Raster Layers Research Articles

An integrated GIS-MCDM framework for zoning, ranking and sensitivity analysis of municipal landfill sites

ABSTRACT This study introduces a comprehensive Geographic Information System – Multi Criteria Decision-Making (GIS-MCDM) framework to identify and evaluate potential municipal landfill sites in Nashik, India. The approach integrates topographic, geomorphological, environmental, and infrastructure criteria to create thematic raster layers representing suitability for landfill development. Criteria include ground slope, elevation, land use, proximity to water bodies, land value, and infrastructure proximity. The Analytical Hierarchy Process (AHP) was employed to determine the relative weights of these criteria, facilitating the computation of the Landfill Suitability Index (LSI) and the creation of a suitability map. The analysis classified the 480 sq. km study area into five categories, with 6.79% of the area deemed “Very High” suitability. The TOPSIS method ranked 16 potential sites within high-suitability regions, identifying L10 near Adagaon as the most optimal site. Sensitivity analysis confirmed the stability of site rankings under various weight adjustments, providing robust tool for evidence-based landfill site selection.

GIS Based Spatial Analysis of Landslide Hazard in Tehsil Balakot, KP, Pakistan

The Himalayan region is highly susceptible to landslides due to intense rainfall and seismic activity, posing significant risks to slope stability. Due to landslides, Tehsil Balakot valley has suffered substantial damage to infrastructure, roads, and the tourism sector. This research aims to develop a landslide susceptibility map and identify hazard-prone areas to support sustainable development and mitigate risks in Balakot valley. The study employs a weighted overlay analysis using primary and secondary data raster layers, including slope, aspect, precipitation, and seismic raster maps. The slope and aspect maps are derived from a 30-meter ASTER digital elevation model, while precipitation maps are generated using Inverse Distance Weighted (IDW) interpolating data from the Pakistan Meteorological Department. Seismic data is acquired from the Geological Survey of Pakistan (GSP). The resulting landslide susceptibility map classifies the region into five hazard classes: very high, high, medium, low, and very low. The landslide susceptibility maps provide valuable insights for identifying areas at varying levels of landslide risk.

Estimación de analitos del suelo en la altillanura, con teledetección sentinel-2 y modelos de regresión

Contextualization: Incorporation of new techniques and technologies to know the characteristics and composition of soils used in agricultural production is booming. The free license products such as Sentinel-2 satellite images, allow correlating the edaphic properties with spectral indices. Knowledge gap: In the agricultural sector, the indices calculated from satellite images have been centered on characterization of vegetation. To a lesser proportion, studies have been carried out with indexes used in mineral prospecting to correlate with the chemical characteristics of the soil. Purpose: This study was developed to evaluate the correlation of ferric oxide, ferrous silicate, ferrous iron, and alteration indices with the content of chemical elements of importance in crop production. Methodology: A crop field of 282 hectares in the municipality of Puerto López in the department of Meta was selected. The evaluated indices from a set of Sentinel-2 imagery were calculated. After indices obtention, 40 sites were selected to do a soil composite sampling and determine the following characteristics: pH in water (1:1 potentiometric), Organic Matter (Walkey and Black), P (Bray II), Al (KCl 1N), Ca, Mg, K y Na (Ammonium acetate), Fe (DTPA). With the obtained information, a multiple regression analysis using the stepwise method was performed; as independent variables, the raster layers of the indexes calculated were used, and the variable to predict were the properties determined in the laboratory. Results and conclusions: The evaluated indices showed a higher correlation to 0.60 with Ca and Mg content. A correlation between 0.40 and 0.60 was found between the indices and organic carbon y Fe. The regression models calculated give adjustments of 0.54, 0.47, and 0.33 for Mg, Ca, and Fe, respectively. The findings of this research allow us to understand from Sentinel-2 images that it is possible to obtain relevant indexes for agricultural production in conditions of the Colombian Plateau.

Moats & comets - A methodology to explore for submerged prehistory at sea floor erosional features formed at artificial reefs, Atlantic continental shelf, GeorgiaUSA.

