Search Results (1,234)

The habitat landscape pattern of Mrs Hume’s pheasant in Jinzhongshan, northwestern Guangxi, was studied using field survey data and the LANDSAT satellite images by the ArcGIS 10.8 and Fragstats 3.3 software. The results showed that the Jinzhongshan region covers 38,716.6 hm², including 1708 patches and 11 landscape types. Although the area of farmland and village only occupies 10%, their number and density have led Jinzhongshan habitats to fragment. The degree of connection of suitable habitat was found to be relatively low, and seven landscape indices were below 0.5, which implied that the extent of habitat fragmentation in Jinzhongshan for Mrs Hume’s Pheasant is very high. The fragmentation index of Jinzhongshan Nature Reserve is 0.9887, landscape connectivity is 1.861, and AWS index is 425.3024. The broad-leaved forest, considered a matrix in the Jinzhongshan area, was the dominant landscape type controlling structure, function, and dynamic changes. The total suitable habitat of Mrs Hume’s pheasant (Syrmaticus humiae) was determined to be 29,552.3 hm², accounting for 76.3% of the total study area; the suitable habitat of Mrs Hume’s pheasant in Jinzhongshan Nature Reserve was determined to be 16,990.1 hm², accounting for 81.14% of the protected area. It is absolutely necessary and urgent to strengthen the management and protection of Mrs Hume’s pheasant’s habitat to control its fragmentation. Therefore, we have provided some useful advice, such as habitat restoration and corridor reconstruction, which are beneficial to the conservation of Mrs Hume’s pheasant in this sensitive region. Full article

Wetland loss and human activities are forcing migratory waterbirds to rely on alternative habitats such as croplands, yet their adaptive habitat use across contrasting landscape contexts remains unclear. The Bar-headed Goose (Anser indicus) is a key indicator species in the wetland ecosystems of the Yunnan–Guizhou Plateau. Comparing differences in its wintering habitat selection and utilization is of great significance for understanding its ecological adaptation mechanisms and formulating regional wetland conservation strategies. In this study, we compared the diurnal habitat use during the wintering period of Bar-headed Geese at three wetlands (Nianhu, Caohai, and Napahai) representing distinct landscape contexts. We used GPS satellite tracking and dynamic Brownian bridge movement modeling, combined with random forest analysis of environmental variables, to quantify diurnal habitat use and selection at each site. Our results revealed significant regional differences in habitat use. In the agriculture-dominated wetlands (Nianhu and Caohai), geese primarily utilized cropland and marsh habitats (Nianhu: cropland 45.88% ± 30.70%, marsh 42.55% ± 33.17%; Caohai: cropland 62.33% ± 12.16%, marsh 28.61% ± 13.62%). In contrast, at Napahai, which is dominated by natural habitats, geese primarily used grassland (65.92% ± 20.01%) and marsh (26.85% ± 21.88%), with minimal use of cropland (4.21% ± 7.00%). Diurnal habitat selection was influenced by multiple environmental factors, with distinct regional differences identified through random forest modeling. In Nianhu, key factors included distance to supplemental feeding site, distance to grassland, distance to woodland, and distance to open water. In Caohai, distance to grassland, distance to nocturnal roost site, distance to settlement, and distance to open water were significant drivers. In Napahai, distance to nocturnal roost site, distance to open water, and distance to marsh were the most influential (all with p < 0.01), reflecting flexible behavioral responses. Based on these findings, we recommend region-specific conservation management strategies. Specifically, supplemental feeding at Nianhu should be strictly regulated. Agricultural planning in farming areas should account for the habitat needs of wintering waterbirds. Grassland and marsh habitats at Napahai should also be more effectively protected. Full article

