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The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study's methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map's accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research's results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Satellite remote sensing is currently an established, effective, and constantly used tool and methodology for monitoring agriculture and fertilisation. At the same time, in recent years, the need for the detection of livestock manure and digestate spreading on the soil is emerging, and the development of spectral indices and classification processes based on satellite multispectral data acquisitions is growing. However, the application of such indicators is still underutilised and, given the polluting impact of livestock manure and digestate on soil, groundwater, and air, an in-depth study is needed to improve the monitoring of this practice. Additionally, this paper aims at exposing a new spectral index capable of detecting the land affected by livestock manure and digestate spreading. This indicator was created by studying the spectral response of bare soil and livestock manure and digestate, using Copernicus Sentinel-2 MSI satellite acquisitions and ancillary datasets (e.g., soil moisture, precipitation, regional thematic maps). In particular, time series of multispectral satellite acquisitions and ancillary data were analysed, covering a survey period of 13 months between February 2022 and February 2023. As no previous indications on fertilisation practices are available, the proposed approach consists of investigating a broad-spectrum area, without investigations of specific test sites. A large area of approximately 236,344 hectares covering three provinces of the Emilia-Romagna Region (Italy) was therefore examined. A series of ground truth points were also collected for assessing accuracy by filling in the confusion matrix. Based on the definition of the spectral index, a value of the latter greater than three provides the most conservative threshold for detecting livestock manure and digestate spreading with an accuracy of 62.53%. Such results are robust to variations in the spectral response of the soil. On the basis of these very encouraging results, it is considered plausible that the proposed index could improve the techniques for detecting the spreading of livestock manure and digestate on bare ground, classifying the areas themselves with a notable saving of energy compared to the current investigation methodologies directly on the ground. [ABSTRACT FROM AUTHOR]

Soil erosion represents a complex ecological issue that is present on a global level, with negative consequences for environmental quality, the conservation and availability of natural resources, population safety, and material security, both in rural and urban areas. To mitigate the harmful effects of soil erosion, a soil erosion map can be created. Broadly applied in the Balkan Peninsula region (Serbia, Bosnia and Herzegovina, Croatia, Slovenia, Montenegro, North Macedonia, Romania, Bulgaria, and Greece), the Erosion Potential Method (EPM) is an empirical erosion model that is widely applied in the process of creating soil erosion maps. In this study, an innovation in the process of the identification and mapping of erosion processes was made, creating a coefficient of the types and extent of erosion and slumps (φ), representing one of the most sensitive parameters in the EPM. The process of creating the coefficient (φ) consisted of applying remote sensing methods and satellite images from a Landsat mission. The research area for which the satellite images were obtained and thematic maps of erosion processes (coefficient φ) were created is the area of the Federation of Bosnia and Herzegovina and the Brčko District (situated in Bosnia and Herzegovina). The Google Earth Engine (GEE) platform was employed to process and retrieve Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) satellite imagery over a period of ten years (from 1 January 2010 to 31 December 2020). The mapping and identification of erosion processes were performed based on the Bare Soil Index (BSI) and by applying the equation for fractional bare soil cover. The spatial–temporal distribution of fractional bare soil cover enabled the definition of coefficient (φ) values in the field. An accuracy assessment was conducted based on 190 reference samples from the field using a confusion matrix, overall accuracy (OA), user accuracy (UA), producer accuracy (PA), and the Kappa statistic. Using the confusion matrix, an OA of 85.79% was obtained, while UA ranged from 33% to 100%, and PA ranged from 50% to 100%. Applying the Kappa statistic, an accuracy of 0.82 was obtained, indicating a high level of accuracy. The availability of a time series of multispectral satellite images for each month is a crucial element in monitoring the occurrence of erosion processes of various types (surface, mixed, and deep) in the field. Additionally, it contributes significantly to decision-making, strategies, and plans in the domain of erosion control work, the development of plans for identifying erosion-prone areas, plans for defense against torrential floods, and the creation of soil erosion maps at local, regional, and national levels. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.

Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time. [ABSTRACT FROM AUTHOR]

Wildfires are a common disturbance factor worldwide, especially over the last decade due to global climate change. Monitoring postfire forest regrowth provides fundamental information needed to enhance the management and support of ecosystem recovery after fires. The purpose of this study is to propose an algorithm for postfire forest regrowth monitoring using tasseled-cap-derived indices. A complex approach is used for its implementation, for which a model is developed based on three components—Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA). The final product—postfire regrowth (PFIR)—allows for a quantitative assessment of the intensity of regrowth. The proposed methodology is based on the linear orthogonal transformation of multispectral satellite images—tasseled cap transformation (TCT)—that increases the degree of identification of the three main components that change during a fire—soil, vegetation, and water/moisture—and implies a higher accuracy of the assessments. The results provide a thematic raster representing the intensity of the regrowth classes, which are defined after the PFIR threshold values are determined (HRI—high regrowth intensity; MRI—moderate regrowth intensity; and LRI—low regrowth intensity). The accuracy assessment procedure is conducted using very-high-resolution (VHR) aerial and satellite data from World View (WV) sensors, as well as multispectral Sentinel 2A images. Three different forest test sites affected by fire in Bulgaria are examined. The results show that the classified thematic raster maps are distinguished by a good performance in monitoring the regrowth dynamics, with an average overall accuracy of 62.1% for all three test sites, ranging from 73.9% to 48.4% for the individual forests. [ABSTRACT FROM AUTHOR]

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands. [ABSTRACT FROM AUTHOR]