Our primary objective is to demonstrate a method that uses marine processes, principally seafloor scour, to broadly expose prehistoric landforms and associated localities. Too often, the loss of prehistoric strata to scour processes such as ravinement during broad-scale, continental shelf transgressions, are decried without the recognition of the potential for erosion at the local scale can expose those same strata for systematic study. The analysis of sea floor erosional features formed around artificial reefs by turbulent flows in the Georgia Bight have used interferometric side-scan sonar (SSS), Remotely Operated Vehicles (ROVs), ultra-high-definition videography (UHD), and sedimentological, palynological, paleontological, archaeological analytical techniques. These techniques were integrated within a geoarchaeological methodology focused on exploring the Continental Shelf for preserved evidence of prehistoric landforms, flora and fauna once thereon. A stepwise protocol was developed to first gather interferometric side-scan sonar data to be then processed by commercial software packages such as HYPACK Hydrographic and HYSWEEP together with HYSCAN and SONAR Wiz software. The sonar mosaics were imported to ArcGIS/QGIS software to characterize scour features/targets around artificial reefs that had exposed paleo-land surfaces previously obscured by the modern palimpsest sand sheet. Analysis of scours and depressions - “moats and comets” - adjacent to reef structures were evaluated using the profile tool within the HYPACK-HYSWEEP editor package. Symbology for the bathymetric raster layers was edited to provide a clear contrast and measurement at scour features. Measured GIS images were then classified as to scour extent and depth with respect to prevailing current directions responsible for producing turbulence. A ranking of the artificial reefs by factors such as scour depth and extent was used to guide subsequent investigation by ROVs. Sediment cores and grab samples were collected, and those samples underwent particle size analysis (PSA), macro-and micro-botanical analyses. All data – geophysical, GIS, video and geological – were collated and synthesized to produce an assessment of the potential of sea floor scour features to expose previously buried geomorphological/geological, paleontological and/or archaeological features of interest.

High-resolution freshwater dissolved calcium and pH data layers for Canada and the United States

Freshwater ecosystems are biologically important habitats that provide many ecosystem services. Calcium concentration and pH are two key variables that are linked to multiple chemical processes in these environments, influence the biology of organisms from diverse taxa, and can be important factors affecting the distribution of native and non-native species. However, it can be challenging to obtain high-resolution data for these variables at regional and national scales. To address this data gap, water quality data for lakes and rivers in Canada and the continental USA were compiled and used to generate high-resolution (10 × 10 km) interpolated raster layers, after comparing multiple spatial interpolation approaches. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. This will aid the development of conservation and management strategies for these vital habitats.

A Geomedical Survey: Is There an Association Between Climatic Conditions and Leishmania Species Distribution in Iran During the Years 1999–2021?

PurposeIran is among the high-risk leishmaniasis regions in the world. WHO recommends the use of GIS as an ideal tool for healthcare authorities to predict the evolution of a disease, delimit the risk of outbreaks and identify critical areas. The aim of this research is to find the association between the main species of Leishmania (L. major, L. tropica, L. infantum) dispersion and climatic variables in Iran.MethodsAll molecular-based reports of leishmaniasis from Iran between 1999 and 2021 were gathered from reliable medical sources. Meteorological data (air and soil temperatures, annual rainfall and humidity) of the country along the study period were obtained from the Iranian Climatological Research Centre. The data concerning species distribution and climatic conditions during this period were moved to a base-map through raster layers using ArcGIS 10.4.1 software. The relationship between parasitological and climatic models was examined using ANOVA.ResultsHigh risk area maps, based on the cut-off thresholds, were generated for Leishmania major, L. tropica and L. infantum. According to the molecular-based reports, the L. major distribution was significantly related to all climatic variables, while L. tropica was merely related to rainfall and humidity, and the L. infantum distribution was significantly associated with rainfall, soil and air temperatures.ConclusionThe association between climatic conditions and Leishmania species distribution in Iran has been confirmed. Consequently, both, the relationship between climatic conditions and the geographical distribution of Leishmania species, and the use of GIS to better understand the spatial epidemiology of leishmaniasis, have been reaffirmed.

