Land Degradation Vulnerability Research Articles

Risk of Land Degradation: A Case Study of Phu Yen Province, Vietnam

The issue of the land degradation vulnerability index (LDVI) is multifaceted, encompassing climate, soil, vegetation, policy formulation, and human actions. In Vietnam, the convergence of climatic fluctuations and human impact results in phenomena, such as soil sealing, erosion, salinization, and landscape fragmentation. These phenomena are recognized as significant triggers of land degradation. This paper seeks to present a method for assessing a land's susceptibility to degradation by utilizing ten ecological 10 criteria: NDVI; slope; bulk density (cg/cm3); cation exchange capacity in the soil (CEC; mmol(c)/kg); Soil organic carbon stock (SOC; dg/kg), pH; Nitrogen (N; cg/kg); soil thickness (cm); soil surface temperature LST (0C); precipitation of the driest quarter (mm). The research results show that Song Hinh and Son Hoa communes are standing on the most land degradation vulnerability. Some criteria that are considered important in assessing land degradation by the analytic hierarchy process (AHP) technique are NDVI, followed by slope, nitrogen, bulk density, and soil thickness. The results of the study are consistent with records in localities that are often under pressure from drought. Extreme LDVI areas were larger identified on low mountains, slope terrain, and precipitation of driest quarter under 200mm, expanding on the agricultural areas with 40km2 total province agriculture area, followed by grassland (20.3 km2), natural forests (17.2 km2), plantation forests (8.2 km2), residences (8.2 km2), and bare land (8.15 km2). Poor land management practices, such as improper construction, inadequate water management, and lack of terracing, can contribute to soil erosion and land degradation. This LDVI assessment process can be applied to some tropical countries. The NDVI index combined with the slope, nitrogen, bulk density, and soil thickness can be exploratory indicators of land sensitivity to land degradation.

Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models.

Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP-based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF), and support vector machine (AHP-SVM) model. The model performances were assessed through an area under the receiver operating characteristic (AUC). The AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977), and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.

Modeling and Assessment of Land Degradation Vulnerability in Arid Ecosystem of Rajasthan Using Analytical Hierarchy Process and Geospatial Techniques

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin.

Evaluation of decadal land degradation dynamics in old coal mine areas of Central India

AbstractThe present study attempts to understand land use land cover change dynamics in an area subjected to opencast and underground coal mining for the last few decades in Kotma Coalmines of Anuppur District in Madhya Pradesh, India through geospatial techniques. Land use and land cover (LULC) change detection analysis was performed digitally classifying LANDSAT‐5 (2001) as well as LANDSAT‐8 (2020) satellite data using a maximum likelihood algorithm. Results revealed that area under dense native vegetation decreased drastically (by 13.74 km2) with the gradual and consistent expansion in the activities of coal mines which showed the highest increase in area over time (15.84 km2). Bivariate regression analysis showed the positive empirical relationships between vegetation indices and soil physico‐chemical parameters. Studies suggested that soil and vegetation is degraded over the large mining areas consistently over a long time period. Despite the continuous reforestation activities on mined areas, the decline area under dense vegetation and sparse vegetation over the twenty‐year time‐scale indicates that the reclamation activities are still in its infancy. A land degradation vulnerability Index (LDVI) map was generated to understand the extent of decadal land degradation trends and it shows that 8.60% of the area is highly vulnerable to degradation. The land degradation index (LDI) inputs will help the planners to develop alternate strategies to tackle vulnerability zones for safe mining. Monthly estimation of various meteorological parameters was also recorded to generate heat plots for the period 2001–2020. The study concludes that monitoring and assessment of fragile ecosystems are indispensable for holistic environmental management.

Mapping of Land Degradation Vulnerability in the Semi-Arid Watershed of Rajasthan, India

Global soils are under extreme pressure from various threats due to population expansion, economic development, and climate change. Mapping of land degradation vulnerability (LDV) using geospatial techniques play a significant role and has great importance, especially in semi-arid climates for the management of natural resources in a sustainable manner. The present study was conducted to assess the spatial distribution of land degradation hotspots based on some important parameters such as land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), terrain characteristics (Topographic Wetness Index and Multi-Resolution Index of Valley Bottom Flatness), climatic parameters (land surface temperature and mean annual rainfall), and pedological attributes (soil texture and soil organic carbon) by using Analytical Hierarchical Process (AHP) and GIS techniques in the semi-arid region of the Bundi district, Rajasthan, India. Land surface temperature (LST) and NDVI products were derived from time-series Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall data products from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), terrain characteristics from Shuttle Radar Topography Mission (SRTM), LULC from Landsat 9, and pedological variables from legacy soil datasets. Weights derived for thematic layers from the AHP in the studied area were as follows: LULC (0.38) > NDVI (0.23) > ST (0.15) > LST (0.08) > TWI (0.06) > MAR (0.05) > SOC (0.03) > MRVBF (0.02). The consistency ratio (CR) for all studied parameters was <0.10, indicating the high accuracy of the AHP. The results show that about 20.52% and 23.54% of study area was under moderate and high to very high vulnerability of land degradation, respectively. Validation of LDV zones with the help of ultra-high-resolution Google Earth imageries indicates good agreement with the model outputs. The research aids in a better understanding of the influence of land degradation on long-term land management and development at the watershed level.

