نشریه مهندسی اکوسیستم بیابان
پیاپی 17 (زمستان 1396)

Introduction Vegetation cover is an important indicator in the arid and semi-arid areas and plays an important role in balancing the ecosystems. Monitoring vegetation cover changes is of great importance because of vegetation effects on the environment. This monitoring provides detailed quantitative and qualitative information. It is therefore important to monitor dynamic changes in vegetation and to investigate the factors that are driving these changes in order to guide regional environmental management. Changes in vegetation could be tracked by satellite time-series data and trend analysis. Analysis of vegetation time series improves considerably our understanding of vegetation annual changes. Touran Biosphere Reserve accommodates three areas of Wildlife Refuge, National Park, and Protected Area. This reserve is the second largest biosphere reserve in the world. In terms of international categories, Touran is a special habitat of steppe grasslands in Central Asia and Desert-Saharas peculiar to West Asia. The aim of this research is to evaluate vegetation trend in Touran Protected Area using MODIS vegetation time series data from 2001 to 2015.
Materials and methods The 16-day composite MODIS VI product with 250m spatial resolution from Terra (MOD13Q1) was downloaded from the Land Processes Distributed Active Archive Centre (LP DAAC) for the Touran protected the area and the years from 2001 to 2015. The MOD13Q1 product includes normalized difference vegetation index (NDVI), Enhanced Vegetation Index (EVI), infrared band, red band, and quality assessment (QA) information. Before the time series analysis, different vegetation indices (NDVI, EVI, TSAVI1, and PVI1) were created. Then, the vegetation indices were aggregated to monthly composites by applying the arithmetic max of each month and standardized anomalies (Z score) of the monthly vegetation indices data were calculated to remove the seasonality. Several studies using the more robust Theil-Sen trend analysis and Mann-Kendall tests were conducted to explore the trend analysis of vegetation using NDVI, EVI, TSAVI1, and PVI1. A field survey was conducted to select a suitable vegetation index.
ResultAccuracy assessment results showed the Kappa coefficient for the PVI1, TSAVI1, EVI, and NDVI indices to be 0.78971, 0.5674, 0.5288, and 0.4159, respectively. Field survey results showed that PVI1 is the best index in the study area. According to the results of the Theil-Sen slope analysis and the Mann-Kendall test, the trend was divided into 3 levels. Vegetation with Theil-Sen slope 0, but with Z values ranging from -1.96 to 1.96 has no trend, and vegetation with Theil-Sen slope > 0 and Z > 1.96 has a significantly increasing trend. Results related to inter-annual trends show that vegetation degradation or decreasing trend occur in the northeast, east, and southeast, as well as the west and southwest region. Vegetation cover improves in central and southern of Touran protected area. Areas without a trend or stable regions are dispersed in the entire region. Vegetation cover improves in central and southern of Touran protected area. Areas without trend or stable regions are dispersed in the entire region. Results indicated that vegetation cover declines in 30% of Touran protected area, vegetation cover increases in 26% of study area but in 44% of Touran protected area no trend in vegetation cover was detected.
Discussion and Conclusion Combining methods including the coefficient of variation, the Theil-Sen median trend analysis, and the Mann-Kendall test provide an effective way to investigate the characteristics of variations in vegetation. According to experts in this area, the overgrazing on rangelands of this area is one of the dangers that threaten the biosphere reserve. Overall, this study showed that the creation of distant-base vegetation indexes from the MODIS Time Series product could improve the evaluation of the long-term trend on a local scale. The method of this paper provides useful information for identifying vegetation changes in arid and semi-arid areas. For future studies, it is suggested studying the changes in vegetation trends in arid ecosystems in order to evaluate remote-sensing time-series images.

