Search Results (848)

The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like. Full article

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system. Full article

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km² by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions. Full article

Gold mining is an activity that has developed in Colombia due to the great availability of mineral resources geographically distributed throughout the territory. The extraction techniques used are linked to the domain of illegality and to armed actors who have generated notable landscape impacts. This study, focused on the Municipality of Ayapel, Colombia, identifies the landscape units and analyzes the changes in land use and cover resulting from gold mining between the years 1987, 2002, and 2022, applying the CORINE Land Cover methodology, an adapted legend for Colombia, using Landsat satellite images. For this, the recognition of the physical geographical characteristics of the area was carried out in order to group homogeneous landscape units through a cartographic overlay of various layers of information, considering variables such as topography, geomorphology, and lithology. This research identifies a total of 16 landscape units, 8 of which were intervened in 1987, mainly associated with denudational hills. However, in 2022, 13 landscape units were intervened, with a considerable increase in the affected area. Particularly noteworthy is the occupation of landscape units associated with alluvial valleys, with an average of more than 30% of their total area. This demonstrates that they are the most attractive and vulnerable areas for mining exploitation, as they are the zones with the greatest potential for hosting mineral deposits. This impact has worsened over the last decade due to the introduction of other extraction techniques with machinery (dredges, dragon boats, backhoes, and bulldozers) that generate higher productive and economic yields but, at the same time, cause deep environmental liabilities due to the lack of administrative controls. The changes in extraction techniques, the increase in the international price of the commodity, and the absence of government attention have been the breeding ground that has driven gold mining activity. Full article

Dynamic development of tourism activities and rapid urbanisation in Special Economic Zones (SEZs) can lead to significant land use and land cover changes (LULCCs) and environmental degradation, particularly in ecologically sensitive areas. This study examines the transformation of land use and its associated impacts on habitat quality and thermal environment in Phu Quoc Island (Vietnam) over a 20-year period (2003–2023). Using multi-temporal Landsat satellite imagery and random forest classification, we quantify LULCCs and assess the environmental consequences of urban expansion on habitat degradation and intensification of the island’s thermal environment, focusing on land surface temperature (LST) changes. Our analysis reveals that rapid urbanisation, driven by large-scale tourism and infrastructure developments, has led to a significant loss of forest and farmland, leading to a 5.6% decline in habitat quality and a marked increase in LST. The study also highlights the uneven distribution of urban growth, with the majority of expansion occurring in the southern and central regions of the island. By applying the InVEST Habitat Quality Model, we identify key zones of habitat degradation and offer insights into the spatial patterns of environmental sensitivity and changes. Our findings underscore the need for integrated land use planning and sustainable development strategies to mitigate the negative environmental impacts of SEZ-driven urbanisation on island ecosystems. This research provides critical guidance for policymakers, planners, and environmental managers to balance economic growth with environmental conservation in fragile island environments. Full article

Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City. Full article

Sea-level rise has important implications for the economic and infrastructure security of coastal cities. Land subsidence further exacerbates relative sea-level rise. The Beijing–Tianjin–Hebei region (BTHR) along the Bohai Bay is one of the areas most severely affected by ground subsidence in the world. This study applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) method to analyze 47 ALOS PALSAR-2 images with five frames, mapping subsidence across 21,677.7 km² and revealing spatial patterns and trends over time from 2015 to 2021. This is one of the few published research studies for large-scale and long-term analysis of its kind using ALOS-2 data in this region. The results reveal the existence of six major areas affected by severe subsidence in the study area, with the most pronounced in Jinzhan Town, Beijing, with the maximum subsiding velocity of −94.42 mm/y. Except for the two subsidence areas located in Chaoyang District of Beijing and Guangyang District of Langfang City, the other areas with serious subsidence detected are all located in suburban areas; this means that the strict regulations of controlling urban subsidence for downtown areas in the BTHR have worked. The accumulated subsidence is highly correlated with the time in the time series. Moreover, the subsidence of 161.4 km of the Beijing–Tianjin Inter-City High-Speed Railway (HSR) and 194.5 km of the Beijing–Shanghai HSR (out of a total length of 1318 km) were analyzed. It is the first time that PALSAR-2 data have been used to simultaneously investigate the subsidence along two important HSR lines in China and to analyze relatively long sections of the routes. The above two railways intersect five and seven subsiding areas, respectively. Within the range of the monitored railway line, the percentage of the section with subsidence velocity below −10 mm/y in the monitoring length range is 11.2% and 27.9%; this indicates that the Beijing–Shanghai HSR has suffered more serious subsidence than the Beijing–Tianjin Inter-City HSR within the monitoring period. This research is also beneficial for assessing the subsidence risk associated with different railways. In addition, this study further analyzed the potential reasons for the serious land subsidence of the identified areas. The results of the geological interpretation still indicate that the main cause of subsidence in the area is due to hydrogeological characteristics and underground water withdrawal. Full article

