completed in 2050), but also resists to the raging winds from Ala Mountain and controls mobile sand dunes. Therefore the GLHFNNR is the one of the most important areas for protecting the natural environment of this arid area [20]. In recent years, with the reduction of Haloxylon forest area, the influence of changes in local rainfall and lower humidities have affected the changes in the patterns of the Haloxylon forest ecosystem and landscape [21]. Because of high evapotranspiration and low rainfall, regions where evaporation occurs on the edges or shallow areas of the Ebinur Lake area are prone to salinization. In addition, salinity often leads to other major soil degradation processes such as soil compaction and dispersion, as well as increased corrosion from the saline soil around man-made structures [22]. Therefore, soil salinization in Ebinur Lake can and will threaten the natural environment and related resources such as human land use of these areas and may limit our ability to maintain sustainable development [23]. Figure 2 provides photos of the physical appearances of the ELWNNR and GLHFNNR landscape. 2.2. Image Processing Four Landsat images were used in this study as follows: Multi-spectral Scanner images collected on 25 September 1972; Thematic Mapper images from 2 September 1998 and 11 September 2007; Operational Land Imager images from 2 September 2013. All the information about the data sources is shown in Table 1. All of these images were downloaded from the United States Geological Survey website (http://glovis.usgs.gov/) for LCLU classification in this study. Images have a minimal cloud cover of 2–3%; these satellite images were originally rectified to Universal Transverse Mercator projection. The images were acquired on different dates with slightly different seasons, the images were geometrically rectified to the local coordinate system of Ebinur Lake watershed using 50 ground control points symmetrically distributed across the images. A nearest neighbor method was used for resampling when conducting rectification, with an error of less than one pixel. Finally, the resolution of all images is resampled to 30 m, so it was crucial to make a radiance calibration and atmospheric correction to all images, using the FLAASH tools provided by ENVI 5.1 (ENVI, Version 5.1, Exelis Visual Information Solutions, Boulder, CO, USA, 2013) to convert the radiance to reflectance [12,24].

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Figure Figure1.1. Location Location maps maps of of the the study study area: area: (a) (a) vicinity vicinity map map showing showing the the location location of of the the study study area area within within Xinjiang Xinjiang and and China; China; (b) (b) map map of of the the Ebinur Ebinur Lake Lake vicinity; vicinity; (c) (c) Ebinur Ebinur Lake Lake Wetland Wetland Nature Nature Reserve; Reserve;(d) (d)Ganjia GanjiaLake LakeHaloxylon HaloxylonForest ForestNature NatureReserve. Reserve. Forest Nature Reserve.

(a) (a)

(b) (b)

(c) (c)

(d) (d)

Figure2. Landscape photos of study (Photographed by Fei Zhang): (a,b) Lake Figure 2.2.Landscape Landscape photos ofstudy study areaarea (Photographed Zhang): (a) are and (b) are areLake Ebinur Lake Figure photos of area (Photographed byby FeiFei Zhang): (a,b) Ebinur Wetland Wetland National Reserve; (c,d) are Ganjia Lake Haloxylon Forest National Nature Reserve; Wetland National Nature (c) and (d) are Ganjia Lake Haloxylon Forest National Nature National Nature Reserve; (c,d) are Ganjia Lake Haloxylon Forest National Nature Reserve; (a,b) show (a,b) show Ebinur withmarshes sparse vegetation. (c,d) show the desert with Reserve; Figure 2a,b Lake show Ebinurvegetation. Lake with sparse vegetation. Figure 2c,dappearance show the forest. desert Ebinur Lake marshes withmarshes sparse (c,d) show the desert appearance with Haloxylon Haloxylon forest. appearance with Haloxylon forest).


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Table 1. Data sources of landscape information in ELWNNR and GLHFNNR. Number

Imaging Data

Sensor

Resolution (m)

Spectral Bands

1 2 3 4

21 September 1972 25 September 1998 18 September 2007 26 August 2013

MSS TM TM OLI_TIRS

80 30 30 30

B1, B2, B3, B4 B1, B2, B3, B4, B5, B7 B1, B2, B3, B4, B5, B7 B1, B2, B3, B4, B5, B6, B7

2.3. Methods 2.3.1. LCLU Classification Methods Researchers have proposed and experimented with many LCLU classification methods in recent years [25–27]. In comparison with various novel classifiers, the traditional maximum likelihood classifier has generally been used because of its ease in application, simple operation and good performance [28]. In this study, a supervised classification based on the maximum likelihood classification (MLC) method was employed to classify the individual images independently. MLC is the most common type of supervised classification and has been widely used in the literature [24,29–32]. This method has considered not only the mean or average values in assigning classification, but also the variability of brightness values in each class [33]. Therefore, in this paper, the landscape of the ELWNNR was classified using six LCLU types. Land use types included water body and forestland. Land cover types included salinized land, desert, wetland and other objects including medium-, low-cover grasslands, and bare land. The GLHFNNR was classified using four land cover types, i.e., high (HDHF), medium (MDHF), and low (LDHF) density Haloxylon forest as well as desert. We optimized the classification accuracy and adjusted it by field sampling using GPS device (G350, UniStrong, Beijing, China, 2014). Both computer classification obtained by using ENVI 5.1 software and manual interpretation were used to obtain the land use information [1]. Then, we collected 100 random samples as training data and combined this data with field data that were used in the maximum likelihood classifier to do the classification. Based on the land-use classification system of the China Agricultural Planning Committee (1984) and the actual situation of ELWNNR, six land use/cover categories were delineated: water body (including lakes, rivers, ponds, and reservoirs), vegetation (trees, grass, bushes, sparse trees, shrubs, and other vegetation), salinized land, desert, wetland (including salt marshes, beaches, flood plains, swamps, and bogs) and others objects (including medium-, and low-cover grasslands, and bare land). For this paper we selected more than 600 pixels of each land use/cover type as areas of interest. 2.3.2. Accuracy Assessment The accuracy of the different thematic maps produced from the classifiers; accuracy assessment was performed based on the computation of the error matrix statistics. As a result, the overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA) and the kappa coefficient (Kc) were computed [34]. The classification accuracy was verified according to the Kappa coefficient that is a statistic which measures inter-rater agreement for qualitative items [35]. Fleiss’s [36] equally arbitrary guidelines characterize kappa coefficient over 0.75 as excellent, 0.40 to 0.75 as fair to good, and below 0.40 as poor.


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2.3.3. Land-Cover and Land-Use Transition Matrix A land-cover and land-use transition matrix was generated to reflect the changes of land-cover and land-use types in two stages. We used the following equation to calculate the matrix [37]:    p=  

p11 p21 .. . pi1

p12 p22 .. . pi2

··· ··· .. . ···

p1j p2j .. . pij

     

where pij indicates the area in transition from landscape i to j. Each element of the transition matrix is n

(1) pij is non-negative and (2) ∑ pij = 1. j =1

Sankey diagrams are often used to analyze energy or material flows, with arrows representing flows and the thickness of the arrow representing the magnitude of the flow [38]. For two categorical LCLU maps that used the same set of N categories, there are N × (N − 1) potential forms of map differences, consisting of pixels classified as category i in one map and category j in the other map. There are N instances of map differences or similarity, or groups of pixels that are classified as the same category in both maps. Map differences and similarity are associated with land use changes, i.e., differences for a change and similarity for no change, respectively. These change status in the transition matrix can be represented in the Sankey diagram by a persistence or transition line [39]. Sankey diagram were used on visualization of LCLUC dynamics in the ELWNNR and GLHFNNR. This diagram provided the classification results from four maps, and the LCLU dynamics observed in three time intervals: 1972–1998, 1998–2007 and 2007–2013. A transition matrix, which compares the two maps within each time interval, is also presented. 2.3.4. Landscape Index A landscape pattern index is an indicator of spatial landscape patterns, which reflects the characteristics of the landscape composition and spatial configuration. Quantification is one of the essential goals of landscape ecology [1,40]. Because landscape metrics are highly correlated [41,42], the correlated metrics were deselected to reduce reduce redundancy, then nine landscape indices were chosen to display the land cover/land use changes in the study area. These indices include number of patches (NP), largest patch index (LPI), landscape shape index (LSI), contagion index (CONTAG), Shannon diversity index (SHDI), Shannon evenness index (SHEI), interspersion and juxtaposition index (IJI), fragmentation index (FI) and aggregation index (AI). The landscape parameters for each LCLU type was calculated for 1972, 1998, 2007 and 2013 using the FRAGSTATS 3.3 software [13]. Landscape indices in FRAGSTATS 3.3 are quite effective for describing landscape changes and refer to both human activities and natural effects [2,17,43] (Table 2). Table 2. Landscape metrics used in the present study. Index

Symbol

Definition

Formula

Number of Patches

NP

Number of patches divided by area.

NP = Ni

LPI

LPI quantifies the percentage of total landscape area comprised by the largest patch. As such, it is a simple measure of dominance.

Largest Patch Index

LPI =

Max ( a1 ...... an ) (100) A


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Table 2. Cont. Index

Landscape Shape Index

Contagion Index

Symbol

Definition

LSI

A perimeter-to-area ratio that measures the overall geometric complexity of the landscape. LSI provides a standardize measure of total edge or edge density that adjusts for the size of the landscape.

CONTAG

Measures the extent to which patch types are aggregated or clumped. CONTAG is inversely related to edge density.

CONTAG = m n P ln( P ) [1 + ∑ ∑ 2ij ln(mij) ](100)

SHDI is a popular measure of diversity in community ecology, applied here to landscape. SHDI is somewhat more sensitive to rare patch types than Simpson’s diversity index.

