Integrated Remote Sensing Research Articles

Quantitative Evaluations of Pumping-Induced Land Subsidence and Mitigation Strategies by Integrated Remote Sensing and Site-Specific Hydrogeological Observations

Land subsidence is an environmental hazard occurring gradually over time, potentially posing significant threats to the structural stability of civilian buildings and essential infrastructures. This study presented a workflow using the SBAS-PSInSAR approach to analyze surface deformation in the Choushui River Fluvial Plain (CRFP) based on Sentinel-1 SAR images and validated against precise leveling. Integrating the InSAR results with hydrogeological data, such as groundwater levels (GWLS), multilayer compactions, and borehole loggings, a straightforward model was proposed to estimate appropriate groundwater level drops to minimize further subsidence. The results showed a huge subsidence bowl centered in Yunlin, with maximal sinking at an average 60 mm/year rate. High-resolution subsidence maps enable the quantitative analyses of safety issues for Taiwan High-Speed Rail (THSR) across the areas with considerable subsidence. In addition, the analysis of hydrogeological data revealed that half of the major compaction in the study area occurred at shallow depths that mainly included the first and second aquifers. Based on a maximal subsidence control rate of 40 mm/year specified in the CRFP, the model results indicated that the groundwater level drops from wet to dry seasons needed to be maintained from 3 to 5 m for the shallowest aquifer and 4–6 m for Aquifers 3 and 4. The workflow demonstrated the compatibility of InSAR with traditional geodetic methods and the effectiveness of integrating multiple data sources to assess the complex nature of land subsidence in the CRFP.

LSTM Model Integrated Remote Sensing Data for Drought Prediction: A Study on Climate Change Impacts on Water Availability in the Arid Region

Climate change is one of the trending terms in the world nowadays due to its profound impact on human health and activity. Extreme drought events and desertification are some of the results of climate change. This study utilized the power of AI tools by using the long short-term memory (LSTM) model to predict the drought index for Anbar Province, Iraq. The data from the standardized precipitation evapotranspiration index (SPEI) for 118 years have been used for the current study. The proposed model employed seven different optimizers to enhance the prediction performance. Based on different performance indicators, the results show that the RMSprop and Adamax optimizers achieved the highest accuracy (90.93% and 90.61%, respectively). Additionally, the models forecasted the next 40 years of the SPEI for the study area, where all the models showed an upward trend in the SPEI. In contrast, the best models expected no increase in the severity of drought. This research highlights the vital role of machine learning models and remote sensing in drought forecasting and the significance of these applications by providing accurate climate data for better water resources management, especially in arid regions like that of Anbar province.

Integrated Remote Sensing Investigation of Suspected Landslides: A Case Study of the Genie Slope on the Tibetan Plateau, China

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability.

Identifying Groundwater Potential Regions in Sokoto Basin, Northwestern Nigeria: An Integrated Remote Sensing, GIS, and MIF Techniques

Identifying Groundwater Potential Regions in Sokoto Basin, Northwestern Nigeria: An Integrated Remote Sensing, GIS, and MIF Techniques

Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery

This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbonate minerals, OH-bearing minerals, and iron oxide minerals. Due to its high spectral resolution in the short-wave infrared region (SWIR), the ASTER sensor provided the most accurate results for mapping carbonate and OH-bearing minerals compared to the Sentinel-2 and Landsat-8 sensors. Conversely, Sentinel-2 offers high spectral and spatial resolution in visible and near-infrared (VNIR) corresponding to the regions where iron oxide minerals exhibit their characteristic absorption peaks. The results confirm the advantages of remote sensing technologies in the geological and mineralogical exploration of the study area and the importance of selecting the appropriate sensors for specific mapping objectives.

An Integrated Remote Sensing and GIS-Based Technique for Mapping Groundwater Recharge Zones: A Case Study of SW Riyadh, Central Saudi Arabia

It might be difficult to find possible groundwater reservoir zones, especially in arid or hilly regions. In the twenty-first century, remotely sensed satellite imagery may present a new opportunity to locate surface and subsurface water resources more quickly and affordably. In order to identify groundwater potential zones, the current study was conducted in Central Saudi Arabia, southwest of Riyadh. The present analysis employed a multi-criteria approach that relies on remote sensing and geographic information systems. The variables employed in this technique include geology, rainfall, elevation, slope, aspect, hillshade, drainage density, lineaments density, and Land Use/Land Cover (LULC). The Analytical Hierarchical Process (AHP) was used for assigning weights to the parameters, and the corresponding significance of each parameter’s several classes for groundwater potentiality. Different groundwater potential zones were identified by the study: very high (16.8%), high (30%), medium (26.7%), low (18.6%), and very low (7.9%). Only two of the observation wells were located in the “medium” potential zone, but the other ten wells were observed in the “very high and high” potential zones, according to the validation survey. Consequently, the results may demonstrate that the current approach, which combines improved conceptualization with AHP to define and map groundwater potential zones, has a greater chance of producing accurate results and can be used to reduce the threat of drought in broader arid regions.

