Remote Sensing Data Research Articles

Monitoring and Predicting Potential Fishing Grounds of Trachurus mediterraneus Using Remotely Sensed Data Along the North Sinai Coastal Zone, Egypt

Monitoring and Predicting Potential Fishing Grounds of Trachurus mediterraneus Using Remotely Sensed Data Along the North Sinai Coastal Zone, Egypt

Estimation of Soil Loss using Remote Sensing Data in a Regional Tropical Humid Catchment Area

Soil erosion has been and continue to be a major threat to environmental degradation especially in the developing countries. Accurate estimation of soil loss will provide reliable information in the management and mitigation solutions to soil erosion. In this study, the soil loss in an erosion prone Anambra State of South East region of Nigeria was estimated. Due to the complex nature of the catchment characteristics of Anambra State, soil loss cannot be estimated precisely by mere application of conventional soil erosion model. Hence a site-specific methodology was developed and applied. Revised Universal Soil Loss Equation (RUSLE) was integrated with the Geographic Information System (GIS) of the environment using remote sensing to build the model. 40-years rainfall data was collated from the Nigeria Meteorological Agency and analyzed. The various parameter of RUSLE which includes: Rainfall Erosivity (R), Soil Erodibility (K), Topography (LS), Land Use and Land Cover (C), and Erosion Control practices (P) were developed and imposed into ArcGIS 10.6 to estimate the amount of annual soil loss in the area. The result indicated that about 27.58km2 (0.59%) of the study area have very low erosion rate of 0 – 5 t ha1year-1 , while the rates of erosion in 1311.52km2 (28.01%), 538.59km2 (11.50%), 1649.08km2 (35.22%), 959.09km2 (20.48%), and 196.76km2 (4.20%) of the study area are 5–10, 10–15, 15–25, 25–50 and >50 t ha-1year-1respectively. This knowledge will help decision makers in managing the land degradation problems in Anambra State of Nigeria. Doi: 10.28991/CEJ-2024-010-07-014 Full Text: PDF

Enhancing Forest‐Steppe Ecotone Mapping Accuracy through Synthetic ApertureRadar‐Optical Remote Sensing Data Fusion and Object-based Analysis

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest‐grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar‐optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region.

Remote Sensing Methods and GIS Approaches for Carbon Sequestration Measurement: A General Review

Geospatial technologies like Remote Sensing (RS) and Geographic Information Systems (GIS) provide a platform for swiftly evaluating terrestrial Carbon Stock (CS) across extensive regions. Employing an integrated RS-GIS method for estimating Above-Ground Biomass (AGB) and precise carbon management emerges as a timely and economical strategy for implementing effective management plans on a localized and regional level. This study reviews different RS-related techniques utilized in CS assessment, particularly in arid lands, shedding light on the challenges, opportunities, and future trends associated with the process. As global warming poses adverse impacts on major ecosystems through temperature and precipitation changes, professionals have a call to develop evidence-based interventions to mitigate them. Carbon sequestration involves harnessing and storing carbon stocks from the atmosphere to minimize the adverse effects of climate change. The review explores the effectiveness of integrating remote sensing and GIS methodologies in quantifying carbon sequestration within agroforestry landscapes. In addition, this review also assesses the traditional methods, including their limitations, and deeply delves into recent techniques, emphasizing key remote sensing (RS) variables for biophysical predictions. This study showcases the efficacy of geospatial technologies in evaluating terrestrial carbon stock, particularly in arid regions. The study reviews diverse techniques and sensors, like optical Radio Detection and Ranging (RADAR), and Light Detection and Ranging (LiDAR), extensively employed for above-ground biomass (AGB) estimation and carbon stock assessment with RS data, introducing and discussing new methods. Existing literature was examined to present knowledge and evidence on the effectiveness of these technologies in carbon sequestration. The key findings of this review will inform future research and integration of technology, policy formulation, and carbon sequestration management to mitigate the impacts of climate change.