Carrying out land use statistics through remote sensors and Geographic Information Systems (GIS) allows obtaining not only numerical information but also thematic mapping of land uses. It is an important element for the analysis of territorial dynamics, allowing for interpretations to be made about the functioning and structure of the space to be analyzed, which results in very useful information for researchers or decision makers in general. This article presents the results of the analysis of the mapping of peri-urban land uses obtained by remote sensing in the period 1990-2023, for four neighborhoods in the city of Santa Rosa, La Pampa. The dynamics of the main land uses of the study area are analyzed, through the quantification and spatial distribution of eight identified categories: continuous and discontinuous urban; in the process of urbanization or vacant; industrial, commercial and other non-residential uses; urban equipment; horticultural crops and water. For the study and evolution of spatial distribution patterns of these categories, a cartographic overlay methodology is proposed by using GIS tools. [ABSTRACT FROM AUTHOR]

Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Desertification assessment is vital to develop actions plans aimed to prevent desertification issues. Utilizing Geographic Information Systems (GIS) to manipulate and analyze data retrieved from various sources of data including satellite images and thematic maps, as well as the use of remote sensing through satellite images to retrieve environmental information on the earth's surface, are crucial tools to understand related environmental dangers, which is used to monitor desertification and investigate different desert conditions. In this study, different physical and environmental factors associated with land desertification including vegetation cover, water, drainage systems, geomorphology, and soil moisture, were extracted and evaluated using various remote sensing techniques. In addition, multi-temporal analysis of Landsat TM data is used in this study to examine the state of vegetation and growth of irrigated area between 1983 and 1997. A GIS tool is utilized to aggregate and analyze different factors including soil, land use, and climate. A decrease in vegetation cover was clearly observed based on these analysis results. A severe desertification of the natural vegetation cover is the primary cause of this predicament. Finally, many maps describing the actual situation of study area is obtained following the GIS layers examination, however, the most important maps are soil erosion, vegetation degradation, salinization and desertification. [ABSTRACT FROM AUTHOR]

In arid and hyper-arid areas, groundwater is a precious and rare resource. The need for water supply has grown over the past few decades as a result of population growth, urbanization, and agricultural endeavors. This research aims to locate groundwater recharge potential zones (GWPZs) using multi-criteria evaluation (MCE) in the Wadi Qena Basin, Eastern Desert of Egypt, which represents one of the most promising valleys on which the government depends for land reclamations and developments. These approaches have been used to integrate and delineate the locations of high groundwater recharge and the potential of the Quaternary aquifer in the Wadi Qena basin. After allocating weight factors to identify features in each case based on infiltration, land use/land cover, slope, geology, topology, soil, drainage density, lineament density, rainfall, flow accumulation, and flow direction, these thematic maps were combined. The results of the GIS modeling led to the division of the area's groundwater recharge potential into five groups, ranging from very high (in the western part) to very low (in the eastern part of the basin). The zones with the best prospects for groundwater exploration turned out to be the alluvial and flood plains, with their thick strata of sand and gravel. The groundwater recharge potential map was validated using data from the field and earlier investigations. The promising recharging areas show high suitability for soil cultivation. The results overall reveal that RS and GIS methodologies offer insightful instruments for more precise assessment, planning, and monitoring of water resources in arid regions and anywhere with similar setups for groundwater prospecting and management. [ABSTRACT FROM AUTHOR]

There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU's policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R 2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model's interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector. [ABSTRACT FROM AUTHOR]

The West Bank is characterized by the diversity of its climate despite its small area. It includes four climatic regions:- a humid, semi-humid, arid, and semi-arid climate. This in turn affected the geographical distribution of vegetation cover seasonally and over the years. This study investigated changes in the West Bank, Palestine vegetation cover using multitemporal Landsat data. Four images were selected for this purpose – two corresponding to 2001 and the other two corresponding to 2021. Seasonal change of the Normalized Difference Vegetation Index (NDVI) was investigated for the acquired images. ArcGIS 10.8 software was used for image processing and analysis. Results showed that the negative change of the NDVI of autumn for both dates is much higher than the positive change. About 98.89 percent of the West Bank area scored a negative change in 2001, while about 80.79 percent of the West Bank scored a negative change in 2021. The negative change in autumn is due to the dryness of summer season, which in turn led to the drying of the green grasses and herbs that grow among the trees and on the eastern slopes of the highlands. Concerning the NDVI change between 2001 and 2021, results showed a considerable positive change in the NDVI. Around 90.91% of the surface area of the West Bank (5132.304 km²) has positive change, while only 9.09% (513.405 km²) which mainly represents urban centers has negative change. The main reason that the NDVI in 2021 was higher than the NDVI in 2001 outside the urban centers is that the amount of rain in that year was greater, especially in the northern areas. Thematic maps of NDVI for the two dates were produced, and changes in vegetation cover were extracted from the four maps. Based on the results of the study, it is recommended that decision-makers in the Ministry of Agriculture and Environmental organizations develop plans to increase permanent green spaces, especially in arid and semi-arid areas of the West Bank. [ABSTRACT FROM AUTHOR]

