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Abstract. Land degradation reduces the production of biomass and vegetation cover for all forms of land use. The lack of specific data related to degradation is a severe limitation for its monitoring. Assessment of the current state of land degradation or desertification is very difficult because this phenomenon includes several complex processes. For that reason, no common agreement has been achieved among the scientific community for its assessment. This study was carried out as an attempt to develop a new approach for land degradation assessment, based on its current state by modifying of Food and Agriculture Organization (FAO)–United Nations Environment Programme (UNEP) index and the normalized difference vegetation index (NDVI) index in Khuzestan province, southwestern Iran. Using the proposed evaluation method it is easy to understand the degree of destruction caused by the pursuit of low costs and in order to save time. Results showed that based on the percent of hazard classes in the current condition of land degradation, the most and least widespread areas of hazard classes are moderate (38.6 %) and no hazard (0.65 %) classes, respectively. Results in the desert component of the study area showed that the severe class is much more widespread than the other hazard classes, which could indicate an environmentally dangerous situation. Statistical results indicated that degradation is highest in deserts and rangeland areas compared to dry cultivated areas and forests. Statistical tests also showed that the average degradation amount in the arid region is higher than in other climates. It is hoped that this study's use of geospatial techniques will be found to be applicable in other regions of the world and can also contribute to better planning and management of land.

Front Cover -- Water, Land, and Forest Susceptibility and Sustainability -- Science of Sustainable Systems: Water, Land, and Forest Susceptibility and Sustainability: Geospatial Approaches and Modeling Volume I -- Copyright -- Contents -- Contributors -- Foreword -- I - Introduction: Theoretical framework and -- 1 - Theoretical framework and approaches of susceptibility and sustainability: issues and drivers -- 1.1 Introduction -- 1.2 Global perspective of sustainability and susceptibility -- 1.3 Theoretical framework of sustainability and ecosystem services -- 1.3.1 Water, land, and forest: integral component of ecosystem -- 1.3.2 Sustainability and ecosystem services -- 1.3.3 Degradation of ecosystem services -- 1.4 Drivers and issues of susceptibility and sustainability of ecosystems -- 1.4.1 Susceptibility and sustainability of water resources -- 1.4.2 Susceptibility and sustainability of land resources -- 1.4.3 Susceptibility and sustainability of forest resources -- 1.5 Approaches for susceptibility/degradation assessment of ecosystems -- 1.6 Conclusions -- References -- II - Water resource susceptibility and sustainability -- 2 - Trap efficiency of reservoirs: concept, review, and application -- 2.1 Introduction -- 2.1.1 Reservoir sedimentation -- 2.1.2 Reservoir storage capacity reduction -- 2.1.3 Determination of quantity of sediment deposited in reservoir -- 2.1.3.1 Quantity of incoming sediment load -- 2.1.3.2 Quantity of outgoing sediment load -- 2.1.4 Determination of trap efficiency -- 2.2 Limitations of the study -- 2.2.1 Empirical methods -- 2.2.2 Artificial neural networks -- 2.3 Literature review -- 2.4 Materials and methods -- 2.4.1 Empirical methods -- 2.4.1.1 Brown's method -- 2.4.1.2 Capacity-inflow method (Brune method) -- 2.4.1.3 Gills method -- 2.4.1.4 Sediment index method (Churchill method).

PurposeIn a tropical country like Malaysia, forest fire is a very common natural and man‐made disaster that prevails in the whole South East Asian region throughout the year. Recently, the haze problem in Malaysia has created a lot of awareness among the government and eco‐tourism sectors. Therefore, detection of the hotspot is very important to delineate the forest fire susceptibility mapping. In this study, remote sensing and geographical information systems (GIS) have been used to evaluate forest fire susceptibility at Sungai Karang and Raja Muda Musa Forest Reserve, Selangor, Malaysia. Frequency ratio model has been applied for the delineation of forest fire mapping for the study area.Design/methodology/approachForest fire locations were identified in the study area from historical hotspots data from year 2000 to 2005 using AVHRR NOAA 12 and NOAA 16 satellite images. Various other supported data such as soil map, topographic data, and agro climate were collected and created using GIS. These data were constructed into a spatial database using GIS. The factors that influence fire occurrence, such as fuel type and Normalized Differential Vegetation Index (NDVI), were extracted from classified Landsat‐7 ETM imagery. Slope and aspect of topography were calculated from topographic database. Soil type was extracted from soil database and dry month code from agroclimate data. Forest fire susceptibility was analyzed using the forest fire occurrence factors by likelihood ratio method.FindingsA new statistical method has been applied for the forest fire susceptibility mapping. The results of the analysis were verified using forest fire location data with the help of a newly written programming code. The validation results show satisfactory agreement between the susceptibility map and the existing data on forest fire location. The GIS was used to analyze the vast amount efficiently, and statistical programs were used to maintain the specificity and accuracy. The result can be used for early warning, fire suppression resources planning and allocation.Originality/valueAll data used in this study are original. The forest fire susceptibility mapping has been done in this study area for the first time. A new program has been coded to cross‐verify the susceptibility map. The results were also verified with field data and other supporting weather data.

