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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 12: 1933 - 1952

DOI: https://doi.org/10.1007/s11442-018-1572-z

Research Articles

Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran

  • OMID Abdi ,
  • ZEINAB Shirvani ,
  • MANFRED F. Buchroithner
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  • Institute for Cartography, Geosciences Department, TU Dresden, Germany

Author: Omid Abdi (1981-), PhD Candidate, specialized in GIS, remote sensing and natural resources. E-mail: ;

Received date: 2016-07-15

  Accepted date: 2017-03-27

  Online published: 2018-12-20

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps: (1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986-2000 and 2000-2015, (2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation (BiLISA), and (3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag (SL) model (wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error (SE) model (wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.

Key words： rangeland degradation; Landsat; GIS; anthropogenic driving forces; BiLISA; spatial regression

Cite this article

OMID Abdi , ZEINAB Shirvani , MANFRED F. Buchroithner . Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran[J]. Journal of Geographical Sciences, 2018 , 28(12) : 1933 -1952 . DOI: 10.1007/s11442-018-1572-z

1 Introduction

In many developing countries, rangelands represent an important land-cover class which are affected by numerous natural and anthropogenic drivers and then caused serious environmental and socioeconomic problems (Bedunah and Angerer, 2012). Changes in the environment, from a natural state to intensified human uses with multiple interacting components, have spatially and functionally altered the Earth (Turner 2002). It is essential to explore the effect of these drivers on ecosystems and to assess the impacts of land degradation on ecosystems on small, medium and large scales (Serneels and Lambin, 2001).
Many land-cover change studies have been performed using Landsat data due to their reasonable spatial, spectral and temporal resolutions (Röder et al., 2008; Basnet and Vodacek, 2015). Moreover, long-term, medium-resolution, time-series Landsat imagery from as far back as 1972 is freely available on the global scale and provides information that can be used to reveal and track human-caused land-cover changes (Hansen et al., 2012). Numerous change-detection methods have been developed based on satellite data, and new approaches are continuously developed (Hussain et al., 2013). For example, Mahyou et al. (2016) used a knowledge-based approach to analyze Landsat TM data to assess rangeland degradation through vegetation characteristics, grazing and cultivation intensity in the arid rangelands of North Africa. Likewise, the post-classification technique was used in studies that needed more detail about the change direction (Bouziani et al., 2010). This technique used a change matrix to compare bitemporal classified images to measure changes (Yuan et al., 2005). Pre-classification change-detection approaches have also been developed based on the evaluation of individual pixels using statistical operators, such as the direct comparison (Coppin et al., 2004) and image transformation methods (Erener and Düzgün, 2009).
Several studies have shown that increasing anthropogenic activities such as expansion of farmlands (White et al., 2000; Elias et al., 2015), construction areas (Hoekstra et al., 2005; Hu et al., 2008), and grazing intensity (Hein et al., 2006; Miehe et al., 2010; Kiage 2013; Angassa, 2014) are the main driving forces of degradation in rangeland ecosystems. For example, Li et al. (2013) introduced anthropogenic activities as the main driving forces of rangeland degradation in the Qinghai-Tibet Plateau, where approximately one-third of these rangelands are considered to have medium to severe degradation. Gaitán et al. (2017) found that grazing pressure has degraded the Patagonian rangelands as the grassland density and the species diversity of palatable grasses have declined. Cameron et al. (2014) mapped rangeland conversion using temporal GIS data and satellite images in California’s rangeland ecosystems from 1984 to 2008, their results showed that the majority of rangeland changes occurred by sprawling residential areas and farming lands. Whereas, Sheehy and Damiran (2012) have reported livestock density as the primary driver of the accelerating rate of degradation in the Mongolian grasslands from 1997 to 2009.
Rangelands are the largest terrestrial ecosystem in Iran by covering approximately 54.60% of the total land area, i.e., 90 million ha, and nearly 65% of natural resources (Eskandari and Chavoshi, 2002; Badripour et al., 2006). These natural habitats have gotten less attention of conservation rather than other major ecosystems in Iran. The DEI report (2003) shows that nearly 500,000 ha of rangelands are removed every year due to increasing anthropogenic activities such as cultivation, overgrazing and other natural variables. In the last six decades, farmlands have expanded from 2.6 million ha (Wilber, 2014) to 18.5 million ha (DEI, 2010), and about 9.1% of Irano-Turanian rangelands converted to other land-use types from 1955 to 1995 (Ansari et al., 2009a). Some scholars argue that the main drivers of Iran’s rangeland degradation include conversion to farmlands and increasing livestock density (Khorshidi and Ansari, 2003; Shahraki and Barani, 2012; Jamshidi and Amini, 2013). Ansari et al. (2009b) introduced overgrazing (47.2%), cultivation (35.9%) and harvesting (16.9%) as influencing factors. Davudirad et al. (2016) showed that the vegetation coverage of the Shazand Watershed in Iran had intensively been degraded and replaced by residential areas, industrial zones and agricultural lands in the last four decades. Many rangeland degradation studies in Iran were performed by traditional methods, such as questionnaires and field surveys, which have lower accuracies than advanced remote sensing approaches. Therefore, exploring degraded rangeland ecosystems and finding driving forces using remote-sensing data and GIS-spatial approaches are necessary in local and regional Iran. Moreover, the statistical and spatiotemporal analyzes between anthropogenic dynamics and changes in natural ecosystems are simultaneously essential at a reasonable scale (Fortin and Dale, 2005; Cressie and Wikle, 2011). In many studies, statistical models are applied to analyze effective factors of land-use changes. Spatial regression techniques assess possible spatial dependencies between geographic phenomena and measure their parameters more accurately (Fortin et al., 2012; Anselin and Rey, 2014), while statistical regression methods without spatial dependence may lead to errors in the coefficient estimates, R-squared, t-ratios and p-value (Rao, 2002). Some spatial regression methods, including the spatial lag (SL) and spatial error (SE) models have been extensively applied to interpret the spatial determinants of explicit data (Aguiar et al., 2007; Anselin and Rey, 2014; Zhou et al., 2014), and analyze the determining drivers of land-use patterns (Overmars et al., 2003), and detect changes in land-use/land-cover (Lesschen et al., 2005; Zhang et al., 2015; Chen et al., 2016). The visualization and quantification of rangeland degradation have been given little attention in previous studies. Therefore, the study objectives are as follows: (1) to map the trend of degraded rangelands and expanded anthropogenic dynamics for the 1986-2000 and 2000-2015 timeframes, (2) to visualize the effects of anthropogenic driving forces on rangeland degradation using BiLISA during each period and (3) to quantify the spatial association between anthropogenic driving forces and rangeland degradation in northeast Iran using the best suitable spatial regression approaches.

2 Material and methods

2.1 Study area information

The study area is a part of a 9550 km2 of Atrak and Gorgan-Rood basins in the Golestan province, northeast Iran, located in the east of the Caspian Sea (Figure 1). It includes 178 rangeland management plans (RMPs) with areas ranging from 560 ha to 1680 ha. The main structure of the RMPs is determined by rangeland boundary, grazing capacity and grazing seasons, grazing system and rehabilitation programs (Badripour et al., 2006). The land management regime of these rangelands is the same, which are managed by the government, as a result their speed of degradation has been increased by human activities such as conversion to the farmlands. The type of rangelands is semi-steppe with a high livestock population, as livestock density is three times higher than the carrying capacity determined for the RMPs. The elevation range reaches from -27 to 1480 m with a mean of 206 m. Annual precipitation across the entire region lies between 160 to 520 mm and generally increases from north to south. The mean temperature reads about 17°C.
Figure 1 The procedure of GCPs collecting for (a) 1986 on the color composite image of Landsat 5, (b) 2000 on the merged image of Landsat 7, (c) 2015 on the merged image of Landsat 8, and (d) location of the study area and an example view

2.2 Data description

We used different data from diverse sources to detect land-cover types and their changes in the study area. We obtained Landsat images from the online database of USGS available at http://earthexplorer.usgs.gov/. Moreover, we used digital topographic maps at 1:25,000, aerial photos of 1966 (1:20,000) and 1991 (1:40,000), raw maps of the RMPs, Google Earth, field observations, and the national census of housing, population and agriculture. We classified land-cover 1986 by Landsat 5 TM data of May 19, 1986, land-cover 2000 by Landsat 7 ETM+ images of May, 20 and 31, 2000, and land-cover 2015 by Landsat 8 OLI data of May 19 and 26, 2015. Moreover, we collected the ground control points (GCPs) by designing a cluster network sampling in the study area (Figure 1). The major advantage of this method is the reduction in travel time and the costs of sampling in the field (Congalton and Green, 2009). The GCPs of 1986 were collected through interpretation of the aerial photos of 1966 and 1991, and the spectral image composites of TM 5 as well. The GCPs of 2000 were obtained from digital topographic maps (1:25,000) and pan-sharpened combinations of ETM+. The GCPs of 2015 were collected through field observations, Google Earth (2010 to 2014) and the pan-sharpened image of OLI. Digital elevation model (DEM) was created from 3D digital topographic maps (1:25,000). Furthermore, the statistics of RMPs include livestock units and their distributions in the RMPs were used based on the available information in the Department of Natural Resources and Watershed Management of Golestan province, and also the agricultural census of 2015.