Assessment of land use spatiotemporal changes provide valuable data for managers to elaborate plans. Land use change modeling is one of the methods used by planers to manage land use changes. Detection of such changes may help decision makers and planners to understand the factors in land use and land cover changes in order to take effective and useful measures. Remote sensing (RS) and geographic information system (GIS) techniques are among the effective tools to detect and assess land use changes. Land cover mapping and change detection have increasingly been recognized as one of the most effective tools for environmental resource management. The latter is recently one of the most widely used techniques to predict land use through the variation of this model. The Markov prediction methods can serve to analyze the dynamic behavior of land use in a time-space pattern to provide forecasts of future changes that can help in making decisions. The present study aims to predict land use changes using Markov chain model in Samal watershed in Bushehr province.
Methods The study area is located in the southwestern of Iran, in the Bushehr Province, with a surface area of 29,750 ha. Geographically, it is located between longitudes 51°7′ to 51 25′E and latitudes 28°59′ and 29°10.5′ N. Land use maps of the study area were prepared from Landsat images (L5-TM-1992, L7-ETM+2002 and -L8-OLI-2013). Firstly, the pre-processing and the necessary processing such as geometric and atmospheric correction, as well as the vegetation index were made. NDVI and principal component analysis were used to separate the green cover and barren land, respectively. The classification accuracy can be assessed by an error matrix. Many measurements such as Kappa coefficient have been proposed to improve the interpretation of the error matrix. In the region, three major uses including grassland, bare land, and agriculture lands were identified. In this study, to classify the supervised classification, maximum likelihood method and to determine the comparative method of classification changes used. Each of the land use and land cover map was compared to the reference data to assess the accuracy of the classification. The reference data were prepared by considering random sample points, the field knowledge, and Google earth data. The ground truth dataset was obtained and used to verify the classification accuracy. In order to predict land use for 2023, a Markov Chains and Cellular Automata (CA), which are based on probabilistic modeling techniques, were employed. The combination of Markov and Cellular Automata (CA_Markov) allows simulating the evolution of the geographical area represented by pixels. Each pixel can take a value from a finite set of states. In this research, we use the 1992 and 2002 land cover maps to predict the 2013 land cover map and then use the 2002 and 2013 land cover maps to predict the 2001 land cover map.
Results The results showed that TM, ETM, and OLI images were classified respectively with 84.45, 99, and 98% accuracy and 0.82, 0.98, 0.97 kappa coefficients. The analysis of the change dynamics was assessed based on the results of calibration periods using kappa coefficients showed that the period 2002-2013 has the highest accuracy to predict 2023 land use map. The results of the land use changes showed that over the period 2002-2013, the decrease rate in grassland and agriculture land was 2.19 and 4.18%, respectively. Also during this period, bare land increased 56.13%. Overall, the results showed that rangeland has the most stability and non-vegetated lands have the least stability. Changes in the extent of bare land of the study area were further projected until 2028, indicating that the area of bare land could be continuously reduced.
Discussion and ConclusionThis study employs three time-period changes to better account for the trend and the modeling exercise. The Markov chain analysis describes the change of one land cover to another and predicts its trend. Modeling of land cover change plays a major role to understand the impacts of the changes. The results show that the RS and GIS technology is an effective approach in the analysis of land use change modeling with Markov. Finally, using Markov chain analysis land cover area statistics were predicted for the year 2020. This analysis would help to have an aggregate view of the future setting of Samal Watershed use to guide the policymakers.

Introduction Today, a significant portion of the water consumption in Iran, especially in the drinking sector, is provided by water resources. Exploitation of groundwater resources requires knowledge of the quantitative and qualitative status of aquifers. By determining the chemical quality of groundwater, an estimate of the health status of these water resources can be obtained and, depending on its state, the type of use is determined. In this regard, direct and indirect methods can be used to understand the qualitative characteristics of water. Direct methods, despite their high precision, require a high size of observational data, involves substantial time and cost. Hence, numerous indirect methods have been developed for simulating natural systems and estimating their parameters using a computer based on complex calculations. The main advantage of these methods is the ability to learn time series and prediction. One of these methods is modeling or hydrological simulation. The modeling of groundwater quality is an important tool for planning and decision-making in the management of water resources. The goal of this research is to identify the ability of intelligent model of Support Vector Machines (SVM), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for modeling groundwater quality variables (EC, SAR, TDS, and TH) in Gero plain and zoning these variables. Therefore, it can provide an appropriate management tool for controlling quality parameters for drinking and farming.