SHDI = − ∑ ( Pi · ln Pi )

Shannon’s Diversity Index

SHDI

Shannon‘s Evenness Index

SHEI

SHEI is expressed such that an even distribution of area among patch types results in maximum evenness.

Interspersion and Juxtaposition Index

IJI

IJI is based on patch adjacencies, not cell adjacencies like the contagion index.

Fragmentation Index

FI

FI is appealing because it reflects shape complexity across a range of spatial scales (patch sizes).

AI

AI takes into account only the like adjacencies involving the focal class, not adjacencies with other patch types.

Aggregation

Formula

LSI =

25∑m √k=1 eik A

i =1 j =1

m

i =1

SHEI = m0

− ∑ [(

IJI =

k =1

m

∑i=1 ( Pi ·ln Pi ) ln m

eik eik 0 eik ) ln( ∑m0 eik )] ∑m k =1 k =1

ln(m0−1)

FN =

(100)

Ni CAi

m

n

AI = 2 ln(m) + ∑ ∑ Pij ln( Pij ) i =1 j =1

Note: i = 1, ..., m patch types (classes); j = 1, ..., n patches; k = 1, ..., m patch types (classes); A = total landscape area (m2 ); aij = area (m2 ) of patch ij; Pij = perimeter (m) of patch ij. eik = total length (m) of edge in landscape between patch types (classes) i and k, includes landscape boundary; N = total number of patches in the landscape, excluding any background patches; Ni = number of patches in the landscape of patch type (class) i; m = number of patch types (classes) present in the landscape, excluding the landscape border if present; Pi = proportion of the landscape occupied by patch type (class) i.

2.3.5. Driving Factor Analysis LCLUC is a central factor related to changes in the Earth’s climate, the environment in a broad sense and human society. Policymakers seek for scientific information about the forces driving LCLUC so that they may not only focus on symptoms, but on the causes of LCLUC [44]. The analysis on driving forces of LCLUC is one of the vital parts of LCLUC research. The relationship between LCLUC and driving forces of LCLUC is often quantified by combining a use conceptual model with a mathematical model, introducing mathematical/statistical methods and adopting both historical and current LCLUC data. The driving factors of LCLUC include natural processes and human interventions, such as topography, climate change, human activity and government policies. It is crucial for us to evaluate the predominant factors in LCLUC to allow policy makers to deal with this change. The authors chose representative driving forces (natural, human and policy driving factors) to explore the relationships between driving factors and LCLUC [44]. Figure 3 shows a technical flowchart of this research study.

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Figure land-use change change and and landscape landscapepattern patternchanges changesininthe the Figure3.3. Flowchart Flowchart for for the the land-cover land-cover and land-use study studyarea. area.

Resultsand and Analyses Analyses 3.3.Results 3.1.Land-Cover Land-Cover and and Land-Use Land-Use Changes in the ELWNNR 3.1. The four four classified classified images images in (Figure 4) were used to calculate The calculate each each landscape landscapeindex. index.Table Table33 showsthe thesummary summary of of LCLUC LCLUC in the ELWNNR in the last shows last 21 21 years. years. Land Landuse usepolicies policiesare areregulated regulated bythe thenational nationaland andregional regional government government agencies. The by The Chinese Chinese government governmentpromotes promotes“cottons “cottonsasas thekey keyto tothe theeconomy”, economy”, in in this this area area and and encouraged the the the development developmentof ofwestern westernChina, China,including including EbinurLake LakeWatershed Watershed for for intensive intensive cotton farming, farming, and Ebinur and these these policies policiesresult resultin inthat thatsalinized salinizedland land increased to to the the greatest greatest extent, extent, with with an annual average increased average growth growth rate rate of of 4.83%. 4.83%. Wetland Wetlandarea areaalso also decreased and water area shrunk significantly. Forestland areas increased during the study period, decreased and water area shrunk significantly. Forestland areas increased during the study period, althoughthe thearea area covered covered by by trees trees near near the the river although river decreased decreased and andthe thearea areacovered coveredby byshrubs shrubsfar farfrom from water increased. Forestland was preferred for agricultural reclamation, thus forestland areas water increased. Forestland was preferred for agricultural reclamation, thus forestland areas decreased decreased duringperiod the study period because of excessive land reclamation, andleads the change leads toof during the study because of excessive land reclamation, and the change to succession succession of LCLU structure [45]. Table 4 shows the confusion matrix to verify the classification LCLU structure [45]. Table 4 shows the confusion matrix to verify the classification results between results between 1972,2013 1998, and 2013 in in the last 21 years. 1972, 1998, 2007 and in 2007 the ELWNNR inthe theELWNNR last 21 years.


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Figure 4. The land cover/land classification maps in ELWNNR in 1972 1998 2007 and Figure 4. The land cover/land useuse classification maps in ELWNNR in 1972 (a);(a); 1998 (b);(b); 2007 (c) (c) and 2013 2013 (d).(d). Table 3. Changes land use/cover types in the ELWNNR as measured in 1972, 1998, 2007 and 2013. Table 3. Changes in in land use/cover types in the ELWNNR as measured in 1972, 1998, 2007 and 2013.

1972 1998 2007 2013 1972 1998 2007 2013 Area Area Area Area Area Area Ratio Area Area Ratio Area Area Ratio Area Area Ratio Area Area Area Area Ratio Ratio Ratio Ratio 2 2 2 2 (km (%) (km2) ) (%) (km2) ) (%) (km2)) (%) 2) ) (km (km (km (km (%) (%) (%) (%) Water body 538.44 16.98 506.66 15.98 437.56 13.80 406.77 12.83 Water bodyland 538.44 16.98 506.66 15.98 437.56 13.80 406.77 12.83 Salinized 1203.62 37.96 1243.42 39.21 1180.31 37.22 1371.44 43.25 Forestland 90.67 2.86 114.16 3.60 373.45 11.78 249.46 7.87 Salinized land 1203.62 37.96 1243.42 39.21 1180.31 37.22 1371.44 43.25 Other objects 229.76 7.25 128.52 4.05 171.73 5.42 200.74 6.33 Forestland 90.67 2.86 114.16 3.60 373.45 11.78 249.46 7.87 Desert 354.47 11.18 380.53 12.00 351.22 11.08 354.06 11.17 Other objects 229.76 7.25 128.52 4.05 171.73 5.42 200.74 6.33 Wetland 754.58 23.80 798.13 25.17 657.22 20.73 588.73 18.57 Desert 354.47 11.18 380.53 12.00 351.22 11.08 354.06 11.17 Wetland 754.58 23.80 798.13 25.17 657.22 20.73 588.73 18.57 Table 4. Calculation of confusion matrix by Maximum likelihood supervised classification in ELWNNR. Land Land Cover/Use Cover/Use Types Types

Table 4. Calculation of confusion matrix by Maximum likelihood supervised classification User’s in Salinized Other Water Body ForestLand Desert Wetland Total ELWNNR. Land Objects Accuracy (%) Water body Salinized Water Bodyland Forestland WaterDesert body 1972 Salinized land Wetland Other objects Forestland Total Desert Producer’s Wetland accuracy (%) 1972 Other objects Water body Total land Salinized Forestland Producer’s Desert 1998 accuracy (%) Wetland Other objects Water body Total Salinized land Producer’s Forestland 1998 accuracy (%) Desert Wetland Other objects

144 Salinized 0 Land 0 1444 00 00 4118

0 57 ForestLand 46 00 57 0 0 46 103 0

0 99.61 0 99 1180 0 0 99.61 17 990 0116

0 55.34 0 0 103 121 0 0 55.34 0 00 121 121 0 100 0 0 0

0 85.34 0 17 0

0 0 0 0Desert 0 Wetland 19 101 0 0 00 0 114 0 0 0 0 19 99 0 101 0 0 0 0 101 0 114114 0 118 99 91.67 100 100 0 0 0 0 0 0 101 114 0 118 7 0 0 131 0 0 100 100 0 91.67 105 71 0 0 49 0 0 0 0 0 120 0 0 131 7 112

0 59.17 0 71 49

131100 0 0 0

0 93.75 105 0 0

0 Other 26 Objects 0 00 2617 0 77 0120

144 100 User’s 102 55.88 Total (%) 147 Accuracy 68.71 144118 10096.61 102116 55.88 85.34 14777 68.71100 Overall accuracy = 83.38% 118 96.61

17 64.17 77 0 1200 0 64.170 0 0128 0128

116 Kappa 85.34 = 0.80 77 100 99 100= Overall accuracy 128 94.53 83.38% 131 100 105 Kappa = 0.80100 88 80.68 99177 10073.32 Overall accuracy = 89.97% 128 94.53

0100 0 0 128

131 Kappa =100 0.88 105 100 88 80.68 177 73.32


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Table 4. Cont. Water Body

2007

2013

Other Objects

0 0 120 0 11 0 131

0 7 0 104 1 0 112

0 0 0 1 0 127 128

99 100 152 75 137 87.59 105 99.05 108 82.41 127 100 Overall accuracy = 89.7%

74.17

91.6

92.86

99.22

Kappa = 0.88

0 76 0 0 27 0 103

0 0 0 0 101 0 101

0 0 114 0 0 0 114

0 15 0 99 0 4 118

0 21 0 17 0 82 120

111 100 112 67.86 135 74.81 116 85.34 135 74.81 86 95.35 Overall accuracy = 86.5%