Distribution and co-occurrence patterns of charophytes and angiosperms in the northern Baltic Sea

The distribution data of 11 soft substrate charophyte and angiosperm species were analyzed. Our study aimed to elucidate the co-occurrence patterns among these sympatric macrophyte species and quantify their distribution areas. The central hypothesis of this study proposed that the observed co-occurrence patterns among the studied species deviate from what would be expected by random chance. Macrophyte occurrence data was derived from an extensive field sampling database. Environmental variables available as georeferenced raster layers including topographical, hydrodynamic, geological, physical, chemical, and biological variables were used as predictor variables in the random forest models to predict the spatial distribution of the species. Permutation tests revealed statistically significant deviations from random co-occurrence patterns. The analysis demonstrated that species tended to co-occur more frequently within their taxonomic groups (i.e., within charophytes and within angiosperms) than between these groups. The most extensive distribution overlap was observed between Chara aspera Willd. and Chara canescens Loisel., while Zostera marina L. exhibited the least overlap with the other species. The mean number of co-occurring species was the highest in Chara baltica (Hartman) Bruzelius while Z. marina had the largest share of single-species occurrences. Based on the distribution models, Stuckenia pectinata (L.) Börner had the largest distribution area.

An Evaluation of Groundwater Vulnerability Utilizing GIS-Based Model

Effective groundwater management requires assessing vulnerability to pollution. While the SINTACS method is widely used, it has limitations by not adequately considering hydrogeological parameters and supplementary data. To address SINTACS limitations, a modified approach (MSVILuLn) incorporates land use/cover and lineaments. Parameter weights were determined using analytic hierarchy process (MSVILuLn-AHP) and network process (MSVILuLn-ANP). Raster layers of nine parameters were analyzed in GIS to evaluate groundwater vulnerability index using SINTACS, MSVILuLn, MSVILuLn-AHP, and MSVILuLn-ANP. Nitrate concentrations from 48 wells were compared to validate results. MSVILuLn-ANP identified more very high/low vulnerability zones compared to others. It showed highest accuracy (91.7%) correlating vulnerability and nitrate levels. MSVILuLn-ANP offers a more comprehensive vulnerability assessment for groundwater management in Raipur city. Integrating SINTACS with GIS and objective weighting methods enhances spatial analysis of sensitive areas and prevents contamination.

Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium

Land use change scenarios, and their projected impacts on biodiversity, are highly relevant at local scales but not adequately captured by the coarse spatial resolutions of global land use models. In this study, we used the land use allocation tool of the GLOBIO 4 model to downscale the Land Use Harmonization v2 (LUH2) data from their original spatial resolution (0.25°) to 100 m and 10 m resolutions, using the country of Belgium as an example. Inputs to the tool included: (1) a reference present-day land cover map at the high spatial resolution, (2) regional land demand projections for three future scenarios, Sustainability (SSP1xRCP2.6), Regional Rivalry (SSP3xRCP6.0), and Fossil-fuelled Development (SSP5xRCP8.5), and (3) raster layers representing the suitability of the grid cells for different land use types. We further investigated the impact of using different reference land cover maps (CORINE at 100 m resolution and ESA WorldCover at 100 m and 10 m resolutions) on the downscaling outcomes. Comparison of downscaled current and future land use maps with the original LUH2 dataset showed that the use of ESA WorldCover as a reference map provides better agreement (RSR: 0.11–0.24, overall accuracy: 0.94–0.98, Kappa: 0.91–0.97) than CORINE (RSR: 0.28–0.33, overall accuracy: 0.90–0.93, Kappa: 0.90–0.91). Additionally, the validation of the present-day downscaled maps showed a good agreement with the independent Copernicus Global Land Service dataset. Our findings suggest that the choice of reference land cover map influences the degree of agreement between the downscaled and the original coarse-grain land-use maps. Moreover, the land use maps produced using our downscaling approach can provide valuable insights into the potential impacts of land use change on biodiversity and can guide local decision-making processes for sustainable land management and conservation efforts.