Precision Mapping of Coastal Wetlands: An Integrated Remote Sensing Approach Using Unoccupied Aerial Systems Light Detection and Ranging and Multispectral Data

Coastal wetlands, crucial for global biodiversity and climate adaptation, provide essential ecosystem services such as carbon storage and flood protection. These vital areas are increasingly threatened by both natural and human-induced changes, prompting the need for advanced monitoring techniques. This study employs unmanned aerial systems (UASs) equipped with light detection and ranging (LiDAR) and multispectral sensors to survey diverse wetland types across 8 sites in North Carolina. Utilizing high-resolution elevation data and detailed vegetation analysis, coupled with sophisticated machine learning algorithms, we achieved differentiated and highly precise classifications of wetland types. Classification accuracies varied by type, with estuarine intertidal emergent wetlands showing the highest classification accuracies due to less complex vegetation structure and clearer spectral signatures, especially when collections account for tidal influence. In contrast, palustrine forested and scrub–shrub wetlands presented lower accuracies, often due to the denser, mixed, and more complex vegetation structure and variable inundation levels, which complicate spectral differentiation and ground returns from LiDAR sensors. Overall, our integrated UAS-derived LiDAR and multispectral approach not only enhances the accuracy of wetland mapping but also offers a scalable, efficient, and cost-effective method that substantially advances conservation efforts and informs policy-making for coastal resilience. By demonstrating the usefulness of small-scale aerial data collection in ecological mapping, this study highlights the transformative potential of merging advanced technologies in environmental monitoring, underscoring their critical role in sustaining natural habitats and aiding in climate change mitigation strategies.

Analysis of the Spatial Distribution and Deformation Types of Active Landslides in the Upper Jinsha River, China, Using Integrated Remote Sensing Technologies

The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep terrain or otherwise inaccessible to humans, extensive landslide studies remain challenging using traditional geological surveys and monitoring equipment. Stacking interferometry synthetic aperture radar (stacking-InSAR) technology, optical satellite images and unmanned aerial vehicle (UAV) photography are applied to landslide identification. Small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) was used to obtain time-series deformation curves of samples to reveal the deformation types of active landslides. A total of 246 active landslides were identified within the study area, of which 207 were concentrated in three zones (zones I, II and III). Among the 31 landslides chosen as research samples, six were linear-type landslides, three were upward concave-type landslides, 10 were downward concave-type landslides, and 12 were step-type landslides based on the curve morphology. The results can aid in monitoring and early-warning systems for active landslides within the UJSR and provide insights for future studies on active landslides within the basin.

Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food security. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions.

Integrated Remote Sensing Observations of Radiative Properties and Sources of the Aerosols in Southeast Asia: The Case of Thailand

Aerosols in Southeast Asia (SEA) are entangled with complex land–sea–atmosphere–human interactions, and it is difficult for scientists to understand their dynamic behaviors. This study aims to provide an insightful understanding of aerosols across SEA with respect to their radiative properties using several lines of evidence obtained from remote sensing instruments, including those from onboard Earth observation satellites (MODIS/Terra and MODIS/Aqua, CALIOP/CALIPSO) and from ground-based observation (AERONET). The findings, obtained from cluster analysis of aerosol optical properties, showed seven aerosol types which were dominant across the country, exhibiting diverse radiative forcing potentials. The light-absorbing (prone to warm the atmosphere) aerosols were likely found in mainland SEA, both for background and high-aerosol events. The light-scattering aerosols were associated with aging processes and hygroscopic growth. The neutral potential, which comprised a mixture of oceanic and local anthropogenic aerosols, was predominant in background aerosols in insular SEA. Further studies should focus on carbonaceous aerosols (organic carbons, black carbon, and brown carbon), the aging processes, and the hygroscopic growth of these aerosols, since they play significant roles in the regional aerosol optical properties.