This study aims at identifying the suitable lands for urban development in Bandar Abbas city based on its real world use regarding specific criteria and sub-criteria. The city of Bandar Abbas is considered as the most important commercial and economic city of Iran. It is also considered as one of the major cities of Iran which has played a pivotal role in the country's development and progress in recent years especially after the end of Iran-Iraq war owing to its embracing the country's main commercial ports. This process has caused the immigration rate into the city to rise significantly over the past 20 years. Thus, the development of the city is meanwhile considered as a high priority. Bandar Abbas city does not have a rich capacity for growth and development due to its special geographical situation being located in coastal border. Among the limitations placed in the city's development way, natural limitations (heights and sea shore) in the northern and southern parts of the city and structural limitations (military centers) in the east and west sides of the city may be referred. Therefore, identifying the suitable lands for urban development within Bandar Abbas city limits is becoming an essential priority. Therefore, different quantitative and qualitative criteria have been studied in order to select and identify these lands. The structures of qualitative criteria for most parts involve ambiguities and vagueness. This leads us to use Fuzzy logic in this study as a natural method for determining the solutions for problems of Multi-criteria decision making (MCDM). In the current research, a combination of MCDM methods has been presented for analysis. To assignee weights of the criteria Fuzzy AHP (analytic hierarchy process) is used for land selection and Fuzzy TOPSIS (method for order priority by similarity to ideal solution) is utilized to choose the alternative that is the most appropriate through these criteria weights. The sensitivity analysis of the results is included in the research.

Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- 1 Introduction to Spatial Modeling of Flood Risk and Hazard: Societal Implication -- Abstract -- 1.1 Concept of Floods -- 1.2 Impact of Flood-Global Scenario -- 1.3 Floods in India-A Case Study -- 1.4 Need for Floods Prediction Map -- 1.5 Role of Geospatial Technology for Floods Prediction -- 1.6 Key Aims of the Book -- 1.7 Individual Chapters -- References -- 2 Flood Susceptibility Mapping Using Morphometric Parameters and GIS -- Abstract -- 2.1 Introduction -- 2.2 Study Area -- 2.3 Data and Methods -- 2.4 Results and Discussion -- 2.4.1 Morphometric Parameters -- 2.4.1.1 Linear Parameters -- 2.4.1.2 Areal Parameters -- 2.4.1.3 Relief Parameters -- 2.4.2 Prioritization of the Sub-basin for Flood Susceptibility -- 2.4.3 Validation -- 2.5 Conclusion -- References -- 3 Palaeohydrologic Estimates of Flood Discharge of Lower Ramganga River Catchment of Ganga Basin, India, Using Slackwater Deposits -- Abstract -- 3.1 Introduction -- 3.2 Study Reach -- 3.3 Database and Methodology -- 3.3.1 Flood Frequency Analysis by Log-Pearson Type III Distribution -- 3.3.2 Palaeoflood Hydrological Investigations -- 3.3.3 Grain Size Measurement -- 3.4 Result and Discussion -- 3.4.1 Hydrological Characteristics -- 3.4.2 Estimation of Probable Flood Discharge and Flood Recurrence Interval -- 3.4.3 Estimation of Palaeohydraulic Flood Discharge Using Slack Water Deposits -- 3.4.4 Stratigraphy and Grain Size Analysis of Slackwater Deposits -- 3.4.5 Accuracy Assessment -- 3.5 Conclusion -- References -- 4 Flood Risk Zone Identification Using Multi-criteria Decision Approach -- Abstract -- 4.1 Introduction -- 4.2 Study Area -- 4.3 Materials and Method -- 4.3.1 Collection of Secondary Data -- 4.3.2 Analysis of Flood Frequency -- 4.3.3 Satellite Data Acquisition and Preprocessing.

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.