Mangroves play a critical role in global carbon sequestration, biodiversity conservation, and climate change mitigation. Accurately quantifying mangrove biomass is essential for sustainable forest management and carbon accounting. Yet, the structural complexity and species diversity of mangrove ecosystems pose significant challenges for accurate estimation. In this study, we developed an integrated model that combines multispectral imagery and radar data. Using Sentinel-2 and ALOS-2 satellite imagery combined with field measurements, these data were used to construct linear regression and random forest models for the Matang Mangrove Reserve, Malaysia. We further analyzed the relationships between vegetation indices, radar polarization modes, and biomass. Results indicate that the average biomass is approximately 146 t/ha. The Optimized Soil-Adjusted Vegetation Index (OSAVI) and horizontal–vertical (HV) polarization showed the strongest correlation with field-measured biomass, with an R² of 0.735 and a root mean square error (RMSE) of 46.794 t/ha. This study provides a scientific basis and technical support for mangrove carbon stock assessment, ecosystem management, and climate change mitigation strategies, and highlights the potential of integrating optical and radar remote sensing for large-scale mangrove biomass monitoring. Full article

The Beaufort Sea has experienced significant sea ice retreat in recent decades, driven by both thermodynamic and dynamic processes. This study investigates the drivers and predictability of summer sea ice retreat in the Beaufort Sea by integrating an ocean–sea ice model with satellite-derived sea ice concentration data and atmospheric reanalysis products. Model diagnostics from 1994 to 2019 reveal that thermodynamic processes dominate annual sea ice loss (approximately 90%), with vertical heat flux accounting for roughly 85% of total oceanic heat input. The summer sea ice minimum area and the day of opening, derived from either model results and satellite observations, have a strong correlation with R² = 0.60 and R² = 0.77, respectively, enabling regression equations based solely on remote sensing data. Further multiple linear regression incorporating preceding winter (January to April) accumulated temperature and easterly wind yields moderately robust forecasts of minimum sea ice area (R² = 0.49) during 1998–2020. Additionally, analysis of reanalysis wind data shows that the timing of minimum sea ice area is significantly influenced by the frequency and intensity of sub-seasonal easterly wind events during melt season. These results demonstrate the critical importance of remote sensing in monitoring Arctic sea ice variability and enhancing seasonal prediction capability under a rapidly changing climate. Full article

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions. Full article

Groundwater is a vital resource in arid regions, where it sustains agriculture, industry, and livelihoods. In northwestern China’s Shule River Basin, located in the Hexi Corridor, increasing water stress has raised concerns about the sustainability of groundwater use. However, the relative contributions of climate variability and human activities to groundwater depletion in this region remain poorly quantified. This study investigates long-term groundwater storage changes in the Shule River Basin from 2003 to 2023 using GRACE satellite data combined with GLDAS land surface models. A water balance approach was applied to isolate natural (climatic) and anthropogenic contributions to groundwater storage anomalies (GWSAs). In addition, land use transitions and socioeconomic indicators were incorporated to assess the impact of human development on subsurface water dynamics. The results show a persistent downward trend in GWSA, with an average annual loss rate of −0.31 cm·yr⁻¹. Spatially, the central and lower reaches of the basin exhibit the most significant depletion, driven by intensive irrigation and urban growth. Contribution analysis indicates that natural factors accounted for 61% of the groundwater loss across the study period, while anthropogenic drivers became increasingly dominant over time, particularly after 2016, accounting for over 40% of total depletion in recent years. Strong correlations were found between groundwater decline and the expansion of cropland, impervious surfaces, and GDP. These findings highlight the intensifying role of human activities in shaping groundwater trends in arid inland basins. This study provides a data-driven framework to support sustainable groundwater management and offers transferable insights for similar water-stressed regions globally. Full article

Monitoring the hydrological processes of lakes can provide reliable data for regional water resources assessment. This paper analyzed changes in the lake area and water level of Hala Lake from 2011 to 2023, subsequently estimating its lake water storage change (LWSC). We used image data from Landsat series satellites and multi-source satellite altimetry data, and then quantitatively assessed the influence of various driving factors on the LWSC in combination with hydrological and meteorological models. The results show three stages of parallel changes in the area, water level and LWSC of Hala Lake in the past 13 years. The first stage is from 2011 to 2014, when the lake expanded slightly, the second stage is from 2015 to 2019, when the lake expanded rapidly, and the last stage is from 2020 to 2023, with relatively stable conditions. Over the entire study period, the LWSC increased with a trend of 0.192 ± 0.009 km³/a. Lake surface precipitation, precipitation-caused runoff, and glacier meltwater contributed to the total recharge input by 51%, 40.96%, and 8.04%, respectively, while the lake surface evaporation accounted for 59.37% of the total recharge input as water loss. Thus, the left 40.63% of the input caused the LWSC increase. Although lake surface precipitation provided the primary contribution to the Hala Lake LWSC, precipitation-caused runoff was the key factor forming the three stages in the LWSC. The results of this study provide valuable information for the rational development and utilization of water resources by government departments and are also beneficial to the study of global change. Full article