2.3 Methods

The main framework of the visualization and quantification of degraded rangelands is illustrated in Figure 2, and the methodology is described in the five steps as follows.
Figure 2 The procedures of visualization and quantification of degraded rangeland: (A) data preprocessing and image enhancement, (B) GCPs and training set selection, (C) image classification and land-cover change detection, (D) creation of a database for Rangeland Management Plans (RMPs), and (E) spatial autocorrelation approaches.
2.3.1 Data preprocessing and image enhancement
Box A of Figure 2 shows the orthorectification of aerial photos, preprocessing and enhancement of Landsat images in the study area. In the first step, we generated a 30-m DEM from contour lines and elevation points of 3D topographic maps using interpolation tools in ArcGIS (ESRI). In the next step, we applied both polynomial rectification and orthorectification on the aerial photos using GCPs and DEM in Erdas Imagine. In the third step, we rectified Landsat images as image-to-image by the available model for geometric and orthometric corrections of Landsat sensors in Erdas Imagine. Afterwards, radiometric corrections performed based on the de-hazing and atmospheric/topographic algorithms using ATCOR3 model for Imagine (Richter, 2001). In the spatial enhancement step, we implemented the HPF (High Pass Filter) resolution merge function (Gangkofner et al., 2008) for merging the panchromatic image, as a high-resolution band, with the multi-spectral layers of Landsat 7 ETM+ and Landsat 8 OLI. Likewise, we applied spectral enhancements to the merged data for ETM+, OLI and multi-spectral of TM. We used Principal Components Analysis (PCA) to compact data into fewer bands with the least correlation and a maximum variance (Daffertshofer et al., 2004) and the Tasseled Cap transformation to maximize visibility of vegetation on the Landsat data. We utilized the Soil Adjusted Vegetation Index (SAVI), equation 1, to lessen the influences of the soil backdrop on the vegetation signal by using a canopy background modification factor, L, which abundant used value is 0.5 for the regions with dominant sporadic vegetation, where the soil under the tree canopy is visible (Huete, 1988).
$SAVI=\frac{1.5*(NIR-\operatorname{R})}{(NIR+R+0.5)}$ (1)
where NIR is the near infrared red spectral band and R is the red spectral band of Landsat data.
2.3.2 GCPs data and signature pixels
Box B of Figure 2 shows the procedure of generating GCPs and signature pixels through orthophotos and the composite images of Landsat for 1986, 2000 and 2015. The GCPs of 1986 were interpreted visually on the large-scale black-and-white orthophotos (1:20,000 and 1:40,000) of 1966 and 1991 based on the pictorial element’s characteristics such as tone, texture, pattern, size and shape. The GCPs of 2000 and 2015 were produced based on the visual interpretation of land-cover types by the Natural Color images of ETM+ and OLI, and the Google Earth images between 2010 and 2014. Furthermore, the signature dataset for three images were obtained based on the truth samples and visual interpretation of the color composite images in 1986, 2000 and 2015. Fifty-fifty of the GCPs dataset were randomly divided into spectral training data for classification and ground truth data for accuracy assessment of the classification results.
2.3.3 Classification and change detection based on Landsat imagery
We applied supervised classifiers for classification of the images through consideration the features of the training dataset. A set combination of bands was created for each Landsat data include the main spectral bands and derived bands from spectral enhancements (e.g., three primitive channels of PCA, Tassled cape and SAVI data) for each scene of the TM, ETM+ and OLI data. Transformed divergence method was performed to calculate the spectral distance between the average components of the paired signatures for all band combinations. Next, we classified multi-spectral images using the Support Vector Machine (SVM), Random Forests (RF) and Maximum Likelihood (ML) classifiers. The superiority of the SVM and RF, as two non-parametric classifiers, has been shown for classification of multi-spectral imagery data in several studies (Otukei et al., 2010; Guo et al., 2011; Lu et al., 2011; Basnet and Vodacek, 2015). Furthermore, the ML and Fuzzy methods have demonstrated superior performance in land-cover classification among parametric algorithms, provided that the distribution of training data is normal (Otukei et al., 2010; Churches et al., 2014). We evaluated the validity of land-cover classification using generation of a confusion matrix (Canty, 2014) and the results of producer’s accuracy, user’s accuracy for subclasses, overall accuracy and Kappa statistic for the classifiers (Cohen, 1960; Congalton and Green, 2009). We employed remained 50% of GCPs dataset (stage B) as reference points in the confusion matrix.
In the final step, land-cover changes interpreted using post-classification comparison method. However numerous methods are formulated to evaluate the changes in environment (Lu et al., 2004; Hussain et al., 2013), the post-classification approach is introduced as the most distinct quantitative change detection technique due to (i) its detailed information from matrix of change detection (Bouziani et al., 2010), (ii) no limitation of the atmospheric and environmental calibrations, and (iii) its visual interpretation (Giri, 2012). By this approach, the land-cover maps were compared to measure changes from 1986 to 2000 and 2000 to 2015 in the study area, as all classes were compared as one-to-one in each period.
2.3.4 Creating a database for degraded rangeland of the RMPs
We build a geodatabase, including the feature classes related to the driving forces of rangeland degradation in the study area (Box D, Figure 2). The database consists of 178 RMPs, degraded areas of rangelands and expansion of anthropogenic drivers including construction areas, dry-farming, irrigated farming, orchards and water bodies in two periods. Also, we imported livestock population into the database of the RMPs for each period, and livestock density calculated regarding this information and the rangelands’ areas in the RMPs. The final database was included the dependent and all required independent drivers for analyzing spatial autocorrelation in the next step (Box E, Figure 2).
2.3.5 Spatial autocorrelation techniques
Spatial autocorrelation is an assessment way to examine the dependence of one variable by considering the similarity simultaneously between its spatial location and field attribute (Cliff and Ord, 1973). Moran’s I is the most common test statistic for spatial autocorrelation, which includes tests for visualization of the clusters using both local and global tests and creation of the significant and cluster maps using the local test statistics (Anselin and Rey, 2014). These maps describe the features with significant local Moran statistics and are classified through a sort of association. In this study, the univariate and bivariate local Moran’s I were tested to assess spatial autocorrelation of degraded areas of rangelands in the RMPs during the studied periods.
(1) Visualizing of degraded rangelands
Local indicators of spatial autocorrelation were measured using Moran’s I for visualization of degraded rangelands over the spatial extent of the rangelands. The typical formula to measure of local Moran’s I is presented in the equation 2.
${{I}_{i}}=\frac{({{x}_{i}}-\bar{x})}{{{m}_{0}}}\sum\limits_{j}{{{w}_{ij}}}({{x}_{i}}-\bar{x})\ \ \ \ \ \ {{m}_{0}}=\sum\limits_{i}^{{}}{\frac{{{({{x}_{i}}-\bar{x})}^{2}}}{n}},$ (2)
where n is the number of RMPs, xi is the degraded rate of rangelands for the location i, $\bar{x}$is the average rate of degradation for all the 178 RMPs, and wij is the spatial weight matrix. LISA (Local Indicators of Spatial Association), based on the local Moran statistic (Anselin and Rey, 2014), was applied to visualize spatial patterns of rangeland degradation by significance map and cluster map in this study. The LISA significance map depicts locations with significant values of Moran’s I and the LISA cluster map classifies these locations based on a sort of spatial association by the Moran scatterplot (Anselin et al., 2006). Moreover, the bivariate local Moran’s I statistic interprets the spatial association between two variables in a specific location (Anselin et al., 2006). We used BiLISA to visualize the significant impacts of anthropogenic drivers (construction areas, dry-farming, irrigated farming, orchards, water bodies and livestock density) on the rangeland changes in 1986-2000 and 2000-2015 in all the 178 RMPs. The outputs of BiLISA include significance maps, cluster maps and Moran scatter plots (Kalkhan, 2011) as well. We assessed the spatial sensitivity of LISA and BiLISA clusters/outliers based on the minimum 999 permutations at the pseudo significance levels of 0.01 and 0.05 (Anselin et al., 2006). We coded the cluster maps of LISA and BiLISA into four spatial association categories using the Moran scatterplot (Oyana and Margai, 2015), and also one Not Significant class. Two of these categories indicate positive spatial association; High-High cluster appears wherever a high value was surrounded by the same high observed values, while Low-Low cluster when appears a low value in a location was surrounded by neighbors with low values. In contrast, the negative spatial association expresses wherever a high value was surrounded by low values (High-Low) and vice versa (Low-High), which indicate the spatial outliers. The fifth class, Not Significant, shows no spatial patterns at the corresponding weight matrix. The locations with significant local Moran’s I statistics and the categories of spatial association are shown as the LISA and BiLISA significance cluster maps in Figures 4 and 5.
(2) Quantifying the influences of anthropogenic drivers
We used three regression approaches, including ordinary least squares (OLS), spatial lag (SL), and spatial error (SE) to quantify the intensity of degraded rangelands affected by different anthropogenic drivers in the RMPs. Since the OLS assumes that observations are independent and also it does not take into account the spatial dependence for variables (Rao, 2002), we carried out the spatial regression models such as SL and SE available in GeoDaSpace (Lloyd, 2010; Anselin and Rey, 2014) which take into account the spatial dependence in observations. In this study, rangeland degradation is included as a dependent variable, and expansion of the six anthropogenic drivers are included as the explanatory variables in all three regression equations. The equation of OLS is given as bellows:
$Y={{\beta }_{0}}+{{x}_{1}}{{\beta }_{1}}+{{x}_{2}}{{\beta }_{2}}++{{x}_{n}}{{\beta }_{n}}+\varepsilon ,$ (3)
where Y is degraded areas of rangelands, β0 is the constant value, β1 to βn are the estimates of explanatory variables (x1, x2,… and xn) including anthropogenic drivers, and ε is a vector of independent variables with zero mean and variance σ2. In the SL method equation 4, the error term (ε) in the OLS is decomposed into a spatially lagged term that is connected to the dependent variable, and an independent error term (Ward and Gleditsch, 2008).
$Y={{\beta }_{0}}+{{x}_{1}}{{\beta }_{1}}+{{x}_{2}}{{\beta }_{2}}++{{x}_{n}}{{\beta }_{n}}+\rho WY+\varepsilon ,$ (4)
where ρWY is defined as the spatially lagged of the dependent variable, W is the mean weight values of the spatial connectivity matrix obtained by K-nearest neighbor’s method in GeoDaSpace (Anselin and Rey, 2014).
In the SE method (Equation 5) the error term is decomposed into the error term v, and the spatially none correlated error (Ward and Gleditsch, 2008).
$Y={{\beta }_{0}}+{{x}_{1}}{{\beta }_{1}}+{{x}_{2}}{{\beta }_{2}}++{{x}_{n}}{{\beta }_{n}}+\lambda W\nu +\mu ,$ (5)
where Lambda (λWv) represents the spatial autoregressive coefficient, and μ is an independent criterion with a normal distribution.
We compared the performance of the OLS, SL and SE models by adjusted Akaike’s information criterion (AICc). AICc, as an information-theoretic approach (Burnham and Anderson, 2002), was applied to measure the goodness-of-fit for three models. We computed AICc (Hurvich and Tsai, 1989) as follows:
$AI{{C}_{c}}=-2\log L\left( {\overset{\scriptscriptstyle\frown}{\theta }} \right)+\frac{2k\left( k+1 \right)}{n-k-1},$ (6)
where L($\overset{\scriptscriptstyle\frown}{\theta }$) is the fitted maximum likelihood of the estimator $\overset{\scriptscriptstyle\frown}{\theta }$ for the parameter θ, k indicates the number of estimable parameters in the model, and n represents the sample size. Then, we computed AICc differences (Δi) for comparing and ranking models and are as well as useful in calculating Akaike weights, which was:
${{\Delta }_{i}}=AIC\ {{c}_{i}}-AIC\ {{c}_{\min }},$ (7)
where the smaller Δi is more considerable and leads to obtaining the fitted model. Then, Akaike weight (wi) was computed based on Equation 8; it is an important index to rank and interpret the Δi values of the alternative models (k).
${{w}_{i}}=\frac{\exp \left( \frac{-{{\Delta }_{i}}}{2} \right)}{\sum\limits_{i=1}^{k}{\exp \left( \frac{-{{\Delta }_{k}}}{2} \right)}}$ (8)
We computed the evidence ratios between the models to determine the best-fitted model. The evidence ratio for the model i versus model j, (Ei,j), was computed by dividing wi to wj. The ratio above one shows that there is robust evidence that the model i has superior performance than the model j (Burnham and Anderson, 2002).

