Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change

Mangrove forest stores large organic carbon stocks in a setting that is highly vulnerable to climate change and direct anthropogenic influences. As such there is a need to elucidate the causes and consequences of land use change on these ecosystems that have high value in terms of ecosystem services. We examine the areal pattern of land types in a coastal region located in southern Iran over a period of 14 years to predict future loss and gain in land types to the year 2025. We applied a CA–Markov model to simulate and predict mangrove forest change. Landsat satellite images from 2000 to 2014 were used to analyze the land cover changes between soil, open water and mangroves. Major changes during this period were observed in soil and water which could be attributed to rising sea level. Furthermore, the mangrove area in the more seaward position was converted to open water due to sea-level rise. A cellular automata model was then used to predict the land cover changes that would occur by the year 2025. Results demonstrated that approximately 21 ha of mangrove area will be converted to open water, while mangroves are projected to expand by approximately 28 ha in landward direction. These changes need to be delineated to better inform precise mitigation and adaptation measures.     

Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.     

Land cover change dynamics mapping and predictions using EO data and a GIS-cellular automata model: the case of Al-Baha region,Kingdom of Saudi Arabia

The present study designed to monitor and predict land cover change (LCC) in addition to characterizing LCC and its dynamics over Al-Baha region, Kingdom of Saudi Arabia, by utilizing remote sensing and GIS-cellular automata model (Markov-CA). Moreover, to determine the effect of rainwater storage reservoirs as a driver to the expansion of irrigated cropland. Eight Landsat 5/7 TM/ETM images from 1975 to 2010 were analyzed and ultimately utilized in categorizing LC. The LC maps classified into four main classes: bare soil, sparsely vegetated, forest and shrub land, and irrigated cropland. The quantification of LCC for the analyzed categories showed that bare soil and sparsely vegetated was the largest classes throughout the study period, followed by forest, shrubland, and irrigated cropland. The processes of LCC in the study area were not constant, and varied from one class to another. There were two stages in bare soil change, an increase stage (1975–1995) and decline stage (1995–2010), and the construction of 25 rainwater-harvesting dams in the region was the turning point in bare soil change. The greatest increase was observed in irrigated cropland after 1995 in the expense of the other three categories as an effect of extensive rainwater harvesting practices. Losses were evident in forest and shrubland and sparsely vegetated land during the first stage (1975–1995) with 5.4 and 25.6 % of total area in 1995, while in 1975, they covered more than 13.8 and 32.7 % of total area. During the second stage (1995–2010), forest and shrubland witnessed a significant increase from 1569.17 km² in 1975 to 1840.87 km² in 2010. Irrigated cropland underwent the greatest growth (from 422.766 km² in 1975 to 1819.931 km² in 2010) during the entire study period, and this agriculture expansion reached its zenith in the 2000s. Markov-CA simulation in 2050 predicts a continuing upward trend in irrigated cropland and forest and shrubland areas, as well as a downward trend in bare soil and sparsely vegetated areas; the spatial distribution prediction indicates that irrigated cropland will expand around reservoirs and the mountain areas. The validation result showed that the model successfully identified the state of land cover in 2010 with 97 % agreement between the actual and projected cover. The output of this study would be useful for decision makers and LC/land use planners in Saudi Arabia and similar arid regions.     

Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok,India

In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.     

The prehistoric and preindustrial deforestation of Europe

Humans have transformed Europe's landscapes since the establishment of the first agricultural societies in the mid-Holocene. The most important anthropogenic alteration of the natural environment was the clearing of forests to establish cropland and pasture, and the exploitation of forests for fuel wood and construction materials. While the archaeological and paleoecological record documents the time history of anthropogenic deforestation at numerous individual sites, to study the effect that prehistoric and preindustrial deforestation had on continental-scale carbon and water cycles we require spatially explicit maps of changing forest cover through time. Previous attempts to map preindustrial anthropogenic land use and land cover change addressed only the recent past, or relied on simplistic extrapolations of present day land use patterns to past conditions. In this study we created a very high resolution, annually resolved time series of anthropogenic deforestation in Europe over the past three millennia by 1) digitizing and synthesizing a database of population history for Europe and surrounding areas, 2) developing a model to simulate anthropogenic deforestation based on population density that handles technological progress, and 3) applying the database and model to a gridded dataset of land suitability for agriculture and pasture to simulate spatial and temporal trends in anthropogenic deforestation. Our model results provide reasonable estimations of deforestation in Europe when compared to historical accounts. We simulate extensive European deforestation at 1000 BC, implying that past attempts to quantify anthropogenic perturbation of the Holocene carbon cycle may have greatly underestimated early human impact on the climate system.     