73.79

100

100

83.9

68.33

Kappa = 0.84

ForestLand

99 0 17 0 0 0 116

0 114 0 0 7 0 121

0 31 0 0 89 0 120

85.34

94.21

111 0 0 0 7 0 118 94.07

Water body Salinized land Forestland Desert Wetland Other objects Total Producer’s accuracy (%) Water body Salinized land Forestland Desert Wetland Other objects Total Producer’s accuracy (%)

User’s Accuracy (%)

Wetland

Salinized Land

Desert

Total

3.2. Land-Cover Transition Matrix in the ELWNNR Land-cover maps from 1972, 1998, 2007 and 2013 were analyzed to generate the land cover transition matrices (Table 5). Between 1972 and 1998, 1998 and 2007, and 2007 and 2013, approximately 251.50 km2 (7.93%), 122.70 km2 (3.87%), and 195.40 km2 (6.16%) of wetland were turned into salinized land, respectively. During the 21-year study period, most of the changes of LCLU in ELWNNR were due to anthropogenic factors (i.e., human disturbance) and natural forcing such as ecological succession [19]. The population rate is continuously high and this led to the human disturbance to ELWNNR that caused the increase of farmland. Rising temperatures led to the high evaporation and the balance between evaporation and precipitation is being disturbed and led to the ecological succession in ELWNNR [21]. Table 5. Land-cover and land-use change transition matrix from 1972 to 1998, 1998 to 2007 and 2007–2013 (unit: % change). Periods

Salinized Land

Wetland

Water Body

Desert

1972–1998

Salinized land Wetland Water body Desert Forestland Other objects

25.41 7.93 0.00 3.64 0.93 0.00

10.61 12.17 0.05 0.63 0.33 0.01

0.03 0.77 15.92 0.00 0.27 0.00

2.54 1.81 0.00 6.44 0.20 0.16

Forestland Other Objects 0.27 0.24 0.00 0.58 1.81 0.01

0.33 2.28 0.00 0.70 0.05 3.87

1998–2007


27.03 6.16 0.00 2.31 3.62 0.09

9.02 12.31 0.01 1.13 1.87 0.82

0.13 1.94 13.78 0.00 0.13 0.00

0.88 0.00 0.00 7.43 3.04 0.65

0.15 0.30 0.01 0.00 3.12 0.02

0.00 0.00 0.00 0.21 0.00 3.84

2007–2013


30.56 3.87 0.01 1.05 0.76 0.96

5.68 14.40 0.09 0.00 0.34 0.21

0.93 0.16 12.72 0.00 0.00 0.00

2.44 0.00 0.00 8.17 0.14 0.31

3.50 0.13 0.01 1.24 6.51 0.40

0.13 0.01 0.00 0.71 0.11 4.45

Figure 5 shows a Sankey diagram of extent of LCLUC in the ELWNNR and change occurring from 1972 to 1998, 1998 to 2007 and 2007 to 2013 The MDHF and LDHF NP values showed the tendency of growth as time goes by, indicating fragmentation of MDHF and LDHF caused by human


11 of 23

tendency of growth Sustainability 2017, 9, 724

as time goes by, indicating fragmentation of MDHF and LDHF caused 11 ofby 22 human disturbance like agricultural reclamation, high population. The agricultural reclamation changed the fragment of HDHF, decreased the HDHF into MDHF as well as LDHF. The stacked bars disturbance like agricultural hightype population. reclamation changed the show the relative abundancereclamation, of each LCLU in 1972, The 1998,agricultural 2007 and 2013. The height of each fragment of HDHF, decreased the HDHF into MDHF as well as LDHF. The stacked bars show the component is proportional to the relative abundance of the represented land cover, and classified relative abundance each LCLU type in largest 1972, 1998, 2007smallest. and 2013. The height of each component is types are arrangedofvertically from the to the Table 5 provides three transition proportional to the relative abundance of the represented land cover, and classified types are arranged matrices showing the extent of LCLUC for each LCLU type as well as the size and change trajectories vertically the largest to the smallest. Table 5 provides three transition matrices extent5 of LCLUCfrom during the three time intervals analyzed here. Four stacked verticalshowing bars inthe Figure of LCLUC for each LCLU as well as the and change trajectories ofpositioned LCLUC during the three represents land cover of atype specific year, andsize three sets of transition lines between each time intervals analyzed here. Four stacked vertical bars in Figure 5 represents land cover of a specific pair of stacked bars. year, The and salinized three setsland of transition lines positioned between each(Figure pair of 5), stacked bars. 37.90%, 39.20%, type is predominant in all four years comprising The salinized land type is predominant in all four years (Figure 5), comprising 37.90%, and 39.20%, 37.20% and 43.20% of the total area in 1972, 1998, 2007, and 2013, respectively. The wetland the 37.20% and 43.20% of the total area in 1972, 1998, 2007, and 2013, respectively. The wetland and the small-extent forestland areas, water body, desert and other objects types all remained relatively small-extent forestland body, objects types relatively stable stable throughout the areas, studywater period. Atdesert last, and the other spatial extent of all theremained water body decreased throughout the study period. At last, the spatial extent of the water body decreased throughout the throughout the examined time periods of 1972–1998, 1998–2007 and 2007–2013. examined time periods of 1972–1998, 1998–2007 and 2007–2013. The four stacked bars have the same horizontal width with varying lengths of lines The four to stacked bars have same horizontal width darker with varying proportional proportional the length of thethe time interval. A slightly shade lengths of colorofinlines Figure 5 express to the length of the time interval. A slightly darker shade of color in Figure 5 express transition lines transition lines for each category. To reduce visual confusion, the threshold value was set to 0.30% of for reduce visual thetothreshold value was set to 0.30% of the total map the each total category. map area To when making thisconfusion, stacked bar exclude smaller changes. area when bar to smallerdeclined changes.(Figure 5). The increase in the spatial From making 1972 to this 2013,stacked the extent ofexclude water bodies From 1972 to 2013, the extent of water bodies declined (Figure 5).toThe increase in the spatial extent of the salinized land occurring from 1972 to 1998 and from 2007 2013 are attributed to the extent of theofsalinized occurring fromof1972 19981972 and from 2007 to 2013 are1998 attributed toand the conversion wetlandsland (Table 5: 10.61% maptofrom to 1998, 9.02% from to 2007 conversion of wetlands (Table 5: 10.61% of map from 1972 to 1998, 9.02% from 1998 to 2007 and 5.68% 5.68% from 2007 to 2013), and to a lesser extent desert (2.54% of map from 1972 to 1998, 0.88% from from 2007 to and 2013), and from to a lesser extent desert (2.54% of map from 1972 to 1998, 0.88% from 1998 to 1998 to 2007 2.44% 2007 to 2013). 2007 and 2.44% from 2007 to 2013).

Figure 5. Sankey diagram for comparison of land-cover and land-use dynamics in three time intervals Figure 5. diagram for comparison of years land-cover and land-use three Lake time defined bySankey four land cover/use maps from the 1972, 1998, 2007 and dynamics 2013 in theinEbinur intervals Nature definedReserve. by four land cover/use maps from the years 1972, 1998, 2007 and 2013 in the Ebinur National Lake National Nature Reserve.

3.3. Landscape Pattern Analysis—ELWNNR 3.3. Landscape Pattern Analysis—ELWNNR The NP of desert peaked in 2013 (Figure 6a), indicating that desert patches were small, highly The NP of and desert peaked in The 2013NP (Figure 6a), indicating thathighest, desert patches were highly heterogeneous, fragmented. of wetland was second indicating thatsmall, agricultural heterogeneous, and fragmented. The NP of wetland second highest, indicatingwetlands. that agricultural development and reclamation have decreased the sizewas of wetlands and fragmented The NP development and reclamation have decreased the size of wetlands and fragmented wetlands. The of water body was the lowest that showed slight disturbance. Largest patch index (LPI) demonstrated NP of water body was the lowest that showed slight disturbance. Largest patch index (LPI) three development stages (Figure 6b). The LPI of salinized land was the highest as the dominant LCLU demonstrated three development stages (Figure 6b).suggested The LPI ofthat salinized land was the highest asand the type in the ELWNNR. The increasing trend in LPI the degree of fragmentation dominant LCLU type in the ELWNNR. The increasing trend in LPI suggested that the degree of human disturbance are low in the study area. Landscape shape index (LSI) is an effective measure for fragmentation and human disturbance are low in the study area. Landscape shape index (LSI) is an cluster landscapes. There are also a rising trend of the LSI of water and forest land, showed a more effective patch measure for by cluster landscapes. are also a rising of the LSIland of water and forest complex shape contrast to other There landscape types (Figuretrend 6c). Salinized displayed high land, showed a more complex patch shape by contrast to other landscape types (Figure 6c). Salinized LSI than other landscape types, showed irregular shapes of the region. On the contrary, the LSI of water land displayed highand LSIindicated than other landscape types, of the region. On the body are the lowest that there were fewershowed changesirregular of water shapes as the time goes by. Different contrary, the LSI of landscapes water body have are the lowest and indicated there were fewer changes of water types of ecological quite different degrees that of aggregation (Figure 6d), indicating as the time goes by. Different types of ecological landscapes have quite different degrees of that the variety of landscapes with varying degrees of patch connection is different. The CONTAG and SHDI showed an inconsistent trend in the ELWNNR (Figure 6e,f). The CONTAG showed less