3 Results

3.1 Spatiotemporal of rangeland degradation and expansion of anthropogenic drivers

The accuracy assessment reports indicate that detected land-cover maps from Landsat images using different classifiers show satisfying agreement results based on the confusion matrices during all periods. The most accurate classifiers were Maximum Likelihood with an overall accuracy of 95% and an overall kappa of 0.91 in 1986, Fuzzy Maximum Likelihood with an overall accuracy of 97% and an overall kappa of 0.94 in 2000, and Random Forests with an overall accuracy of 96% and an overall kappa of 0.87 in 2015. The areas of rangelands obtained 647,011 ha in 1986, 573,264 ha in 2000, and 480,602 ha in 2015 using verified classifiers.
The temporal pattern of rangeland changes is shown in Figure 3. About 10.68% (69,082 ha) and 8.4% (46,320 ha) of rangelands are changed into other land-cover types in 2000 and 2015, respectively. The results show that the annual rates of rangeland degradation obtained about 0.77% in 1986-2000, and 0.56% in 2000-2015. The majority of degraded rangelands are replaced by dry-farming and irrigated farming in both periods; as approximately 66% and 33% of degraded rangelands are affected by expansion of these two land-cover types in the first period, respectively. The expansion of other land-cover types was less than one percent of degraded rangelands during the first period (Figure 3a). Moreover, dry-farming has increased from 220,159 ha in 2000 to 287,879 ha in 2015, about 95% of decreasing rangeland areas are happened due to dry-farming expansion during this period. Although irrigated farming has decreased from 85,153 ha in 2000 to 80,914 ha in 2015, approximately two percent of rangelands is converted into irrigated farming in 2015. The remaining areas have altered by water bodies, construction areas and orchards (Figure 3b). The average livestock density calculated at about 1.6 n.ha‒1 in the first period (Figure 3c), while this amount has doubled in the RMPs in 2015 (Figure 3d). Some locations have high livestock densities in the southern parts of the rangelands in the first period. There are several RMPs with livestock densities above the standard carrying capacity allocated for them (1.5 n.ha‒1) in the second period. The average livestock density was calculated about 3.2 n.ha‒1 in the study area in this period, which is higher than the estimated average livestock density for other Iran’s rangelands, i.e., 2.5 n.ha‒1 (Badripour et al., 2006).
Figure 3 The expansion of anthropogenic land-cover and livestock density from 1986 to 2000 (a, c) and from 2000 to 2015 (b, d) in the RMPs

3.2 Degradation patterns of rangelands

Results of univariate local Moran’s I statistic demonstrated that spatial autocorrelation of degraded rates of rangelands was larger than that would be expected by chance in two studied periods, with a slightly more in the second period (I= 0.522) than the first period (I= 0.485). The LISA cluster maps, illustrated in Figure 4, show the locations with four of significant spatial autocorrelation categories (two-cluster and two-outlier) by the Moran scatterplot (the corresponding p-values were above 0.05 and a number of 999 random permutations). According to these maps, 22 and 21 RMPs are located in the High-High cluster in 1986-2000 and 2000-2015, respectively. Spatial visualization depicts about 76% of High-High clusters are common in the first and second periods and about 10% of Low-High outlier polygons in the first period are converted into the High-High cluster in the second period, and also 14% of the remaining clusters did not show a significant autocorrelation in the first period (Figure 4a). It can be inferred that these areas were probably affected by various drivers as that such a severe degradation rate has occurred not only in the first period, but also has continued during the second period. Although some locations emerged with high degradation rates in the second period (Figure 4b), they did not show significant autocorrelation in the first period (Figure 4a), while they were surrounded by clusters with high degradation rate. The LISA presented here is easy to implement and lend itself readily to visualize degraded rangelands. Nevertheless, some questions remain about influencing of driving forces and the degradation rate of rangelands that LISA cannot solve them. The following results show how we could find reasonable responses to these important questions using BiLISA and spatial regression techniques in this study.
Figure 4 LISA clustering during the period of 1986-2000 (a) and the period of 2000-2015 (b) with p < 0.05

3.3 BiLISA’s outputs of driving forces influencing degraded rangelands

The spatial autocorrelation between degraded rangelands and expanded driving forces is summarized by local bivariate Moran’s I in the BiLISA cluster maps (Figure 5) and summary statistics (Table 1) for two studied periods. The highest values of Moran’s I were obtained for dry-farming in both periods, with about 0.533 in 1986-2000 and 0.509 in 2000-2015. The BiLISA cluster maps showed that 16 and 22 of the RMPs are occupied by dry-farming (High-High cluster) in the first and second periods (Figures 5a and 5b), respectively; about 59% of locations with High-High cluster were common in both periods, 9% were in the Low-High outlier, and 32% entered from Not Significant class of the first period (Figure 5b). It can be inferred that degradation of rangelands has been increased due to conversion to dry-farming during the second period. Moreover, 75% of Low-High outliers in the first period are changed into the High-High clusters in the second period, namely, rangelands with low degradation rates in 1986-2000 were affected by higher rates of dry-farming expansion and then gained high rates of degradation in 2000-2015 (Figure 5e). Furthermore, irrigated farming scored high values of Moran’s I at 0.200 in the first period and slightly lower at about 0.157 in the second period. The number of High-High clusters induced by irrigated farming was nine (in the first period) and seven (in the second period) locations, which are less than affected locations by dry-farming in both periods. Locations with High-Low outliers observed in one and two RMPs in the first and second periods, respectively (Figure 5).
Figure 5 BiLISA clustering showing driving forces (dry-farming, irrigated farming, construction areas, orchards, water bodies and livestock density) influencing degradation of rangelands with four categories of High-High (a, b), High-Low (c, d), Low-High (e, f) and Low-Low (h, I) in the periods of 1986-2000 and 2000-2015 (b) with p < 0.05
Table 1 Bivariate local Moran’s I statistics: spatial autocorrelation between degradation of rangelands and expansion of anthropogenic drivers (empirical pseudo significance based on 999 random permutations)
1986-2000 2000-2015
Drivers Local
Moran’s I		 Cluster/
Outlier		 Number of RMPs Degradation rate (%) Local Moran’s I Cluster/ Outlier Number
of RMPs		 Degradation rate (%)
Dry-farming (DF) 0.533*** High-High 16 22

0.509***		 High-High 22 17.31
Low-Low 40 1.30 Low-Low 30 0.89
Low-High 4 8.10 Low-High 7 0.54
High-Low 0 0 High-Low 1 3.27
Not Significant 118 9 Not Significant 118 4.04
Irrigated Farming (IF) 0.200*** High-High 9 16.92

0.157***		 High-High 7 20.77
Low-Low 17 0.89 Low-Low 24 0.97
Low-High 7 3.46 Low-High 5 0.93
High-Low 1 14.38 High-Low 2 11.64
Not Significant 144 10.67 Not Significant 140 7.33
Orchards (OR) -0.00987ns High-High 3 11.96

-0.0273ns		 High-High 1 13.69
Low-Low 0 0 Low-Low 0 0
Low-High 7 1.40 Low-High 8 0.58
High-Low 43 19.36 High-Low 25 17.38
Not Significant 125 3.86 Not Significant 144 2.73
Construction Area (CA) 0.0754* High-High 4 14.33

0.293***		 High-High 7 17.58
Low-Low 1 2.03 Low-Low 5 0.54
Low-High 10 1.65 Low-High 10 2.25
High-Low 10 17.97 High-Low 0 0
Not Significant 153 9.86 Not Significant 156 6.14
Water Body (WB) - High-High - -