Material and methods In this study, data from 14 wells over the 2008-2016 period was used in order to model the variations in quality variables of Gero plain groundwater. The observed values for Na, Mg, Ca, SO4, Cl, and HCO3 are considered as independent variables and values of EC, SAR, TDS, and TH are considered as dependent variables. An SVM, an ANN, and an ANFIS design were used to model groundwater quality. Input data are randomly divided into two sets such that 80% of data are assigned to the training set and the remaining data (20%) form the test set.
Results Results showed that the ANFIS system had the best performance in the estimation of EC (R2 = 0.99, RMSE=109.13, CE=0.99), SAR (R2 = 0.98, RMSE=0.28, CE=0.98), and TH (R2 = 0.99, RMSE=0.49, CE=0.99) among considered methods for the modeling of groundwater quality. Results also indicated that the ANN had the best performance in estimating TDS (R2 = 0.99, RMSE=109.13, CE=0.99). Furthermore, Schoeller and Wilcox water quality classifications, for drinking and agricultural water, were respectively employed to perform groundwater quality zoning based on outcomes of the considered methods. According to Schoeller classification, TDS has three classes: inappropriate (21.1%), bad (74.59%), and non-dirking (4.31%) and TH variable has four class: good (0.84%), acceptable (23.48%), inappropriate (67.55%), and bad (8.16%). According to Wilcox classification, EC has three classes: excellent (9.41%), good (89.79%), middle (0.8%) and SAR has two classes: excellent (19%) and good (81%).
Discussion and Conclusion ANFIS for a better estimation of EC, SAR, and TH variables outperforms two models of ANN and SVM. The ANFIS system, using the if-then rules, describes that these rules are implemented in a network structure that can be used for learning algorithms used in ANN. Due to this structure, the fuzzy-comparative neural network model has more transparency for analysis and interpretation. The zoning of qualitative variables (TDS and TH) based on the classification of Schoeller drinking water showed that in the TDS variable, the groundwater quality has three classes: bad, inappropriate, and non-drinkable, with the most inadequate plain, southeastern plain bad status and the west of the plain has a terrible situation. The TH zoning map presents that the plain is in good, acceptable, inappropriate, and bad classes. The most part of the plain is in the inappropriate class, the west of the plain in the bad class, and the southeast plain is in an acceptable class. The results of zoning the variables EC and SAR based on the Wilcox agricultural classification showed that groundwater quality is acceptable four agricultural purposes. Therefore, it is essential to take measures to improve the quality of drinking water in the region.

Introduction Water erosion is one of the most important factors in land degradation in large parts of Iran destroying fertile soils and agricultural land. The impact of soil erosion and related sediments decreases significantly water quality and reservoir capacity. Especially in semiarid areas like in the Menderjan watershed in the west Isfahan province sheet and rill erosion contributes to the sediment dynamics in a significant way. Particularly, sheet and rill erosion processes and related forms and features are very common in this region. Hence, this study is aimed at identifying and quantifying the major erosion process dynamics. Therefore, we applied an integrated approach combining the USPED and RUSLE models with data mining, remote sensing, and GIS methods.
Materials and methods The study area is Menderjan watershed locating at east Isfahan and has an area of about 21,100 hectares. In this study, the USPED and RUSLE was used. The USPED model is based on the assumption that soil erosion depends on the detachment capacity and the sediment transport capacity of surface runoff. However, the USPED models do not consider the sediment yields from gullies, stream banks, and stream bed erosion. In the USPED model erosion and deposition (ED) are computed as the change in sediment flow in the direction of flow.