Impact of coal mining on land use dynamics and soil quality: Assessment of land degradation vulnerability through conjunctive use of analytical hierarchy process and geospatial techniques

AbstractCoal is the major source of energy in India, where 66% of the thermal power is generated from different grades of coal. The surging energy demand for domestic and industrial sectors has accelerated coal production through intensive coal mining from existing mines and there is exploration for potential areas across the Country. More than 80% of the coal excavated from open cast mining, which is a damaging form of mining that is often associated with undesirable land use changes, soil degradation, and deforestation and is responsible for other environmental degradation. The present study has been conducted in Manendragarh coal mining area located in northern Chhattisgarh, Central India using the analytical hierarchy process (AHP) and geospatial tools. LANDSAT‐TM and OLI satellite data from 1990 and 2020 were digitally analyzed applying a maximum likelihood supervised classification technique in ERDAS IMAGINE and Arc‐GIS platforms. Spatial analysis of satellite data revealed that the area under forest, agriculture‐fallow, and waterbodies fell by 14.5%, 12.9%, and 2.52%, respectively, due mainly to conversion into degraded land, while the mining area and built‐up areas were proportionally enhanced by 3.2% and 0.59% between 1990 and 2020. The intense mining resulted in deterioration of soil health, where the organic C, pH, and soil depth were considerably decreased in mined sites and degraded lands, compared to agricultural and forest areas. The major determinant biophysical, topographic, and soil factors were integrated in a GIS platform to derive a land degradation vulnerability index (LDVI), which identified five vulnerability classes. The salient findings of study provides the critical inputs for the policy makers/environmentalists in prioritization and decision‐making for implementing remedial measures for the eco‐restoration of mining degraded landscapes and also the improvement of ecology and environment along with sustainable coal mining.

Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI / TIRS, LST, and NDVI approach

This research aims to find a relation between measured Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) for assessing land degradation and desertification in semi-arid regions using LANDSAT 8. In the twenty-first century, soil loss has been a serious environmental problem due to anthropogenic pressure and climate change. Monitoring and assessing land degradation vulnerability is becoming increasingly important for sustainable land resource management, especially in semi-arid ecosystems. The purpose of this research is to retrieve the LST and examine the relationship between LST and NDVI, in addition to LST and Landsat 8 bands. The association between LST and Landsat 8 bands is found to be positive, as well as the regression coefficient from NDVI to LST is also found to be strong Positive. The resultant of the NDVI is further classified into five categories based on the pixel values, and they are reclassified into five categories for land cover assessment, they are namely waterbodies, degraded lands, desertified lands, vegetation, and built-up lands. The results reveal that the desertified lands are occupying an area of 13.62%, degraded lands occupy 48.91%, built-up lands occupy 8.39%, vegetation lands occupy 27.34%, and water bodies occupy 1.71% in the study region. These research findings can be used to optimize the technique for future research in the field of degradation assessment, thermal comfort zone assessment, and sustainable agricultural growth.

Modelling and Accessing Land Degradation Vulnerability using Remote Sensing Techniques and the Analytical Hierarchy Process Approach

Land degradation and desertification have recently become a critical problem in Ethiopia. Accordingly, identification of land degradation vulnerable zonation and mapping was conducted in Wabe Shebele River Basin, Ethiopia. Precipitation derived from Global Precipitation Measurement Mission (GMP), the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized difference vegetation index (NDVI) and land surface temperature (LST), topography (slope), and pedological properties (i.e., soil depth, soil pH, soil texture, and soil drainage) were used in the current study. NDVI has been considered as the most significant parameter followed by the slope, precipitation and temperature. Geospatial techniques and the Analytical Hierarchy Process (AHP) approach were used to model the land degradation vulnerable index. Validation of the results with google earth image shows the applicability of the model in the study. The result is classified into very highly vulnerable (17.06%), highly vulnerable (15.01%), moderately vulnerable (32.72%), slightly vulnerable (16.40%), and very slightly vulnerable (18.81%) to land degradation. Due to the small rate of precipitation which is vulnerable to evaporation by high temperature in the region, the downstream section of the basis is categorized as highly vulnerable to Land Degradation (LD) and vice versa in the upstream section of the basin. Moreover, the validation using the Receiver Operating Characteristic (ROC) curve analysis shows an area under the ROC curve value of 80.92% which approves the prediction accuracy of the AHP method in assessing and modelling LD vulnerability zone in the study area. The study provides a substantial understanding of the effect of land degradation on sustainable land use management and development in the basin.