Many research fields, such as urban planning, urban climate and environmental assessment, require information on the distribution of buildings. In this study, we used U-Net to segment buildings from WorldView-3 imagery. To improve the accuracy of building segmentation, we undertook two endeavors. First, we investigated the optimal order of atmospheric correction (AC) and panchromatic sharpening (pan-sharpening) and found that performing AC before pan-sharpening results in higher building segmentation accuracy than after pan-sharpening, increasing the average IoU by 9.4%. Second, we developed a new multi-task self-supervised learning (SSL) network to pre-train VGG19 backbone using 21 unlabeled WorldView images. The new multi-task SSL network includes two pretext tasks specifically designed to take into account the characteristics of buildings in satellite imagery (size, distribution pattern, multispectral, etc.). Performance evaluation shows that U-Net combined with an SSL pre-trained VGG19 backbone improves building segmentation accuracy by 15.3% compared to U-Net combined with a VGG19 backbone trained from scratch. Comparative analysis also shows that the new multi-task SSL network outperforms other existing SSL methods, improving building segmentation accuracy by 3.5–13.7%. Moreover, the proposed method significantly saves computational costs and can effectively work on a personal computer. Full article

Arid mountainous regions are vulnerable to extreme hydrological events such as floods and droughts. Providing accurate and continuous rainfall records with no gaps is crucial for effective flood mitigation and water resource management in these and downstream areas. Satellite data and geospatial interpolation can be employed for this purpose and to provide continuous data series. However, it is essential to thoroughly assess these methods to avoid an increase in errors and uncertainties in the design of flood protection and water resource management systems. The current study focuses on the mountainous region in northern Oman, which covers approximately 50,000 square kilometers, accounting for 16% of Oman’s total area. The study utilizes data from 279 rain gauges spanning from 1975 to 2009, with varying annual data gaps. Due to the limited accuracy of satellite data in arid and mountainous regions, 51 geospatial interpolations were used to fill data gaps to yield maximum annual and total yearly precipitation data records. The root mean square error (RMSE) and correlation coefficient (R) were used to assess the most suitable geospatial interpolation technique. The selected geospatial interpolation technique was utilized to generate the spatial distribution of annual maxima and total yearly precipitation over the study area for the period from 1975 to 2009. Furthermore, gamma, normal, and extreme value families of probability density functions (PDFs) were evaluated to fit the rain gauge gap-filled datasets. Finally, maximum annual precipitation values for return periods of 2, 5, 10, 25, 50, and 100 years were generated for each rain gauge. The results show that the geostatistical interpolation techniques outperformed the deterministic interpolation techniques in generating the spatial distribution of maximum and total yearly records over the study area. Full article

Global navigation satellite System (GNSS) precise point positioning (PPP) enables high-precision global positioning using a single receiver, yet its widespread application is hindered by long convergence times. In contrast, pseudolite system (PLS) transmitters are located relatively close to receivers, and the movement of receivers induces rapid spatial geometry changes, which greatly facilitate fast parameter convergence. Therefore, leveraging the fast-converging PLS to augment GNSS PPP presents a promising solution. This study proposes a tightly coupled PLS and GNSS observation-level integration model. A key factor influencing the augmentation effectiveness is the strategy of ambiguity resolution. In this work, we design a novel strategy of ambiguity resolution, in which the fast convergence property of PLS is taken into account, and the PLS ambiguities are picked out to be fixed independently. This strategy can resolve the PLS ambiguities, GNSS wide-lane (WL) ambiguities, and GNSS L1 ambiguities cascadingly. Further, the fixed ambiguities can be treated as constraints in the filtering process. The experimental results demonstrate that the proposed strategy substantially improves the ambiguity fixing rates, especially in short-duration augmentation. Full article