12 of 23

aggregation (Figure 6d), indicating that the variety of landscapes with varying degrees of patch 12 of 22 connection is different. The CONTAG and SHDI showed an inconsistent trend in the ELWNNR (Figure 6e,f). The CONTAG showed less fragmentation and enhanced connectivity in the landscape. The negative trend of SHDI indicated a decrease in the wetland landscape types. The SHEI was fragmentation and enhanced connectivity in the landscape. The negative trend of SHDI indicated the even distribution of area among patch types, whichthe results in maximumof aapplied decreasetoindescribe the wetland landscape types. The SHEI was applied to describe even distribution evenness, astypes, evenness as results the complement of dominance (Figure 6g). Therefore, the spatial area amongsuch patch which in maximum evenness, such as evenness as the complement continuity of landscape patches had also and transformed significantly. The IJI also exhibited an of dominance (Figure 6g). Therefore, thechanged spatial continuity of landscape patches had changed increasing trendsignificantly. (Figure 6h)The indicating that an patches became inter-conjugated and patches better and transformed IJI exhibited increasing trendmore (Figure 6h) indicating that connected large scale patches, leaving only a few dominant and leading The FI became moreininter-conjugated and better connected in large scale patches, leavingLCLU only atypes. few dominant demonstrated a decrease from in 1972 to 0.04 in 2013from (Figure Severe fragmentation would and leading LCLU types. The FI0.06 demonstrated a decrease 0.066i). in 1972 to 0.04 in 2013 (Figure 6i). influence the development of agriculture and pasture livestock. So, the ELWNNR shifted toward a Severe fragmentation would influence the development of agriculture and pasture livestock. So, mono-culture land status that hard to preserve its biodiversity, and assumedly resulting from the ELWNNR shifted toward a mono-culture land status that hard to preserve its biodiversity, and increased resulting human activities and related domestic, industrial, and agricultural assumedly from increased human disturbances activities and for related disturbances for domestic, industrial, purposes [46]. and agricultural purposes [46].


800

15

1972 1998 2007

1998

2013

2007 2013

9

LPI

NP

600

1972

12

400

6 200

3

0 Water body

Forest land Salinized land

Desert

Wetland

0

Other objects

Water body


Land cover/use types

Desert

Wetland

Other objects


(a)

(b)

40

120 1972

100

1998

30

2007

80 AI

LSI

2013 20

60 1972

40

10

1998 20

0 Water body


Desert

Wetland

Other objects

2007 2013

0 Water body


Forest land

Desert

Wetland

Other objects


(c)

(d)

58.0

1.36

57.5

1.35

y = 0.2584x + 56.123

1.34

57.0 SHDI Index

CONTAG Index

Salinized land

56.5 56.0

1.33 y = 0.0019x + 1.3259

1.32 1.31 1.30

55.5

1.29

55.0

1.28 1972

54.5 1972

1998

2007

1998

2007 Year

2013

Year

(e)

(f) Figure 6. Cont.

2013


13 of 22 13 of 23 68

0.70

67 66

0.68

IJI

SHEI Index

0.69

y = 0.001x + 0.6814

65 64

0.67

y = 0.5102x + 64.204

63 0.66

62

0.65

61 1972

1998

2007

2013

1972

1998

Year

2007

2013

Year

(g)

(h) 0.07 0.06 0.05 FI

0.04 0.03 0.02

y = -0.0067x + 0.0622

0.01 0 1972

1998

2007

2013

Year

(i) Figure (c)(c) LSI, landscape Figure 6. 6. Comparison Comparisonofof(a) (a)NP, NP,number numberofofpatches; patches;(b) (b)LPI, LPI,largest largestpatch patchindex; index; LSI, landscape shape index; (d) AI, aggregation index; (e) CONTAG, contagion index; (f) SHDI, Shannon diversity shape index; (d) AI, aggregation index; (e) CONTAG, contagion index; (f) SHDI, Shannon diversity index; and juxtaposition index; and (i) (i) FI,FI, index; (g) (g) SHEI, SHEI,Shannon Shannonevenness evennessindex; index;(h) (h)IJI, IJI,interspersion interspersion and juxtaposition index; and fragmentation index values, in the Ebinur Lake Wetland National Nature Reserve at five points in fragmentation index values, in the Ebinur Lake Wetland National Nature Reserve at five points in time: time: 20072013. and 2013. 1972, 1972, 1998, 1998, 2007 and

3.4. Land-Cover and Land-Use Changes in the GLHFNNR 3.4. Land-Cover and Land-Use Changes in the GLHFNNR LCLUC were estimated according to the classification results in the GLHFNNR (Figure 7) from LCLUC were estimated according to the classification results in the GLHFNNR (Figure 7) from satellite images captured in 1972, 1998, 2007 and 2013. Table 6 shows that the main LCLU types in satellite images captured in 1972, 1998, 2007 and 2013. Table 6 shows that the main LCLU types in the GLHFNNR are LDHF and MDHF. LDHF area decreased by 19.02 km2, MDHF area decreased by 2 the GLHFNNR decreased by 19.02 km , MDHF area decreased Sustainability 2017, 9,are 724LDHF and MDHF. LDHF area 14 of 23 2 between 1972 and 2013. This indicated that the 3.36 km2, and HDHF area increased by 26.59 km by 3.36 km2 , and HDHF area increased by 26.59 km2 between 1972 and 2013. This indicated that areas of LDHF lost were result from both human and natural factors like the increase in human areas of of LDHF lostlost were result from both human and the areas LDHF were result from both human andnatural naturalfactors factorslike likethe theincrease increaseininhuman human disturbance, increased temperature, etc. [46,47]. As described the driving factors before, human disturbance, increased temperature, etc. [46,47]. As described the driving factors before, disturbance, increased temperature, etc. [46,47]. As described the driving factors before, humanhuman factors factors like high population and excessive agricultural reclamation have changed the structure of factors high population and excessive agricultural reclamation havethe changed theofstructure of like highlike population and excessive agricultural reclamation have changed structure GLHFNNR. GLHFNNR. The increased temperatures led to high evaporation, and disturbed the balance between GLHFNNR. The increased temperatures led to high evaporation, and disturbed the balance between The increased temperatures led to high evaporation, and disturbed the balance between evaporation evaporation and precipitation [21,46]. Table 7 shows the confusion matrix to verify the classification evaporation and [21,46]. precipitation [21,46]. Table 7 shows matrix the confusion matrix to verify theresults classification and precipitation Table shows theinconfusion tothe verify between results between 1972, 1998, 20077and 2013 the GLHFNNR in lastthe 21 classification years. results between 1972,2013 1998, and 2013 inin the GLHFNNR in the last 21 years. 1972, 1998, 2007 and in 2007 the GLHFNNR the last 21 years.

Figure 7. Cont.


14 of 22 14 of 23

Figure 7. Land-cover and land-use classification maps in Ganjia Lake Haloxylon Forest National Nature Figure 7. Land-cover and land-use classification maps in Ganjia Lake Haloxylon Forest National Reserve at five points in 1972 (a); 1998 (b); 2007 (c) and 2013 (d). Nature Reserve at five points in 1972 (a); 1998 (b); 2007 (c) and 2013 (d). Table land-use changes in the LakeLake Haloxylon ForestForest National NatureNature Reserve Table6.6.Land-cover Land-coverand and land-use changes in Ganjia the Ganjia Haloxylon National as measured in 1972, 1998, 2007 and 2013. Reserve as measured in 1972, 1998, 2007 and 2013. Land Land Cover Cover Types Types LDHF LDHF MDHF MDHF HDHF HDHF Desert

Desert

Area Areainin 1972 1972 2 (km (km)2 ) 228.32 228.32 166.52 166.52 38.11 38.11 131.86

131.86

Area Area Ratio in inRatio 1972 (%) 1972 (%) 40.42 40.42 29.48 29.48 6.75 6.75 23.35

23.35

Area Area in in 1998 1998 (km22)) (km 169.65 169.65 215.32 215.32 49.87 49.87 129.97

129.97

Area Area Ratio inRatio 1998 in (%) 1998 (%)

Area in in Area 2007 2007 (km22)) (km

30.04 30.04 38.12 38.12 8.83 8.83 23.01

226.43 226.43 150.68 150.68 61.49 61.49 126.21

23.01

Area Area Ratio Ratio in 2007in (%) 2007 (%) 40.09

40.09 26.68 26.68 10.89 10.89 22.35 22.35

126.21

Areain in Area 2013 2013 2 )2) (km (km 209.30

209.30 163.15 163.15 64.70 64.70 127.66 127.66

Area AreaRatio Ratio in2013 2013(%) in (%) 37.06

37.06 28.89 28.89 11.46 11.46 22.60 22.60

Table 7. Calculation of confusion matrix by Maximum likelihood supervised classification in Table 7. Calculation of confusion matrix by Maximum likelihood supervised classification GLHFNNR. in GLHFNNR. High Density Haloxylon Medium Density Forest Haloxylon Forest High Density Medium Density High density haloxylon Haloxylon Forest Haloxylon 33 Forest forest High density haloxylon forest 33 Medium density haloxylonn Medium density haloxylonn 00 forest forest 1972 1972 Low haloxylon forest Low density haloxylon 0 0 Desert 0 forest Total Desert 033 Producer’s 100 Totalaccuracy (%) 33 Producer’s accuracy (%)forest 100 High density haloxylon 43 Medium density haloxylonn High density haloxylon 430 forest forest 1998 Low density haloxylon forest 0 Medium density haloxylonn Desert 00 forest Total 43 Low density haloxylon 1998 Producer’s accuracy (%) 0100 forest High density haloxylon forest 36 Desert 0 Medium density haloxylonn Total 430 forest 2007 Producer’s accuracy (%)forest 1000 Low density haloxylon High density haloxylon Desert 0 36 Total 36 forest Producer’s (%) 100 Medium densityaccuracy haloxylonn 0 foresthaloxylon forest High density 39 Medium density haloxylonn Low density haloxylon 2007 00 forest forest 2013 Low density haloxylon forest Desert 00 Desert 0 Total 36 Total 39 Producer’s accuracy (%) 100 Producer’s accuracy (%) 100 High density haloxylon 39 2013 forest Medium density haloxylonn 0