Multitemporal Analysis of Slow-Moving Landslides and Channel Dynamics through Integrated Remote Sensing and In Situ Techniques

The relationships between hillslope and fluvial processes were studied in a mountainous area of the Northern Apennines (Italy) where intermittent landslide activity has interacted for a long time with river morphodynamics. The aim of the study was to analyse such relationships in two study sites of the Scoltenna catchment. The sites were analysed in detail and monitored through time. A long-term analysis was carried out based on multitemporal photointerpretation of aerial photos. Slope morphological changes and land use modifications since 1954 were detected and compared with the evolution of the channel morphology. A short-term analysis was also performed based on two monitoring campaigns accomplished in 2021 and 2022 in order to detect possible slope displacements and channel-bed-level changes. The techniques used are global navigation satellite systems and drone photogrammetry accompanied by geomorphological surveys and mapping. The multitemporal data collected allowed us to characterise slope surface deformations and quantify morphological changes. The combination of various techniques of remote and proximal sensing proved to be a useful tool for the analysis of the surface deformations and for the investigation of the interaction between slope and fluvial dynamics, showing the important role of fluvial processes in the remobilisation of the landslide toe causing the displacement of a significant volume of sediment into the stream.

Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning

This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data processing issues, selecting a suitable machine learning model that can learn from few available time-series data, and understanding the non-linear relationship between historical crop yield and remote sensing and meteorological factors. This study applied a series of data processing techniques and a customized ML model to improve the accuracy of crop yield estimation at the district level in Nepal. It was found that remote sensing-derived NDVI product alone was not sufficient for accurate estimation of crop yield. After incorporating other meteorological variables into the ML models, estimation accuracy improved dramatically. Along with NDVI, the meteorological variables of rainfall, soil moisture, and evapotranspiration also exhibited a strong association with rice yield. This study also found that stacking multiple tree-based regression models together could achieve better accuracy than benchmark linear regression or standalone ML models. Due to the unique and distinct physio-geographical setting of each district, a variation in estimation accuracy from district to district could be observed. Our data processing and ML model workflow achieved an average of 92% accuracy of yield estimation with RMSE 328.06 kg/ha and MAE 317.21 kg/ha. This methodological workflow can be replicated in other study areas and the results can help the local authorities and stakeholders understand the factors affecting crop yields as well as estimating crop yield before harvesting season to ensure food security and sustainability.

Groundwater Potential Zone Mapping in the Ghaggar River Basin, North-West India, Using Integrated Remote Sensing and GIS Techniques

The immense dependence of the growing population on groundwater has resulted in depletion at a fast pace can be seen nowadays. Identifying a groundwater potential zone can be proved as an aid to provide insight to the decision-makers and local authorities for planning purposes. This study evaluated the delineation of groundwater potential zones using integrated remote sensing and GIS approach. Various thematic layers such as geology, geomorphology, lineament, slope, drainage, soil, land use/land cover, and rainfall were considered in this study as these have influence on the occurrence of groundwater and its cycle, and maps have been prepared in GIS domain. Afterward, appropriate weights were assigned to these layers based on multi-criteria decision analysis, i.e., Analytical Hierarchy Process (AHP). Groundwater potentiality has been delineated in different zones (low, moderate, high, and very high) in the study region based on weighted overlay analysis. The study reveals zones with different groundwater prospects viz. low (1.27%), moderate (15.65%), high (75.54%), and very high (7.29%). The ground survey data provided by CGWB (Central Ground Water Board) of nearly 100 wells/dug wells/borewells/piezometers have been used for validation purposes, showing comparable results with the groundwater prospects zones. It also confirms that the majority of these wells fall under very high or high groundwater potential zones. They were also found to be thereby indicating that there is the existence of a permeable reservoir with considerable water storage in the subsurface. One of the most important issues for users and governments is groundwater depletion. Planning for the available groundwater resource is made easier by identifying the potential for groundwater (low to high).

Groundwater Potential Mapping in Lapan Gwari Community Using Integrated Remote Sensing and Electrical Resistivity Soundings

Groundwater Potential Mapping in Lapan Gwari Community Using Integrated Remote Sensing and Electrical Resistivity Soundings

The Investigating Water Infiltration Conditions Caused by Annual Urban Flooding Using Integrated Remote Sensing and Geographic Information Systems

Flood disasters always hit densely populated urban areas during the rainy season. The causes of flooding that will examine in this scientific article are the condition of water infiltration into the soil. The case study was conducted in the urban area of Denpasar, Bali, Indonesia. Remote sensing data derived from various satellite images i.e., Sentinel-2 (BSI and NDVI extraction), Alos Palsar Imagery (slope extraction), CHIRPS (annual rainfall), and soil texture by laboratory analysis. Acquisition of remote sensing data using a Cloud Computing platform named Google Earth Engine (GEE). Data analysis using weighted overlay with ArcGIS 10.8 and threshold classification using natural breaks (Jenks). Denpasar City has the potential for water infiltration is good to very critical conditions. The correlation of the water infiltration map was carried out by comparing flood events in Denpasar City. The correlation results show (R2 = 0.84), (r = 0.916), (RMSE = 0.138), and p-value <0.05, these values indicate very high relation. Flood events often occur in zones with very critical water infiltration with high building density and low vegetated land cover. The condition of water infiltration critical to very critical category, spatially at the proportion of land cover vegetation < 1% and built-up area > 37%.