The distribution of malaria is characterised by microgeographic variations determined by a range of factors, including the local environment. A study on the spatial distribution of malaria and land cover patterns was carried out by sampling Primary Health Centres in Ogoni Land. Nine Primary Healthcare Centres (PHCs) were selected across four local government areas (LGA) using Systematic Grid Point Sampling. Human blood samples were obtained from 318 consented individuals, and questionnaires were administered to obtain demographic data. Plasmodium species were identified through microscopy using thick and thin blood films. A geodatabase was created and imported into ArcGIS 10.7 to produce a thematic map of the study area. A cloud-free Landsat-8 Operational Land Imager (OLI) was employed for land cover analysis. Both supervised and unsupervised classifications of land cover were performed to generate the land cover classes. Pearson correlation was carried out to determine the significance between malaria distribution and land cover. Of the 318 individuals, 169 were infected with an overall prevalence of 53.1%.Only P. falciparum was identified and malaria distribution showed spatial variations. Across the PHCs sampled, the highest point prevalence was recorded in Model Primary Health Centre Koroma in Tai LGA whereas the lowest was recorded in MPHC Okwale in Khana LGA. Cumulatively, Kwawa PHC recorded the highest malaria prevalence whereas MPHC Bunu in Tai recorded the lowest prevalence. The highest prevalence was recorded in Khana LGA while the lowest was recorded in Eleme LGA. Land cover analysis revealed that Ogoni Land has a total land cover mass of 982.97km.2 Sparse vegetation dominated the study area (471.06km2) while dense vegetation covers a total mass of 213.1km2. Bivariate analysis showed a significant correlation between malaria prevalence and dense vegetation (p<0.05, 0.952). Dense vegetation played a significant role in malaria transmission in Ogoni Land. The study concludes that the presence of dense vegetation is associated with high malaria prevalence in the study area. [ABSTRACT FROM AUTHOR]

Water erosion caused by rainfall and runoff, in the Morocco is the main threat of fertile agricultural soil losses causing important silting-up of dams. Most watersheds are characterized by excessive values of Soil Specific Degradation (Average annual soil loss per year and per Km2), exceeding in many regions 2000T/Km2/year. The main objective of this study is to investigate this phenomenon in the Tamdrost watershed located in northwestern Morocco. The methodology is based on integrating water erosion RUSLE model to Geographic Information System and satellite image processing. The aim of the study is to develop digital mapping for the main factors involved in the erosion processes as well as the variation of the average annual quantity of soil losses. The results outcome are: (i) the average value of the specific degradation is about 80 (T/ha/year), reaching a maximum value of 800 T/ha/year and a minimum value of 3 T/ha/year; (ii) The main factors that control water erosion by order of importance are successively: R, LS, K, and C factors. Finally, different maps representing erosion and the main factors involved, are be very helpful for decision makers to better assess this phenomenon and to implement anti-erosion measures in the threatened areas to support and control the water erosion.

Flooding is one of the most catastrophic natural disasters in terms of provoking socio-economic losses. The current study is to foster a flood susceptibility map of Krishna District in Andhra Pradesh (AP) through integrating remote sensing data, geographical information system (GIS), and the analytical hierarchy process (AHP). Eleven factors, including elevation, slope, aspect, land use/land cover (LULC), drainage density, topographic wetness index, stream power index, lithology, soil, precipitation, and distance from the streams, are considered for identifying and evaluating the spatial distribution of critical flood-susceptible regions. Thematic maps of different factors were derived in GIS using remote sensing data obtained from Sentinel-2A (satellite sensor), shuttle radar topography mission digital elevation model (SRTM DEM v3), and other scientific data products. An analytical hierarchy process is a mathematical approach for decision support, primarily based on the weight and rank of different causative factors. AHP technique is implemented for flood hazard modeling and ascertaining the Flood Hazard Index (FHI) to produce a flood susceptibility map. Different thematic maps weighed with the AHP framework are combined using overlay analysis to produce the flood susceptibility map of the study region. The outcomes of the study demonstrate the potential of GIS and AHP in providing a premise to recognize the vulnerable areas that are susceptible to flood. According to the findings, the Flood Hazard Index is 42% and the study region is classified into very high, high, moderate, low, and very low susceptible, respectively. Following that, historical flood data was used to validate the accuracy of the generated flood susceptibility map. This shows that a maximum of 90% of the data points are within floodplain. [ABSTRACT FROM AUTHOR]

The article is a book review of "Top 20 Essential Skills for ArcGIS Pro" by Bonnie Shrewsbury and Barry Waite. The book focuses on teaching novices or beginners the necessary skills for using ArcGIS Pro, Esri's desktop GIS software. It covers 21 chapters, each addressing a specific skill, such as exploring the interface, creating thematic maps, and working with imagery. The reviewer found the book to be a useful and clear guide, with realistic and practical exercises that can help beginners start using the software professionally. However, they also noted some areas where the book could have provided more information, such as accessing and editing metadata and using network tracing tools. [Extracted from the article]

The largest reservoir of drinkable water on Earth is surface water. It is crucial for maintaining ecosystems and enabling people to adapt to diverse climate changes. Despite surface freshwater is essential for life, the current research shows a striking lack of understanding in its spatial and temporal dynamics of variations in outflow and storage across a sizable country: India. Numerous restrictions apply to current research, including the use of insufficient machine learning techniques and limited data series. This work uses cutting-edge and SOTA-method to use the available data and machine learning to accurately understand spatial and temporal dynamics of variations in surface freshwater outflow and storage using extended data series. The authors did the examination of thematic maps produced using ArcMap 10.8 from June'2005 to June'2020 using JRC dataset to track changes in the intensity of surface water. Google Earth Engine in Python API has been devised to detect changes in surface water levels and quantifying shifting map trends. Raster image viewing, editing, and calculation are done with ArcMap. For determining the relationship between declines in Surface water levels, changes in rainfall intensity and land surface temperature, variables were averaged over 13 rivers for 15 years. The change in surface water is reliant on independent variables of change in land surface temperature and rainfall intensity. The authors use the correlation between these parameters to achieve an average R-squared adjusted value of 0.402. The study's findings contribute to a better understanding of the matter and can be used across the world. [ABSTRACT FROM AUTHOR]