Low Density Desert Haloxylon Low DensityForest Desert Haloxylon Forest 0 3 0 3 0 0 3 0 3333 3 0 8 8 0 44 0 7544 3 75

84 0 87 96 0

0 87 96

34 3

2

0

6 034 43 79 6

86 0 88 97

3

0

0 3343 8 79 0 3 44 75 33 3

84

2 86

0 3 88 84 97 0 0 87 96 3 0

33 8

3

12 0 0 44 48 6875

80 0 87 91

84 0 87 96

0 0 94 94 94 100 94 0 100 0 0 94 94 100 0 0 0 94 94 100 0 0

0 0 0 94 94 100 0 0 0

0 94 94 94 94 100 100

Total Total 36 36 36 36

User’s Accuracy (%) User’s Accuracy (%) 91 91 9191

92 91 92 91 94 100 Overall = 93.25 94 accuracy100 Kappa = 0.89 = 93.25 Overall accuracy 46 Kappa = 0.89 93 36 46

9394 92 93 94 36 94100 Overall accuracy = 95 92 Kappa = 0.90 93 39 92 94 100 36 Overall accuracy91= 95 92 Kappa = 0.90 100 94 100 39 92 Overall accuracy = 95.75 Kappa = 0.91 36 91 42 92 36 92

10091

92 94 10086 94 100 Overall accuracy = 95.75 Overall accuracy = 92.25 Kappa = 0.91 Kappa = 0.84

3

0

0

42

92

33

3

0

36

91

forest Low density haloxylon forest Desert Total Sustainability 2017, 9, 724 Producer’s accuracy (%)

0

12

80

0

0 39 100

0 48 68

0 87 91

94 94 100

92

86

94 100 Overall accuracy = 92.25 15 of 22 Kappa = 0.84

3.5.Land-Cover Land-Coverand andLand-Use Land-UseTransition TransitionMatrix MatrixininthetheGLHFNNR GLHFNNR 3.5. 2 2(11.64%) Asshown shownininTable Table8,8,between between1972 1972and and1998, 1998,65.74 65.74km km (11.64%)ofofMDHF MDHFwere wereturned turnedinto into As 2 (0.67%) of Medium density MDHF were turned 2 LDHF. In addition, between 1998 and 2007, 3.77 km LDHF. In addition, between 1998 and 2007, 3.77 km (0.67%) of Medium density MDHF were turned 2 2(4.68%) intoLDHF. LDHF.Meanwhile, Meanwhile,between between2007 2007and and2013, 2013,26.42 26.42km km (4.68%)ofofMDHF MDHFwere wereturned turnedinto intoLDHF. LDHF. into

Table 8. Land-cover and land-use transition matrix in the Ganjia Lake Haloxylon Forest National Table 8. Land-cover and land-use transition matrix in the Ganjia Lake Haloxylon Forest National Nature Nature Reserve from 1972 to 1998, 1998 to 2007 and 2007 to 2013 (unit: % change). Reserve from 1972 to 1998, 1998 to 2007 and 2007 to 2013 (unit: % change). Periods 1972-1998

1998-2007

2007-2013

Periods HDHF MDHF LDHF 1972–1998 Desert HDHF MDHF LDHF 1998–2007 Desert HDHF MDHF LDHF 2007–2013 Desert

HDHF HDHF 5.61 HDHF0.84 5.61 MDHF0.24 0.84 LDHF0.00 0.24 Desert 0.00 8.30 HDHF0.27 8.30 MDHF0.26 0.27 LDHF 0.26 0.01 Desert 0.01 9.61 HDHF0.86 9.61 MDHF 0.86 0.39 LDHF 0.39 0.02 Desert 0.02

MDHF MDHF 1.85 1.85 25.35 25.35 2.05 2.05 0.16 0.16 2.09 2.09 25.43 25.43 10.34 10.34 0.26 0.26 0.60 0.60 23.22 23.22 2.58 2.58 0.28 0.28

LDHF 1.60 11.64 26.67 0.65 0.49 0.67 28.50 0.38 1.24 4.68 33.51 0.66

LDHF Desert 1.60 0.01 11.64 0.26 26.67 1.01 0.65 22.06 0.49 0.00 0.67 0.31 28.50 1.00 0.38 21.70 1.24 0.00 4.68 0.13 33.51 0.58 0.66 21.64

Desert 0.01 0.26 1.01 22.06 0.00 0.31 1.00 21.70 0.00 0.13 0.58 21.64

Figure 8 shows the map difference in a form of stacked vertical bars. The LDHF type is Figure 8 shows the map difference in a form of stacked vertical bars. The LDHF type is predominant in all four years, comprising 40.56%, 29.98%, 40.09% and 37.06% of the total map area predominant in all four years, comprising 40.56%, 29.98%, 40.09% HDHF and 37.06% the total areawere in in 1972, 1998, 2007 and 2013, respectively (Table 6). MDHF, and of desert landmap types 1972, 1998, 2007 and 2013, respectively (Table 6). MDHF, HDHF and desert land types were relatively relatively stable in the total extent of each land type throughout the study period. stable A in slightly the total darker extent of eachinland type8throughout study period. color Figure represents the transition lines. To reduce visual chaos in the A slightly darker Figure 8 represents chaos diagram, we used the color same in0.30% threshold value transition in Sankey lines. diagramTotoreduce excludevisual unnecessary ininformation. the diagram, we used the same 0.30% threshold value in Sankey diagram to exclude unnecessary information. The desert category remained unchanged during the study period. In the spatial extent, the net The desert remained unchanged the 2007 studytoperiod. In attributed the spatialto extent, the netof increase of thecategory HDHF type from 1972 to 1998during and from 2013 are conversion increase of the HDHF type from 1972 to 1998 and from 2007 to 2013 are attributed to conversion MDHF (Table 8: 1.85% of map from 1972 to 1998, 2.09% from 1998 to 2007 and 0.60% from 2007ofto MDHF of mapthe from 1972 to 1998, 2.09%(1.60% from 1998 to 2007 from 2007from to 2013), (Table and, to8:a 1.85% lesser extent, conversion of LDMF of map from and 19720.60% to 1998, 0.49% 2013), and, to a lesser extent, the conversion of LDMF (1.60% of map from 1972 to 1998, 0.49% from 1998 to 2007 and 1.24% from 2007 to 2013). 1998 to 2007 and 1.24% from 2007 to 2013).

Figure 8. Sankey diagram of land cover dynamics from the years 1972, 1998, 2007 and 2013 in the Figure 8. Sankey diagram land cover dynamics Ganjia Lake Haloxylon Forest of National Nature Reserve.from the years 1972, 1998, 2007 and 2013 in the Ganjia Lake Haloxylon Forest National Nature Reserve.

3.6. Landscape Pattern Analysis—GLHFNNR The landscape indices of each LCLU type for the GLHFNNR were shown in Figure 9. In 1972–2013, the NP of desert are the lowest because the fragmentation degree in the surrounding of Ebinur Lake region was less (Figure 9a), and biodiversity is lesser than any other types. The LPI was compared for three different period (Figure 9b). MDHF LPI value showed a rising trend from 1972 to 2013,

3.6. Landscape Pattern Analysis—GLHFNNR The landscape indices of each LCLU type for the GLHFNNR were shown in Figure 9. In 1972–2013, the NP of desert are the lowest because the fragmentation degree in the surrounding of Ebinur Lake region was less (Figure 9a), and biodiversity is lesser than any other types. The LPI was Sustainability 2017,three 9, 724different period (Figure 9b). MDHF LPI value showed a rising trend from 1972 16 of to 22 compared for 2013, suggesting that dispersed patches were merged into larger areas, decreasing the fragmentation of this LCLU type. LDHF LPI declined from 12.9725% in 1972 to 11.336% in 2013, showing that suggesting that dispersed patches were merged into larger areas, decreasing the fragmentation of this increased human activities disturbance caused dramatic fragmentation. The LSI of desert was the LCLU type. LDHF LPI declined from 12.9725% in 1972 to 11.336% in 2013, showing that increased smallest because its shapes were more regular (Figure 9c). Different types of ecological landscapes human activities disturbance caused dramatic fragmentation. The LSI of desert was the smallest have quite different degrees of aggregation (Figure 9d) indicating that the variety of landscapes with because its shapes were more regular (Figure 9c). Different types of ecological landscapes have quite varying degrees of patches connection is different. CONTAG values in the GLHFNNR decreased different degrees of aggregation (Figure 9d) indicating that the variety of landscapes with varying during each period (47.43, 46.98, 45.44 and 45.30 in 1972, 1998, 2007 and 2013, respectively), degrees of patches connection is different. CONTAG values in the GLHFNNR decreased during each indicating high levels of landscape fragmentation and declining connectivity (Figure 9e). The SHDI period (47.43, 46.98, 45.44 and 45.30 in 1972, 1998, 2007 and 2013, respectively), indicating high levels increased over time (1.38, 1.40, 1.40 and 1.41 in 1972, 1998, 2007 and 2013, respectively; Figure 9f), of landscape fragmentation and declining connectivity (Figure 9e). The SHDI increased over time (1.38, which indicated a little increase in diversity of HDHF and a trend toward monoculture land status. 1.40, 1.40 and 1.41 in 1972, 1998, 2007 and 2013, respectively; Figure 9f), which indicated a little increase SHEI and IJI also had an increasing trend (Figure 9g,h), indicating that patches became more in diversity of HDHF and a trend toward monoculture land status. SHEI and IJI also had an increasing inter-conjugated and better connected into large-scale patches. The FI had a dramatic increase from trend (Figure 9g,h), indicating that patches became more inter-conjugated and better connected into 0.21 in 1972 to 0.29 in 2013 (Figure 9i). large-scale patches. The FI had a dramatic increase from 0.21 in 1972 to 0.29 in 2013 (Figure 9i).