Land cover and land use change related to shrimp farming in coastal areas of Quang Ninh,Vietnam using remotely sensed data

Rapid development of shrimp farming may lead to unrecognized and undesirable changes of land cover/land use patterns in coastal areas. Of special concern is the loss of mangrove forest in coastal areas such as Quang Ninh, Vietnam, which is adjacent to the World Heritage-listed Ha Long Bay. Understanding the status and changes of land cover/land use for coastal shrimp farms and mangrove forests can support environmental protection and decision-making for sustainable development in coastal areas. Within this context, this paper uses the 1999/2001 Landsat ETM+ and the 2008 ALOS AVNIR-2 imagery to investigate the contraction and expansion of shrimp farms and mangrove forests in coastal areas of Ha Long and Mong Cai, which now have a high concentration of intensive and semi-intensive shrimp farms. Images were separately analyzed and classified before using post-classification comparisons to detect land cover/land use changes in the study area. The results of this study found that the area of mangrove forest has been reduced by an estimated 927.5 ha in Ha Long and 1,144.4 ha in Mong Cai, while shrimp farming areas increased by an estimated 1,195.9 and 1,702.5 ha, respectively, over the same period. The majority of shrimp farms in Mong Cai were established at the expense of mangrove forest (49.4 %) while shrimp farms in Ha Long were mainly constructed on areas previously occupied by bare ground (46.5 %) and a significant proportion also replaced mangroves (23.9 %). The remarkable rate of mangrove loss and shrimp farming expansion detected in this study, over a relatively short time scale indicate that greater awareness of environmental impacts of shrimp farm expansion is required if this industry is to be sustainable, the important estuarine and coastal marine ecosystems are to be protected over the long term, and the capturing and storing of carbon in mangrove systems are to be enhanced for global climate change mitigation and for use as carbon offsets.     

Land use/cover change and driving force analyses in parts of northern Iran using RS and GIS techniques

To accomplish integrated watershed management and land use planning, it is necessary to study the dynamic spatial pattern of land use and cover change related to socioeconomical and physical parameters. In this study, land use and cover change detection was applied to the Lajimrood Drainage Basin in northern parts of Iran, an area characterized by rich and diversified agricultural and forest mosaic. The main of changes in the study area were forest–arable land transformation, which was only considered in this study. In order to detect these changes, at first, based on 1:25,000 digital topographic maps dated 1967 and 1994 and ETM⁺ satellite image dated 2002, land use map in these three dates were prepared. The results showed that the area with forest land use decreased about 3.2% in transition 1967–2002. Also, arable land increased about 36.9%. We suggested a method to analyze the driving forces and the spatial distribution of land use change. The maps of elevation, slope, and aspect were derived and classified by using digital elevation model (DEM). Also, the maps of distance from road, drainage network, and building area were selected as socioeconomical factors. These maps were overlaid and crossed with land use change map and land use change area ratio was computed. The results showed that the elevation, slope, and aspect were physical effective factors in land use changing. Also, by increasing the distance from building area and roads, deforestation rate was reduced.     