Land use change is related to a series of core issues of global environmental change, such as environmental quality improvement, sustainable utilization of resources, energy reuse and climate change. In this study, Google Earth Engine (GEE), a remote sensing natural environment monitoring and analysis platform, was used to realize the combination of Landsat TM/OLI data images with spectral features and topographic features, and the random forest machine learning classification method was used to supervise and classify the low-cloud composite image data of Ordos City. The results show that: (1) GEE has a powerful computing function, which can realize efficient and high-precision in-depth analysis of long-term multi-temporal remote sensing images and monitoring of land use change, and the accuracy of acquisition can reach 87%. Compared with other data sets in the same period, the overall and local classification results are more distinct than ESRI (Environmental Systems Research Institute) and GlobeLand 30 data products. Slightly lower than the Institute of Aerospace Information Innovation of the Chinese Academy of Sciences to obtain global 30 m of land cover fine classification products. (2) The overall accuracy of the land cover data of Ordos City from 2003 to 2023 is between 79–87%, and the Kappa coefficient is between 0.79–0.84. (3) Climate, terrain, population and other interactive factors combined with socio-economic population data and national and local policies are the main factors affecting land use change between 2003 and 2023. Full article

Mapping land cover in Uruguay is essential to meet the growing demand for accurate data to support sustainable development policies and manage natural resources, while also addressing the United Nations Sustainable Development Goals (SDGs) and other international conventions. In recent decades, collaboration between the FAO and the Government of Uruguay has led to the development of key products that strengthen the country’s planning processes, including a detailed, standardized national land cover database. By using the FAO’s Land Cover Classification System (LCCS), Uruguay has achieved a multitemporal national land cover database, through a legend specifically adapted to its national context and with classification accuracy improving from 85% in earlier products to 95% in the most recent ones. The use of LCCS has ensured semantic interoperability and provided reliable, up-to-date information on land cover distribution and change analysis. This progress has been supported by the enhancement of national capacities for change analysis, using international standards, remote sensing, and GIS technologies, integrated with national data. This article reviews the historical evolution and methodological advancements in the implementation of the LCCS in Uruguay, emphasizing the improvements in methodology and technology, and their impact on the sustainable management of the country’s territory. Full article

Activities, particularly harbor construction, often exert significant and non-negligible impacts on coastal environments. Therefore, it is of great practical importance to quantitatively assess the effects of such construction on the surrounding topography, such as tidal flats. This study focuses on the coast of Jiangsu Binhai Harbor. Using multi-source and multi-temporal remote sensing images, digital elevation models of tidal flats surrounding Binhai Harbor were generated for the years 2013, 2015, and 2017 through the waterline method. A quantitative analysis was conducted utilizing GIS spatial analysis techniques to examine erosion–deposition patterns, contour changes, and typical cross-sectional comparisons. The findings reveal that, although the overall coastline is in a state of erosion, the localized impacts of harbor construction are evident. Between 2013 and 2017, the northern tidal flats experienced overall erosion, whereas deposition occurred near the harbor’s root areas. Compared to 2013–2015, there was a significant decrease in erosion between 2015 and 2017, indicating that the construction of the project had a significant impact on the northern tidal flats. Throughout the five-year study period, the tidal flats within the breakwater underwent continuous adjustment, shifting from being close to the shoreline to being concentrated on both sides of the breakwater. Significant siltation was observed on the inner side of the breakwater at Binhai Harbor between 2015 and 2017, with an increase of 0.86 km² in the area above −2 m. This study demonstrates that remote sensing technology is highly effective in monitoring changes in coastal topography, especially under the influence of human activities. Full article