-0.0407ns		 High-High 3 7.29
Low-Low - - Low-Low 1 0.048
Low-High - - Low-High 9 0.82
High-Low - - High-Low 13 13.57
Not Significant - - Not Significant 152 6.34

Livestock Density (LD)

0.005ns		 High-High 7 14.47

0.0601ns		 High-High 6 29.28
Low-Low 8 2.01 Low-Low 15 0.58
Low-High 3 15.98 Low-High 4 0.67
High-Low 1 17.83 High-Low 3 8.38
Not Significant 159 9.71 Not Significant 150 7.30

Significance levels: ns р-value >0.1; *р-value<0.05; **р-value<0.01; ***р-value<0.001

In addition, sprawling construction showed a high value of spatial autocorrelation, especially in the second period with about 0.293 of Moran’s I (Table 1), which High-High clusters affected by sprawling construction areas increased in the second period, as five of them did not show a significant relationship with this variable in the first period (Figure 5). Although the remaining drivers did not show a meaningful spatial autocorrelation on the entire rangelands, they were significant in a few specific RMPs. Livestock density showed strong spatial autocorrelation with degraded areas of rangelands in the second period with a value of 0.06, it recorded a lower value in comparison with the three previous variables in the first period; as the rates of degradation of High-High spatial clusters were almost half of the second period (Table 1). The least Moran’s value was recorded for orchards with a higher value of about 0.027 by only one location of High-High cluster in the second period. The last variable, water body, has also shown low spatial autocorrelation (I= 0.04) with degraded rangelands; nevertheless, this factor has emerged in the second period (Table 1).

3.4 Analysis of spatial regression techniques

The results of regression models show that the superiority of the SL and SE is about 1.41 and 1.17 times higher than the OLS, respectively, in 1986-2000. The estimates of driving forces for three spatial regression techniques are shown in Table 2. Dry-farming and irrigated farming show a strong influence in explaining the spatial degradation of rangeland rates at a level of 0.001 in all models. The other three variables did not show a significant spatial autocorrelation (Table 2). In contrast, the SE achieved higher performance than the other two models in explaining the influence of anthropogenic drivers on the degraded rangelands in 2000-2015. The model fit measures describe that the evidence ratio of the SE is 1.515 times higher than the OLS. Livestock density and dry-farming show significant spatial autocorrelation with all models in the second period (Table 3). Irrigated farming shows only significant spatial autocorrelation with the SE by a value of 1.99 at a level of 0.05. Also, the spatial autocorrelation coefficient, lambda, as an important component of the SE model, was entered in this model with a coefficient of 0.369 during the second period (p<0.05). Other independent drivers (construction areas, orchards and water bodies) did not show significant influence in degradation of rangelands during the second period (p>0.1) (Table 3).
Table 2 Regression analyses of spatial dependencies between degradation rates of rangelands with expansion rates of anthropogenic drivers and livestock density in 1986-2000
Ordinary Least Squares Spatial Lag Spatial Error
Model estimation Coefficient (β) t-
Statistic		 Standard Error Coefficient (β) z-Statistic Standard Error Coefficient (β) z-Statistic Standard Error
Constant (β0) 24.40ns 1.27 19.15 14.63ns 0.751 19.49 23.353ns 1.183 19.736
Livestock density (x1) -4.43ns -0.578 7.663 -6.266ns 7.599 -0.825 -3.579ns -0.468 7.642
Dry-farming (x2) 0.908*** 39.008 .0233 0.877*** 29.543 0.029 0.905*** 37.824 0.024
Irrigated farming (x3) 1.004*** 24.234 0.041 0.991*** 24.013 0.041 1.002*** 24.121 0.041
Orchards (x4) 0.695ns 0.598 1.162 0.606ns 0.534 1.135 0.633ns 0.553 1.144
Construction areas (x5) 1.917ns 0.837 2.291 2.083ns 0.931 2.236 2.092ns 0.923 2.266
Ƿwy - - - 0.0502* 1.611 0.031 - - -
Lambda (ʎwv) - - - 0.078ns 0.775 0.100
The model fit criteria Coefficient Coefficient Coefficient
AICc 2339.371 2338.643 2338.955
Log likelihood -1163.685 -1163.321 -1163.477
wi 0.2789 0.3943 0.3266
Ei**** 1.000 1.4139 1.1710

Significance levels: ns р-value >0.1; *р-value <0.05; **р-value <0.01; ***р-value <0.001;****Compared with OLS

Table 3 Regression analyses of spatial dependencies between degradation rates of rangelands with expansion rates of anthropogenic drivers and livestock density in 2000-2015
Ordinary Least Squares Spatial Lag Spatial Error
Model estimation Coefficient (β) t-
Statistic		 Standard Error Coefficient (β) z-
Statistic		 Standard Error Coefficient (β) z-
Statistic		 Standard Error
Constant (β0) -2.004ns -0.0811 24.692 -8.783ns -0.355 24.755 -20.536ns -0.625 32.868
Livestock density
(x1)		 8.474* 2.318 3.655 8.182* 2.262 3.617 12.236*** 3.445 3.552
Dry-farming (x2) 0.879*** 21.917 0.0401 0.853*** 18.298 0.047 0.878*** 20.836 0.042
Irrigated farming
(x3)		 -0.162ns -0.134 1.212 -0.213ns -0.176 1.202 1.997* 1.729 1.155
Orchards (x4) -12.993ns -0.415 31.285 -12.287ns -0.402 30.571 -2.805ns -0.094 29.799
Construction areas (x5) 5.283ns 0.259 20.373 6.011ns 0.302 19.908 5.674ns 0.295 19.225
Water bodies (x6) 0.656ns 0.935 0.702 0.640ns 0.933 0.687 -0.220ns -0.341 0.647
ρwy - - - 0.050 ns 0.977 0.051 - - -
Lambda (λwv) - - - - - - 0.369*** 4.304 0.086
The model
fit criteria		 Coefficient Coefficient Coefficient
AICc 2503.395 2502.560 2502.545
Log likelihood -1244.281 -1243.780 -1243.272
wi 0.3202 0.4833 0.4853
Ei**** 1.000 1.509 1.515

4 Discussion

The overall validities of the land-cover classification were greater than the minimum threshold, i.e., 85% (Anderson, 1976), and the observed agreements of sub-classes were greater than 70% (Thomlinson et al., 1999) as well. Furthermore, in this research, the Maximum Likelihood, Fuzzy Maximum Likelihood, and Random Forest classifiers performed reasonable accuracies, which are verified by prior scholars (Otukei et al., 2010; Churches et al., 2014; Li et al., 2014; Basnet and Vodacek, 2015). We carried out adequate training samples, which resulted in higher classification accuracy using the classifiers (Li et al., 2014).
The annual rangeland degradation rates were about 0.77% in 1986-2000 and 0.56% in 2000-2015. These findings are different from the results of Ansari et al. (2009b), which reported an annual change rate of 0.22% in Irano-Turanian rangelands from 1955 to 1995. The semi-arid climate and fertile soils of the northeast of Iran compared to the arid climate of Irano-Turanian rangelands have provided a suitable condition for farming, which resulted in conversion of rangelands to farmlands in this region (Tabari et al., 2014). Our results indicate that the expansion of agricultural land was about 1.16 times per decade in the first period, this finding is consistent with the report of DEI (2010); considering that the expansion rate of irrigated farming (≈1.64) is higher than dry-farming (≈1.05) during this period. During the second time, the expansion rate of farmlands slightly decreased to almost 0.81 per decade, meanwhile, the expansion rate of dry-farming is higher than irrigated farming.
The results of BiLISA indicate a high spatial association between rangeland degradation and farmland expansion whether dry-farming or irrigated farming in the northeast of Iran. Some other studies reported that agricultural land expansion is the main cause of land-cover changes in the world (White et al., 2000; Elias et al., 2015), especially in developing countries such as Iran, where converting rangelands to agricultural lands has been accelerating over the recent decades (Ansari et al., 2009a; Shahraki and Barani 2012; Jamshidi and Amini 2013; Kardavani 2015). Moreover, residential growth shows a significant correlation with rangeland degradation in the second period. It depicts that sprawling construction area is a serious threat to the degradation of northeastern Iran’s rangelands in the future, it was introduced as an influencing driver of land-cover changes in previous studies (Hoekstra et al., 2005; Hu et al., 2008) as well.
The spatial regression models showed higher performance than statistical regression model in quantifying driving forces influencing rangeland degradation due to considering spatial dependence for variables, which is addressed in several earlier research for modeling land use/land-cover changes (Lesschen et al., 2005; Chen et al., 2016). Without considering the model-fit parameters, there is no guarantee that spatial models are superior to each other. For instance, Zhang et al. (2015) showed that the SL and SE models have an irregular performance to each other based on the different selected scales. Similarly, our findings indicated that the model-fit parameters of explanatory variables are varying depending on the selected model in the studied periods (Tables 2 and 3). Whereas, Chen et al. (2016) argued that the performance of the SE is slightly higher than the SL. The numerical results of the spatial models indicate that the coefficients of the fitted model (SE) for dry-farming and irrigated farming are lower than livestock density in the rangelands (Table 3), it can be inferred that although the influence of agricultural expansion is remarkable but increasing livestock density is a serious threat. This result is consistent with the findings of some scholars (Ansari et al., 2009a), which have shown the influence of overgrazing is higher than agricultural land expansion in rangeland degradation.
The spatial association between the expansion of farmlands and land-cover changes has been well documented in previous studies (White et al., 2000; Elias et al., 2015), our study shows that rangelands with higher degradation rates are occupied by dry-farming or irrigated farming in both studied periods. Furthermore, there is an important concern that livestock density has emerged as one of the main significant drivers of degraded rangelands during 2000-2015, while it was not significant in 1986-2000. This finding is consistent with some studies that argue overgrazing is a main cause of degradation in California’s rangeland ecosystems in 1984-2008 (Cameron et al., 2014), Mongolian rangelands in 1997-2009 (Sheehy and Damiran, 2012), and Patagonian rangelands (Gaitán et al., 2017). Moreover, Iran’s livestock population has been approximately doubled during the past five decades from 1960 to 2015; as about 65% of them grazing in rangelands (Bobek, 1962). The implementation of forest conservation programs was a main cause of increasing livestock in the rangelands; some relocation efforts have been performed to out of livestock from forests by the government since 2000 in the northeast of Iran (PDOSPC, 2011), which about 56% of livestock and 11% of forest dwellers are transmigrated to outside of forest lands (http://www.frw.org.ir/). Consequently, this led to a massive migration of livestock holders from forests to rangelands and an increase in the livestock density in these areas as well. However, this issue needs more studies in the future. Likewise, converting rangelands to farmlands and overgrazing have been reported as two major driving forces of rangeland degradation from other regions of Iran (Khorshidi and Ansari, 2003; Shahraki and Barani, 2012; Jamshidi and Amini, 2013; Davudirad et al., 2016).