ED=d×(T×cosa)/ dx d×(T×sina) /dy
Where a is the aspect of the terrain surface, dx, dy is the grid resolution, and T is the sediment flow at transport capacity. ED can be positive, indicating soil deposition, or negative, indicating soil erosion. Transport capacity (A) is expressed as: A=R×K×C×P×L m×(sin S) n
Where A: Average yearly soil erosion (t ha-1 y-1), R: yearly rainfall erosivity factor (MJ mm ha-1 year-1h-1), K: the soil edibility factor, LS: Slope Length and Steepness factor, C: cover management factor and P: Support Practice Factor, S is the slope, L is the upslope contributing area, and m and n are constants. For prevailing rill erosion m = 1.6, n = 1.3, while for prevailing sheet erosion, m = n = 1. The USPED and RUSLE model was applied using Arcmap10. In this study, the Landsat satellite images 8 (OLI) and rainfall data, soil properties and digital elevation model (DEM) were used, and GIS plays a major role in preparing thematic layers and estimating soil erosion.
Result The range of obtained R factor values range was from 82 to 118 MJ mm/ ha h year. The average values for the Menderjan watershed amount to 265.2 MJ mm/ ha year-1. According to the soil laboratory analysis soil texture is dominated by silt clay loam and clay loam and thus is highly susceptible to soil erosion. The amount of organic matter in all samples was 2 %. Soil organic matter reduces the erodibility of soil. In many arid and semiarid areas soil organic matter is low due to scarce vegetation, and hence, soil is more susceptible to erosion. The annual average soil erodibility of this basin 0.04 (t h MJ-1 mm-1). The amount of topography for RUSLE varies from 0.001 to 16.7 and USPED varies 0.01 to 30 Support Practice Factor of the study area is variable from 0.1 to 1 and C factor value varies was 0.2 to 0.5. According to result more than 35 % of the area is affected by high to very high erosion and deposition process intensities. The stable areas and low erosion and deposition zones cover about 15 % of the area. However, some of the mapped and predicted sheet and rill processes are located in the stable and low intensity soil erosion classes. The extreme values are characterized by steep slopes in ridge positions in the northern and southern parts of the watershed.
Discussion and Conclusions During recent years, the role of water erosion as one of the land degradation factors in arid and semi-arid areas of large parts of Iran has increased. In our study we applied a combined approach using the RUSLE and USPED models rill/inter-rill (sheet) erosion processes, and deposition processes. To the knowledge of the authors, this is the first attempt integrating different erosion processes and deposition dynamics in Iran. In the study area soil loss is concentrated especially in the abandoned bare land areas. The protection of bare soil to reduce soil loss should be ensured by appropriate cultivations. According to the results a large part of severe erosion occurs in the steep areas in the north and southwest of the study area. Agricultural cultivations may change the land cover, leading to poorer vegetation cover or bare land, especially after harvest and thus increase erosion processes and land degradation. Thus, control of soil erosion targeted to the area not only reduces direct costs of soil erosion; however, it also it diminishes the implementation costs of control operations for decreasing soil erosion.

Introduction The attraction model algorithm spatially depends on the neighborhoods of the central pixels that are attracting surrounding sub-pixels. Another possibility is the hypothesis of subpixel interaction as introduced by Mertens et al. (2003) and Atkinson (2005). In order to reach a pixel state with the maximum number of sub-pixels of identical classes neighboring, there are several methods such as genetic algorithms (Mertens et al., 2003) and pixel swapping (Atkinson, 2005) that the techniques use the initial pixel fraction values as a constraint.
In this study, for the first time, an attraction subpixel model is applied on digital elevation models (DEM). The attraction model uses the surrounding pixels around the main pixel and tries to find the best matching value for each sub-pixel in the central pixel. There are two main methods in attraction model in order to select surrounding pixels for each sub-pixel in the central pixel. Each pixel can be divided into 2, 3, and 4 subpixels. To find the best model with a higher accuracy, an RMSE index is calculated and then using the best model rivers’ shorelines are extracted. To validate the shorelines and lake border data using Landsat 8 images an NDVI index is extracted and then water area is extracted and the results are compared with attraction models output.