Water erosion caused by rainfall and runoff, in the Morocco is the main threat of fertile agricultural soil losses causing important silting-up of dams. Most watersheds are characterized by excessive values of Soil Specific Degradation (Average annual soil loss per year and per km²), exceeding in many regions 2000 T/km²/year. The main objective of this study is to investigate this phenomenon in the Tamdrost watershed located in northwestern Morocco. The methodology is based on integrating water erosion RUSLE model to Geographic Information System and satellite image processing. The aim of the study is to develop digital mapping for the main factors involved in the erosion processes as well as the variation of the average annual quantity of soil losses. The results outcome are: (i) the average value of the specific degradation is about 80 (T/ha/year), reaching a maximum value of 800 T/ha/year and a minimum value of 3 T/ha/year; (ii) The main factors that control water erosion by order of importance are successively: R, LS, K, and C factors. Finally, different maps representing erosion and the main factors involved, are be very helpful for decision makers to better assess this phenomenon and to implement antierosion measures in the threatened areas to support and control the water erosion. [ABSTRACT FROM AUTHOR]

Groundwater is an essential reservoir of freshwater. It is becoming more critical for arid and semi-arid regions due to climate changes and long drought periods. The proposed work aims at providing a solution for data scarcity regions in addition to the improvement of the reliability of machine learning methods for groundwater mapping due to low samples. We propose the use of neighboring data by defining an extended study area that includes the study area where extra boreholes are known. In addition, we propose the use of piezometric contours to augment the size of the dataset using artificial boreholes. Seven thematic maps are prepared to characterize the study area and the extended area: digital elevation, slope, drainage, lithology, lineaments, rainfall, and land use. On the other hand, random forest, SVM, and neural networks are used to learn an appropriate model on the augmented dataset. To validate the work, 233 boreholes are classified based on their groundwater static level. In addition, several metrics are used to validate the performance such as ROC-AUC, accuracy, and κ score. The Random forest shows the best performance in terms of all measures: AUC = 0.95, accuracy = 79.82%, and κ = 0.73. The obtained maps show that 7% of the study area can have very high potential which can be used for resource management. [ABSTRACT FROM AUTHOR]

Remote sensing, regardless of the nature of the applications, uses electromagnetic radiation to obtain images of bodies from a certain altitude (plane, satellite, balloon, helicopter) because in this way, the image can be used to obtain maps and plans, and interpretation of objects is optimal and easy. The remote sening (RS) techniques are used in a lot of studies, in the dynamics of changes in the cover of the vegetal carpet, in forecasting studies, in various fields such as: agriculture, environmental protection, forestry. Remote sensing systems are developing rapidly and support the decision-making process due to the effectiveness of remote sensing images that can provide useful spectral information. The present research had as its main purpose the study of the dynamics of entire UAT Satchinez, based on the spectral information obtained from Ladsat 8 satellite images and processed with specialized GIS and remote sensing programs namely ERDAS Imagery and ArcGIS, on the basis of which thematic maps were created with various combinations of spectral bands and 3 normalized indices of differentiation, useful in monitoring and analysis processes of the territory. It was downloaded and processed a satellite scene from the LANDSAT 8 remote sensing system, who receives satellite images in 11 spectral bands. Based on the spectral bands, various differentiation indexes useful in territorial analysis can be calculated: NDVI, SAVI, NDMI. These indices are calculated based on mathematical formulas that use the spectral bands of the remote sensing system used. [ABSTRACT FROM AUTHOR]

The use of remote sensing and Geographic Information System (GIS) are among the most efficient modern tools to study the varied natural resources in terms of localization, identification of characteristics, and the study of its dynamics. Thus, the aim of this study is to show the importance of remote sensing and Geographical Information System in studying the Guercif irrigated plain. We will first process and analyze satellite images using the program (Erdas IMAGINE 15. 00) and then create thematic maps illustrating the irrigated area's evolution (ArcGIS 10.8). The results revealed that since the late 20th century, the area of Guercif Plain has expanded significantly, with the total irrigated space that has been doubled many times by 2021. Significant landscape transformations accompanied this evolution, for Guercif Plain has turned from a barren and dry to a green agricultural space. [ABSTRACT FROM AUTHOR]

The concept of planetary mapping constitutes different activities within different contexts. Much like the field of cartography, it is an amalgamation of science, techniques, and artistic disciplines. It has undergone considerable changes over the last decades to cope with increasing demands related to data management, analysis, and visualization. Planetary mapping employs abstraction, which involves simplifications and generalizations. It aims to produce accessible visualization of planetary surfaces to gain insights and knowledge. Here, we show that different manifestations of this concept are interdependent and we discuss how different mapping concepts relate to each other semantically. We reason that knowledge gain can only be achieved through thematic mapping. The reasoning for systematic mapping and exploration is an intellectual product of thematic mapping. In order to highlight these relationships, we (a) develop in-depth definitions for different types of planetary mapping, (b) discuss data and knowledge flow across different mapping concepts, and (c) highlight systemic limitations related to data that we acquire and attempt to abstract through models. We finally develop a semantic proto-model that focuses on the transformation of information and knowledge between mapping domains. We furthermore argue that due to compositionality, map products suffer not only from abstraction but also from limitations related to uncertainties during data processing. We conclude that a complete database is needed for mapping in order to establish contextualization and extract knowledge. That knowledge is needed for reasoning for planning and operational decision making. This work furthermore aims to motivate future community-based discussions on functional semantic models and ontologies for the future development of knowledge extraction from thematic maps. [ABSTRACT FROM AUTHOR]

Remote sensing methods provide information on both the natural objects on the earth's surface, water bodies, water-land boundaries, and relief and relief forms. Satellite images were used to determine the characteristics of the coastline and Black Sea area (about 50 km distance) of the Republic of Bulgaria. Its fractal dimension and curvature are determined. A number of thematic maps have been generated through the digital elevation model to assist in the interpretation and geomorphologic characteristics of the study area. A database was created as part of the project MARINEGEOHAZARD - "Set-up and implementation of key core components of a regional earlywarning system for marine geohazards of risk to the Romanian-Bulgarian Black Sea coastal area" from CBC Romania-Bulgaria Programme. [ABSTRACT FROM AUTHOR]

Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services. [ABSTRACT FROM AUTHOR]

Preservation and revitalization of our cultural and natural heritage has a vital role not only in understanding our past, but also in our future development as humans. This paper aims to present the current directions and the use of remote sensing satellite imagery in identifying different terrain forms, routes, objectives and analyzing their evolution over time both for cultural and environmental purposes. A case study was carried out to investigate the contribution of processing remote sensing images to the research and detection of an important former commercial connection between Europe and Asia, the old Silk Road. The analysis of the multi-spectral remote sensing data was performed with the help of innovative programs developed by PIESAT Information Technology Co Ltd. These softwares use an object-oriented analyzing method and have made a qualitative leap in image information extraction. The actuality of the paper is given by the fact that preserving cultural heritage and historical sites represents an important issue that must be taken into account when sustainable development projects are required for environmental policies. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring and assessing the phytosanitary status of forests is a crucial activity in forest protection field, and the use of satellite remote sensing has become increasingly prevalent in this regard. Satellite images offer valuable information in terms of time and space, particularly through the analysis of vegetation and biophysical indices. Within this context, the aim of the study was to explore the potential of satellite remote sensing methods to monitor and assess the extent of tree defoliation caused by the gypsy moth (Lymantria dispar). The research study was focused on a forest situated in the Oltenia region of Romania, which experienced a gradation period for the defoliator L. dispar between 2018 and 2020. To determine the extent of defoliation caused by L. dispar in 2020 and 2021, a rectangular network consisting of 70 one-hectare sample areas was implemented. Each sample area had a square shape with a side of 100 m. In each corner of the sample areas, sub-sample circles with a radius of 12.62 meters were established, in which visual observations regarding the defoliation levels were conducted. The study involved a comparison between thematic maps derived from ground observations and those based on specific remote sensing indices derived through the processing of Sentinel 2 satellite images, of the Ciuturica forest area during the years 2020 and 2021. The research study indicated that the canopy water content (CWC) index was the most suitable for assessing defoliation caused by L. dispar. Furthermore, the study highlighted that remote sensing methods could be very effective and easily applicable, complementing the field groundbased efforts in monitoring and assessing the impact of L. dispar infestations. [ABSTRACT FROM AUTHOR]

In this article, a part of Wadi Al-Sahba, Saudi Arabia, is tested for revealing and modelling potential area of groundwater using integrated data derived from remote-sensing, geology and hydrology through GIS-based analytical hierarchy process method. Nine thematic maps e.g. geology, stream-networks, topography, slope, lineaments, depressions, radar intensity, rainfall and land use/cover that control the occurrence of groundwater were weighted and combined to quantify the potential area of groundwater resources. The output prospection map revealed five predicting zones of groundwater (GPZs) are very low (11.55%), low (28.18%), moderate (29.14%), high (22.41%) and very high (8.73%). The most predicted areas are covering wide part of Al Kharj and downstream of Wadi Al-Sahba. Information derived from well data (# 59), thermal sensor and interferometric SAR coherence change detection data are validated the prospective map. Overall, integration of remote sensing data has the capability for revealing and predicting potential areas of groundwater resources. [ABSTRACT FROM AUTHOR]

In this article, GIS, remote sensing (RS) and fuzzy AHP are used to develop a methodology to delineate the groundwater potential zones. GIS-RS technique is used to prepare the different thematic maps that influence the groundwater potentiality. Fuzzy AHP method is used to assign weights and fuzzy ratings to different thematic layers and their attributes classes, respectively. After assigning the weight and rating, Groundwater potential index is calculated by multiplying each thematic layer weights to its attribute class and adding all of them. Thus, the calculated groundwater potential index (GWPI) is used to classify the different groundwater potential zones. The groundwater in the study area is classified into five different classes such as very low (0.51% of total area), low (13.58%), moderate (61.25%), high (23.42%) and very high (1.24%). The delineated groundwater potential zone map is verified using the well depth data, obtained from the central groundwater board (CGWB), India. [ABSTRACT FROM AUTHOR]

In recent years, deep learning (DL) algorithms have been widely integrated for remote sensing image classification, but fewer studies have applied it for land consumption (LC). LC is the main factor in land transformation dynamics and it is the first cause of natural habitat loss; therefore, monitoring this phenomenon is extremely important for establishing effective policies and sustainable planning. This paper aims to test a DL algorithm on high-resolution aerial images to verify its applicability to land consumption monitoring. For this purpose, we applied a convolutional neural networks (CNNs) architecture called ResNet50 on a reference dataset of six high-spatial-resolution aerial images for the automatic production of thematic maps with the aim of improving accuracy and reducing costs and time compared with traditional techniques. The comparison with the National Land Consumption Map (LCM) of ISPRA suggests that although deep learning techniques are not widely exploited to map consumed land and to monitor land consumption, it might be a valuable support for monitoring and reporting data on highly dynamic peri-urban areas, especially in view of the rapid evolution of these techniques. [ABSTRACT FROM AUTHOR]

A study was conducted in the Muttom–Mandaikadu coastal region, which is among the profitable coastal sectors in Tamil Nadu, to find the groundwater potential as well as its quality by an integrated geospatial, geophysical and geochemical approach. The GIS-based weighted overlay analysis was used to merge five thematic layers to create the groundwater potential zone map. The geophysical resistivity survey was performed in the study area at 26 stations by applying Schlumberger vertical electrical sounding technique. The observed data were inverted to develop a subsurface lithology model and its electrical properties using one-dimensional software AGI Earth Imager. The combined vertical electrical sounding result and remote sensing thematic maps have exposed the potential zone of groundwater in the study area. From the inferred results, it was observed that 20.8% of the area has ample groundwater potential and 7.7% of the area has scanty groundwater potential. The saltwater intrusion zone had been predicted by validating aquifer resistivity with Dar-Zarrouck (D-Z) parameter. From the geophysical and geochemical interpreted results, it was found that aquifers in 34.6% of the study area are vulnerable to saline contamination. The 4-D model with integrated groundwater quantity and quality suggests that the study area's Western part falls under excellent-to-good groundwater potential zone and excellent water quality. [ABSTRACT FROM AUTHOR]