5 Conclusions

We found that above 19% of rangelands degraded in the northeast of Iran during the past three decades, with a high annual change rate (≈0.77%) in 1986-2000 and continued with a slight down rate (≈0.56%) in 2000-2015. Meanwhile, expanding anthropogenic activities was remarkable. The most expanded areas were farmlands, as dry-farming in 2015 was 3.2 times higher than its area in 1986, and this number was about 3.5 times for irrigated farming. Likewise, the livestock density was about 1.6 n.ha‒1 in the first period and it was doubled in the second period.
The statistical results of univariate local Moran’s I showed that there is a strong and positive autocorrelation among degraded rangelands. LISA cluster maps describe rangelands with High-High class are centered in the northwest of the study area, while about 76% of them between the first and second periods were the same. Specifically, we visualized the effects of significant anthropogenic drivers in changing of rangelands by bivariate local Moran’s I in each rangeland plan. The greatest Moran’s I was recorded for dry-farming with 16 and 22 locations in High-High class in 1986-2000 and 2000-2015, respectively. Irrigated farming was another induced factor that scored high values of Moran’s I with nine and seven High-High clusters, chronologically. The sprawling construction areas was the last factor that has shown a significant value of Moran’s I with seven High-High clusters in the second period. Furthermore, we found that the SL regression model performed better than the OLS and SE in modeling degradation rates of rangelands in 1986-2000 (wi= 0.3943, Ei=1.4139). Among all predictor variables, dry-farming by a coefficient of ≈0.88 and irrigated farming with a coefficient of ≈0.99 show the most significant influence in explanation of the spatial degradation rates of rangelands at the level of 0.001 (Table 2). Meanwhile, the SE regression model indicates stronger performance than the other two models in explaining driving forces influencing rangeland degradation in 2000-2015 (wi=0.4853, Ei=1.515), which has shown efficacious spatial dependence with livestock density (β=12.23), dry farming (β=0.87) and irrigated farming (β=1.99) during the second period (Table 3).
To conclude, the number of driving forces influencing rangeland degradation has been increased depending on the SE model and BiLISA in the second period. High degradation rates of rangelands show a strong autocorrelation with the increasing rates of dry-farming and irrigated farming based on the results of BiLISA and spatial regression methods in both periods in the RMPs. Meanwhile, the increasing construction areas and livestock density, as significant variables, are emerged in the second period. Evidence of maps and models indicates that there is a serious concern about intensifying the impacts of human drivers with ongoing conditions on the degradation of northeast Iran’s rangelands in the future.

The authors have declared that no competing interests exist.

References

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Churches C E, Wampler P J, Sun Wet al., 2014. Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data.International Journal of Applied Earth Observation and Geoinformation, 30: 203-216. doi: 10.1016/j.jag.2014.01.020.This study uses 2010 2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009, and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010鈥2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship (R2=0.996 for tree cover) between spatial resolution and land cover estimates.

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Cliff A D, Ord J K, 1973. Spatial Autocorrelation. London: Pion Limited.

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Cohen J, 1960. A coefficient of agreement for nominal scales.Educational and Psychological Measurement, 20: 37-46.A Coefficient of agreement for nominal Scales COHEN J. Educational and Psychological Measurement 20(1), 37-46, 1960

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Congalton R G, Green K, 2009. Assessing the Accuracy of Remotely Sensed Data:Principles and Practices. 2nd ed. Boca Raton: CRC Press, 200pp.

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Coppin P, Jonckheere I, Nackaerts Ket al., 2004. Review article digital change detection methods in ecosystem monitoring: A review.International Journal of Remote Sensing, 25: 1565-1596. doi: 10.1080/ 0143116031000101675.

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Cressie N A C, Wikle C K, 2011. Statistics for Spatio-Temporal Data. Hoboken, NJ: Wiley, 624pp.Berkeley Electronic Press Selected Works

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Daffertshofer A, Lamoth C J C, Meijer O Get al., 2004. PCA in studying coordination and variability: A tutorial.Clinical Biomechanics, 19: 415-428. doi: 10.1016/j.clinbiomech.2004.01.005.RelevancePrincipal component analysis can be successfully applied to movement data, both as feature extractor and as data-driven filter. Its potential for the (clinical) study of human movement sciences (e.g., diagnostics and evaluation of interventions) is evident but still largely untapped.

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[24]
Davudirad A K, Sadeghi S H, Sadoddin A, 2016. The impact of development plans on hydrological changes in the Shazand watershed, Iran.Land Degradation & Development, 27(4): 1236-1244. doi: 10.1002/ldr.2523.Development plans are mainly responsible for population changes and the conversion of forest and rangelands into agricultural lands and human settlements. Qualitative and quantitative analysis of population and land use changes are necessary to assess the impacts of change on hydrological processes. However, such important issues have been less considered worldwide particularly in developing countries. Therefore, we selected the Shazand Watershed (174065km2) because of rapid industrialization to track the effects of land use and population changes on streamflow and sediment yield. The data were collected from statistical yearbooks and satellite imageries from 1973 to 2008. All available measurements on discharge and suspended sediment concentration at the Pole doab hydrological station were also collected. The study was conducted for the whole period, as well as the pre-1991 and post-1991 as a basis for the economic development growth in the region. We found that the land use and population changes have occurred in the Shazand Watershed, especially in the vicinity of industrial zones. The results showed that the cities, industrial zones, roads, and bare lands quickly increased from 5865·658 to 13465·65365km2 during post-1991. The flow durations, sediment rating curves and trend analyses indicated distinct variations in the relationship between streamflow and sediment and also caused changes within different periods. Based on the results, the mean annual flow and sediment yield in post industrialization (1991–2008) were respectively 065·6584 and 165·6519 times of those for pre-industrialization period and the annual sediment yield increased from 25,000 to 29,85065Mg. Copyright 08 2016 John Wiley & Sons, Ltd.

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[25]
DEI, 2003. Iran’s Initial National Communication to UNFCCC. Department of Environment and United Nation Department Programme: Tehran, 206pp.

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DEI, 2010. Iran second national communication to UNFCCC. National Climate Change Office of Iran at the Department of Environment: Tehran, 205pp.

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Elias M, Hensel O, Richter Uet al., 2015. Land conversion dynamics in the Borana rangelands of Southern Ethiopia: an integrated assessment using remote sensing techniques and field survey data. Environments,7; 2(1):1-31. doi: 10.3390/environments2010001.

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Erener A, Düzgün H S, 2009. A methodology for land use change detection of high resolution pan images based on texture analysis.Italian Journal of Remote Sensing, 41: 47-59.

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Eskandari Z, Chavoshi S, 2002. Effects of livestock management on the erosion control: Case study of Isfahan Rangelands.Iranian Journal of Rang and Desert Research, 9: 943-957.

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Fortin M-J, Dale M R T, 2005. Spatial Analysis:A Guide for Ecologists. Cambridge: Cambridge University Press, 1-30.

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Gaitán J J, Bran D E, Geng Jet al., 2017. Aridity and overgrazing have convergent effects on ecosystem structure and functioning in Patagonian rangelands.Land Degradation & Development, 29(2): 210-218. doi: 10.1002/ldr.2694.Abstract Over 65% of drylands are used for grazing of managed livestock. Understanding what drives grazing effects on the structure and functioning of rangelands is critical for achieving their sustainability. We studied a network of 239 sites across Patagonian rangelands (Argentina), which constitute one of the world's largest rangeland area. We aimed to (i) evaluate how aridity and grazing affect ecosystem structure and functioning and (ii) test the usefulness of the landscape function analysis (LFA) indices (stability, infiltration and nutrient cycling) as surrogates of soil functioning. Aridity decreased species richness and the cover of palatable grasses but increased the cover of palatable shrubs. Grazing pressure negatively impacted the cover of palatable grasses and species richness but did not affect the cover of shrubs. Aridity had direct and indirect negative relationships with the LFA indices. Grazing pressure had no direct effects on the LFA indices but had an indirect negative effect on them by affecting vegetation structure. The LFA indices were positively and negatively correlated with soil organic carbon and sand contents, respectively, suggesting that these indices are useful proxies of soil functional processes in Patagonian rangelands. Our findings indicate that aridity and overgrazing have convergent effects on the structure and functioning of ecosystems, as both promoted reductions in species richness, the cover of palatable grasses and soil functioning. Rangeland management activities should aim to enhance species richness and the cover of palatable grasses, as these actions could contribute to offset adverse effects of ongoing increases in aridity on drylands.