Comparative analyses of East Texas forest cover maps generated from Landsat and AVHRR data

Comparing satellite data derived map products are affected by differences in data characteristics, image acquisition dates, processing techniques, and classification schemes used for assigning pixels to a thematic class. By comparing two forest maps generated from Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Very High Resolution Radiometer (AVHRR) images acquired on the same day, and processed using identical classification scheme and methods these differences were minimized. The ETM+ derived map had higher classification accuracy values and more precise area estimates than the AVHRR derived map. In the ETM+ derived map, 87 of the 599 verification data were misclassified, whereas in the AVHRR derived map, 155 of the 469 verification data were misclassified. Detailed error analyses by land cover class revealed that a land use based definition of forest accounted for 74% (64 out of 87) and 57% (89 out of 155) of the classification errors in ETM+ and AVHRR derived maps, respectively.     

Impact assessment of land cover changes on the runoff changes on the extreme flood events in the Kelantan River basin

In the current years, changing the land cover/land use had serious hydrological impacts affecting the flood events in the Kelantan River basin. The flood events at the east coast of the peninsular Malaysia got highly affected in the recent decades due to several factors like urbanisation, rapid changes in the utilisation of land and lack of meteorological (i.e. change in climate) and developmental monitoring and planning. The Kelantan River basin has been highly influenced due to a rapid change in land use during 1984 to 2013, which occurred in the form of transformation of agricultural area and deforestation (logging activities). In order to evaluate the influence of the modifications in land cover on the flood events, two hydrological regional models of rainfall-induced runoff event, the Hydrologic Engineering Center (HEC)-Hydrologic Modeling System (HMS) model and improved transient rainfall infiltration and grid-based regional model (Improved TRIGRS), were employed in this study. The responses of land cover changes on the peak flow and runoff volume were investigated using 10 days of hourly rainfall events from 20 December to the end of December 2014 at the study area. The usage of two hydrological models defined that the changes in land use/land cover caused momentous changes in hydrological response towards water flow. The outcomes also revealed that the increase of severe water flow at the study area is a function of urbanisation and deforestation, particularly in the conversion of the forest area to the less canopy coverage, for example, oil palm, mixed agriculture and rubber. The monsoon season floods and runoff escalate in the cleared land or low-density vegetation area, while the normal flow gets the contribution from interflow generated from secondary jungle and forested areas.     

Identification of suitable sites for groundwater recharge in Awaspi watershed using GIS and remote sensing techniques

Groundwater is a valuable natural resource for drinking, domestic, livestock use, and irrigation, especially in arid and semi-arid regions like the Garmiyan belt in Kurdistan region. The Awaspi watershed is located 50 km east of Kirkuk city, south Kurdistan, Iraq; and covers an area of 2146 km². The paper presents result of a study aimed at: (1) mapping and preparing thematic layers of factors that control groundwater recharge areas, and (2) determination of sites suitable for groundwater recharge. We used available data such as geological map, groundwater depth map, digital elevation model (DEM), Landsat 8 imagery, and tropical rainfall measuring mission (TRMM) data for this study. These data, supplemented by slope features, lithology, land use land cover, rainfall, groundwater depth, drainage density, landform, lineament density, elevation and topographic position index, were utilized to create thematic maps to identify suitable areas of groundwater recharge, using GIS and remote sensing techniques. Analytic hierarchy process (AHP) was applied to weight, rank, and reclassify these maps in the ArcGIS 10.3 environment, to determine the suitable sites for groundwater recharge within the Awaspi watershed. Fifty-five percent of the total area of the watershed was found to be suitable for groundwater recharge; whereas 45% of the area was determined to have poor suitability for groundwater recharge, but can be used for surface water harvesting.     

Determination of potential runoff coefficient for Al-Baha Region,Saudi Arabia using GIS

The curve number (CN) is a hydrologic parameter used to describe the stormwater runoff potential for drainage areas, and it is a function of land use, soil type, and soil moisture. This study was conducted to estimate the potential runoff coefficient (PRC) using geographic information system (GIS) based on the area’s hydrologic soil group, land use, and slope and to determine the runoff volume. The soil map for the study area was developed using GPS data carried on to identify the soil texture to be used in building a soil hydrological groups map. Unsupervised and supervised classifications were done to Landsat 5/7 TM/ETM image to generate land-use and land-cover map. This map was reclassified into four main classes (forest, grass and shrub, cropland, and bare soil). Slope map for Al-Baha was generated from a 30-m digital elevation model. The GIS technique was used to combine the previous three maps into one map to generate PRC map. Annual runoff depth is derived based on the annual rainfall surplus and runoff coefficient per pixel using raster calculator tool in ArcGIS. An indication that in the absence of reliable ground measurements of rainfall product, it can satisfactorily be applied to estimate the spatial rainfall distribution based on values of R and R ² (0.9998) obtained. Annual runoff generation from the study area ranged from 0 to 82 % of the total rainfall. Rainfall distribution in the study area shows the wise use of identifying suitable sites for rainwater harvesting, where most of the constructed dams are located in the higher rainfall areas.     

Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Assessment of soil erosion risk using SWAT model

Soil erosion is one of the most serious land degradation problems and the primary environmental issue in Mediterranean regions. Estimation of soil erosion loss in these regions is often difficult due to the complex interplay of many factors such as climate, land uses, topography, and human activities. The purpose of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The study area is the Sarrath river catchment (1,491 km²), north of Tunisia. Based on the estimated soil loss rates, the catchment was divided into four priority categories for conservation intervention. Results showed that a larger part of the watershed (90 %) fell under low and moderate soil erosion risk and only 10 % of the watershed was vulnerable to soil erosion with an estimated sediment loss exceeding 10 t?ha^?1?year^?1. Results indicated that spatial differences in erosion rates within the Sarrath catchment are mainly caused by differences in land cover type and gradient slope. Application of the SWAT model demonstrated that the model provides a useful tool to predict surface runoff and soil erosion hazard and can successfully be used for prioritization of vulnerable areas over semi-arid catchments.     

Large-scale rainfall–runoff–inundation modeling for upper Citarum River watershed,Indonesia

The Citarum River is one of the strategic rivers in West Java, Indonesia. Its total watershed area is approximately 1800 km². Almost every year, the overflow from the Citarum River causes the inundation of most of the upper Citarum River watershed. To prevent and mitigate flood damage, it is necessary to understand the flooding characteristics. The region, however, suffers from a lack of observational data. Therefore, to analyze the inundation caused by flooding in the upper Citarum River watershed, a rainfall–runoff–inundation (RRI) model was employed. It used the following multiple satellite-derived datasets as input data as well as for model verification: Global Satellite Mapping of Precipitation, Hydrological data and maps based on Shuttle elevation Derivatives at multiple scales, Global Mosaics of the standard MODIS land cover type data product, and Landsat 7 satellite images. Parameter calibration was performed using a Monte Carlo simulation. The simulation was performed for February 2010. The results of this study show that the RRI model identifies inundation areas in large-scale river watersheds more effectively when using multiple satellite-derived datasets compared with the observed inundation map obtained from JICA in 2010 and Landsat 7 images. The model results can be improved if high-quality observed rainfall data, topographic data, and river cross-sectional data are available.     

Long term changes in forest cover and land use of Similipal Biosphere Reserve of India using satellite remote sensing data

The spatial changes in forest cover of Similipal biosphere reserve, Odisha, India over eight decades (1930–2012) has been quantified by using multi-temporal data from different sources. Over the period, the forest cover reduced by 970.8 km² (23.6% of the total forest), and most significantly during the period, 1930–1975. Human-induced activities like conversion of forest land for agriculture, construction of dams and mining activities have been identified as major drivers of deforestation. Spatial analysis indicates that 399 grids (1 grid = 1 × 1 km) have undergone large-scale changes in forest cover (>75 ha) during 1930–1975, while only 3 grids have shown >75 ha loss during 1975–1990. Annual net rate of deforestation was 0.58 during 1930–1975, which has been reduced substantially during 1975–1990 (0.04). Annual gross rate of deforestation in 2006–2012 is indeed low (0.01) as compared to the national and global average. This study highlights the impact and effectiveness of conservation practices in minimizing the rate of deforestation and protecting the Similipal Biosphere Reserve.     