Modeling assessment of groundwater vulnerability to contamination risk in a typical basement terrain using TOPSIS-entropy developed vulnerability data mining technique

This study involved a comparative analysis in the groundwater vulnerability domain, which is a crucial component of groundwater management decision support systems (DSS). This was achieved by creating models that covered the range of algorithms from the subjective to the data-driven. The study was conducted in a basement complex area. Databases of climatic, remote sensing, and geophysical datasets were created using varieties of data acquisition techniques. The datasets included in this assessment were: rainfall (R), land use (LU), bedrock topography (BT), recharge rate (Re), and slope (S). The slope and rainfall were determined to have the highest (0.78) and lowest (0.01) weighted factors, respectively, using the entropy method. For the development of the TOPSIS-Entropy model algorithm, the weights results were combined with the TOPSIS outranking method. To generate the Groundwater Vulnerability Model map of the study area, the hybrid model was applied to griddled raster layers of the factors. Also, the TOPSIS and Entropy-WLA model algorithms were also explored and used to generate groundwater vulnerability maps. The TOPSIS-Entropy algorithms produced an accuracy of 70%, while TOPSIS and Entropy-WLA produced accuracy of 50 and 47%, respectively. The resulting model maps were validated by using correlation technique on the produced map and the longitudinal conductance map of the study area. The TOPSIS-Entropy, which followed an object-oriented model pattern, demonstrates greater accuracy and has the potential to provide appropriate insights and alternatives to decision-making in the field of groundwater hydrology in the study area and other regions of the world with comparable geology.

Modeling the Distribution of the Chytrid Fungus Batrachochytrium dendrobatidis with Special Reference to Ukraine.

Amphibians are the most threatened group of vertebrates. While habitat loss poses the greatest threat to amphibians, a spreading fungal disease caused by Batrachochytrium dendrobatidis Longcore, Pessier & D.K. Nichols 1999 (Bd) is seriously affecting an increasing number of species. Although Bd is widely prevalent, there are identifiable heterogeneities in the pathogen's distribution that are linked to environmental parameters. Our objective was to identify conditions that affect the geographic distribution of this pathogen using species distribution models (SDMs) with a special focus on Eastern Europe. SDMs can help identify hotspots for future outbreaks of Bd but perhaps more importantly identify locations that may be environmental refuges ("coldspots") from infection. In general, climate is considered a major factor driving amphibian disease dynamics, but temperature in particular has received increased attention. Here, 42 environmental raster layers containing data on climate, soil, and human impact were used. The mean annual temperature range (or 'continentality') was found to have the strongest constraint on the geographic distribution of this pathogen. The modeling allowed to distinguish presumable locations that may be environmental refuges from infection and set up a framework to guide future search (sampling) of chytridiomycosis in Eastern Europe.

Mikrubi: a model for species distributions using region‐based records

Many species occurrence records from specimens and publications are based on regions such as administrative units. These region‐based records are accessible and dependable, and sometimes they are the only available data source; however, few species distribution models accept such data as direct input. In this paper, we present a method named Mikrubi for robust prediction of species distributions from region‐based occurrence data and a Julia package implementing the algorithms. The package ‘Mikrubi' requires a map describing disjoint regions, climatic raster layers, and a list of occupied regions. Mikrubi then rasterizes the regions, reduces the environmental dimensionality, parameterizes the niche, and finally estimates the parameters by maximizing the likelihood. In a simulation study, we find Mikrubi effective in accurate estimation in most cases; in a case study of Allium wallichii in China, Mikrubi significantly outperforms four modeling strategies that adapt region‐based records to conventional models according to different principles. The package has many prospective applications in addition to modeling distributions on region‐based records: 1) it accepts supplementary coordinates; 2) it is a new solution for distribution modeling using deviated coordinates; and 3) its probabilistic region‐based outputs have special uses in conservation and biodiversity science.

Annual biomass spatial data for southern California (2001-2021): Above- and belowground, standing dead, and litter.