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[32]
Gangkofner U G, Pradhan P S, Holcomb D W, 2008. Optimizing the high-pass filter addition technique for image fusion.Photogrammetric Engineering and Remote Sensing, 74(9): 1107-1118. doi: https://doi.org/10.14358/ PERS.73.9.1107.Pixel-level image fusion combines complementary image data, most commonly low spectral-high spatial resolution data with high spectral-low spatial resolution optical data. The presented study aims at refining and improving the High-Pass Filter Additive (HPFA) fusion method towards a tunable and versatile, yet standardized image fusion tool. HPFA is an image fusion method in the spatial domain, which inserts structural and textural details of the higher resolution image into the lower resolution image, whose spectral properties are thereby largely retained. Using various input image pairs, workable sets of HPFA parameters have been derived with regard to high-pass filter properties and injection weights. Improvements are the standardization of the HPFA parameters over a wide range of image resolution ratios and the controlled trade-off between resulting image sharpness and spectral properties. The results are evaluated visually and by spectral and spatial metrics in comparison with wavelet-based image fusion results as a benchmark.

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[33]
Giri C P, 2012. Remote Sensing of Land Use and Land Cover:Principles and Applications. Boca Raton: CRC Press, 477pp.

[34]
Guo L, Chehata N, Mallet Cet al., 2011. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests.ISPRS Journal of Photogrammetry and Remote Sensing, 66(1): 56-66. doi: 10.1016/j.isprsjprs. 2010.08.007.Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregularly distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm the relevance of input features for urban classification. The quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.

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[35]
Hansen M C, Loveland T R, 2012. A review of large area monitoring of land cover change using Landsat data.Remote Sensing of Environment, 122: 66-74. doi: 10.1016/j.rse.2011.08.024.Landsat data constitute the longest record of global-scale medium spatial resolution earth observation data. As a result, the current methods for large area monitoring of land cover change using medium spatial resolution imagery (10 50m) typically employ Landsat data. Most large area products quantify forest cover change. Forests are a comparatively easy cover type to map as well as a current focus of environmental monitoring concerning the global carbon cycle and biodiversity loss. Among existing change products, supervised or knowledge-based characterization methods predominate. Radiometric correction methods vary significantly, largely as a function of geographic/algorithmic scale. For instance, products created by mosaicking per scene characterizations do not require radiometric normalization. On the other hand, methods that employ a single index or classification model over an entire study area do require radiometric normalization. Temporal updating of cover change varies between existing products as a function of regional acquisition frequency, cloud cover and seasonality. With the Landsat archive opened for free access to terrain-corrected data, future product generation will be more data intensive. Per scene, interactive analyses will no longer be viable. Coupling free and open access to large data volumes with improved processing power will result in automated image pre-processing and land cover characterization methods. Such methods will need to leverage high-performance computing capabilities in advancing the land cover monitoring discipline. Robust validation efforts will be required to quantify product accuracies in determining the optimal change characterization methodologies.

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[36]
Hein L, De Ridder N, 2006. Desertification in the Sahel: A reinterpretation.Global Change Biology, 12: 751-758. doi: 10.1111/j.1365-2486.2006.01135.x.The impact of human management, in particular livestock grazing, on the vegetation cover of the Sahel is still debated. In a range of studies, satellite images have been used to analyze the development of the Sahelian vegetation cover over time. These studies did not reveal any significant degradation of the Sahel in the last two decades. In this paper, we examine the ecological assumptions underlying the use of satellite imagery to analyze degradation of the Sahel. Specifically, we analyze the variability of the rain-use efficiency (RUE), which is often used as an indicator for the state of the vegetation cover. We detect a fundamental flaw in the way RUE has been handled in most remote sensing studies; they ignored the relation between annual rainfall variation and RUE. Because of the upward trend in annual rainfall that occurred during the 1980s and 1990s, this leads to a bias in the interpretation of the satellite images. In this paper, we show the importance of the variability in RUE for the analysis of remote sensing imagery of semiarid rangelands. Our analysis also shows that it is likely that there has been anthropogenic degradation of the Sahelian vegetation cover in the last two decades. This has important consequences for the debate on the impacts of grazing on semiarid rangelands. Furthermore, the occurrence of anthropogenic degradation is relevant to explain the magnitude of 20th century Sahelian droughts. The analyses also indicate that the population of the Sahel may be more vulnerable for droughts than currently assumed.

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[37]
Hoekstra J M, Boucher T M, Ricketts T Het al., 2005. Confronting a biome crisis: Global disparities of habitat loss and protection.Ecology Letters, 8: 23-29. doi: 10.1111/j.1461-0248.2004.00686.x.Abstract Human impacts on the natural environment have reached such proportions that in addition to an ‘extinction crisis’, we now also face a broader ‘biome crisis’. Here we identify the world's terrestrial biomes and, at a finer spatial scale, ecoregions in which biodiversity and ecological function are at greatest risk because of extensive habitat conversion and limited habitat protection. Habitat conversion exceeds habitat protection by a ratio of 8:1 in temperate grasslands and Mediterranean biomes, and 10:1 in more than 140 ecoregions. These regions include some of the most biologically distinctive, species rich ecosystems on Earth, as well as the last home of many threatened and endangered species. Confronting the biome crisis requires a concerted and comprehensive response aimed at protecting not only species, but the variety of landscapes, ecological interactions, and evolutionary pressures that sustain biodiversity, generate ecosystem services, and evolve new species in the future.

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[38]
Hu D, Yang G, Wu Qet al., 2008. Analyzing land use changes in the Metropolitan Jilin City of Northeastern China using remote sensing and GIS.Sensors, 8: 5449-5465. doi: 10.3390/s8095449.Remote sensing and GIS have been widely employed to study temporal and spatial urban land use changes in southern and southeastern China. However, few studies have been conducted in northeastern regions. This study analyzed land use change and spatial patterns of urban expansion in the metropolitan area of Jilin City, located on the extension of Changbai Mountain, based on aerial photos from 1989 and 2005 Spot images. The results indicated that urban land and transportation land increased dramatically (by 94.04% and 211.20%, respectively); isolated industrial and mining land decreased moderately (by 29.54%); rural residential land increased moderately (by 26.48%); dry land and paddy fields increased slightly (by 15.68% and 11.78%, respectively); forest and orchards decreased slightly (by 5.27% and 4.61%, respectively); grasslands and unused land decreased dramatically (by 99.12% and 86.04%, respectively). Sloped dry land (more than 4 degrees) was mainly distributed on the land below 10 degrees with an east, southeastern and south sunny direction aspect, and most sloped dry land transformed to forest was located on an east aspect lower than 12 degrees, while forest changed to dry land were mainly distributed on east and south aspects lower than 10 degrees. A spatial dependency analysis of land use change showed that the increased urban land was a logarithmic function of distance to the Songhua River. This study also provided some data with spatial details about the uneven land development in the upstream areas of Songhua River basin.

DOI PMID

[39]
Huete A R, 1988. A soil-adjusted vegetation index (SAVI).Remote Sensing of Environment, 25: 295-309. doi: 10.1016/0034- 4257(88)90106-X.A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton ( Gossypium hirsutum L. var DPI-70) and range grass ( Eragrostics lehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple lobal that can describe dynamic soil-vegetation systems from remotely sensed data.

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[40]
Hurvich C, Tsai C L, 1989. Regression and time series model selection in small samples.Biometrika, 76: 297-307. doi: 10.1093/biomet/76.2.297.A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICCis found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.

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[41]
Hussain M, Chen D, Cheng Aet al., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches.ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106. doi: 10.1016/j.isprsjprs.2013.03.006.The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

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[42]
Jamshidi A R, Amini A M, 2013. Evaluation of factors affecting on natural resource degradation from the viewpoint of expert management of natural resources in Ilam province.Journal of Conservation and Utilization of Natural Resources, 1(4): 91-105. (in Persian)

[43]
Kalkhan M A, 2011. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping. CRC Pres, 184pp.Geospatial information modeling and mapping has become an important tool for the investigation and management of natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems (GIS), and GPS as well as their integration into landscape-scale geospatial statistical models and maps. The book explores how to extract information from remotely sensed imagery, GIS, and GPS, and how to combine this with field data—vegetation, soil, and environmental—to produce a spatial model that can be reconstructed and displayed using GIS software. Readers learn the requirements and limitations of each geospatial modeling and mapping tool. Case studies with real-life examples illustrate important applications of the models.Topics covered in this book include:An overview of the geospatial information sciences and technology and spatial statistics Sampling methods and applications, including probability sampling and nonrandom sampling, and issues to consider in sampling and plot design Fine and coarse scale variabilitySpatial sampling schemes and spatial patternLinear and spatial correlation statistics, including Moran’s I, Geary’s C, cross-correlation statistics, and inverse distance weightingGeospatial statistics analysis using stepwise regression, ordinary least squares (OLS), variogram, kriging, spatial auto-regression, binary classification trees, cokriging, and geospatial models for presence and absence data How to use R statistical software to work on statistical analyses and case studies, and to develop a geospatial statistical model The book includes practical examples and laboratory exercises using ArcInfo, ArcView, ArcGIS, and other popular software for geospatial modeling. It is accessible to readers from various fields, without requiring advanced knowledge of geospatial information sciences or quantitative methods.

[44]
Kardavani P, 2015. Pastures,Problems and Solutions in Iran. 7th ed. Tehran: University of Tehran Press (UTP), 540pp.