Materials and methods In this study, a subpixel spatial attraction model is used to enhance the spatial resolution of DEM. The subpixel attraction model is based on neighboring values located around each subpixel inside a central pixel. In most studies, a set of methods are used to separate different neighboring methods. In this study, two quadrant and touching neighboring methods are used.
In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. A sample of two neighborhood methods with different scale factors is shown in Fig. 2 (Mertens et al., 2006). For the quadrant neighborhood and S=3 and touching method, the darkest shaded subpixel inside the center pixel is attracted only by the right middle pixel and the gray subpixel is attracted by the left top, top middle, and left middle pixels. Shaded sub-pixels without corresponding pixels refer to sub-pixels that are not attracted by any of the pixels, as is the case for the center sub-pixels with S=3 for the touching and quadrant neighborhood. In the present work, two neighborhood methods with S=2, 3, and 4 are examined.
It must be noted that both neighboring methods are the same when S=2. The neighborhoods previously defined can now be formulated as Eq. 1 (Mertens et al., 2006): N Touching neighborhood.

Background and objectives Gully erosion is one of the destructive forms of soil erosions that may lead to a considerable volume of soil loss. This erosion type in addition to on-site and off-site effects has an important role in land degradation and forming in some watersheds. In this regard, due to gully erosion in some provinces of Iran such as Hormozgan, Bushehr, Fars, Khorasan, Semnan, and Zanjan vast agricultural areas are under threat of gully erosion. There are biological and mechanical methods that are available for controlling this type of erosion. One of the most important issues before designing any biological or mechanical practices is knowledge about morphological characteristics of gullies and process of their development. Therefore, this study was planned to assess morphological changes and development of gullies during 1955-2016 years in Idelo watershed in Zanjan Province, Iran.
Materials and Methods In this study, morphological characteristics of 22 gullies were mapped using aerial photos, IRS satellite imagery, and GPS over two periods (1955-2008 and 2008-2016) of time. Also, in the downstream of the watershed, six gullies were surveyed in the four sections to obtain dimensions of the gullies especially depth. One-Way ANOVA and Duncan test were used to compare characteristics of gullies such as area, length, width, and radius of the head cut in years 1955, 2008, and 2016.
Results The results showed that 77% of the gullies are located in downstream of the watershed especially on the red gypsiferous marl geologic unit, which latter cases are bigger than others. The areas occupied by direct gully erosion were 2.9, 4, and 7.4 hectares in 1955, 2008, and 2016 respectively. Also, the number and dimensions of morphological characteristics of the gullies have been grown during 1955-2016. The results of estimation and measurements of the extension of the gullies showed that gullies located on the red gypsiferous marl geologic unit had the highest rate of extension. Accordingly, the mean values of the length growth of the gullies located in marl and other geological units (i.e., young alluvial deposits and old alluvial clastics) were 2.15 and 0.3 m/year during 1955-2008 and 11.23 and 0.96 m/year during 2008-2016, respectively. The results of surveying of the gullies showed that there is a consistency between the volume of soil loss with depth and length of the gullies.
Conclusion overall, regarding the obtained results, the extension and growth of the morphological characteristics of the gullies in 1955-2008 are more severe than those in 2008-2016 especially in the red gypsiferous marl geologic unit. Accordingly, low levels of extension of the gullies during 1955 until 2008 can be attributed to the initial phase of the gullies, subsistence agriculture especially until 1990 and designed control measures such as gabions and check dams. Furthermore, according to the existing of the calcareous soils of the study area and field evidence, the severe extension of the gullies during 2008 until 2016 can be attributed to piping erosion and consequently appearance of hidden sections of the gullies due to ceiling collapse. However, for a comprehensive understanding and conclusion about this matter, further studies should be conducted.