Search Results (1,377)

Dryland lakes are indispensable to regional water resource systems. Ebinur Lake, the largest saline lake in Xinjiang Uygur Autonomous Region, is vital for regional biodiversity and environmental stability but has been facing the predicament of gradual shrinkage in recent decades. In this study, we proposed a new dual-index method for Landsat (-5, -7, -8, and -9) data to extract water with the combinations of the normalized difference water index (NDWI) and the modified NDWI for turbid waters (NDWI_turbid). The dual-index method showed a high overall accuracy of 96.36% for Ebinur Lake. Landsat series images from 1992 to 2023 were employed to acquire the water areas of Ebinur Lake. The results showed that, over the past three decades, the area of Ebinur Lake exhibited a fluctuating decreasing trend, with an average lake area of 568.74 ± 152.43 km². The northwest intermittent water areas showed significant changes, and there was a close connection between the northwest and core water areas. Seasonally, the lake area decreased from spring to autumn. River inflow, driven by rainfall and human activities, was the primary factor affecting the inter/inner annual changes in Ebinur Lake. Furthermore, due to the valley effects, wind was found to be a critical factor in the diurnal changes in the water areas. This study should deepen the understanding of the variations of Ebinur Lake and benefit local water resource management. Full article

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. Full article

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability. Full article

In the Loess Plateau of China, loess is widely distributed and forms a unique geomorphic feature of the world. Meanwhile, the Yellow River water and sediment regulation system is under construction. Nonetheless, the morphological characteristics of constrained meandering rivers in the Loess Plateau are still unknown due to the difficulty of extracting the sediment-laden water body. An improved method is proposed based on Landsat 8 imagery, which automatically extracts the multi-band spectral relationship of high-sediment-concentration rivers in valleys. This study analyzes the morphological characteristics of constrained meandering rivers in the middle reaches of the Yellow River Basin, including their sinuosity, periodicity, curvature, and skewness based on the deflection points bend segmentation and continuous wavelet transform methods. These characteristics are then compared with those of other constrained meandering rivers and alluvial meandering rivers. The results show that the sinuosity of the constrained river bends is generally low (with an average of 1.55) due to limitations imposed by the riverbanks, which prevent full development. The average dimensionless curvature radius of the constrained rivers is 18.72, lower than that of alluvial rivers. The skewing angle of the constrained river bends typically inclines upstream, with a proportion reaching 59.44%. In constrained river bends, as the sinuosity increases, the proportion of bends skewing upstream initially increases and then gradually decreases. This indicates that constrained river bends can develop similarly to alluvial bends at lower sinuosity but are limited by the mountains on both sides at medium sinuosity. The analysis of river characteristics in regions with different geological structures reveals the effect of geological structures on the formation of constrained rivers in the Loess Plateau. These findings can provide a reference for selecting reservoir dam sites and are important for the dredging engineering layout in the middle reaches of the Loess Plateau. They also offer quantitative explanations for the meandering characteristics of these rivers. Full article

Soil erosion and subsequent sedimentation pose significant challenges in the Sikkim Himalayas. In this study, we conducted an assessment of the impact of rainfall-induced soil erosion and sediment loss in South Sikkim, which falls within the Teesta Basin, employing Revised Universal Soil Loss Equation (RUSLE) and Sediment Yield Index (SYI) models. Leveraging mean annual precipitation data, a detailed soil map, geomorphological landforms, Digital Elevation Models (DEMs), and LANDSAT 8 OLI data were used to prepare the factorial maps of South Sikkim. The results of the RUSLE and SYI models revealed annual soil loss >200 t ha⁻¹ yr⁻¹, whereas mean values were estimated to be 93.42 t ha⁻¹ yr⁻¹ and 70.3 t ha⁻¹ yr⁻¹, respectively. Interestingly, both models displayed similar degrees of soil loss in corresponding regions under the various severity classes. Notably, low-severity erosion <50 t ha⁻¹ yr⁻¹ was predominantly observed in the valley sides in low-elevation zones, while areas with severe erosion rates >200 t ha⁻¹ yr⁻¹were concentrated in the upper reaches, characterized by steep slopes. These findings underscore the strong correlation between erosion rates and topography, which makes the region highly vulnerable to erosion. The prioritization of such regions and potential conservation methods need to be adopted to protect such precious natural resources in mountainous regions. Full article