Identifying and Ranking Landfill Sites for Municipal Solid Waste Management: An Integrated Remote Sensing and GIS Approach

Disposal of municipal solid waste (MSW) is one of the significant global issues that is more evident in developing nations. One of the key methods for disposing of the MSW is locating, assessing, and planning for landfill sites. Faisalabad is one of the largest industrial cities in Pakistan. It has many sustainability challenges and planning problems, including MSW management. This study uses Faisalabad as a case study area and humbly attempts to provide a framework for identifying and ranking landfill sites and addressing MSW concerns in Faisalabad. This method can be extended and applied to similar industrial cities. The landfill sites were identified using remote sensing (RS) and geographic information system (GIS). Multiple datasets, including normalized difference vegetation, water, and built-up areas indices (NDVI, NDWI, and NDBI) and physical factors including water bodies, roads, and the population that influence the landfill site selection were used to identify, rank, and select the most suitable site. The target area was distributed into 9 Thiessen polygons and ranked based on their favorability for the development and expansion of landfill sites. 70% of the area was favorable for developing and expanding landfill sites, whereas 30% was deemed unsuitable. Polygon 6, having more vegetation, a smaller population, and built-up areas was declared the best region for developing landfill sites and expansion as per rank mean indices and standard deviation (SD) of RS and vector data. The current study provides a reliable integrated mechanism based on GIS and RS that can be implemented in similar study areas and expanded to other developing countries. Accordingly, urban planning and city management can be improved, and MSW can be managed with dexterity.

Integrated Remote Sensing to Assess Disease Control: Evidence from Flat Island Quarantine Station, Mauritius

This article presents an integrated approach used in archaeology and heritage studies to examine health and disease management during the colonial period in the Indian Ocean. Long-distance labor migrations had dire health consequences to both immigrants and host populations. Focusing on the quarantine station on Flat Island, Mauritius, this study analyzes a historical social setting and natural environment that were radically altered due to the implementation of health management. Using aerial and satellite imagery, digital elevation models, RTK and total station raw data, 3D modeling, and GIS mapping, we reconstructed the spatial organization and the built landscape of this institution to assess the gap between the benefits claimed by European colonizers and the actual effects on immigrant health conditions through the promotion of public health practices.

Qualitative and Quantitative Characterization of Municipal Waste in Uncontrolled Dumpsites and Landfills Using Integrated Remote Sensing, Geological and Geophysical Data: A Case Study

The conducted research offers an environmental assessment of municipal waste (MW) using remote sensing (RS), geological, and geophysical datasets. As a test site, the present study aims to characterize one of the largest uncontrolled dumpsites in Egypt. RS data analysis indicates that high temperature values are concentrated at the MW and landfill site allocations as a result of the decomposition process, leading to fire risks. Moreover, the geological and structural data draw attention to the structural-controlled old topography role on MW distributions. Consequently, the dumpsite MW has larger surface quantities near the downthrows of the mapped faults. For MW characterization, geophysical data are acquired to produce2D/3D resistivity models. Because MW has the ability to become soft clay, the municipal organic waste and landfill leachate resistivities are lower than that of municipal solid waste and sandy soils. The geophysical inversion results indicate that the maximum thickness of MW calibrating with the drilled borehole data is 60 m. Furthermore, the estimated MW dumpsite volume is about 42.32 MCM. Accordingly, the MW can be characterized in an accurate qualitative and quantitative manner. Our findings, therefore, help the efforts of uncontrolled dumpsite development and thus contribute to sustainability plans.

An Integrated Remote Sensing & GIS Techniques Based Approach to Study Spatial Distribution of Parameters Controlling Groundwater Contamination in Parbhani Tehsil of Maharashtra State, India

An Integrated Remote Sensing & GIS Techniques Based Approach to Study Spatial Distribution of Parameters Controlling Groundwater Contamination in Parbhani Tehsil of Maharashtra State, India

An Integrated Remote Sensing and GIS in Monitoring Landuse and Land Cover Change in Egbeda Local Government Area, Oyo State, Nigeria

An Integrated Remote Sensing and GIS in Monitoring Landuse and Land Cover Change in Egbeda Local Government Area, Oyo State, Nigeria