Biomass estimates for shrub-dominated ecosystems in southern California have been generated at national and statewide extents. However, existing data tend to underestimate biomass in shrub vegetation types, are limited to one point in time, or estimate aboveground live biomass only. In this study, we extended our previously developed estimates of aboveground live biomass (AGLBM) based on the empirical relationship of plot-based field biomass measurements to Landsat NDVI and multiple environmental factors to include other vegetative pools of biomass. AGLBM estimates were made by extracting plot values from elevation, solar radiation, aspect, slope, soil type, landform, climatic water deficit, evapotranspiration, and precipitation rasters and then using a Random Forest model to estimate per-pixel AGLBM across our southern California study area. By substituting year-specific Landsat NDVI and precipitation data, we created a stack of annual above ground live biomass raster layers for each year from 2001 - 2021. Using these AGLBM data as a foundation, we developed decision rules to estimate belowground, standing dead, and litter biomass pools. These rules were based on relationships between AGLBM and the biomass of the other vegetative pools derived primarily from peer reviewed literature and an existing spatial dataset. For shrub vegetation types (our primary focus), rules were based on literature estimates by the post-fire regeneration strategy of each species (obligate seeder, facultative seeder, obligate resprouter). Similarly, for non-shrub vegetation types (grasslands, woodlands) we used literature and existing spatial datasets specific to each vegetation type to define rules to estimate the other pools from AGLBM. Using a Python language script that accessed ESRI raster GIS utilities, we applied decision rules to create raster layers for each of the non-AGLBM pools for the years 2001-2021. The resulting spatial data archive contains a zipped file for each year; each of these files contains four 32-bit tiff files for each of the four biomass pools (AGLBM, standing dead, litter, and below ground). The biomass units are g/m2 . We estimated the uncertainty of our biomass data by conducting a Monte Carlo analysis of the inputs used to generate the data. Our Monte Carlo technique used randomly generated values for each of the literature-based and spatial inputs based on their expected distribution. We conducted 200 Monte Carlo iterations, which produced percent uncertainty values for each of the biomass pools. Results showed, using 2010 as an example, mean biomass for the study area and percent uncertainty for each of the pools as: AGLBM (905.4 g/m2 , 14.4%); standing dead (644.9 g/m2 , 1.3 %); litter (731.2 g/m2 , 1.2 %); and belowground (776.2 g/m2 , 17.2 %). Because our methods are consistently applied across each year, the data produced can be used to inform changes in biomass pools due to disturbance and subsequent recovery. As such, these data provide an important contribution to supporting the management of shrub-dominated ecosystems for monitoring trends in carbon storage and assessing the impacts of wildfire and management activities, such as fuel management and restoration. There are no copyright restrictions on the data set; please cite this paper and the data package when using these data. This article is protected by copyright. All rights reserved.

Spatial patterns of biomass change across Finland in 2009–2015

Forest characteristics vary largely at the regional level and in smaller geographic areas in Finland. The amount of greenhouse gas emissions is related to changes in biomass and the soil type (e.g. upland soils vs. peatlands). In this paper, estimating and explaining spatial patterns of tree biomass change across Finland was the main interest. We analysed biomass changes on different soil and site types between the years 2009 and 2015 using the Finnish multi-source national forest inventory (MS-NFI) raster layers. MS-NFI method is based on combining information from satellite imagery, digital maps and national forest inventory (NFI) field data. Automatic segmentation was used to create silvicultural management and treatment units. An average biomass estimate of the segmented MS-NFI (MS–NFI–seg) map was 73.9 tons ha−1 compared to the national forest inventory estimate of 76.5 tons ha−1 in 2015. Forest soil type had a similar effect on average biomass in MS–NFI–seg and NFI data. Despite good regional and country-level results, segmentation narrowed the biomass distributions. Hence, biomass changes on segments can be considered only approximate values; also, those small differences in average biomass may accumulate when map layers from more than one time point are compared. A kappa of 0.44 was achieved for precision when comparing undisturbed and disturbed forest stands in the segmented Global Forest Change data (GFC-seg) and MS–NFI–seg map. Compared to NFI, 69% and 62% of disturbed areas were detected by GFC-seg and MS–NFI–seg, respectively. Spatially accurate map data of biomass changes on forest land improve the ability to suggest optimal management alternatives for any patch of land, e.g. in terms of climate change mitigation.

