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The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates (UAE). The current study aims to delineate AGR potential zone mapping using the traditional analytical hierarchy process (AHP) and a hybrid deep learning model namely, Convolutional Neural Network-Xtreme Gradient Boosting (CNN-XGB) was used for the optimal prediction-based suitability assessment. A total of nine hydrogeological factors were considered for AGR mapping. First, the influence of each parameter was determined based on expert opinion and literature reviews for the AHP approach (0.007 consistency ratio). Second, a hybrid CNN-XGB model (90.8 % accuracy) predicted the AGR and non-AGR classes as part of binary classification and generated an AGR potential zone map. Moreover, the contributing factors were analyzed deeply for the AGR site selection to understand the intercorrelation, importance, and prediction interaction. Using both approaches, a comparative assessment was conducted in the eastern, central, and western parts of Sharjah. The AGR zone based on the CNN-XGB model achieved a precision of (0.8168), recall (0.7873), and F1-score (0.8018). The critical contributing factors for AGR mapping were found to be geology (20%), geomorphology (15%), rainfall (10%), and groundwater level (10%). The AGR map is expected to help explore new sites with potentially higher favourability to retain water, deal with water scarcity, and improve water management in the UAE. [ABSTRACT FROM AUTHOR]

Understanding urban sprawl and its drivers is crucial for sustainable urban development. Most studies on Chinese urbanization have focused on coastal areas, paying little attention to urban centers in western China. This study examines urban expansion based on the Google Earth Engine (GEE), remotely sensed image, urban expansion model, and analysis of buffer and quadrant location in the Geographic Information System (GIS). Additionally, driving forces of urban expansion are examined based on the principle component analysis (PCA). Results indicate that urban land area increased more than 5.60 times, reaching 124,723 ha, an increase of over 400 % during 1990–2020. The urban expansion rate and intensity significantly increased and exhibited spatio-temporal heterogeneity. We identified that urban spatial expansion patterns changed from patch filling to patch border expansion, and urban expansion direction was mainly in the southern, northeastern, southwestern, and northwestern regions, extending along the traffic corridor, ring road, and adjacent cities. We suggest that economic development, population, and urbanization have become the driving factors of urban expansion. The GEE provides a new geographic processing algorithm based on massive image datasets, facilitating remote sensing processing. The results revealed that Chengdu is following trends witnessed in coastal cities of China; however, the significance of various drivers of urban expansion in these cities differs from that of the eastern cities. This study will help formulate policies for better urban land management and sustainable land development. [ABSTRACT FROM AUTHOR]

[Display omitted] • GSREI regarding accumulated subsidence, subsidence rate, and elevation. • VEI from population, GDP, construction land, viaduct and metro density. • Risk assessment model and the GSHI index. Urban ground subsidence (GS) causes ground elevation loss, threatens the safe operation of various facilities, and affects surface runoff and hydrological circulation. Therefore, it is vital to monitor the current status of GS to predict and evaluate potential risks to sustainable urban development. Sentinel SAR images from 2019 to 2020 combined with the data from the 2019–2020 Statistical Yearbook were used as data sources. Based on PS-InSAR and SBAS-InSAR, GIS was integrated with the AHP method. A ground subsidence risk evaluation index (REI) suitable for Shanghai was developed regarding three aspects of the amount of accumulated land subsidence (RS obtained): subsidence rate, and terrain elevation. The vulnerability evaluation index (VEI) was established from five aspects: population density, GDP per unit area, the proportion of construction land, viaduct density, and metro density. We introduced the fuzzy AHP method to complete the risk assessment of ground subsidence in Shanghai and obtained the ground subsidence hazard index (GSHI). We used GIS superimposed remote sensing images to visualize the individual evaluation indicators REI , VEI , and GSHI. Using GIS superimposed remote sensing images, various evaluation indicators and GSHI were visualized. Based on the evaluation results, targeted prevention and control recommendations are provided. The results of this research can provide a basis for scientific decision-making regarding Shanghai's territorial spatial planning and socio-economic development. [ABSTRACT FROM AUTHOR]

The present research aims to find the groundwater prospective areas by using Geographic Information System and Remote Sensing based frequency ratio technique. The data sets that affect groundwater such as geomorphology, lithology, slope, soil, rainfall, drainage density, lineament density and land use/ land cover were taken under consideration for finding the groundwater prospective areas in the Bundelkhand region, India. Thematic maps of all the above-mentioned factors have been prepared from the remotely sensed data, data taken from the different organizations and departments. The weights of each attribute class of each thematic layer are assigned according to the frequency ratio technique. A total, 799 numbers of spring locations and discharge rate data of 108 numbers of well locations have been taken under consideration for obtaining the frequency ratio of each attribute class of every parameter used and testing the obtained result, respectively. Finally, all layers were combined by raster calculator command in ArcGIS software to develop the map of groundwater prospective zones in the Bundelkhand region. The produced groundwater prospective areas were classified into five different zones such as very high, high, moderate, low and very low prospective zones and verified by actual well yield data. [ABSTRACT FROM AUTHOR]

