Applied Geomatics最新文献

One of the most significant natural resources, groundwater is essential to providing a long-term, reliable and sustainable global water supply. Therefore, delineating Groundwater potential zones (GWPZs) is crucial in effectively managing groundwater reserves. The present study attempts to delineate GWPZs in the Siliguri subdivision of West Bengal using integrated Remote Sensing (RS), Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) in the light of a considerable shift in the patterns of groundwater usage, especially considering the ongoing rise in demand for groundwater owing to a variety of causes. Raster layers of fourteen causative factors Viz. geomorphology, lithology, lineament density, soil texture, elevation, slope, land use and land cover (LULC), river density, rainfall, pre-monsoon groundwater depth, post-monsoon groundwater depth, groundwater fluctuation, topographic wetness index (TWI) and topographic roughness index (TRI) are used to delineate GWPZs using AHP in GIS software. The final GWPZs map was categorised into five classes: very high (25.67%), High (31.77%), moderate (20.73%), low (17.67%) and very low (4.15%). The results are further validated by evaluating the receiver operating characteristic (ROC) curve with the groundwater level depth from 39 dug wells. The ROC curve shows that the AUC value is 0.818, representing a prediction accuracy of 81.80%. The comprehensive map of GWPZs will enhance managing natural assets to guarantee the continued preservation of water resources and the development of agriculture. The method utilised in this research may be used in other natural contexts with a comparable environment.

The saturated soil hydraulic conductivity (K_sat) exhibits high spatial variability due to the various physical, chemical, and biological processes that act simultaneously with different intensities in soil formation. The use of geostatistics as a tool to study soil heterogeneity facilitates the understanding of the spatial variability of K_sat. This study aimed to simulate the spatial variability of K_sat and evaluate its uncertainties using sequential Gaussian simulation (SSG) in a tropical watershed located in southern Brazil. Soil sampling was conducted in an experimental watershed-scale grid with a sample spacing of 300 m, and K_sat was analyzed. Descriptive statistics were applied to assess the behavior of K_sat spatial variability, followed by geostatistical analysis, specifically SSG. Variogram parameters were defined, and SSG was used to generate 100 equiprobable random fields. The results showed that K_sat in the Santa Rita watershed (SRW) is heterogeneous, and uncertainties among the hundred fields ranged from 58.70 to 81.10 mm h-1 for the 5th and 95th percentiles, respectively, possibly influenced by soil type, land use, density, and texture. The criteria for validating SSG simulation were met and successfully described the spatial continuity of K_sat in the SRW. Thus, SSG proved to be an effective tool for understanding the magnitude and structure of K_sat spatial variability at the watershed scale, contributing to effective soil and water management in the SRW.

Water is a vital resource used in effective sanitation, hygiene, drinking and agricultural uses. This study was in Obudu Plateau covering an area of 3,053.08km². Analysis of remote sensing, geographic information system (GIS), and global positioning system (GPS) data obtained from satellite imageries and digital elevation model (DEM) assessed groundwater potential. The model was validated using borehole data in the area. Thematic layers of geology, lineament density, slope, geomorphology, land use and land cover and drainage density were integrated using GIS software. Multicriteria evaluation of the layers was by analytic hierarchy process (AHP). Pairwise comparison matrix shows consistency the consistency ratio is 0.07 or 7%. This shows the comparison of groundwater controlling factors is within acceptable limit of consistency. Overlay analysis produced groundwater potential map classified into five zones of very high 2.66% (81.31km²), high 6.92% (211.38km²) very low 9.60% (292.69km²), moderate 46.95% (1,433.46 km²) and low 33.87% (1,034.23km²). Structural geological setting determines largely the suitability of an area to groundwater occurrence. Overlaying each thematic layer with lineament density map produced a more credible groundwater potential model compared to preceding related works. This method is suitable for both local and regional groundwater development.