Estimation of Soil Erosion Using RUSLE Model and GIS Tools: A Study of Chilika Lake, Odisha

Abstract Chilika Lake is the largest coastal lagoon on the eastern coast of India. The aerial extension of the lake fluctuates between 1165 km2 to 906 km2. The opening of new inlet into the lake has enriched its biodiversity but also has increased the rate of siltation, which inturn affected the lake’s water holding capacity, causing ecological repercussions and impacting its flora and fauna. The RUSLE model integrated with remote sensing and GIS techniques was used to assess the soil loss in watershed of Chilika Lake. The model takes into account various factor viz. Crop /cover management factor (C), Soil erodibility factor (K), slope length (L), Conservation support practice factor (P), Rainfall erosivity factor (R), and steepness factor (S) to estimate potential soil loss. The results of the study reveal that 486.92 km2 i.e., 73.16% area of the Chilika watershed falls under least risk category of soil erosion, 44.17 km2 (6.6%) under moderate category, while, about 11% is at high, 8.8% at very high, and 0.043 at severe risk of soil erosion. The analysis estimates the annual average soil loss rate in the catchment of Chilika Lake to be 32.41 ton/ha/yr. The raster layers developed to study spatial soil loss indicate that the catchment of the lake is under the grip of soil erosion and siltation problems. Therefore, the study recommends immediate action of conservation and management planning processes to save the lake from further degradation.

The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling

Northern Eurasia is extensive and includes terrestrial and aquatic ecosystems that cover several natural zones and access to the seas of three oceans. As a result, it has been invaded by numerous invasive alien species (IAS) over large temporal and spatial scales. The purpose of this research was to assess invasion trends and construct species distribution models for the Russian TOP-100 most dangerous IAS. Environmentally suitable regions for IAS were established based on alien species attribute databases, datasets of 169,709 species occurrence records (SOR) and raster layers of environmental variables using species distribution modelling (MaxEnt). The objectives of this research were to (1) create databases of SOR for the TOP-100 IAS in Russia; 2) determine pathways, residence time, donor regions and trends of invasions; (3) determine the main types of spatial distributions of invasive species and their relation to residence time; and (4) distinguish regions with the highest richness of IAS that have a strong impact on the terrestrial and aquatic ecosystems of Russia. We found that although species invasions date back over 400 years, the number of naturalized IAS has increased non-linearly over the past 76 years. The TOP-100 list is mainly represented by unintentionally introduced species (62%) which are characterized by different introduction pathways. Species occurrence records revealed that 56 IAS are distributed locally, 26 are distributed regionally and 18 are widespread in Russia. Species with local, regional or widespread distributions were characterized by residence times of 55, 126 or 190 years, respectively. We found that IAS with local distribution can expand their range into suitable regions more extensively (expected increase by 32%) than widespread species (expected increase by only 7%). The procedure of identifying hot/cold spots locations based on SOR allowed us to identify the Russian regions with the highest richness of IAS. Our results and the integrated database that we created provide a framework for studying IAS over large temporal and spatial scales that can be used in the development of management plans for dangerous IAS.

Improved Automatic Classification of Litho-Geomorphological Units by Using Raster Image Blending, Vipava Valley (SW Slovenia)

Automatic landslide classification based on digital elevation models has become a powerful complementary tool to field mapping. Many studies focus on the automatic classification of landslides’ geomorphological features, such as their steep main scarps, but in many cases, the scarps and other morphological features are difficult for algorithms to detect. In this study, we performed an automatic classification of different litho-geomorphological units to differentiate slope mass movements in field maps by using Maximum Likelihood Classification. The classification was based on high-resolution lidar-derived DEM of the Vipava Valley, SW Slovenia. The results show an improvement over previous approaches as we used a blended image (VAT, which included four different raster layers with different weights) along with other common raster layers for morphometric analysis of the surface (e.g., slope, elevation, aspect, TRI, curvature, etc.). The newly created map showed better classification of the five classes we used in the study and recognizes alluvial deposits, carbonate cliffs (including landslide scarps), carbonate plateaus, flysch, and slope deposits better than previous studies. Multivariate statistics recognized the VAT layer as the most important layer with the highest eigenvalues, and when combined with Aspect and Elevation layers, it explained 90% of the total variance. The paper also discusses the correlations between the different layers and which layers are better suited for certain geomorphological surface analyses.