Regional-scale assessment of the damage caused by earthquakes to structures is crucial for post-disaster management. While remote sensing techniques can be of great help for a quick post-event structural assessment of large areas, currently available methods are limited to the detection of severely-damaged buildings. Furthermore, remote sensing-based assessment methods typically provide only qualitative results, as they lack integration with information on the building's behaviour in response to seismic-induced ground shaking. In this study, we developed a new methodology that uses airborne Light Detection And Ranging (LiDAR) data in combination with structural indicators of building response to provide a quantitative assessment of earthquake-induced damage at a regional scale. LiDAR datasets collected before and after an earthquake are used to measure residual displacements of building roofs. The resulting lateral drift estimations are used to quantify the level of damage for a specific building typology. Application to the LiDAR datasets collected before and after the 2014 earthquake in Napa Valley, California, demonstrates the capability of the proposed method to detect moderate levels of structural damage, proving its potential for faster and more accurate support to post-disaster management. • Regional-scale building damage assessment using remote sensing LiDAR data. • Quantitative building damage assessment by integrating remote sensing and structural analysis. • Successful detection of moderate levels of building damage. [ABSTRACT FROM AUTHOR]

The main aim of this review paper is to evaluate and make recommendations on how current and emerging remote sensing (RS) technology might be best used to improve vegetation condition assessment and monitoring. This research reviews the vegetation attributes used in various approaches to vegetation condition assessment, the most efficient and rapid techniques to assess those attributes, and proposes applicable suggestions for future vegetation condition assessment using fusion and ensemble techniques. The attributes are those that have strong correlations with components of vegetation condition and are expected to produce trustable indications of change. Further to this, it aims to identify those vegetation attributes that can be best assessed using field survey and those that can be remotely measured world-wide. Vegetation has various structural, functional and compositional characteristics. To measure specific vegetation characteristics, the suitable type of RS sensor is required. Multi-spectral, hyperspectral, Radio Detection And Ranging (RADAR) and Light Detection And Ranging (LiDAR) are the main types of RS sensors, and each type has a range of applications. A variety of automated and repeatable methods are provided by RS technology to monitor the indicators of vegetation condition. However, dependency on site-based data remains. Further work is essential to find a rapid, cost effective and transferable RS method to map and monitor vegetation condition. Moreover, near future improvements in RS, such as Sentinel products, are expected to ease the process of vegetation condition assessment and enhance the outcomes. [ABSTRACT FROM AUTHOR]

Environmental management of an area describes a policy for its systematic and sustainable environmental protection. In the present study, regional environmental vulnerability assessment in Hirakud command area of Odisha, India is envisaged based on Grey Analytic Hierarchy Process method (Grey–AHP) using integrated remote sensing (RS) and geographic information system (GIS) techniques. Grey–AHP combines the advantages of classical analytic hierarchy process (AHP) and grey clustering method for accurate estimation of weight coefficients. It is a new method for environmental vulnerability assessment. Environmental vulnerability index (EVI) uses natural, environmental and human impact related factors, e.g., soil, geology, elevation, slope, rainfall, temperature, wind speed, normalized difference vegetation index, drainage density, crop intensity, agricultural DRASTIC value, population density and road density. EVI map has been classified into four environmental vulnerability zones (EVZs) namely: ‘low’, ‘moderate’ ‘high’, and ‘extreme’ encompassing 17.87%, 44.44%, 27.81% and 9.88% of the study area, respectively. EVI map indicates that the northern part of the study area is more vulnerable from an environmental point of view. EVI map shows close correlation with elevation. Effectiveness of the zone classification is evaluated by using grey clustering method. General effectiveness is in between “better” and “common classes”. This analysis demonstrates the potential applicability of the methodology. [ABSTRACT FROM AUTHOR]

Abstract: Researching the long-term and dynamics law of runoff has important scientific and practical significance for the sustainable using and planning the water resources. Studying the relationship between changes of vegetation cover and runoff dynamics is the basis for regulation of water resources. Vegetation regulation of runoff was affected by vegetation type, age, spatial distribution of coverage. Application of remote sensing (RS) and geographic information system (GIS) analyzes the changes of vegetation cover in watershed, and using the gray relationship analysis researches the relevancy of vegetation change and the runoff, and discusses the laws and mechanisms of vegetation cover affecting the runoff. Reliable results, but still facing many challenging scientific problems, theories and methods must be resolved. [Copyright &y& Elsevier]

