Since in most mapping models geometric mean of different criteria are used to determine the desertification intensity, one of the most important issues in desertification studies is understanding the similar areas, which require similar management after determining the desertification intensity map. Two similar classes of desertification intensity may require different management due to differences in the criteria that affect its desertification severity. Therefore, after determining the geomorphological facies as the working units in Sistan plain, we used hierarchical cluster analysis to identify the homogeneous environmental management units (HEMUs) based on indices of MEDALUS model. According to the MEDALUS model, the studied area was divided into two categories namely medium and high desertification classes. Working units (geomorphological facies) are classified into five clusters according to HEMUs analysis based on climate, soil, vegetation, and wind erosion criteria. The first cluster (C11) include six facies with moderate and severe desertification; in all of these units the main effective factor was wind erosion, so they need the same management decisions controlling wind erosion. Two working units (1 and 4) with the same desertification severity were placed in two different clusters due to the main factors affecting each other. The results of the Mann-Whitney test showed that the value of the test statistics was 79. Also, the value of Asymp.Sig was obtained to be 0.018, which is less than 0.025 (two-tailed test), and it can be concluded that the classification of work units in the two models, clustering and desertification, is not equal (P<0.05). So It seems that using cluster analysis to identify the same units, which need the same management decision after preparing the desertification intensity, is necessary.

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According to the extent of damage, various effects and complexity of desertification process, selecting appropriate alternatives considering all effective desertification criterions is one of the main concerns of Iran in the field of natural resources. This can be effective in controlling, reclamation of disturbed lands and avoiding destruction areas at desertification risk. This paper tries to provide a systematic and optimal alternatives in a group decision-making model. For this aim, PROMETHEE II method was used for ranking desertification alternatives. At the first in the framework of Multiple Attribute Decision-making (MADM), normalized decision matrix was provided by Delphi model. Then, to ease and accuracy in estimating the criteria preference and alternatives priority, the normalized decision matrix data were entered in Visual PROMETHEE software. Based on the results, the alternatives of prevention of unsuitable land use changes (A18), vegetation cover development and reclamation (A23) and modification of ground water harvesting (A31) with pure out ranking progress of =0.3660, 0.1909 and -0.0887 were selected as the main combating desertification altarnative in the study area, respectively. Therefore, it is suggested that the obtained results and ranking should be considered in projects of controlling and reducing the effects of desertification and rehabilitatyion of degraded lands plans.

Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

Abstract. Desertification is the third most important global challenge after crisis of water shortage and drought in the 21st century. Many countries, especially developing countries, are faced this phenomenon. Identifying the regions exposed to this phenomenon is so important for combating desertification. Regarding to the importance of this phenomenon, in this study, we assessed desertification in Kashan, Iran, using IMDPA model and GIS software. According to the region condition, water was selected as the criterion and some indices were identified for it. For monitoring, the effect of criterion and indices changes was investigated during 1966–1974, 1974–1992, 1992–2002 and 2002–2011. Finally, for providing the desertification early warning system, data related to the criterion and indices were collected during 2003–2011 and the threshold of each index was determined. The results showed that the class of desertification based on Electrical Conductivity index was very severe in the study area, so this index is one of the most important desertification indices in arid areas in Iran.