800

1998

1972 1998

15

2007 2013

NP

600

20

1972

LPI

1000

2007 2013

10

400

5 200

0 0 HDHF

MDHF

LDHF

HDHF

Desert

MDHF

LDHF

Desert



(a)

(b) 100

40 1972

90

1998

30

2013

20

AI

LSI

2007

10

1972 1998 2007 2013

80

70

0

60

HDHF

MDHF

LDHF

Desert

HDHF


48.0 47.5

1.41

47.0 46.5

SHDI Index

CONTAG Index

Desert

(d) 1.42

46.0 45.5

1.40 1.39

y = 0.0113x + 1.3691

1.38 1.37

y = -0.7925x + 48.269

44.5

LDHF


(c)

45.0

MDHF

1.36

44.0 43.5

1.35 1972

1998

2007

2013

1972

1998

Year

2007 Year

(e)

(f) Figure 9. Cont.

2013

Sustainability2017, 2017,9,9,724 724 Sustainability

1717ofof2322

58

0.89

56

0.88

54

IJI

SHEI Index

60 0.90

52

0.87

y = 0.007x + 0.8506

y = 2.0948x + 50.192

50

0.86

48 0.85 1972

1998

2007

46

2013

1972

Year

1998

2007

2013

Year

(g)

(h) 0.30

0.25 FI

y = 0.0253x + 0.1811 0.20

0.15 1972

1998

2007

2013

Year

(i) Figure Figure9.9.Comparison Comparisonofof(a) (a)NP, NP,number numberofofpatches; patches;(b) (b)LPI, LPI,largest largestpatch patchindex; index;(c) (c)LSI, LSI,landscape landscape shape index; (d) AI, aggregation index; (e) CONTAG, contagion index; (f) SHDI, Shannon shape index; (d) AI, aggregation index; (e) CONTAG, contagion index; (f) SHDI, Shannondiversity diversity index; index;(g) (g)SHEI, SHEI,Shannon Shannonevenness evennessindex; index;(h) (h)IJI, IJI,interspersion interspersionand andjuxtaposition juxtapositionindex, index,and and(i)(i)FI,FI, fragmentation fragmentationindex, index,values valuesininthe theGanjia GanjiaLake LakeHaloxylon HaloxylonForest ForestNational NationalNature NatureReserve Reserveatatfive fivepoints points inintime: 1972 1998, 2007 and 2013. Note: LDHF, MDHF and HDHF are low, medium and high density time: 1972 1998, 2007 and 2013. Note: LDHF, MDHF and HDHF are low, medium and high density Haloxylon Haloxylonforest, forest,respectively. respectively.

4.4.Discussion Discussion 4.1. 4.1.Influence InfluenceofofClimatic ClimaticFactors Factorson onELWNNR ELWNNRand andGLHFNNR GLHFNNR Natural Naturalfactors factorsasasaasingle singleforce forcewas wasone oneofofthe themajor majordriving drivingforces forcesofofLCLUC LCLUCprocesses processesininthe the region and could influence land degradation at the regional level of the landscapes [48]. “Wetland region and could influence land degradation at the regional level of the landscapes [48]. “Wetland ecosystems ecosystemsare areparticularly particularlysensitive sensitivetotoclimate climatechange changethat thataffects affectshydrology, hydrology,biogeochemical biogeochemicalprocesses, processes,plant plant communities communitiesand andecosystem ecosystemfunction” function”[49]. [49].Precipitation Precipitationand andtemperature temperaturedetermine determinethe thewet wetorordry dry local climate, affecting the formation and geographical distribution of runoff [50]. We selected local climate, affecting the formation and geographical distribution of runoff [50]. We selected annual annual average temperature and precipitation study how factors naturalaffected factors affected the of ecology of average temperature and precipitation to studytohow natural the ecology wetlands. wetlands. (Figure 10). Data from the China Meteorological Data Sharing Service System (Figure 10). Data from the China Meteorological Data Sharing Service System (http://cdc.nmic.cn/ (http://cdc.nmic.cn/home.do). Figure 10a,b showand theprecipitation air temperature and precipitation data, home.do). Figure 10a,b show the air temperature data, respectively. respectively. Changes of temperature and precipitation had a significant effect on the wetland areas in the Changes of temperature and precipitation hadevaporation a significantineffect on the wetlandthere areasare in high the study areas. Changes in temperature leads to high wetlands. Although study areas. Changes in temperature leads to high evaporation in wetlands. Although there are high precipitation time to time in ELW, but the precipitation cannot supply the higher evaporation, thus precipitation time to in time ELW, butinthe precipitation cannotin supply theand higher evaporation, rising temperatures thein ELW result a prominent reduction wetland LDHF areas in thethus both rising temperatures in the ELW result in a prominent reduction in wetland and LDHF areas in the ELWNNR and GLHFNNR region from 1959 to 2005 [19,21]. Although the annual average temperature both and GLHFNNR regionfrom from 1959 to 2005 the annual increased average and ELWNNR precipitation increased gradually 1959 to 2005, the[19,21]. annual Although average precipitation temperature and precipitation increased gradually from 1959 to 2005, the annual average slightly in a way that leads to a significant change in water resources. precipitation increased slightly in a way that leads to a significant change in water resources.


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Figure 10. Average annualannual temperature (a) and precipitation at the theAlashankou, Alashankou, Bole, Figure 10. Average temperature (a) and precipitation(b) (b)for for1959–2005 1959–2005 at Bole, Jinghe,and Wenquan, Wusu meteorological stations the study area. Jinghe, Wenquan, Wusuand meteorological stations in theinstudy area. 4.2. Influence of Human Factors on ELWNNR and GLHFNNR

4.2. Influence of Human Factors on ELWNNR and GLHFNNR Human activities appeared to be the most important factors responsible for the formation of

Human activities appeared to be the important factors responsible forare thecreating formation of wetland ecosystems, that is, humans aremost destroying many more wetlands than they wetland[51,52]. ecosystems, that is, humans are destroying many more wetlands than they Major anthropological factors driving the changes in the study area include landare usecreating policies, [51,52]. population growthfactors and agricultural conservation. Land use policies are regulated Major anthropological driving water the changes in the study area include land by usethepolicies, national and regional government agencies. The Chinese government promote “cottons as the key to by the population growth and agricultural water conservation. Land use policies are regulated the economy”, in this area and encouraged the development of western China, including Ebinur national and regional government agencies. The Chinese government promote “cottons as the key Lake Watershed for intensive cotton farming. The Ebinur Lake Watershed produces 7.5 × 107 kg of to the economy”, in this area and encouraged the development of western China, including cotton per year [53]. The relationship between population and farmland of the Ebinur Lake Ebinur Lake Watershed intensive cotton farming. The Lakethere Watershed × 107 kg of Watershedfor from 1980 to 2007 is shown in Figure 11.Ebinur Since 1950s, have beenproduces two kinds 7.5 of tillage method Xinjiang, which are thebetween land usedpopulation by local farmer the landofintensively large scaled cotton per year in [53]. The relationship and and farmland the Ebinur Lake Watershed used Xinjiang Production and Construction Corps for agriculture [54].two According the from 1980 toby 2007 is shown in Figure 11. Since 1950s, there have been kindstoofstatistics, tillage method in population has increased from 790,000 to 1,380,000 from 1950 to 2006, and has drastically populated Xinjiang, which are the land used by local farmer and the land intensively large scaled used by Xinjiang around ELW. The population rate went straight since 1950s and greatly enhanced the pressure on Production and Construction Corps for agriculture [54]. According to statistics, the population has land use. Water has a critical effect on wetland ecosystems. Xinjiang Production and Construction increased from 790,000 to 1,380,000 fromthe1950 to for 2006, and hasconservation drastically in populated around ELW. Corps constructed dams to preserve water agricultural case for drought The population rateincreased went straight since for 1950s and greatly enhanced onlivestock land use. Water has seasons. The use of water irrigation on agriculture landthe andpressure supporting in the led to a significant decline in groundwater levels (Figure 12). Meanwhile, increase dams a criticalregions effect has on wetland ecosystems. Xinjiang Production and Construction Corpswith constructed in population and farmlands in ELW, water resource and led toseasons. the succession ecology use of to preserve the water for agricultural conservation inexhausted case for drought The of increased water for irrigation on agriculture land and supporting livestock in the regions has led to a significant decline in groundwater levels (Figure 12). Meanwhile, with increase in population and farmlands in ELW, water resource exhausted and led to the succession of ecology in ELW. Forestland was preferred for land reclamation and this change led to the succession of the LCLU structure as revealed in this study. The reclamation of land by adjacent farms caused changes in the internal hydrological