Assessing climate resilience in rice production: measuring the impact of the Millennium Challenge Corporation’s IWRM scheme in the Senegal River Valley using remote sensing and machine learning

Satellite remote sensing (RS) and machine learning can be combined to develop methods for measuring the impacts of climate change on biomass and agricultural systems. From 2015 to 2023, we applied this approach in a critical earth observation-based evaluation of the Irrigation and Water Resources Management component of the Millennium Challenge Corporation’s Senegal Compact. This project, funded by the United States Agency for International Development (USAID), was implemented in the Senegal River Valley from 2010 to 2015. Utilising these techniques, we successfully mapped rice cultivation areas, deciphered cropping practices, and analysed irrigation systems responses to different climatic conditions. A marked increase in cultivated rice area was found particularly in regions targeted by the project intervention. This is despite prolonged drought conditions which underscores a significant climate adaptation benefit from these irrigation works. We observed a notable dip in rice cultivation area in 2020, possibly due to the COVID-19 pandemic, followed by a recovery to pre-pandemic levels in 2023, likely aided by previously funded USAID’s socio-economic resilience programmes in the region. Economic analysis of increased rice yields in the region translates to approximately US$ 61.2 million in market value since 2015, highlighting the economic returns from the project investment. Both the RS data and ground audits identify issues regarding post-project deterioration of irrigation infrastructure, emphasising the need for long-term maintenance of irrigation infrastructure to support climate adaptation benefits arising from irrigation. With a focus on crop irrigation, our findings stress the critical role of climate adaptation interventions for maintaining agricultural productivity in the face of adverse climate shocks. It further highlights the necessity of continuous investment and maintenance for ensuring climate resilient agrifood systems.

Detecting Vegetation Cover Change in Reforestation Sites from 2013 to 2019 in Central Visayas, Philippines Using Remotely Sensed Data

Assessment of reforestation success is indispensable to document the impact of the project and whether it has achieved the intended objectives. Globally, the use of remotely sensed data is a widely accepted method to track changes in reforestation sites. Hence, this study investigated the changes in Normalized Difference in Vegetation Index (NDVI) using LandSat 8 images to assess vegetation cover in National Greening Program reforestation sites in Central Visayas from 2013 to 2019. Results showed that there was a minimal increase of NDVI (0.002 to 0.07) and a 9% (286 ha) gain of dense vegetation cover in the sampling sites which may be attributed to the reforestation activity. Forest gain was highest in Negros Oriental (262 ha) while Cebu lost 29 ha of dense vegetation. Field data confirmed that tree biomass density (245 Mg/ha) was highest in Negros Oriental and lowest in Cebu (78 Mg/ha). Analysis of variance revealed that there was no significant change in forest cover between 2013 and 2019. The program reported a total of 42,000 ha as their accomplishment in the region which was planted with 500 fast-growing tree species per hectare. If the seedlings successfully survived, this could have increased considerably forest cover in the region by up to 60%. Thus, it is important to revisit the reforestation program’s policies and processes to build on the strengths and avoid mistakes for future projects. It is also vital to integrate cost-effective, accurate, and real-time monitoring schemes that ensure the attainment of the key objectives.

Evaluating the Spatial-Temporal Dynamics of Urbanization in Prefecture Cities of China Using SNPP-VIIRS Nighttime Light Remote Sensing Data

Ensuring the well-being of urban communities hinges on sustainable urban planning strategies informed by current data, particularly in China since urbanization has been one of the most significant demographic shifts in recent decades. Therefore, our research aimed to evaluate the spatio-temporal dynamics of urbanization and sub urbanization across prefecture and provincial levels in China by utilizing consistent SNPP-VIIRS-like and NPP-VIIRS nighttime data spanning the years 2000 to 2020. The k-means method was applied to derive urban and sub urban features from above datasets. The findings uncovered a significant expansion of urban entities at the prefecture level, escalating from 16,209 km2 to 89,631 km2 over the specified period showing a 5% growth. Among five main urban agglomerations, the Yangtze River Delta stands out with the highest urbanization rate, witnessing a remarkable expansion of urban entities from 2,684 km2 to 41,465 km2. This growth reflects an average growth rate of 72.2% per annum. The analysis revealed that the overall area of suburbs expanded from 59,151 km2 to 120,339 km2 between 2012 and 2020 indicating a proportional growth rate ranging from 0.4% to 1.9%. The peak growth rate of suburbs was recorded between 2012 and 2014, reaching 18%. Guizhou, Hunan, and Hubei provinces have exhibited growth rates of 334%, 258%, and 246% respectively while Beijing, Guangdong, Tianjin, and Shanghai have experienced relatively low growth rates of 50%, 56%, 46%, and 17%. The analysis of urban growth with GDP, population, and electricity consumption revealed an inverse relationship during the specified period. Therefore, the findings of this research can provide immense support to sustainable urban planning initiatives at both the provincial and prefecture-level cities in China. The findings can assist city planning authorities in making informed decisions regarding optimizing resource distribution, all while prioritizing the preservation of ecological footprint within urban environments. Also, the limitations addressed in our study must be taken into account in future research works aimed at deriving reliable urban extraction results using nighttime light remote sensing data.