[45]
Khorshidi M, Ansari N, 2003. Understanding rural and nomadic communities’ knowledge of natural resources degradation and its affecting factors in the Bazoft Dehestan of the Chaharmahal and Bakhtiari province,Iranian Journal of Range and Desert Research, 10(1): 95-109. (in Persian)

[46]
Kiage L M, 2013. Perspectives on the assumed causes of land degradation in the rangelands of Sub-Saharan Africa.Progress in Physical Geography, 37(5): 664-684. doi: 10.1177/0309133313492543.Soil erosion and land degradation are serious problems in tropical Africa, especially Sub-Saharan Africa, where they are widely recognized as more serious problems than in non-tropical areas. Sub-Saharan Africa experiences deleterious levels of soil erosion, largely due to the interaction between harsh climates of high erosivity, fragile soils of high erodibility, steep slopes, and poor natural resource management. The fundamental challenge is to separate purely background-level soil erosion due to biophysical, geomorphic, topographic, and climatic conditions from what is caused by humans. This review shows that the human-induced causes of soil erosion and land degradation in Sub-Saharan Africa are not fully understood and some of the commonly listed causes may not always stand the test of critical scrutiny. The popular views of human-induced soil erosion and land degradation not only fail to take into consideration the fact that land degradation is primarily a physical process, but also they do injustice to adaptive ecosystem management by the local inhabitants. The review specifically questions the stereotypes of overpopulation, overgrazing, deforestation, overstocking, and general rangeland degradation due to human resource use in Sub-Saharan Africa. Empirical evidence suggests that biophysical factors including soil properties, climatic characteristics, topography, and vegetation can sometimes interact among themselves to yield high soil erosion and degradation rates independent of anthropogenic impacts.

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[47]
Lesschen J P, Verburg P H, 2005. Statistical methods for analysing the spatial dimension of changes in land use and farming systems. LUCC Report Series No.7, LUCC Focus 3 Office and ILRI, 78pp.This Report provides an overview of empirical methods that are frequently used for the analysis of spatial patterns of LUCC based on a survey of recent literature. Because these methods are relevant for wider analysis of system change beyond LUCC (Land Use and Land Cover Change), some examples of analysis of livestock systems are included. The descriptions are not detailed, with the emphasis being instead on explaining the concepts in simple terms, with the aid of illustrations of these methods in land use research. The references refer to detailed descriptions, applications or textbooks. The methods discussed in this Report aim at uses of different types of spatially differentiated data at different scales, including both household-and pixel-level analysis. Furthermore, a number of issues important to spatial analysis of land use and farming system change are discussed, including data representation, spatial autocorrelation and validation issues. This information is believed to facilitate the application of these methods in LUCC and other studies and provide an overview of the possibilities and limitations of empirical methods to unravel the complexity of spatial variation in land use and farming system change.

[48]
Li C, Wang J, Wang Let al., 2014. Comparison of classification algorithms and training sample sizes in urban land classification with Landsat Thematic Mapper Imagery.Remote Sensing, 6(2): 964-983. doi: 10.3390/ rs6020964.Although a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative) training samples than others. Many algorithms improved the overall accuracy marginally with per-segment decision making.

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[49]
Li X L, Gao J, Brierley Get al., 2013. Rangeland degradation on the Qinghai-Tibet Plateau: Implications for rehabilitation.Land Degradation & Development, 24: 72-80. doi: 10.1002/ldr.1108.ABSTRACTWith ever intensifying land use, land degradation is becoming an increasingly important issue around the world, especially in China. This paper evaluates the extent and underlying causes of rangeland degradation on the Qinghai-Tibet Plateau in China through a comprehensive review of the literature. Diverse forms and differing intensities of rangeland degradation have been reported in several regions of the Plateau. Rangeland degradation is particularly severe in South Qinghai, North Tibet and the Qaidam Basin. Anthropogenic activities, especially changing land use practices, are identified as the primary force driving rangeland degradation. Dissimilar to climate change-induced degradation, such anthropogenic degradation is a rather short-term process altering the abiotic properties of the underlying soil. On the basis of these findings, we assessed the prospects of rehabilitating degraded rangeland to productive uses. Different measures are proposed to rehabilitate rangelands that have been degraded by different mechanisms. Reduction in grazing intensity is prescribed to rehabilitate reversibly degraded rangelands. Targeted human intervention in the forms of selective planting of grasses and artificial seeding, in conjunction with ecological and biological control of the plateau rodent population, is recommended to rehabilitate rreversibly degraded rangelands. Our studies suggest it is very difficult or even impossible to rehabilitate new assemblage of species which appear as a result of climate change. Copyright 2011 John Wiley & Sons, Ltd.

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[50]
Lloyd C D, 2010. Local Models for Spatial Analysis. 2nd ed. Boca Raton: CRC Press, 352pp.

[51]
Lu D, Mausel P, Brondízio Eet al., 2004. Change detection techniques.International Journal of Remote Sensing, 25(12): 2365-2401. doi: 10.1080/0143116031000139863.

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[52]
Lu D, Weng Q, Moran E et al., 2011. Remote Sensing Image Classification. Boca Raton: CRC Press, 219-240.

[53]
Mahyou H, Tychon B, Balaghi Ret al., 2016. A knowledge-based approach for mapping land degradation in the arid rangelands of north Africa.Land Degradation & Development, 27: 1574-1585. doi: 10.1002/ldr.2470.Abstract Rangelands cover about 82% of the arid area of Morocco. It is generally acknowledged that these areas are threatened by desertification. Monitoring desertification requires accurate knowledge about the current status of rangeland degradation. Remote sensing is widely used to assess changes in land cover, but its use in arid rangelands has limitations because of spectral confusion among various types of land cover. The objective of this study was to assess the severity and spatial extent of rangeland degradation in the high plateaus of eastern Morocco, using a knowledge-based approach combining remote sensing and ancillary data. This approach relies on analyzing datasets derived from Landsat TM satellite imagery, lithology, bioclimatic data and field measurements. The level of rangeland degradation was assessed using indicators such as vegetation parameters, grazing levels and cultivation intensity, which provided a high level of accuracy for mapping and monitoring the degradation of the arid rangelands. The results showed that the total area of degraded rangeland in the high plateaus of eastern Morocco is about 17,417 m2, accounting for 48% in the studied area. Copyright 2015 John Wiley & Sons, Ltd.

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[54]
Miehe S, Kluge J, von Wehrden Het al., 2010. Long-term degradation of Sahelian rangeland detected by 27 years of field study in Senegal.Journal of Applied Ecology, 47: 692-700. doi: 10.1111/j.1365-2664.2010.01815.x.1. Sustainable management of rangelands will become increasingly important as the climate changes, yet rangeland dynamics are still a challenge to dryland ecologists because degradation patterns are difficult to sample and interpret. There are contradictions between remote sensing-based studies and field-based analyses, for which long-term data are almost non-existent. In the rangelands of North Senegal, remote sensing studies have not revealed any extensive degradation during the past three decades. The present study used a 27-year series of field data from the area to assess the impact of grazing on rangeland degradability. 2. Rainfall, standing crop and floristic data from North Senegal were analysed to quantify the effects of rainfall patterns and grazing on plant composition and the overall rain use efficiency. Monitoring plots of 1 ha comprised five ungrazed and 19 grazed plots with two different grazing treatments. Standing crop was sampled annually at the peak of biomass development. Data were analysed with mixed effect models. 3. Changes in herbaceous production were mainly caused by fluctuations in rainfall, whereas the grazing intensity had a long-term effect, interacting with precipitation dynamics. During the first and drier phase, the rainfall variability masked the grazing influence, whereas during the second phase with above-average rainfall, grazing treatments differed significantly, indicating rangeland degradation. 4. The patterns of productivity and floristic composition followed predominant non-equilibrium dynamics during the first phase (rainfall variability 40%), whereas gradual changes especially in species composition represented characteristics of equilibrium systems during the second phase (rainfall variability 23%). Thus, the study supports the existence of shifts between periods of nonequilibrium conditions and those more typical of equilibrium systems. 5.Synthesis and applications. Our 27 years field study, carried out with the aim of assessing the non-degradability of Sahelian rangelands, revealed long-term degradation trends linked to grazing intensity. Longer observation periods provide an increasing probability of including 'equilibrium phases' that allow the identification of long-term degradation processes. Consequently, both rangeland research and management policies demand monitoring periods that are long enough to account for long-term trends. The grazing experiment in this study has shown that degradation processes are reversible, but long-term exclosure and ranching with fixed stocking rates are less suitable for rangeland amelioration than moderate, production-adjusted grazing regimes mimicking traditional nomadic systems.

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[55]
Otukei J R, Blaschke T, 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms.International Journal of Applied Earth Observation and Geoinformation, 12S: S27-S31. doi: 10.1016/j.jag.2009.11.002.Land cover change assessment is one of the main applications of remote sensed data. A number of pixel based classification algorithms have been developed over the past years for the analysis of remotely sensed data. The most notable include the maximum likelihood classifier (MLC), support vector machines (SVMs) and the decision trees (DTs). The DTs in particular offer advantages not provided by other approaches. They are computationally fast and make no statistical assumptions regarding the distribution of data. The challenge to using DTs lies in the determination of the “best” tree structure and the decision boundaries. Recent developments in the field of data mining have however, provided an alternative for overcoming the above shortcomings. In this study, we analysed the potential of DTs as one technique for data mining for the analysis of the 1986 and 2001 Landsat TM and ETM+ datasets, respectively. The results were compared with those obtained using SVMs, and MLC. Overall, acceptable accuracies of over 85% were obtained in all the cases. In general, the DTs performed better than both MLC and SVMs.

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[56]
Overmars K P, de Koning G H J, Veldkamp A, 2003. Spatial autocorrelation in multi-scale land use models.Ecological Modelling, 164: 257-270. doi: 10.1016/S0304-3800(03)00070-X.In several land use models statistical methods are being used to analyse spatial data. Land use drivers that best describe land use patterns quantitatively are often selected through (logistic) regression analysis. A problem using conventional statistical methods, like (logistic) regression, in spatial land use analysis is that these methods assume the data to be statistically independent. But, spatial land use data have the tendency to be dependent, a phenomenon known as spatial autocorrelation. Values over distance are more similar or less similar than expected for randomly associated pairs of observations. In this paper correlograms of the Moran I are used to describe spatial autocorrelation for a data set of Ecuador. Positive spatial autocorrelation was detected in both dependent and independent variables, and it is shown that the occurrence of spatial autocorrelation is highly dependent on the aggregation level. The residuals of the original regression model also show positive autocorrelation, which indicates that the standard multiple linear regression model cannot capture all spatial dependency in the land use data. To overcome this, mixed regressive patial autoregressive models, which incorporate both regression and spatial autocorrelation, were constructed. These models yield residuals without spatial autocorrelation and have a better goodness-of-fit. The mixed regressive patial autoregressive model is statistically sound in the presence of spatially dependent data, in contrast with the standard linear model which is not. By using spatial models a part of the variance is explained by neighbouring values. This is a way to incorporate spatial interactions that cannot be captured by the independent variables. These interactions are caused by unknown spatial processes such as social relations and market effects.