Tropical and subtropical forests account for approximately one-third of global terrestrial gross primary productivity (GPP), and the diurnal patterns of GPP strongly regulate the land–atmosphere CO₂ interactions and feedback to the climate. Combined with ground eddy-covariance (EC) flux towers, geostationary satellites offer significant advantages for continuously monitoring these diurnal variations in the “breathing of biosphere”. Here we utilized half-hourly optical signals from the Himawari-8 Advanced Himawari Imager (H8/AHI) geostationary satellite and tower-based EC flux data to investigate the diurnal variations in subtropical forest GPP and its drivers. Results showed that three machine learning models well estimated the diurnal patterns of subtropical forest GPP, with the determination coefficient (R²) ranging from 0.71 to 0.76. Photosynthetically active radiation (PAR) is the primary driver of the diurnal cycle of GPP, modulated by temperature, soil water content, and vapor pressure deficit. Moreover, the effect magnitude of PAR on GPP varies across three timescales. This study provides robust technical support for diurnal forest GPP estimations and the possibility for large-scale estimations of diurnal GPP over tropics in the future. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed in this study. The optimization process includes three components. First, a method for calculating the actual equivalent antenna height is introduced, utilizing high-precision remote sensing satellite imagery to obtain communication path profiles. This method accounts for the antenna’s physical length, vehicular height, and local terrain characteristics, thereby providing an accurate representation of the antenna’s effective height within its operational environment. Second, an equivalent substitution method for ground loss is developed, utilizing surface information derived from high-precision remote sensing satellite images. This method integrates ground loss directly into the Egli model’s calculation process, eliminating the need for separate computations and simplifying the model. Third, leveraging the Egli model as a foundation, the least squares method (LSM) is employed to fit the relief height, ensuring the model meets the requirements for ultra-short wave communication distances under line-of-sight (LOS) conditions and enhances suitability for real-world vehicular communication systems. Finally, the validity and accuracy of the optimization model are verified by comparing the measured data with the theoretical calculated values. Compared with the Egli model, the Egli model with additional correction factors, and the measured data, the average error of the optimized model is reduced by 8.98%, 2.09%, and the average error is 0.45%. Full article

Soil fertility is the essential attribute of soil quality. Large-scale coal mining has led to the continuous deterioration of the fragile ecosystems in arid and semi-arid mining areas. As one of the key indicators for land ecological restoration in these coal mining regions, rapidly and accurately monitoring topsoil fertility and its spatial variation information holds significant importance for ecological restoration evaluation. This study takes Wuhai City in the Inner Mongolia Autonomous Region of China as a case study. It establishes and evaluates various soil indicator inversion models using multi-temporal Landsat8 OLI multispectral imagery and measured soil sample nutrient content data. The research constructs a comprehensive evaluation method for surface soil fertility based on multispectral remote sensing monitoring and achieves spatiotemporal variation analysis of soil fertility characteristics. The results show that: (1) The 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version)-SVM (Support Vector Machine) prediction model for surface soil indicators based on Landsat8 OLI imagery achieved prediction accuracy with R² values above 0.85 for all six soil nutrient contents in the study area, thereby establishing for the first time a rapid assessment method for comprehensive topsoil fertility using multispectral remote sensing monitoring. (2) Long-term spatiotemporal evaluation of soil indicators was achieved: From 2015 to 2025, the spatial distribution of soil indicators showed certain variability, with soil organic matter, total phosphorus, available phosphorus, and available potassium contents demonstrating varying degrees of increase within different ranges, though the increases were generally modest. (3) Long-term spatiotemporal evaluation of comprehensive soil fertility was accomplished: Over the 10 years, Grade IV remained the dominant soil fertility level in the study area, accounting for about 32% of the total area. While the overall soil fertility level showed an increasing trend, the differences in soil fertility levels decreased, indicating a trend toward homogenization. Full article