Geographical Information System, Remote Sensing and Multi Influencing Factors Techniques for Delineation of Groundwater Potential Zones in District Charsadda, Khyber Pakhtunkhwa, Pakistan

The effective harvesting of groundwater and its sustainable management is possible with the proper identification of zones on its potential basis. For this purpose, geographical information system (GIS) and Remote Sensing (RS) data approaches were combined and used for the investigation, preservation, and evaluation of groundwater supply. This study aimed to assess groundwater delineation and availability-based potential of groundwater area in Charsadda District, KPK, Pakistan, by applying the approach of RS, GIS, and multi-influencing factors (MIF). For this purpose, digital elevation model (DEM), shuttle radar topographic mission (SRTM), and sentinel 2 satellite images were employed to produce numerous thematic layers, i.e., land use land cover (LULC), drainage density, lineament density, geology, slope soil, and rainfall as MIF in this study. After assigning a fixed score and weight to each thematic layer (MIF techniques), then an analyzed spatial layer was combined with a weighted overlay using ArcGIS software (ArcMap 10.5) and finally potential area for groundwater was defined. The obtained potential area of groundwater was categorized spatially as, very high, high, moderate, and low zones which depict most of the area is covered with moderate (547.66 km2) to high (306.7 Km2), as well as, high highest groundwater potential in 7 km2. The results of this study and the approach will be applicable and insightful for regional and extensive levels of developmental planning and harvesting of groundwater resources.

Federated learning meets remote sensing

Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth’s land surface within the scope of Earth observation (EO). Technological advances in capture instrumentation, coupled with the rise in the number of EO missions aimed at data acquisition, have significantly increased the volume of accessible RS data. This abundance of information has alleviated the challenge of insufficient training samples, a common issue in the application of machine learning (ML) techniques. In this context, crowd-sourced data play a crucial role in gathering diverse information from multiple sources, resulting in heterogeneous datasets that enable applications to harness a more comprehensive spatial coverage of the surface. However, the sensitive nature of RS data requires ensuring the privacy of the complete collection. Consequently, federated learning (FL) emerges as a privacy-preserving solution, allowing collaborators to combine such information from decentralized private data collections to build efficient global models. This paper explores the convergence between the FL and RS domains, specifically in developing data classifiers. To this aim, an extensive set of experiments is conducted to analyze the properties and performance of novel FL methodologies. The main emphasis is on evaluating the influence of such heterogeneous and disjoint data among collaborating clients. Moreover, scalability is evaluated for a growing number of clients, and resilience is assessed against Byzantine attacks. Finally, the work concludes with future directions and serves as the opening of a new research avenue for developing efficient RS applications under the FL paradigm. The source code is publicly available at https://github.com/hpc-unex/FLmeetsRS.

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations’ Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping.

Upgrading Maize Cultivation in Bosnia and Herzegovina from Rainfed to Irrigated Systems: Use of Remote Sensing Data and the Dual Crop Coefficient Approach to Estimate Evapotranspiration

A two-year experiment was conducted with a local maize hybrid under full (F) and deficit (D) drip irrigation and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina (BiH). Three approaches, namely, A&P, SIMDualKc (SD), and vegetation index (VI), to estimate the actual crop coefficient (Kc act), the actual basal crop coefficient (Kcb act), and the actual crop evapotranspiration (ETc act), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences in comparison to A&P of up to 0.24 in D and R conditions, especially in the initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71 to 0.87 for the Kc ini act and from 0.80 to 1.06 for the Kc end act, while the mid-season period showed very good agreement with the literature. The maize crop evapotranspiration range is 769–813 mm, 480–752 mm, and 332–618 mm for F, D, and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The higher observed yields (67.6%) under irrigation conditions emphasize the need to transition from rainfed to irrigation-dependent agriculture in BiH, even for drought-resistant crops like maize.

CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification.

In recent years, the classification and identification of surface materials on earth have emerged as fundamental yet challenging research topics in the fields of geoscience and remote sensing (RS). The classification of multi-modality RS data still poses certain challenges, despite the notable advancements achieved by deep learning technology in RS image classification. In this work, a deep learning architecture based on convolutional neural network (CNN) is proposed for the classification of multimodal RS image data. The network structure introduces a cross modality reconstruction (CMR) module in the multi-modality feature fusion stage, called CMR-Net. In other words, CMR-Net is based on CNN network structure. In the feature fusion stage, a plug-and-play module for cross-modal fusion reconstruction is designed to compactly integrate features extracted from multiple modalities of remote sensing data, enabling effective information exchange and feature integration. In addition, to validate the proposed scheme, extensive experiments were conducted on two multi-modality RS datasets, namely the Houston2013 dataset consisting of hyperspectral (HS) and light detection and ranging (LiDAR) data, as well as the Berlin dataset comprising HS and synthetic aperture radar (SAR) data. The results demonstrate the effectiveness and superiority of our proposed CMR-Net compared to several state-of-the-art methods for multi-modality RS data classification.