Tidal flats, a critical part of coastal wetlands, offer unique ecosystem services and functions. However, in China, these areas are under significant threat from industrialization, urbanization, aquaculture expansion, and coastline reconstruction. There is an urgent need for macroscopic, accurate and periodic tidal flat resource data to support the scientific management and development of coastal resources. At present, the lack of macroscopic, accurate and periodic high-resolution tidal flat maps in China greatly limits the spatio-temporal analysis of the dynamic changes of tidal flats in China, and is insufficient to support practical management efforts. In this study, we used the Google Earth Engine (GEE) platform to construct multi-source intensive time series remote sensing image collection from Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) images, and then automated the execution of improved MSIC-OA (Maximum Spectral Index Composite and Otsu Algorithm) to process the collection, and then extracted and analyzed the tidal flat data of China in 2018 and 2023. The results are as follows: (1) the overall classification accuracy of the tidal flat in 2023 is 95.19%, with an F1 score of 0.92. In 2018, these values are 92.77% and 0.88, respectively. (2) The total tidal flat area in 2018 and 2023 is 8300.34 km² and 8151.54 km², respectively, showing a decrease of 148.80 km². (3) In 2023, estuarine and bay tidal flats account for 54.88% of the total area, with most tidal flats distribute near river inlets and bays. (4) In 2023, the total length of the coastline adjacent to the tidal flat is 10,196.17 km, of which the artificial shoreline accounts for 67.06%. The development degree of the tidal flat is 2.04, indicating that the majority of tidal flats have been developed and utilized. The results can provide a valuable data reference for the protection and scientific planning of tidal flat resources in China. Full article

This study investigates the escalating impact of wildfire smoke on the remote sensing of phytoplankton biomass in freshwater systems. Wildfire smoke disrupts the accuracy of Chlorophyll-a (Chl-a) retrieval models, with Chl-a often used as a proxy for quantifying phytoplankton biomass. Given the increasing frequency and intensity of wildfires, there is a need for the development and refinement of remote sensing methodologies to effectively monitor phytoplankton dynamics under wildfire-impacted conditions. Here we developed a novel approach using Landsat’s coastal/aerosol band (B1) to screen for and categorize levels of wildfire smoke interference. By excluding high-interference data (B1 reflectance > 0.07) from the calibration set, Chl-a retrieval model performance using different Landsat band formulas improved significantly, with R² increasing from 0.55 to as high as 0.80. Our findings demonstrate that Rayleigh-corrected reflectance, combined with B1 screening, provides a robust method for monitoring phytoplankton biomass even under moderate smoke interference, outperforming full atmospheric correction methods. This approach enhances the reliability of remote sensing in the face of increasing wildfire events, offering a valuable tool for the effective management of aquatic environments. Full article

Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. Full article

The two near-identical pushbroom Thermal Infrared Sensors (TIRS) aboard Landsat 8 and 9 are currently imaging the Earth’s surface at 10.9 and 12 microns from similar 705 km altitude, sun-synchronous polar orbits. This work validates the consistency in the imaging data quality, which is vital for harmonization of the data from the two sensors needed for global mapping. The overlapping operation of these two near-identical sensors, launched eight years apart, provides a unique opportunity to assess the sensitivity of the conventionally used metrics to any unexpectedly found nuanced differences in their spatial performance caused by variety of factors. Our study evaluates spatial quality metrics for bands 10 and 11 from 2022, the first complete year during which both TIRS instruments have been operational. The assessment relies on the straight-knife-edge technique, also known as the Edge Method. The study focuses on comparing the consistency and stability of eight separate spatial metrics derived from four separate water–desert boundary scenes. Desert coastal scenes were selected for their high thermal contrast in both the along- and across-track directions with respect to the platforms ground tracks. The analysis makes use of the 30 m upsampled TIRS images. The results show that the Landsat 8 and Landsat 9 TIRS spatial performance are both meeting the spatial performance requirements of the Landsat program, and that the two sensors are consistent and nearly identical in both across- and along-track directions. Better agreement, both with time and in magnitude, is found for the edge slope and line spread function’s full-width at half maximum. The trend of averaged modulation transfer function at Nyquist shows that Landsat 8 TIRS MTF differs more between the along- and across-track scans than that for Landsat 9 TIRS. The across-track MTF is consistently lower than that for the along-track, though the differences are within the scatter seen in the results due to the use of the natural edges. Full article