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[57]
Oyana T J, Margai F, 2015. Spatial Analysis:Statistics, Visualization, and Computational Methods. Boca Raton: CRC Press, 323pp.

[58]
Rao C R, 2002. Linear Statistical Inference and Its Applications. 2nd ed. New York: Wily, 656pp.Rao, Radhakrishna C

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[59]
Richter R, 2001. Atmospheric and Topographic Correction: Model ATCOR3, DLR-IB 564-03/00, Wessling, Germany: DLR-German Aerospace Center.

[60]
Röder A, Udelhoven T, Hill Jet al., 2008. Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece.Remote Sensing of Environment, 112: 2863-2875. doi: 10.1016/j.rse.2008.01.018.Mediterranean rangelands are unique marginal ecosystems, which are characterized by a highly heterogeneous structure and are often interwoven with other ecosystems. Traditionally, rangelands provided resources for livestock grazing in transhumantic rotation schemes. In recent times, there has been a trend towards semi-intensive grazing systems, which is partly connected to the European system of agricultural and infrastructural subsidies, and which effectuates both intensification and extensification. This study employed trend analysis of a remote sensing data time series for a retrospective assessment of rangeland processes, and interpreted these in the light of land-use practices and previous management interventions. We have selected a test area in Northern Greece that is representative of typical land-use transitions of the European Mediterranean. A time series of Landsat TM and ETM+ data covering the years 1984鈥2000 with one image per year was acquired, and for all images a geometric correction including digital elevation information and full radiative transfer modelling were carried out to attain surface reflectance data. For further analyses, proportional vegetation cover was selected as the target indicator, which was derived using Spectral Mixture Analysis. The resulting data set was used in a linear trend analysis to characterize spatio-temporal patterns of vegetation cover development. These could be interpreted based on knowledge of the local grazing regime and factors driving it, as well as using auxiliary spatial data sets. Results showed that temporal trends in the test area reflect the underlying pattern of potential livestock distribution at the per-pixel level, with a spatially differentiated pattern of both positive and negative trends in close proximity. On the other hand, no direct relation could be established between the development of vegetation cover and animal stocking rates at the community level. This suggests that this aggregation level is too coarse given the combination of highly heterogeneous landscapes with semi-intensive to intensive land tenure systems.

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[61]
Serneels S, Lambin E F, 2001. Proximate causes of land-use change in Narok District, Kenya: A spatial statistical model.Agriculture, Ecosystems and Environment, 85: 65-81. doi: 10.1016/S0167-8809(01)00188-8.This study attempts to identify how much understanding of the driving forces of land-use changes can be gained through a spatial, statistical analysis. Hereto, spatial, statistical models of the proximate causes of different processes of land-use change in the Mara Ecosystem (Kenya) were developed, taking into account the spatial variability of the land-use change processes. The descriptive spatial models developed here suggest some important factors driving the land-use changes that can be related to some well-established theoretical frameworks. The explanatory variables of the spatial model of mechanised agriculture suggest a von Th nen-like model, where conversion to agriculture is controlled by the distance to the market, as a proxy for transportation costs, and agro-climatic potential. Expansion of smallholder agriculture and settlements is also controlled by land rent, defined, in this case, by proximity to permanent water, land suitability, location near a tourism market, and vicinity to villages to gain access to social services (e.g. health clinics, schools, local markets). This difference in perception of land rent reflects the widely different social and economic activities and objectives of smallholders versus the large entrepreneurs involved in mechanised farming. Spatial heterogeneity as well as the variability in time of land-use change processes affect our ability to use regression models for wide ranging extrapolations. The models allow evaluating the impact of changes in driving forces that are well represented by proximate causes of land-use change.

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[62]
Shahraki M R, Barani H, 2012. Examining factors on the destruction of Golestan province rangelands.Journal of Conservation and Utilization of Natural Resources, 1(3): 59-78. (in Persian)

[63]
Sheehy D P, Damiran D, 2012. Assessment of Mongolian rangeland condition and trend (1997-2009). Final Report for the World Bank and the Netherlands-Mongolia Trust Fund for Environmental Reform (NEMO), 0-47.

[64]
Tabari H, AghaKouchak A, Willems P, 2014. A perturbation approach for assessing trends in precipitation extremes across Iran.Journal of Hydrology, 519: 1420-1427. doi: 10.1016/j.jhydrol.2014.09.019.Extreme precipitation events have attracted a great deal of attention among the scientific community because of their devastating consequences on human livelihood and socio-economic development. To assess changes in precipitation extremes in a given region, it is essential to analyze decadal oscillations in precipitation extremes. This study examines temporal oscillations in precipitation data in several sub-regions of Iran using a novel quantile perturbation method during 1980–2010. Precipitation data from NASA’s Modern-Era Retrospective Analysis for Research and Applications-Land (MERRA-Land) are used in this study. The results indicate significant anomalies in precipitation extremes in the northwest and southeast regions of Iran. Analysis of extreme precipitation perturbations reveals that perturbations for the monthly aggregation level are generally lower than the annual perturbations. Furthermore, high-oscillation and low-oscillation periods are found in extreme precipitation quantiles across different seasons. In all selected regions, a significant anomaly (i.e., extreme wet/dry conditions) in precipitation extremes is observed during spring.

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[65]
The Presidential Deputy Office of Strategic Planning and Control(PDOSPC), 2011. Law for the Fifth Development Plan of the Islamic Republic of Iran (IR038). Retrieved from .

[66]
Thomlinson J R, Bolstad P V, Cohen W B, 1999. Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products.Remote Sensing of Environment, 70: 16-28. doi: 10.1016/S0034-4257(99)00055-3.

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[67]
Turner B L, 2002. Toward integrated land-change science: Advances in 1.5 decades of sustained international research on land-use and land-cover change. In: Challenges of a Changing Earth. Berlin, Heidelberg: Springer, 21-26.

[68]
Ward M D, Gleditsch K S, 2008. Spatial Regression Models (Vol. 155). Thousand Oaks, CA: Sage, 112.

[69]
White R P, Wanasselt W, 2000. Grasslands in Pieces: Modification and Conversion Take a Toll. World Resource Institute: Washington,DC, USA, 1-4.

[70]
Wilber D N, 2014. Iran,Past and Present:From Monarchy to Islamic Republic (Vol. 529).Princeton University Press, 398pp.This Ninth Edition of the standard work on Iran includes up-to-date statistics and current information on the country. It begins with an account of the history, arts, languages, and religions of Iran from 4000 B.C. to the present. Originally published in 1982. ThePrinceton Legacy Libraryuses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

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[71]
Yuan F, Sawaya K E, Loeffelholz B Cet al., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing.Remote sensing of Environment, 98: 317-328. doi: 10.1016/j.rse.2005.08.006.The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing metropolitan areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) data in the seven-county Twin Cities Metropolitan Area of Minnesota for 1986, 1991, 1998, and 2002. The overall seven-class classification accuracies averaged 94% for the four years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 80% to 90%. The maps showed that between 1986 and 2002 the amount of urban or developed land increased from 23.7% to 32.8% of the total area, while rural cover types of agriculture, forest and wetland decreased from 69.6% to 60.5%. The results quantify the land cover change patterns in the metropolitan area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions.

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[72]
Zhang R, Du Q, Geng Jet al., 2015. An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study.Habitat International, 46: 196-205. doi: 10.1016/j.habitatint.2014.12.001.61This paper formulates a spatial error model to implement mass appraisal of commercial properties.61Fuzzy mathematics, econometrics and GIS are introduced into the proposed method.61Different results are compared between the spatial error model and traditional method (linear regression).61The proposed method has a better performance than traditional mass appraisal method.61The advantage allows us to increase the usages of our technique for property tax reform.

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[73]
Zhou Y, Wang Y, Gold A Jet al., 2014. Assessing impact of urban impervious surface on watershed hydrology using distributed object-oriented simulation and spatial regression.GeoJournal, 79: 155-166. doi: 10.1007/ s10708-013-9506-x.In this study, we investigated the relationship between watershed characteristics and hydrology using high spatial resolution impervious surface area (ISA), hydrologic simulations and spatial regression. We selected 20 watersheds at HUC 12 level with different degrees of urbanization and performed hydrologic simulation using a distributed object-oriented rainfall and runoff simulation model. We extracted the discharge per area and ratio of runoff to base flow from simulation results and used them as indicators of hydrology pattern. We derived percentage of ISA, distance from ISA to streams, and stream density as the watershed characteristics to evaluate the relationship with hydrology pattern in watersheds using ordinary least square, spatial error and spatial lag regression models. The comparison indicates that spatial lag regression model can achieve better performance for the evaluation of relationship between ratio of runoff to base flow and watershed characteristics, and that three models provide similar performance for the evaluation of relationship between discharge per area and watershed characteristics. The results from regression analyses demonstrate that ISA plays an important role in watershed hydrology. Ignorance of spatial dependence in analyses will likely cause inaccurate evaluation for relationship between ISA and watershed hydrology. The hydrologic model, regression methods and relationships between watershed characteristics and hydrology pattern provide important tools and information for decision makers to evaluate the effect of different scenarios in land management.

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