Abstract: The effect of geographical factors on soil erosion is an important aspect of the monitoring environment changes. The spatial relationship between soil erosion and its geographical factors was carried out. The results showed that soil erosion exhibited high selectivity of geographical factors. 94.5% of soil erosion occurred in areas less than 500 m, 85.3% presented on the areas between 15°- 35°, 57.2% distributed on southern slope, and 76.8% of strong erosion were found in garden land. It provided a basis for governing soil erosion, which was an important significance to local economic development. [Copyright &y& Elsevier]

Abstract: Recently there have been reports of forest regrowth occurring in different regions across the world. There is also a growing recognition of the potential beneficial impact that secondary forests may have on the global environment: providing crucial ecosystem services such as soil conservation, stabilization of hydrological cycles, carbon sequestration, and support for forest dependent communities. Consequently, there is a growing awareness of the need to recognize that landscapes are complex shifting mosaics wherein forest clearing and reforestation take place. In this study, the rates of reforestation, deforestation, forest regrowth and degradation were measured using multi-temporal Landsat images of Danjiangkou, China. Landsat data from 1990, 1999 and 2007 were (1) classified as dense forest, open forest and non-forest areas and (2) compared between years to identify forest cutting, regeneration and degradation. The results showed that there was a net gain of 29,315ha of forest area (including dense and open forest) from 1990 to 2007, showing a clear trend of reforestation in the study area. Forest modification (degradation and regrowth) and change categories (deforestation and reforestation) occurred simultaneously during the observation time period. Socioeconomic data from public statistics and environmental attributes allowed the assessment of the socioeconomic factors and the environmental conditions that caused these changes using non-metric multidimensional scaling (NMDS). The research showed that the socioeconomic factors due to different policies were major driving forces of forest transition, whereas environmental attributes of the underlying landscape constrained forest cover changes. These findings have led to a better understanding of forest transition at a local scale in our study region. Comprehensive knowledge of these relationships may be useful to reconstruct past forest transitions and predict future changes, and may help to enhance sustainable management practices aimed at preserving essential ecological functions. [Copyright &y& Elsevier]

Based on remote sensing (RS) which has integrated and realistic characteristics, geographic information system (GIS) which has powerful spatial analysis ability, and artificial neutral network (ANN) which can optimize nonlinear complex systems, the forest volume in Huzhong forest area was estimated. The results showed that there was an obvious negative correlation between the forest volume and infrared band, indicating that infrared band had definite potential in estimating forest volume. The forest volume also negatively correlated with visible band and PC1. Among the topographic factors, altitude exerted more influence than aspect and slope on the estimation of forest volume. The correlation coefficient of predicted value and actual value reached to 0.973, when the optimal ANN parameter, suitable GIS information, and RS bands were adopted. After principal component transformation, the amount of observation data was effectively reduced, while the predicted precision only had a small decline (R² = 0.934). [ABSTRACT FROM AUTHOR]

Abstract: In this study, remote sensing (RS) with computer-based geographic information systems (GIS) techniques are used as a tool for monitoring the water basin area and water quality in Istanbul''s relatively less polluted and comparatively less destroyed catchment of the metropolis drinking water dam reservoir named Terkos. It is necessary to work with recent data to be able to identify the effects of urbanization on the water quality of the Terkos dam catchment area that supplies drinking water to the metropolis. RS is an important tool to monitor water quality and urban terrain. For this aim, a project has been initiated at the Technical University Remote Sensing Laboratory, under the Istanbul Water and Sewerage Administration (ISKI) sponsorship in Istanbul. The project uses SPOT-PAN, XS and IRS-1C/D PAN and satellite data of 1993 and 2000 for urban analysis and Landsat-TM and LISS-III satellite data of 1992 and 2000 for water quality. For calibration and validation, ground truth samples are collected from the experimental area. The RS data was converted into the UTM coordinate system and image enhancement and classification techniques are used. Raster data is converted to vector data to assess the study area for analyzing in GIS for the purpose of planning and decision-making on protected water basin zones. As a result of monitoring land use and water quality changes, recommendations are made for planning and management of the protected environment of the Terkos catchment protected area. Measuring land use change is a very important issue for controlling the future development of the basin, GIS techniques are performed and results are illustrated in established models on the four protected zones of Terkos water basin. [Copyright &y& Elsevier]