Study on the Dynamic Changes of Vegetation Cover in Yan'an City based on Remote Sensing Data

Based on remote sensing data, we analyzed the spatial and temporal dynamic change characteristics of vegetation and meteorology in Yan'an City in the past 20 years, and explored the factors affecting the change of vegetation index. The results showed that the vegetation index of Yan'an City ranged from 0.54 to 0.75 from 2000 to 2019, and its spatial distribution showed the characteristics of high in the south and low in the north, which was consistent with the distribution of land use types in Yan'an City, and in the temporal distribution, it showed a significant upward trend, and the overall rate of increase was 0.095/(10a); during the period of 2000~2019, 79.83% of the areas of Yan'an City had an improved vegetation trend, and degraded areas accounted for 0.34% of Yan'an city. In summary, the vegetation index of Yan'an city showed an increasing trend.

Identification and Analysis of the Geohazards Located in an Alpine Valley Based on Multi-Source Remote Sensing Data.

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work.

Мониторинг изменений сельскохозяйственных угодий с использованием ГИС-технологий (на примере Зерноградского района Ростовской области)

An approach of correcting agricultural land boundaries based on the use of multi-source agrarian soil data from various sources with GIS technology was used to provide a more accurate representation of the status and location of the actual agricultural land boundaries. Archival materials and remote sensing data were the object of the study. The result of the work is the digitization with correction of agricultural land boundaries and assessment of their dynamics. Keywords: GEOINFORMATION SYSTEMS, AGRICULTURAL LANDS, DATA OF REMOTE SENSING DATA, ARABLE LAND, PASTE, PASTURE

Improving Snowmelt Runoff Model (SRM) Performance Incorporating Remotely Sensed Data

Improving Snowmelt Runoff Model (SRM) Performance Incorporating Remotely Sensed Data

Geophysical and Remote Sensing Data for Delineating Structural Elements at El Hammam Area, Northern Part of Western Desert, Egypt

Geophysical and Remote Sensing Data for Delineating Structural Elements at El Hammam Area, Northern Part of Western Desert, Egypt

Mapping Soil Erosion Classes using Remote Sensing Data and Ensemble Models

Mapping Soil Erosion Classes using Remote Sensing Data and Ensemble Models

Assessing Ecological Impacts and Recovery in Coal Mining Areas: A Remote Sensing and Field Data Analysis in Northwest China

In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining equilibrium, through the tumultuous phase of extraction, to the eventual restoration of stability post-reclamation. By harnessing a suite of analytical tools, including sophisticated remote sensing, UAV aerial surveys, and the meticulous ground-level sampling of flora and soil, the study meticulously measures the environmental toll of mining activities and charts the path to ecological restoration. The results are promising, indicating that the restoration initiatives are effectively healing the landscapes, with proactive interventions such as seeding, afforestation, and land rehabilitation proving vital in the swift ecological turnaround. Remote sensing technology, in particular, emerges as a robust ally in tracking ecological shifts, supporting sustainable practices and guiding ecological management strategies. This study offers a promising framework for assessing geological environmental shifts, which may guide policymakers in shaping the future of mining rehabilitation in arid and semi-arid regions.

From green to brown: two decades of darkening coastal water in the Gulf of Riga, the Baltic Sea

Over the past decades, changes in the light conditions of coastal waters, induced by darkening due to water browning, has been gaining momentum within the scientific community. Although there is a general agreement that the darkening of coastal water is caused by shifts in climatic conditions, the actual links between trends in light attenuation caused by water browning and drivers are still being debated, as causality relationships are often assessed through the use of indirect evidence. In this study, we employed algorithmically processed remote-sensing and modelled data with substantially higher resolution than in-situ data as well as in-situ data. Two decades (1998–2007 and 2008–2018) were compared to test impact of parameters known to be altered by climate change, such as freshwater runoff, mobilisation of soil dissolved organic carbon, and alteration of freshwater-saline water balance in coastal waters, on darkening of coastal waters of the Gulf of Riga, Baltic Sea. The study results indicated that the most likely cause of the observed darkening of coastal waters in the study region was the mildening of winter conditions. At the same time, the study results highlighted the urgent need for detailed, time-sensitive data, since none of the conclusions drawn from the analyses of available data were unequivocal.