Abstract Groundwater plays a vital role in the sustainable development of agriculture, society and economy, and it's demand is increasing due to low rainfall, especially in arid and semiarid regions. In this context, delineation of groundwater potential zones is essential for meeting the demand of different sectors. In this research, the integrated approach consisting of analytical hierarchy process (AHP), multiple influence factors (MIF) and receiver operating characteristics (ROC) was applied. The demarcation of groundwater potential zones is based on thematic maps, namely Land Use/Land Cover (LULC), Digital Elevation Model (DEM), hillshade, soil texture, slope, groundwater depth, geomorphology, Normalized Difference Vegetation Index (NDVI), and flow direction and accumulation. The pairwise comparison matrix has been created, and weights are assigned to each thematic layer. The comparative score to every factor was calculated from the overall weight of two major and minor influences. Groundwater potential zones were classified into five classes, namely very poor, poor, moderate, good and very good, which cover an area as follows: 3.33 km2, 785.84 km2, 1147.47 km2, 595.82 km2 and 302.65 km2, respectively, based on AHP method. However, the MIF groundwater potential zones map was classified into five classes: very poor, poor, moderate, good and very good areas covered 3.049 km2, 567.42 km2, 1124.50 km2 868.86 km2 and 266.67 km2, respectively. The results of MIF and AHP techniques were validated using receiver operating characteristics (ROC). The result of this research would be helpful to prepare the sustainable groundwater planning map and policy. The proposed framework has admitted to test and could be implemented in different in various regions around the world to maintain the sustainable practices.

Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual crop type maps is the collection of sufficient training data for supervised classification. Classification based on unsupervised domain adaptation, which uses prior information from the source domain for target domain classification, can solve the impractical problem of collecting sufficient training data. This study presents self-training with domain adversarial network (STDAN), a novel unsupervised domain adaptation framework for crop type classification. The core purpose of STDAN is to combine adversarial training to alleviate spectral discrepancy problems with self-training to automatically generate new training data in the target domain using an existing thematic map or ground truth data. STDAN consists of three analysis stages: (1) initial classification using domain adversarial neural networks; (2) the self-training-based updating of training candidates using constraints specific to crop classification; and (3) the refinement of training candidates using iterative classification and final classification. The potential of STDAN was evaluated by conducting six experiments reflecting various domain discrepancy conditions in unmanned aerial vehicle images acquired at different regions and times. In most cases, the classification performance of STDAN was found to be compatible with the classification using training data collected from the target domain. In particular, the superiority of STDAN was shown to be prominent when the domain discrepancy was substantial. Based on these results, STDAN can be effectively applied to automated cross-domain crop type mapping without analyst intervention when prior information is available in the target domain. [ABSTRACT FROM AUTHOR]

This paper illustrates the GIS-based Applications web marketing: Turkey Historical Site in Khartoum, The Motivation depend on Role Geoinformatics Technology for Evaluate the historical sites. The objectives are Analysis, Design, and Implementation Turkey Historical Site in Khartoum. The Importance to support the decision Makers/ Supports. The Methodology is Geoinformatics Phases (Analysis, Design, and Implementations). The importance Results: Design Turkey GeoHistorical Data Model, Build Turkey GeoHistorical Geodatabase, implementation Turkey GeoHistorical Applications Map, Develop Turkey GeoHistorical thematic Map. We recommendations: Design Turkey GeoHistorical virtual Reality, Implementation Turkey GeoHistorical Map in Sudan, Implementation Turkey GeoHistorical Application in Sudan. The Future Papers: Implementation Turkey GeoHistorical 3D Model in Sudan. [ABSTRACT FROM AUTHOR]

Wheat basic seedling number is one of the most important sources of the total number of wheat ears. In turn, the leading factor can also dominate the wheat yield per unit area. It is an essential prerequisite for the timely and accurate acquisition of the within-field spatial difference information of the wheat basic seedling number. The variable-rate topdressing of nitrogen fertilizer can then be implemented in the manner of precision agriculture. The population density of wheat tillers can often be regulated to realize the fertilizer reduction with a better yield. Unmanned Aerial Vehicle (UAV) remote sensing imagery can be efficiently obtained at the field level in recent years. However, the vegetation and background features can be only processed without considering the influence of mixed pixels of the imagery in the traditional agricultural UAV remote sensing applications. The accuracy and reliability of wheat basic seedling number inversion cannot fully meet the large-scale production in smart agriculture. In this study, the quantitative inversion accuracy of wheat basic seedling numbers was improved using the mixed pixel decomposition model of UAV remote sensing imagery. Firstly, the UAV remote sensing imagery was acquired with a spatial resolution of about 2.5 cm using DJI Mini drone. The relative radiometric calibration was then completed using the invariant target method. Furthermore, the endmembers of vegetation and soil, as well as the mixed pixels were extracted from the reflectance image, which accounted for 2.23%, 0.28%, and 97.49% of the pixels, respectively. The spectral signatures were acquired for the endmembers of vegetation and soil using the reflectance values. Consequently, the decomposition model was established using mixed pixels of UAV remote-sensing images. The linear decomposition was used to divide each mixed pixel into 2 components of vegetation and soil. The abundance data was acquired for each component. The vegetation abundance model was used to calculate the Fractional Vegetation Coverage (FVC) of the experimental field. The proportions of vegetation endmember and abundance were then evaluated over the total area of “1m and 2 rows”. Finally, a linear regression model was established between the FVC and the ground truth data of 15 sets of wheat basic seedling numbers. The determination coefficient R² was 0.87. Besides, the regression model was verified using 3 other ground truth data of wheat basic seedling numbers. The verification results show that the Root Mean Square Error (RMSE) was 1.97 seedlings/m² . The higher inversing accuracy was achieved in this case, compared with the average wheat basic seedling number of 217.442 seedlings/m² for the wheat field. A comparative experiment was performed on the FVC thematic maps. The traditional vegetation index method was used, including the Visible-band Difference Vegetation Index (VDVI), Green Red Difference Index (GRDI), and Green Red Ratio Index (GRRI). The linear regression models were then established between each FVC of VDVI, GRDI, GRRI, and ground truth data of wheat basic seedling number. The determination coefficient R² and RMSE were calculated as 0.79, 0.56, 0.47, and 6.06, 7.04 and 4.43 seedlings/m2, respectively. Therefore, better performance was achieved in the quantitative inversion model of the wheat basic seedling number using the mixed pixels decomposition of UAV remote sensing images. The findings can provide data support for the precise variable topdressing of nitrogen fertilizer at the tillering stage of wheat. [ABSTRACT FROM AUTHOR]