MONITORING OF LAND SURFACE DISPLACEMENT BASED ON SBAS-INSAR TIME-SERIES AND GIS TECHNIQUES: A CASE STUDY OVER THE SHIRAZ METROPOLIS, IRAN

Abstract. Nowadays, subsidence as one of the natural disasters, threaten urban environments. Over the past three decades, the small baseline subset (SBAS) InSAR method due to its capability to monitor medium to large-scale deformations, has emerged as an important tool for estimating surface displacements. In densely vegetated environments, however, the SBAS technique is not effective due to the decorrelation effect. Since the SBAS technique can estimate millions of points for subsidence, these data can be used as input to the Kriging method to estimate the unknown points. Therefore, in this study, we identified the subsidence disaster-stricken-urban areas in the Shiraz metropolis over a 4-year period (between 2017 and 2021) based on the fusion of the SBAS technique and geospatial information science (GIS) using Sentinel-1A time-series images. In the GIS part of this research, the ordinary kriging method was used for interpolation of the SBAS data. Also, the kernel estimation density (KED) was used for the preparation of the raster layers of a population and the built-up density. Based on 33 radar images in ascending and descending orbit, a total of 95 interferograms have been computed. In this research, temporal baselines of less than 100 meters and multi-look window dimensions of 200 meters were used. What is more, the amount of coherence was 0.8 in estimating displacements. Based on the results, the highest displacements occurred in districts 2, 4, 7, and 11. The maximum value of the displacement (30 mm per year) was seen in the southeast of the Shiraz metropolis, in district 7. Moreover, the surface displacement was mainly observed in the lands of the Shiraz international airport and some of the agricultural lands located in district 2. The reasons for the displacements in districts 4 and 11 are related to the high density of the building coverages in these regions. Also, the increased population density was another reason for the subsidence in district 4. It can be concluded that the suggested framework is efficient in determining time-series surface movements in extensive urban and non-urban areas.

APPLICATION OF GIS BASED MODEL IN LANDFILL SITE SELECTION: A CASE STUDY OF LAHORE, PAKISTAN

Open dumping sites for solid wastes in Asian developing countries, including Pakistan, have recently received particular attention in terms of environmental pollution problems. In the Lahore District, Mehmood Booti is the only authorized open dumping site developed by city district government which is now unable to fulfill district’s requirement. Therefore the data was collected to propose a new landfill site in Lahore district, Pakistan. These data sets involved the processing of spatial information that was utilized in GIS environment using Boolean Operation as a tool for land suitability analysis. In order to achieve desired results, five input raster layers; including water bodies, roads, important places, existing dumping site and tube well were prepared. After omission of unsuitable areas, the suitability analysis of the residue areas was accomplished.

Estimating water erosion in the EL-Mador Valley Basin, South-West Matrouh City, Egypt, using revised universal soil loss equation (RUSLE) model through GIS

Water erosion is among the largest and most crucial problems with soil loss and that is through land degradation in semi-arid regions and the North African areas are especially vulnerable to it. In addition, the EL-Mador Valley Basin is exposed to soil degradation due to climate and topographic properties as well as a human activity. In this scope, the aim of this study is to perform an evaluation of soil degradation hazards in the El-Mador Valley Basin by employing the equation RUSLE and the data of RS within a frame GIS. Furthermore, various raster layers have been designed to RUSLE parameters, such (R), (K), (LS), (C), and (P), and these inputs were merged into a GIS-based framework for measuring the degree of soil degradation. The results demonstrate the ranged values from 0 to > 2500 ton ha–1 year–1, with a geographical average of 54.80 ton ha–1 year–1. In addition, a soil erosion hazard map was created which was divided into five hazard categories: Low (56.56%), Moderate (13.88%), High (8.09%), Severe (8.82%), and Extreme (12.65%). Moreover, these results will be important to create mitigation strategies for regions where the soil loss is high, severe, and extreme, and also to assisting the local administration in setting priorities and basis for decision-makers to create suitable actions to decrease soil erosion hazards.