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for using remote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

Evaluation of Livelihood Sustainability in the Context of Natural Forest Land Degradation Vulnerability: A Case Study of Five Counties in China

Land degradation, especially natural forest land degradation (NFLD), is a severe environmental concern in China. This natural disaster itself and its derivative control policies have caused some impacts on surrounding farmers’ livelihood level and strategies, but the literature on the sustainable livelihood of different households in NFLD vulnerability is limited, and there is an urgent need to bridge the gap and conduct studies on the sustainable livelihood of Changting, Libo, Lixian, Menghai and Wuxi, the typical NFLD-prone areas in China. A new livelihood sustainability index (LSI) including livelihood asset, livelihood strategy and sustainability engagement is constructed to assess the basic situation. The results showed that: (1) The overall LSI of five NFLD areas was not high, and the social, financial and natural assets, in particular, were relatively low. A disparity was found among the five areas, and the rank sequence of the LSI value was sorted in a descending order: Changting > Menghai > Libo > Lixian > Wuxi. (2) In detail, farmers in Changting had the relative highest LSI because of the inherent high value of livelihood assets, which constrain the scores of the livelihood strategy and sustainability engagement. (3) Households in Libo, Menghai and Lixian had middle level LSI scores. The relative low livelihood assets in Libo and Menghai drove parts of local farmers to carry out off-farm/forestry employment, leading to high scores of livelihood strategy, while farmers in Lixian had lower livelihood diversification scores and higher sustainability engagement due to their working content for the local forestry bureau. (4) The low scores of the livelihood asset and sustainability engagement restricted farmers in Wuxi. A discussion of LSI in the NFLD vulnerability was conducted to determine the characteristics and analyze the reasons. Accordingly, targeted policy recommendations were proposed to realize a sustainable livelihood in NFLD areas.

Assessment of Land Degradation Vulnerability using Geospatial Technique: A Case Study of Kachchh District of Gujarat, India

Land degradation is decline in productivity of land in terms of bio-diversity and economy, resulting from various causes including climate and human dominance, leading to loss of ecosystem. It is an issue of global concern and threatens productivity of land, water, biodiversity, ecology, economy, and people. India, with 2.4% of global land area, is homeland for around 18% of global human population and 30.4% livestock population, supporting more than 8% of world’s agriculture with more than 69% area falling under drylands. The blend of high population, high agriculture production and diverse agro-climatic conditions results in excessive pressure on resources. This study is an attempt to access land degradation vulnerability considering human-induced factors, biophysical and climate parameters. Hierarchy-based indexing method is used for analysis using geospatial technique. Study reveals that around 67% of the land area falls under high vulnerability, and 27% area falls under moderate vulnerability. The outcome was further compared with MODIS land surface temperature and normalised difference vegetation index data for validation and is observed that more than 85% area under moderate–high vulnerability is related to increase in surface temperature and/or no-change in vegetation index; this area also falls under low NDVI value range (< 0.3), indicating vulnerable to land degradation. The outcome is useful for stakeholders in understanding the issue and for preparing action plans for combating land degradation.

Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS

Globally, land degradation becomes a serious environmental issue in the context of anthropogenic pressure and climate change in the twenty-first century. Modeling and assessment of land degradation vulnerability assume a greater importance especially in the semi-arid ecosystems for sustainable land resource management. Consistent and reliable earth observation satellite datasets, Analytic Hierarchy Process (AHP), and Geographic Information System (GIS) are the powerful tools to model and assess the land degradation vulnerability. The present study was aimed to model and assess the land degradation vulnerability through analysis of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), rainfall, terrain characteristics, and pedological parameters by using AHP and GIS in the semi-arid ecosystem of Rayalaseema region of southern India. The NDVI and LST products derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall products from Tropical Precipitation Measuring Mission (TRMM), terrain characteristics from Shuttle Radar Topography Mission (SRTM) (30 m), and pedological parameters derived from legacy soil datasets were used in the study. The AHP- and GIS-based modeling shows that about 25.3 and 9.9% of the study area were under high and very high vulnerability to land degradation, respectively. Inadequate rainfall and vegetative cover, high temperature, problematic soils, and lack of adequate conservation measures were found to be the main causative environmental factors for land degradation in the study area. The study clearly demonstrates the potential of AHP- and GIS-based modeling in the assessment of land degradation vulnerability by using the time-series MODIS NDVI- and TRMM-based rainfall products, terrain characteristics, and pedological parameters.