Quantile, equal interval, and natural breaks methods are widely used data classification methods in geospatial analysis and cartography. However, when applied to data with skewed distributions, they can only reveal the variations of either high frequent values or extremes, which often leads to undesired and biased classification results. To handle this problem, Esri provided a compromise method, named geometric interval classification (GIC). Although GIC performs well for various classification tasks, its mathematics and solution process remain unclear. Moreover, GIC is theoretically only applicable to single-peak (single-modal), one-dimensional data. This paper first mathematically formulates GIC as a general optimization problem subject to equality constraint. We then further adapt such formulated GIC to handle multi-peak and multi-dimensional data. Both thematic data and remote sensing images are used in this study. The comparison with other classification methods demonstrates the advantage of GIC being able to highlight both middle and extreme values. As such, it can be regarded as a general data classification approach for thematic mapping and other geospatial applications. [ABSTRACT FROM AUTHOR]

In the dryland areas watershed development is one of the major programs of the Government of India. Ever since its initiation (1981), large tracts of degraded lands are treated through this approach. Remote sensing (RS) is a tool that has been successfully applied to capture groundwater potentials in different regions and geological units. In this study RS & GIS tools are applied to correlate groundwater potentials with various geological controls and watershed development activities at Anjeni watershed, Udaipur district, Rajasthan, India. In the study reference periods taken are year 2013 (pre-project) and 2016 (post-project). A hydro-geomorphological approach integrating remote sensing applications, field investigations, geological and hydrological studies were undertaken to delineate and study the groundwater potential along with recharge dynamics before and after intervention situation. Thematic maps were prepared through linking the RS data with morphometric characteristics, topographic analysis, land use/cover assessment and groundwater conditions. Geographical Information System (GIS) was used to integrate all the contributing factors like lithology, geomorphology, LU/LC, lineaments, drainage, and slope with the help of weighted overlay analysis. Merging the characteristics of groundwater potential, hydro-geomorphology, geology, water harvesting structures, morphometric analysis, lineament density studies, and water balance elements provide an effective approach for the delineation of potential sites for groundwater. This integrated approach has successfully captured the changes that occurred in groundwater situation of the Anjeni watershed in form of poor to very high groundwater potential zones. It establishes the importance of application of RS-GIS in flagship development program of the watershed management. [ABSTRACT FROM AUTHOR]

The objective of this work was to carry out a multitemporal analysis of changes in land use and land cover in the municipality of Floresta, Pernambuco State in Brazil. Landsat images were used in the years 1985, 1989, 1993, 1997, 2001, 2005, 2009, 2014, and 2019, and the classes were broken down into areas: water, exposed soil, agriculture, and forestry, and using the Bhattacharya classifier, the classification was carried out for generating land use maps. The data was validated by the Kappa index and points collected in the field, and the projection of the dynamics of use for 2024 was constructed. The thematic maps of land use and coverage from 1985 to 2019 show more significant changes in the forest and exposed soil classes. The increase in the forest class and the consequent reduction in exposed soil are consequences of the interaction between climate and human activities and the quality of the spatial resolution of the satellite images used between the years analyzed. [ABSTRACT FROM AUTHOR]