In the Khaled-Teboursouk basin (Southern Mediterranean Basin), karstic aquifers are the main sources of drinking and irrigation water. They play a crucial role in the socio-economic development of the region. Therefore, the estimation of groundwater recharge is necessary for a good management of water resources, while considering the impacts of climate change. The present study utilizes the application of APLIS method integrated with Geographic Information System (GIS) as a remote sensing technique for geospatial analysis to explore groundwater recharge areas along Khalled-Teboursouk basin, expressed as a percentage of precipitation combined with numerous parameters. The morphology of earth surface features such as Altitude (A), Slope (P), Lithology (L), infiltration (I), and Soil (S) influence the groundwater recharge rate in carbonate aquifers, from the infiltration of rainfall in aquifers in either direct or indirect way. The results revealed that 60–80% of precipitation is identified as high potential for groundwater recharge and it is associated with karstified limestones of Eocene lower age. The gentle slope areas in the Middle-East and Central parts have been moderate potential for groundwater recharge 40–60% of precipitation and they are associated with karstified limestone of Campanian-Maastrichtian age (Abiod Fm.). Hilly terrains with low and very low recharge are the most represented for groundwater recharge processes. They are associated with areas of non-karstified rocks and Quaternary deposits. The dominant water type of the groundwater in this area is Ca–Mg–Cl–SO₄ water type. The Total Dissolved Solids (TDS) of these waters (0.37 to 3.58 g/l) are slow in the recharge area and high in the discharge area. This is caused by rapid circulation of water from the recharge areas to the discharge points. The aquifers have been recharged by rainfall originating from a mixture of Atlantic and Mediterranean vapor masses. The isotope analyses, δ¹⁸O and δ²H ranged from − 6.8 to -5.3‰ (vs. SMOW) and from − 42 to -4‰ (vs. SMOW) respectively, confirm the recent recharge of these carbonate aquifers.

Frequency-Area Distribution (FAD) analyses were introduced to landslides research since the early 2000’s. This technique is a powerful tool that allows assessing the completeness of landslide inventory maps (LIM), used to build both landslides susceptibility and landslides hazard assessment models. However, FAD analyses are not commonly used in such studies despite the significant potential of the technique. The long processing steps needed to generate FAD curves, which involve logarithmic binning and iterative model fitting using various statistical tools, constitutes an energy and time-consuming task that pushes many researchers away from using the technique. In fact, no fully automatic tool capable of generating FAD curves and models exists as of July 2023. Therefore, we attempt to provide a fully automatic computer program capable of binning, fitting FAD curves and assessing their goodness of fit to theoretical models in a fully automatic, one step process. An example is provided using real data from Taounate province, Northern Morocco, so as to demonstrate the ability of the tool to deal with exhaustive datasets. In addition, the Kolmogorov-Smirnov, goodness of fit test is added to provide an objective assessment of the data fitting, which constitutes a better alternative to the subjective visual assessment that most landslides researchers rely on. To sum up, we believe that this tool will help popularize the FAD technique, which will consequently improve the accuracy and objectivity of landslides risk and hazard assessment disciplines.

Rapid urbanisation, especially in developing countries like India, has resulted in unplanned and haphazard urban expansion. With saturated urban cores, growth is observed in the peri-urban areas, resulting in severe challenges for urban planners. The present study aims to study the urban growth patterns of the fast-growing Mumbai Metropolitan Region (MMR) using the Landsat data from 1999 to 2019 and to evaluate the neighbourhood configurations’ effect on urban growth prediction. The urban area maps are classified using a maximum likelihood algorithm and are used along with the potential drivers to test three levels of neighbourhood considerations. The first model assumes no neighbourhood effect, the second incorporates the built-up pixels in the neighbourhood as an additional potential driver variable, and the third uses a Cellular Automata (CA). The CA model explores variations in neighbourhood types and sizes, distance decay and iterations to identify the optimal configuration. The results show an 89.44% increase in built-up areas over two decades (1999-2019). The urban growth prediction model testing reveals the importance of neighbourhood, with the first model without neighbourhood consideration giving the least accuracy (67%) while the inbuilt neighbourhood model gives better results (71%). However, the CA-based model with a 9 × 9 Moore neighbourhood, distance exponent β = 2 and two iterations give the highest accuracy (76%). The growth prediction shows a new wave of peri-urban growth in MMR, with overall urban areas increasing by 25% between 2019 and 2029 and 20% between 2029 and 2039. The results provide urban planners with a valuable tool for informed decision-making and promoting sustainable development.