Integration of social, economic and environmental factors in GIS for land degradation vulnerability assessment in the Pir Panjal Himalaya, Kashmir, India

Land degradation assessment is imperative for developing a mitigation plan to minimize the adverse impacts associated with land degradation. The study involves the integration of various social, economic and environmental indices for identifying the land degradation vulnerable areas in the Pir Panjal Himalaya, Kashmir, India. Analysis of the land use and land cover from the satellite data revealed that ~47% of the area is devoid of any vegetal cover. Slope index revealed that ~40% of the study area is precipitous with slope >30%. The land utilization index indicated that ~51% of the area is over-utilized. Integration of all the environmental indices in GIS environment yielded the Natural Resource Prioritization Index which revealed that ~41% of the area needs attention for land degradation mitigation on top priority. Integration of all the socioeconomic indices yielded the Socio-Economic Priority Zones which revealed that ~35% of the area needs attention for the socio-economic upliftment on priority for combating the land degradation in the study area. Land Degradation Vulnerability Index (LDVI) model was developed in GIS environment based on the unique combination of various natural resource and socioeconomic priority zones using the weighted index approach to assess the cumulative impact of social, economic and environmental factors on land degradation. The model analyses revealed that ~48% of the study area (~603 Km2) shows high to very high LDVI values alluding to its high vulnerability to different forms of land degradation. The approach developed in this research for the integration of various causal factors to identify the lands vulnerable to varying degree and intensity of degradation will go a long way in developing appropriate mitigation strategies for combating land degradation at village level in the Himalaya.

Land degradation vulnerability assessment based on land use changes and FAO suitability analysis in Jordan

The objective of this research was to investigate the pattern of land use change and its impact on land degradation in the Mediterranean regions of Jordan. Land use was interpreted using aerial photos and high-resolution satellite images and fieldwork carried out in 2018. Assessment of the degradation vulnerability degree was based on comparing the current land use with the potential suitability of the land by using FAO framework and spatial analysis techniques. The pattern of land use change from 1958 to 2018 showed that the area of rangeland and field crops declined by 16.1%, and 13.5% respectively; while the potential suitability for land utilization showed that 80% of the catchment is highly suitable for forest and rangeland in classes S&lt;strong&gt;&lt;sub&gt;1&lt;/sub&gt;&lt;/strong&gt; and S&lt;strong&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;/strong&gt; respectively. The degree of vulnerability for land degradation under the current land use was assessed based on the erosion hazard, slope percent, and soil depth. The highest vulnerability class represents 10%, the medium vulnerability class covers 24%, the slightly moderate vulnerability class covers 31%, and the low vulnerability class consists of about 22% of the catchment area. In summary, the main constraints contributing to land degradation are improper land use by cultivation under high erosion hazard and slope degree followed by shallow soil depth.

Cointegration analysis of vulnerability index and standardised precipitation index in Mafeteng district, Lesotho.

Given the high poverty levels in Africa, with most countries’ economy and populations’ livelihood dependent on rain-fed agriculture, land degradation among other environmental hazards has proven to be a major threat to economic growth and food insecurity, respectively. Drought, which is on the increase at the global level and said to create over 78% of other hazards, has aggravated land degradation. Dry conditions lessen soil particles cohesion force, thereby increasing susceptibility of such soils to be lost by wind and water. The current study aimed at estimating land degradation from drought hazard index, standardised precipitation index (SPI) over the drought declared district of Mafeteng Lesotho. Data were provided by Lesotho Meteorological Services for a period of 30 years (1984–2014). All missing values that existed in the collected precipitation data were filled with average values of the months with data. The computation of SPI was performed by using DrinC software in SPI-3 and SPI-Annual time step. The results revealed a constant condition of land degradation vulnerability over a 30-year period, implying a continuous loss of soil fertility, agricultural gross domestic product (GDP), water and bio-energy, malnutrition and increased poverty levels.