This study aims to introduce a semi-automatic classification workflow for the production of a land use/land cover (LULC) map of the island of Sardinia (Italy) following the CORINE legend schema, and a ground spatial resolution compatible with a scale of 1:25.000. The classification is based on free high-resolution satellite imagery from Sentinel-1 and Sentinel-2 collected in 2020, ancillary data derived from Sardinian Geoportal, Joint Research Centre (JRC) and OpenStreetMap. The LULC map production includes three steps: 1) pixel-based classification, realized with two different approaches, that use i) information derived from existing thematic maps eventually re-coded in case of incoherencies observed between datasets and/or satellite data products, and ii) spectral indices and parameter thresholds defined on the basis of multitemporal analysis; 2) segmentation of Sentinel-1 and 2 annual composites, and pre-labelling of segments with the pixel-based classified map, obtaining the preliminary map; 3) visual inspection procedure in order to confirm, or re-assign, classes to polygons. The accuracy of the preliminary map was tested in a sample area and on specific class of non-irrigated crops through ground truth data collected from a detailed photo-interpretation, estimating 97% of overall accuracy. The results show a great improvement from existing thematic maps in terms of detail, with the possibility of a yearly updating of the map via automatic processes. However, some limitations were found, due to the high fragmentation of Sardinian landscape and the high variety of crop types and agricultural practices, that could affect the efficiency of the classifier. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Owing to the extensive global dependency on groundwater and associated increasing water demand, the global groundwater level is declining rapidly. In the case of Islamabad, Pakistan, the groundwater level has lowered five times over the past five years due to extensive pumping by various departments and residents to meet the local water requirements. To address this, water reservoirs and sources need to be delineated, and potential recharge zones are highlighted to assess the recharge potential. Therefore, the current study utilizes an integrated approach based on remote sensing (RS) and GIS using the influence factor (IF) technique to delineate potential groundwater recharge zones in Islamabad, Pakistan. Soil map of Pakistan, Landsat 8TM satellite data, digital elevation model (ASTER DEM), and local geological map were used in the study for the preparation of thematic maps of 15 key contributing factors considered in this study. To generate a combined groundwater recharge map, rate and weightage values were assigned to each factor representing their mutual influence and recharge capabilities. To analyze the final combined recharge map, five different assessment analogies were used in the study: poor, low, medium, high, and best. The final recharge potential map for Islamabad classifies 15% (136.8 km2) of the region as the "best" zone for extracting groundwater. Furthermore, high, medium, low, and poor ranks were assigned to 21%, 24%, 27%, and 13% of the region with respective areas of 191.52 km2, 218.88 km2, 246.24 km2, and 118.56 km2. Overall, this research outlines the best to least favorable zones in Islamabad regarding groundwater recharge potentials. This can help the authorities devise mitigation strategies and preserve the natural terrain in the regions with the best groundwater recharge potential. This is aligned with the aims of the interior ministry of Pakistan for constructing small reservoirs and ponds in the existing natural streams and installing recharging wells to maintain the groundwater level in cities. Other countries can expand upon and adapt this study to delineate local groundwater recharge potentials. [ABSTRACT FROM AUTHOR]

Desertification is a serious problem in most arid and semi-arid areas of the world, and especially in Iran. Combating desertification planning and any preparatory measures require detailed mapping of the current status of desertification as well as its evolvement during the time. This study, basically relying on remote sensing datasets, strives to map desertification in the Sabzevar area of Iran and identify its changes between the years 2001 and 2020. The indicators used in this study included Enhanced Vegetation Index, Vegetation Condition Index, Salinity Index, Synthetized Drought Index, Temperature Condition Index, Precipitation, Normalized Vegetation Index, and Land Surface Temperature. Analytical Hierarchical Process was used to determine the weight of the factors and the fuzzy overlay approach was used for synthesizing the thematic layer maps. According to the expert judgments, precipitation and salinity are the most important desertification factors in the area (~ 50% of the total weight). In terms of land-use changes over the past 20 years, urban, desert, croplands and bare lands grew in size at the expense of rangelands and open canopy forests which shrank greater than 3.5% (435 km2). Based on our findings, the total area of desertification classes has not changed considerably during the period but there has been a growth in very high and high desertification intensity classes by 12%. According to our findings, very high and high classes have increased between the years 2001 and 2020 mostly in the central and southern parts of the study area. The results obtained in this research are very important to the land managers of the Khorasan Razavi Province, for formulating sustainable development programs and avoiding the unwanted consequences of desertification. [ABSTRACT FROM AUTHOR]

The present study was carried out to identify of the groundwater potential zones (GWPZ) in the northern part of the Anantapur district of Andhra Pradesh State, India using Remote Sensing (RS), Geographical information system (GIS), and Analytical Hierarchy Process (AHP) approaches. In this study, various thematic maps categorized viz. geomorphology (GM), lineament density (LD), drainage density (DD), geology, land use/land cover (LULC), soils, slope, and rainfall for assessment of GWPZs, which is generated using RS and GIS technique. Furthermore, the relative weights were allocated to various thematic maps using the AHP approach and the relative rank assigned to each sub-criterion based on expert advice. The combination of the eight thematic layers in ArcGIS resulted in a groundwater potential map, providing the information about very good 2.45% (87.06 km2), good 12.76 (452.56 km2), moderate 63.47% (2250.75 km2), poor 15.99% (567.16 km2), and very poor 5.32% (188.73 km2) groundwater possible zones. The acquired outcomes were validated with the area under the curve (AUC/ROC) method. The results show that there is a strong positive correlation between the GWPZs with 78% validation high performance and decreases to the low yield potential with poor areas. This study concludes that the AHP model will be a more reliable for the assessment of the GWP. Any groundwater management project carried out in these favourable regions would benefit the stack holders. [ABSTRACT FROM AUTHOR]

Southwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-scale mountainous grassland distribution data to support the development of sustainable grassland husbandry. Here, we proposed a sample selection method and comprehensively utilized multi-source data to obtain the quasi-10 m southwest grassland distribution data. The sample selection method was to first determine the sample selection range based on multi-source land use/cover database, and then to randomly select the samples under the constraint of secondary land use types, multiple factors of terrain and pure pixels. This method can deal with the difficulty in identifying the fragmented grassland distribution caused by steep mountains and hills. In addition, a multispectral time series dataset was constructed based on the fusion of Landsat 8 OLI and Sentinel-2A/B data due to cloudy and rainy weather and was used as one of the input features along with synthetic aperture radar Sentinel-1 time series data and the terrain multi-factor data. Finally, a remote sensing method to accurately identify grassland distribution in southwest China was constructed based on the Google Earth Engine (GEE) platform. Taking Zhaotong City, a prefecture-level city in Yunnan Province, as an example, a thematic map of grassland distribution with an overall accuracy of 88.21% was obtained using the above method. This map has been used by the local government of Zhaotong City in their planning of the development of sustainable grassland husbandry. [ABSTRACT FROM AUTHOR]