This study aims to address the challenges associated with rockfall assessment and monitoring, focusing on the coastal cliffs of “Navagio Shipwreck Beach” in Zakynthos. A complete time-series analysis was conducted using state-of-the-art methodologies including a 2020 survey using unmanned aerial systems (UASs) and two subsequent surveys, incorporating terrestrial laser scanning (TLS) and UAS survey techniques in 2023. Achieving high precision and accuracy in georeferencing involving direct georeferencing, the utilization of pseudo ground control points (pGCPs), and integrating post-processing kinematics (PPK) with global navigation satellite system (GNSS) permanent stations’ RINEX data is necessary for co-registering the multitemporal models effectively. For the change detection analysis, UAS surveys were utilized, employing the multiscale model-to-model cloud comparison (M3C2) algorithm, while TLS data were used in a validation methodology due to their very high-resolution model. The synergy of these advanced technologies and methodologies offers a comprehensive understanding of rockfall dynamics, aiding in effective assessment and monitoring strategies for coastal cliffs prone to rockfall risk. Full article

Rapid urbanization causes a high concentration of human population and economic activities that lead to the changes in landscape and spatial growth of the cities. Landscape features play a key role in understanding the land use and land cover (LULC) dynamics of urban areas. This work aims to analyze and quantify the changes in LULC over 24 years (1999 to 2023) in Lucknow District of India. It focuses on different land use types, including built-up area, cropland, water body, vegetation, and fallow land, using satellite imagery. Multi-temporal Landsat satellite data from the years 1999, 2008, 2015, and 2023 were employed to prepare LULC maps including major classes, namely built-up area, cropland, water body, vegetation, and fallow land. Several landscape metrics, such as number of patches (NP), patch density (PD), largest patch index (LPI), landscape shape index (LSI), edge density (ED), and total edge (TE), were calculated to analyze spatial patterns and changes in LULC categories. The study revealed significant changes in the landscape of Lucknow District, characterized by variations in the extent and distribution of the land use categories. Key findings include a remarkable increase in built-up area from 9.04% in 1999 to 25.91% in 2023 and a decrease in vegetation from 26.01% in 1999 to 11.71% in 2023. The PD and ED showed an increased fragmentation, especially in built-up areas where PD increased from 9.18 patches/100 ha in 1999 to 11.85 patches/100 ha in 2023. The LPI for built-up areas significantly grew, indicating larger continuous urban regions. The findings of this study emphasize the importance of monitoring landscape changes using multi-temporal remote sensing images over urban landscapes. Analyzing landscape metrics helps to understand the ongoing changes in LULC, providing essential information for effective sustainable land management practices. Full article

Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry (InSAR) using 224 Sentinel-1A data, spanning from 9 January 2016 to 8 April 2024, to investigate the deformation characteristics of the coastal reclamation embankment in Funing Bay, China. We optimized the phase-unwrapping network by employing ambiguity-detection and redundant-observation methods to facilitate the multitemporal InSAR phase-unwrapping process. The deformation results indicated that the maximum observed land subsidence rate exceeded 50 mm per year. The Funing Bay embankment exhibited a higher level of internal deformation than areas closer to the sea. Time-series analysis revealed a gradual deceleration in the deformation rate. Furthermore, a geotechnical model was utilized to predict future deformation trends. Understanding the spatial dynamics of deformation characteristics in the Funing Bay reclamation embankment will be beneficial for ensuring the safe operation of future coastal reclamation projects. Full article

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha⁻¹ × yr⁻¹ to 1.29 ton × ha⁻¹ × yr⁻¹, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha⁻¹ × yr⁻¹, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables. Full article

Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the multitemporal conditions of a sixth-generation forest fire through the use and implementation of tools such as remote sensing, photointerpretation with geographic information systems (GISs), thematic information on land use, and the use of spatial indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Burned Ratio (NBR), and its difference (dNBR) with satellite images from Sentinel-2. To improve our understanding of the dynamics and changes that occurred due to the devastating forest fire in Los Guájares, Granada, Spain, in September 2022, which affected 5194 hectares and had a perimeter of 150 km, we found that the main land use in the study area was forest, followed by agricultural areas which decreased from 1956 to 2003. We also observed the severity of burning, shown with the dNBR, reflecting moderate–low and moderate–high levels of severity. Health and part of the post-fire recovery process, as indicated by the NDVI, were also observed. This study provides valuable information on the spatial and temporal dimensions of forest fires, which will favor informed decision making and the development of effective prevention strategies. Full article