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20 of 23 20 of 23

160 160 140 140 120 120 100 100 80 80 60 60 40 40 20 20 0 0 1980 1980

50 50 45 45 40

40 35 35 30 30 25 25 20

1985 1985

Population Population

Farmland area Farmland area

1990 1990

2000 2000

1997 1997

Year Year

2003 2003

20 15 15 10 10 5 50 2006 2006

3hm 2 2 3hm area Farmland ×10 area Farmland ×10

4 4 Population ×10 Population ×10

conditions within ELWNNR and GLHFNNR. Wetland areas were drastically reduced, despite1972 their This studythe showed that LCLUC was significant in the ELWNNR and GLHFNNR between This study showed that LCLUC was significant in the ELWNNR and GLHFNNR between 1972 explicit protection status by the regional government. However, HDHF areas increased drastically. and 2013. A large amount of wetland area has become salinized land, desert, and forestland. This and 2013. A large amount wetland area has become salinized land, desert, and forestland. This study showed that of LCLUC was significant in the ELWNNR GLHFNNR between 1972 study found the local water resources were insufficient to support the and expansion of farm land inThis the study found the local water resources were insufficient to support the expansion of farm land instudy the important ofbecome all, the salinized low waterland, tabledesert, in theand surrounding has andEbinur 2013. ALake largeWatershed; amount ofmost wetland area has forestland.region This Ebinur Lake Watershed; most important of the all, ELWNNR thesupport low water table in theofTherefore, surrounding has caused droughts and habitat degradation in and study has found the local water resources were insufficient to the GLHFNNR. expansion farm landour inregion the Ebinur caused droughts and habitat degradation in the ELWNNR and GLHFNNR. Therefore, our study has significant implications for the two national natural reserves that monitor and analyze the dynamics Lake Watershed; most important of all, the low water table in the surrounding region has caused significant implications for the two national natural reserves that monitor and analyze the of LCLUC in the ELWNNR and explored the natural and human to dynamics the study droughts and habitat degradation inGLHFNNR, the ELWNNR and GLHFNNR. Therefore, ourfactors study has significant of LCLUC in the ELWNNR and GLHFNNR, explored the natural and human factors to the study area. implications for the two national natural reserves that monitor and analyze the dynamics of LCLUC in area. the ELWNNR and GLHFNNR, explored the natural and human factors to the study area.

0

Figure 11. The relationship between population and farmland are within the Ebinur Lake Watershed Figure 11.11. The relationship between are within withinthe theEbinur EbinurLake LakeWatershed Watershed Figure The relationship betweenpopulation populationand and farmland farmland are from 1980 to 2007. from 1980 to 2007. from 1980 to 2007.

Figure 12. 12. TheThe increased stocking levels for for livestock in the Ebinur Lake Watershed fromfrom 19901990 to 2010. Figure increased stocking levels livestock in the Ebinur Lake Watershed to Figure 12. The increased stocking levels for livestock in the Ebinur Lake Watershed from 1990 to 2010. 2010. 5. Conclusions

5. Conclusions growth and the national demand for cotton production in China have led to significant 5. Population Conclusions growth the national for cotton in China have1972 led and to LCLUC Population in the ELWNNR andand GLHFNNR, anddemand surrounding Ebinurproduction Lake Watershed between Population growth and the national demand for and cotton productionEbinur in China have led to significant LCLUC in the ELWNNR and GLHFNNR, surrounding Lake Watershed 2013. Analysis of LCLUC showed a dramatic increase in salinized land at the expense of wetlands. significant LCLUC in theAnalysis ELWNNR and GLHFNNR, surrounding Ebinur Lake Watershed between 1972 anddisplayed 2013. of LCLUC showed a and dramatic increase in salinized at the Analysis of LCLUC that salinized land drastically increased at the expense land of wetlands. between 1972 and 2013. Analysis of LCLUC showed a dramatic increase in salinized land at the expense of wetlands. Analysis of LCLUC displayed that salinized land drastically increased atat the Forestland and desert areas changed less significantly, but also dramatically increased in HDHF the expense of and LCLUC displayed that salinized land drastically increased at the expense of of wetlands. wetlands. Analysis Forestland desert areas changed less significantly, but also dramatically expense of MDHF and LDHF. The changes in Desert area are less significant. expense wetlands. Forestland and desert areasand changed significantly, also area dramatically increasedofin HDHF at the expense of MDHF LDHF.less The changes in but Desert are less Landscape patternat analyses displayed a dissimilar overallThe pattern between the nature reserves. increased in HDHF the expense of MDHF and LDHF. changes in Desert area are less significant. Thesignificant. NP of forestland and wetland showed that patches of these habitats were small, highly Landscape pattern analyses displayed a dissimilar overall pattern between the nature reserves. heterogeneous, scattered and fragmented. Water bodyoverall and the forestland landscape shape index pattern analyses displayed a dissimilar between the nature reserves. The Landscape NP of forestland and wetland showed that patches ofpattern these habitats were small, highly exhibited a growth tendency between 1972 and 2013, showed a more complex patch shape structure The NP of forestland and showed thatbody patches habitats were small, heterogeneous, scattered andwetland fragmented. Water and of thethese forestland landscape shapehighly index by heterogeneous, contrast to other landscape types. Different landscapes have landscape quite different scattered and fragmented. Watertypes bodyofand the forestland shape degrees index


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of aggregation index, indicating that the variety of landscapes with varying degrees of patches connection is different. The Shannon evenness index is applied to describe the evenness of the patch types. The Interspersion and juxtaposition index indicated that patches became more inter-conjugated and better connected in large-scale patches, leaving only a few dominant and leading LCLU types. The Fragmentation index decreased from 0.06 in 1972 to 0.04 in 2013. The rising contagion index in the ELWNNR and Shannon diversity index values showed that growing human activities and disturbances result in the increase of wetland landscape diversity. In the nature reserve itself, wetland and forestland were similarly replaced by salinized land although in a lesser extent. Salinized land slightly replaced wetland and forestland in the nature reserve although the extent is not so much. With the ongoing development of agriculture in Xinjiang, the location of the two reserves provides an obvious advantage. Our work demonstrated that landscape diversity in the ELWNNR and GLHFNNR are linked to the broader Ebinur Lake Watershed. To maintain a sustainable environment, making good use of ecological resources is necessary to efforts that facilitate the sustainable development of the environment on the North Slope of the economic belt of the Tianshan Mountains. Local climatic factors also have a strong effect on the formation of wetland landscapes. With the increase of temperature and precipitation since 1990s, wetland areas declined drastically. This study also highlights the prominent influence of national and local government policies on agriculture and regional water usage on wetland ecosystems in the nature reserves. Acknowledgments: Thanks the National Meteorological Information Center data provided meteorological data. The research was carried out with the financial support provided by the National Natural Science Foundation of China (41361045), Xinjiang Local Outstanding Young Talent Cultivation Project of National Natural Science Foundation of China (U1503302). The authors wish to thank the referees for providing helpful suggestions to improve this manuscript. Author Contributions: Fei Zhang designed the study, analyzed the data, and prepared the manuscript with contributions from Hsiang-te Kung and Verner Carl Johnson, who both also contributed data sets to this effort. Conflicts of Interest: The authors declare no conflict of interest.

References 1. 2.

3. 4. 5. 6. 7. 8. 9.

10.

Liu, H.; Weng, Q.H. Landscape metrics for analyzing urbanization-induced land use and land cover changes. Geocarto Int. 2013, 28, 582–593. [CrossRef] Maimaitiyiming, M.; Ghulam, A.; Tiyip, T.; Pla, F.; Latorre-Carmona, P.; Halik, Ü.; Caetano, M. Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS J. Photogramm. Remote Sens. 2014, 89, 59–66. [CrossRef] Jackson, S.T.; Sax, D.F. Balancing biodiversity in a changing environment: Extinction debt, immigration credit and species turnover. Trends Ecol. Evolut. 2010, 25, 153–160. [CrossRef] [PubMed] Baan, L.; Alkemade, R.; Koellner, T. Land use impacts on biodiversity in LCA: A global approach. Int. J. Life Cycle Assess. 2012, 18, 1216–1230. [CrossRef] Dirzo, R.; Raven, P.H. Global state of biodiversity and loss. Ann. Rev. Environ. Resour. 2003, 28, 137–167. [CrossRef] Didham, R.K.; Kapos, V.; Ewers, R.M. Rethinking the conceptual foundations of habitat fragmentation research. Oikos 2012, 121, 161–170. [CrossRef] Ghulam, A. Monitoring Tropical Forest Degradation in Betampona Nature Reserve, Madagascar Using Multisource Remote Sensing Data Fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 4960–4971. [CrossRef] Ghulam, A.; Ghulam, O.; Maimaitijiang, M.; Freeman, K.; Porton, I.; Maimaitiyiming, M. Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways. Remote Sens. 2015, 7, 6257–6279. [CrossRef] Reid, R.S.; Kruska, R.L.; Muthui, N.; Taye, A.; Wotton, S.; Wilson, C.J.; Mulatu, W. Land-use and land-cover dynamics in response to changes in climatic, biological and socio-political forces: The case of southwestern Ethiopia. Landsc. Ecol. 2000, 15, 339–355. [CrossRef] Jin, G.; Wang, P.; Zhao, T.; Bai, Y.P.; Zhao, C.H.; Dong, C. Reviews on land use change induced effects on regional hydrological ecosystem services for integrated water resources management. Phys. Chem. Earth Parts A/B/C 2015, 89, 33–39. [CrossRef]


11.

12. 13.

14.

15. 16.

17.

18. 19.

20. 21.

22. 23. 24.

25. 26. 27. 28.

29. 30. 31. 32.