Processes and Status of Land Degradation in a Plateau Fringe Region of Tropical Environment

The concept of land degradation means a temporary or permanent long-term decline in an ecosystem function and its productive capacity. Land degradation is the result of numerous factors or a combination thereof, including physical (climate change, soil degradation) as well as anthropogenic activities such as unsustainable land management practices. The present study primarily concerns the processes and status of land degradation in a part of the eastern Chotanagpur Plateau, India. The basin falls under the transitional zone, is undulating in nature and situated between the Chotanagpur Plateau area in the west and the greater Ganga plain area in the east. Largely, the basin falls under the Ganga River system. The major part of the population (>70%) of the area is dependent on agriculture which is largely affected by land degradation as well as soil erosion processes. The status of land degradation is estimated from different physical factors (geology, soil, geomorphology, slope, relief, groundwater, drainage density, rainfall intensity, green biomass cover) as well as anthropogenic factors (land use land cover change, population pressure on agricultural land, deforestation, irrigation intensity etc.). All these factors have been weighted with considerable importance to show the resultant varying intensities of land degradation. The different factor weighted raster has been multiplied in the GIS environment. The different vulnerable land degradation areas have been identified on the basis of the final weighted value. It has been shown that the upper reaches of the basin is considerably more prone to degradation due to its rugged topography, poor soil development and high drainage density. In the lower reaches of the Silabati River, the left bank is characterized by a high degreee of slope whereas the right bank shows intensive agricultural density, which couple up, to cause medium to high degree of land degradation vulnerability.

Mapping and assessing land degradation vulnerability in Kangra district using physical and socio-economic indicators

Land degradation is a major problem in the fragile ecosystem of the Himalayan region. The steep slope with low forest cover and increasing human interference are the major factors of land degradation. Therefore, identification of severe degradation prone areas is necessary for implementing conservation strategies to retard the present rate of degradation processes. The aim of this study is to assess the vulnerable land degradation areas based on Space Application Centre (SAC/ISRO) guidelines and MEDALUS model. Indicators used to calculate the degradation vulnerability are geology, slope, aspect, soil type, rainfall, temperature, land use/land cover, population density, non-worker population and illiteracy. The results reveal that the areas with higher rainfall, less forest cover and large population are highly vulnerable to degradation in spite of moderate slope. The degradation vulnerability index values have been classified into five land degradation categories. The Pong dam is a large water body where the land degradation is nil. The low, moderate, high and very high land degradation categories account for 19.01, 22.79, 31.49 and 17.37% area of the district, respectively.

The urgent call for land degradation vulnerability assessment for conserving land quality in the purview of climate change: Perspective from South Indian Coast

Generating an extensive knowledge base on land degradation risk in different locations of the world is essential for developing effective mitigation and adaptation strategies. A changing climate will affect the land, soil and water resources in many ways. In this context, this study aimed to understand the possibility of present and future land degradation in the study area in the purview of global warming through the analyzing aridity index (AI), sea level rise trends and soil analysis. The study area is a semi-arid coastal region characterized by tropical climate. AI analysis shows possible drying trends for the end century period as the simulated AI values decline in the ranges of −3.25 to −7.5 mm/°C. The soil analysis revealed that the mean soil organic carbon (SOC) content in the study area is 0.43% which is far below the world standards and sea levels may likely be raised under climate change in this area. This study also showcases a frame work for assessing land degradation vulnerability in the context of climate change. Conducting land degradation vulnerability assessment is a highly essential prior to any flagship activities on sustainable land management.

Assessing the impact of ecological and economic factors on land degradation vulnerability through multiway analysis

Abstract Land degradation (LD) is a global problem which involves climate, soil, vegetation, economic, and population conditions. In Mediterranean Europe climatic variability and human pressure combine to produce soil sealing, erosion, salinisation, fire risk, and landscape fragmentation, all regarded as important factors to start LD. The aim of this paper is to introduce a time-series evaluation of land vulnerability to degradation based on nine ecological and economic variables. The analysis was carried out over 1970–2000 at the municipality level in Latium (central Italy), a region which has shown increasing land vulnerability in the last years. A multiway data analysis (MDA) was applied in order to explore the relationship among indicators over the study period. Their importance in determining LD vulnerability was estimated through a weighting system based on MDA results. A composite index of land vulnerability (LVI) was obtained as the weighted average of the nine variables transformed into single indicators, according to their relationship with LD. Considerable increases in LVI were observed in dry coastal and lowland municipalities close to Rome, thus indicating that climate aridity, population growth, and land use changes are important determinants of land vulnerability in Latium. LVI was positively correlated to the environmental sensitive area index (ESAI) measured on the same spatial and time scales, thus suggesting that a sound evaluation of land vulnerability is possible through LVI score.