Delineation of potential sites for groundwater recharge using a GIS-based decision support system

In arid and semi-arid regions without perennial water sources such as rivers or lakes, almost all water supply needs are met by groundwater. Groundwater recharge (GWR) is critical to maintain the abundance of groundwater. This paper presents a methodology based on a decision support system (DSS) that combines remote sensing, field survey and geographic information system techniques to identify suitable GWR areas. The DSS was implemented to obtain suitability maps and to evaluate the existing GWR in the study area. The DSS inputs comprised maps of rainfall surplus, slope, potential runoff coefficient, land cover/use and soil texture. The spatial extents of GWR suitability areas were identified by a hierarchical process analysis that considered five layers. The model generated a GWR map with four categories of suitability: excellent, good, moderate and poor and unsuitable. The spatial distribution of these categories showed that 0.08 and 32.3 % of the study area was classified as excellent and good for GWR, respectively, while 63.2 and 4.42 % of the area was classified as moderate and poor and unsuitable, respectively. Most of the areas with excellent to good suitability have slopes of between 4 and 8 % and are intensively cultivated areas. The major soil type in the excellent to good areas is loam, followed by clay loam, and the rainfall in these areas ranges from 150 to 260 mm. Another suitability model, in which all criteria were assigned equal influence, generated a suitability map in which 0.1 % of the study area was rated as excellent, 10.9 % as good, 82 % as moderate and 7 % as poor and unsuitable. The locations of existing GWR dams were compared with the locations indicated on the generated suitability map using the proximity analysis tool in ArcGIS 10.1. Most (77 %) of the existing GWR structures that were categorised as successful were within the excellent and good areas, followed by moderately suitable (23 %).     

Morphometric classification of the biosphere reserve “Eastern Carpathians” (Central Europe) with self-organizing map

In this paper, we propose a semiautomatic method for landscape analysis of biosphere reserve Eastern Carpathians with both spectral and morphometric constituents. The Shuttle Radar Topography Mission (SRTM) has provided digital elevation models for approximately 80 % of the earth’s land surface. SRTM data are used to calculate first derivatives (slope) and second derivatives of elevation (such as minimum curvature, maximum curvatures, and cross-sectional curvature) by fitting a bivariate quadratic surface with a window size of 9 by 9. Together with multispectral remote sensing data like Landsat 7 ETM+ with 28.5 m raster elements, these data provide comprehensive information for the analysis of the landscape in the study area. Unsupervised neural network algorithm—self-organizing map—divided all input vectors into inclusive and exhaustive classes on the basis of similarity between attribute vectors. An optimal self-organizing map with 21 classes using 1,000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. Morphometric analysis, spectral signature analysis, and feature space analysis are used to assign semantic meaning to the classes as landscape elements according to form, cover, and slope, e.g., deciduous forest on ridge (convex landform) with steep slopes. The results revealed the efficiency of self-organizing map to integrate SRTM and Landsat data for landscape classification. This makes it possible to develop an alternative method for fast assessment and comparison of landscapes over large areas. This procedure is reproducible for the same applications with consistent results.     

Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS

Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses.     

Mapping potential areas for groundwater storage in Wadi Aurnah Basin, western Arabian Peninsula, using remote sensing and geographic information system techniques

Water shortage has become a problem in many arid regions where rainfall is low. Wadi Aurnah Basin, in Saudi Arabia (Arabian Peninsula), where the Holy Islamic cities are located, was selected for study, since it represents a water-scarce region. The potential for groundwater storage was investigated. This was achieved using remote sensing and geographic information system (GIS) techniques to cover the whole area (3,113 km²). Satellite images with high spatial resolution were processed to recognize terrain elements controlling the subsurface rock behavior. Landsat 7 ETM+, ASTER and SRTM satellite images were processed using ERDAS IMAGINE software. The influencing factors on groundwater storage were determined and digitally mapped as thematic layers. This included rainfall, lithology, rock fractures, slope, drainage and land cover/use. These factors were integrated in the GIS system (ArcView). A map was produced, indicating potential areas for groundwater storage. The map shows that 12–15% of Wadi Aurnah Basin has potential for groundwater storage, mainly in areas where intensive fracture systems exist.