Land Surface Temperature (LST) is a vital satellite remote sensing-driven indicator of earth heat studies. LST can provide information about urban heat emission, urban climate, and human activities in urban areas. In recent years, the calculated LST for a satellite image pixel has been studied as a parameter affected by urban environment factors such as available land cover types in the same pixel. However, in this study, a scenario in which the calculated LST for a pixel is not only affected by the factors in the same pixel but also by the factors in the neighbor pixels is studied. Firstly, required maps for the calculated LST and influential factors (indicators of vegetation, building, and water surfaces) are produced from satellite remote sensing images. Secondly, the relationship between the LST and influential factors is modeled using the Ordinary Least Squares (OLS) model. Thirdly, Moran’s I and Lagrange Multiplier tests are used to analyze the existence of spatial dependency and correlation in residuals of the OLS model. Fourthly, three spatial regression models (Spatially Lagged X (SLX), Spatial Lag (SL), and Spatial Error (SE) models) are used to model the spatial dependency and correlation between the LST and influential factors. Finally, the outcomes of the models are compared and evaluated. Results present related maps for the variables besides maps for spatial residuals in the spatial regression models. The outcomes of the models are investigated by p-values, log-likelihood, and RMSE. To conclude, the spatial regression models fitted the relation between the dependent and independent variables better than the OLS model.

Rice is the staple food of more than half of the world’s population, especially in Asia, where rice provides more than 50% of the caloric supply for at least 520 million people, most of them are either extremely impoverished or poor. Information on rice production is thus essential for agricultural management and the formulation of food security policies. The objective of this research is to develop an approach combining remote sensing and artificial intelligence (AI) techniques with a crop growth model for enhancing yield estimation and crop management in Taiwan. The data processing involves three main steps: (1) data pre-processing to generate model inputs, (2) crop yield modeling through assimilating satellite-derived leaf area index (LAI) into a crop growth model using the AI particle swarm optimization (PSO) algorithm, and (3) model validation. The assimilation process was performed using a cost function based on the difference between remotely-sensed and simulated LAI values. The optimization process began with an initial parameterization and appropriately adjusted input parameters in the model. The fitness value derived from a cost function was determined using the PSO. The results of yield estimates obtained from the crop growth model based on optimized inputs were evaluated using the government’s yield statistics, revealing close agreement between these two datasets. The root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) for the first crop were 19.8% and 17.1%, and the values for the second crop were 8.4% and 6.3%, respectively. The relative percentage error (RPE) values of 18.5% and − 5.1%, respectively, showed a slight overestimate and underestimate for the first and second crops.

The prioritization of watersheds has increasingly become an optimal and relevant approach for the management and planning against natural hazards. This approach is based on the morphometric analysis of the watersheds according to some parameters and indicators. In this study, we adopted this approach using Geographic Information System (GIS) techniques to identify the priority sub-watersheds of the Inaouene River upstream of the Idris I dam. This watershed, which is part of the Sebou watershed with an area of approximately 3608.2 km², is made of up 38 sub-watersheds and an area of gulleys. The results showed that 57.89% of the Inaouene River’s sub-watersheds have high to very high priority. The most important ones are Lahdar, El Melah 1, Gherghab, Larbaâ, and Mezwarou watersheds. By unveiling the distinctive morphometric characteristics of the watershed, this study enhances our understanding of its hydrological behavior, while providing crucial data to support soil and water conservation measures. This ensures sustainable agriculture, preserves water quality, and prevents sedimentation in the Idris I dam.

Land subsidence (LS) is among the most prominent forms of subsurface erosion and geomorphological hazards. This study used two deep learning (DL) models consisting of the hybrid CNN-RNN and ensemble DL (EDL) merged with two dense models. The main variables controlling LS (consisting of environmental, hydrological, hydrogeological, digital elevation model, and soil characteristics), were used as the input for the predictive DL models. Likewise, to establish the degree of performance of each parameter, different control points have been established. We then trained and tested our DL models using the receiver-operating characteristic-area under curve (ROC-AUC) and precision-recall plots. The measures based on the game theory consisting of permutation feature importance measure (PFIM) and SHapley Additive exPlanations (SHAP) were employed to assess the features relative importance and interpretability of the predictive model output. Our findings show that the ensemble CNN-RNN model performed well with the ROC-AUC curve (0.95) of class 1 (land subsidence) for training data for detecting and mapping land subsidence compared to EDL with the ROC curve (0.93) of class 1 (land subsidence) for training datasets. The CNN-RNN also performed well with the precision-recall curve (0.954) of class 1 for testing data for detecting and mapping land subsidence compared to the EDL model with the precision-recall curve (0.95) of class 1. The results of this research revealed that much of the study area is susceptible to land subsidence. The results of the model sensitivity analysis suggested that the groundwater drop rate is the most sensitive for the model. Based on the SHAP values, the groundwater drop rate was identified as the most contributed feature to the model output. The importance of this study is at a broader level, especially in arid and semiarid environments with similar geomorphological, and climatic conditions.