21 of 22

Meshesha, T.W.; Tripathi, S.K.; Khare, D. Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia. Model. Earth Syst. Environ. 2016, 2, 168–180. [CrossRef] Liu, G.L.; Zhang, L.C.; Zhang, Q.; Musyimi, Z.; Jiang, Q.H. Spatio–temporal dynamics of wetland landscape patterns based on Remote Sensing in Yellow River Delta, China. Wetlands 2014, 34, 787–801. [CrossRef] FRAGSTATS v3: Spatial Pattern Analysis Program for Categorical Maps. Computer Software Program Produced by the Authors at the University of Massachusetts, Amherst. Available online: http://www. umass.edu/landeco/research/fragstats/fragstats.html (accessed on 30 April 2017). Copeland, H.E.; Tessman, S.A.; Girvetz, E.H.; Roberts, L.; Enquist, C.; Orabona, A.; Patla, S.; Kiesecker, J. A geospatial assessment on the distribution, condition, and vulnerability of Wyoming’s wetlands. Ecol. Indic. 2010, 10, 869–879. [CrossRef] Sulman, B.; Desai, A.; Mladenoff, D. Modeling soil and biomass carbon responses to declining water table in a wetland-rich landscape. Ecosystems 2013, 16, 491–507. [CrossRef] Robertson, L.D.; King, D.J.; Davies, C. Assessing Land Cover Change and Anthropogenic Disturbance in Wetlands Using Vegetation Fractions Derived from Landsat 5 TM Imagery (1984–2010). Wetlands 2015, 35, 1077–1091. [CrossRef] Zhang, H.; Qi, Z.F.; Ye, X.Y.; Cai, Y.B.; Ma, W.C.; Chen, M.N. Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Appl. Geogr. 2013, 44, 121–133. [CrossRef] Yao, J.Q.; Liu, Z.H.; Yang, Q.; Meng, X.Y.; Li, C.Z. Responses of Runoff to Climate Change and Human Activities in the Ebinur Lake Catchment, Western China. Water Resour. 2014, 41, 738–747. [CrossRef] Wang, L.; Ding, J.L. Vegetation index feature change and its influencing factors and spatial-temporal process analysis of desert grassland in the Ebinur Lake Nature Reserve, Xinjiang. Acta Prataculturae Sin. 2015, 24, 4–11. [CrossRef] Ma, Q. Land use dynamic change and associated effects on eco-environment in Gan-jia lake wetland. J. Arid Land Resour. Environ. 2012, 26, 189–194. Zhang, F.; Tashpolat, T.; Verner, C.J.; Kung, H.T.; Ding, J.L.; Sun, Q.; Zhou, M.; Ardak, K.; Ilyas, N.; Chan, N.W. The influence of natural and human factors in the shrinking of the Ebinur Lake, Xinjiang, China, during the 1972–2013 period. Environ. Monit. Assess. 2015, 187, 4128–4142. [CrossRef] [PubMed] Wang, Y.; Li, Y.; Xiao, D. Catchment scale spatial variability of soil salt content in agricultural oasis, Northwest China. Environ. Geol. 2008, 56, 439–446. [CrossRef] Metternicht, G.; Zinck, J.A. Remote Sensing of Soil Salinization: Impact on Land Management; CRC Press: Boca Raton, FL, USA, 2008. Jia, K.; Liang, S.L.; Zhang, N.; Wei, X.Q.; Gu, X.F.; Zhao, X.; Yao, Y.J.; Xie, X.H. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS J. Photogramm. Remote Sens. 2014, 93, 49–55. [CrossRef] Friedl, M.A.; Brodley, C.E. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61, 399–409. [CrossRef] Pal, M.; Mather, P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86, 554–565. [CrossRef] Keuchel, J.; Naumann, S.; Heiler, M. Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sens. Environ. 2003, 86, 530–541. [CrossRef] Zhang, F.; Tashpolat, T.; Feng, Z.D.; Kung, H.T.; Verner, C.J.; Ding, J.L.; Nigela, T.; Maimaiti, S.; Gui, D.W. Spatio-temporal patterns of land use/cover changes over the past 20 years in the middle reaches of the Tarim River, Xinjiang, China. Land Degrad. Dev. 2015, 26, 284–299. [CrossRef] Richards, J.A.; Jia, X. Remote Sensing Digital Image Analysis, an Introduction, 4th ed.; Springer: Berlin, Germany, 2006. Pradhan, B.; Sulaiman, Z. Land cover mapping and spectral analysis using multi-sensor satellite data: A case study in Tioman Island, Malaysia. J. Geomat. 2009, 3, 71–78. Biro, K.; Pradhan, B.; Buchroithner, M.; Makeschin, F. Land use/land cover change analysis and its impact on soil properties in the Northern part of Gadarif region, Sudan. Land Degrad. Dev. 2013, 24, 90–102. [CrossRef] Sun, J.B.; Yang, J.Y.; Zhang, C.; Yun, W.J.; Qu, J.Q. Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method. Math. Comput. Model. 2013, 58, 573–581. [CrossRef]


33. 34.

35. 36. 37.

38. 39. 40. 41. 42. 43.

44. 45. 46.

47. 48. 49. 50. 51.

52. 53. 54.

22 of 22

Banerjee, R.; Srivastava, P.K. Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing. Land Use Policy 2013, 34, 193–203. [CrossRef] Shao, Y.; Lunetta, R.S.; Wheeler, B.; Iiames, J.S.; Campbell, J.B. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multitemporal data. Remote Sens. Environ. 2016, 174, 258–265. [CrossRef] Smeeton, N.C. Early History of the Kappa Statistic. Biometrics 1985, 41, 795. Fleiss, J.L. Statistical Methods for Rates and Proportions, 2nd ed.; John Wiley: New York, NY, USA, 1981. Wan, L.H.; Zhang, Y.W.; Zhang, X.Y.; Qi, S.Q.; Na, X.D. Comparison of land use/land cover change and landscape patterns in Honghe National Nature Reserve and the surrounding Jiansanjiang Region, China. Ecol. Indic. 2015, 51, 205–214. [CrossRef] Schmidt, M. The Sankey diagram in energy and material flow management, part I: History. J. Ind. Ecol. 2008, 12, 82–94. [CrossRef] Cuba, N. Research note: Sankey diagrams for visualizing land cover dynamics. Landsc. Urban Plan. 2015, 139, 163–167. [CrossRef] Paukert, C.P.; Pitts, K.L.; Whittier, J.B.; Olden, J.D. Development and assessment of a landscape-scale ecological threat index for the Lower Colorado River Basin. Ecol. Indic. 2011, 11, 304–310. [CrossRef] Seto, K.C.; Fragkias, M. Quantifying spatial-temporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landsc. Ecol. 2005, 20, 871–888. [CrossRef] Xu, K.; Kong, C.F.; Liu, G.; Wu, C.L.; Deng, H.B.; Zhang, Y. Changes of urban wetlands in Wuhan, China, from 1987 to 2005. Prog. Phys. Geogr. 2010, 34, 207–220. EPA (Environmental Protection Agency). A Landscape Approach for Detecting and Evaluating Change in a Semi-Arid Environment, Office of Research and Development, Washington, DC, USA. EPA 620/R-94/009; 2000. Available online: http://www.epa.gov/crdlvweb/land-sci/san-pedro.htm (accessed on 19 January 2017). Fan, Q.D.; Ding, S.Y. Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013. CATENA 2016, 137, 152–160. [CrossRef] Wang, S.; Ding, J.L.; Wang, L.; Niu, Z.Y. Response of ecological service value to land use change in nearly 20 years in Ebinur Lake region of Xinjiang based on remote sensing. Res. Soil Water Conserv. 2014, 21, 144–149. Ma, L.; Wu, J.L.; Liu, W.; Abuduwaili, J.L.L. Distinguishing between anthropogenic and climatic impacts on lake size: A modeling approach using data from Ebinur Lake in arid northwest China. J. Limnol. 2014, 73, 350–357. [CrossRef] Qiang, A.G.; Li, Q.C. Anthropogenic interference analysis of Ganjia Lake Haloxylon forest National Nature Reserve in Xinjiang. Jilin Agric. 2013, 3, 206–207. Dawelbait, M.; Morari, F. Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. J. Arid Environ. 2012, 80, 45–55. [CrossRef] Malekmohammadi, B.; Blouchi, L.R. Ecological risk assessment of wetland ecosystems using multi criteria decision making and geographic information system. Ecol. Indic. 2014, 41, 133–144. [CrossRef] Shi, Y.F.; Shen, Y.P.; Li, D.L.; Zhang, G.W.; Ding, Y.J.; Hu, R.J.; Kang, E. Discussion on the present climate change from WARM-DRY to WARM-WET in northwest CHINA. Quat. Sci. 2003, 23, 152–164. Zhang, F.; Tashpolat, T.; Verner, C.J.; Kung, H.T.; Ding, J.L.; Zhou, M.; Fan, Y.H.; Ardak, K.; Ilyas, N. Evaluation of land desertification from 1990 to 2010 and its causes in Ebinur Lake region, Xinjiang China. Environ. Earth Sci. 2015, 73, 5731–5745. [CrossRef] Wang, T.; Yan, C.Z.; Song, X.; Xie, J.L. Monitoring recent trends in the area of aeolian desertified land using Landsat images in China’s Xinjiang region. ISPRS J. Photogramm. Remote Sens. 2012, 68, 184–190. [CrossRef] Ge, L.M. Study on Climatic Variation and Its Effect in Recent 45 Years in Ebinur Lake Basin of Xinjiang. Master’s Thesis, Xinjiang University, Ürümqi, China, 2006. Wang, S.X.; Wang, S.L. Land use/land cover change and their effects on landscape patterns in the Yanqi Basin, Xinjiang (China). Environ. Monit. Assess. 2013, 185, 9729–9742. [CrossRef] [PubMed] © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).