Surface water is a crucial part of terrestrial ecosystems and is crucial to maintaining ecosystem health, ensuring social stability, and promoting high-quality regional economic development. The surface water in the Yellow River Basin (YRB) has a high sediment content and spatially heterogeneous sediment distribution, presenting a significant challenge for surface water extraction. In this study, we first analyze the applicability of nine water indexes in the YRB by using the Landsat series images (Landsat 5, 7, 8) and then examine the correlation between the accuracy of the water indexes and suspended particulate matter (SPM) concentrations. On this basis, we propose a surface water extraction method considering the SPM concentrations (SWE-CSPM). Finally, we examine the dynamic variations in the surface water in the YRB at four scales: the global scale, the secondary water resource zoning scale, the provincial scale, and the typical water scale. The results indicate that (1) among the nine water indexes, the MBWI has the highest water extraction accuracy, followed by the AWEInsh and WI2021, while the NDWI has the lowest. (2) Compared with the nine water indexes and the multi-index water extraction rule method (MIWER), the SWE-CSPM can effectively reduce the commission errors of surface water extraction, and the water extraction accuracy is the highest (overall accuracy 95.44%, kappa coefficient 90.62%). (3) At the global scale, the maximum water area of the YRB shows a decreasing trend, but the change amount is small. The permanent water area shows an uptrend, whereas the seasonal water area shows a downtrend year by year. The reason may be that the increase in surface runoff and the construction of reservoir projects have led to the transformation of some seasonal water into permanent water. (4) At the secondary water resource zoning scale, the permanent water area of other secondary water resource zonings shows an increasing trend in different degrees, except for the Interior Drainage Area. (5) At the provincial scale, the permanent water area of all provinces shows an uptrend, while the seasonal water areas show a fluctuating downtrend. The maximum water area of Shandong, Inner Mongolia Autonomous Region, and Qinghai increases slowly, while the other provinces show a decreasing trend. (6) At the typical water scale, there are significant differences in the water area variation process in Zhaling Lake, Eling Lake, Wuliangsuhai, Hongjiannao, and Dongping Lake, but the permanent water area and maximum water area of these waters have increased over the past decade. This study offers significant technical support for the dynamic monitoring of surface water and helps to deeply understand the spatiotemporal variations in surface water in the YRB. Full article

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research. Full article

Migration-induced land degradation is a challenging environmental issue in Sub-Saharan Africa. The need for expansion due to urban development has raised the question of effective sustainable measures. Understanding migration and land degradation links is paramount for sustainable urban development and resource use. This is particularly true in Nigeria, where elevated migration levels frequently result in accelerated land degradation due to urban expansion. Given the need to understand the impact of migration on land degradation in the Savannah Region of Nigeria (SRN), this study introduces a novel approach by integrating remote sensing data (NDVI, NDBI) with local community perceptions (mixed-methods approach) to assess the impact of migration on land degradation in four migration destination communities located in two local government areas (LGAs) (Sabon Gari East and Sabon Gari West of Fagge LGA; Zuba and Tungamaje of Gwagwalada LGA). We conducted focus group discussions and a semi-structured survey with 360 household heads to obtain a comprehensive view of perceptions. Our findings revealed that 41.1% and 29.5% of the respondents agreed and strongly agreed that migration significantly contributes to land degradation. We analysed the spatiotemporal patterns of the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-Up Index (NDBI) acquired from Landsat 8 datasets for 2014 to 2023. While increasing NDBI values were observed in all communities, a slight decrease in NDVI was noted in Sabon Gari East and Tungamaje. Our analyses highlighted activities leading to land degradation such as land pressure due to built-up expansion at Sabon Gari East, Sabon Gari West, and Tungamaje, and deforestation at Zuba. Based on the varying challenges of migration-induced land degradation, we recommend adequate community participation in suggesting targeted interventions and policies to foster various adaptive capacities and sustainable environments within SRN communities and Sub-Saharan Africa. Full article

Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas. Full article

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. Full article

Greenscaping, a key sustainable practice, helps cities combat rising temperatures and climate change. Urban parks, a pivotal greenscaping element, mitigate the urban heat island (UHI) effect. In this study, we utilized high-resolution remote sensing imagery (GF-2 and Landsat 8, 9) and in situ measurements to analyze the seasonal thermal regulation of different park types in Zhengzhou, China. We calculated vegetation characteristic indices (VCIs) and landscape patterns (LMs) and employed boosted regression tree models to explore their relative contributions to land surface temperature (LST) across different seasons. Our findings revealed that urban parks lowered temperatures by 0.65 °C, 1.41 °C, and 2.84 °C in spring, summer, and autumn, respectively, but raised them by 1.92 °C in winter. Amusement parks, comprehensive parks, large parks, and water-themed parks had significantly lower LSTs. The VCI significantly influenced LST in autumn, with trees having a stronger cooling effect than shrubs. LMs showed a more prominent effect than VCIs on LST during spring, summer, and winter. Parks with longer perimeters, larger and more dispersed green patches, higher plant species richness, higher vegetation heights, and larger canopies were associated with more efficient thermal reduction in an urban setting. The novelty of this study lies in its detailed analysis of the seasonal thermal regulation effects of different types of urban parks, providing new insights for more effective urban greenspace planning and management. Our findings assist urban managers in mitigating the urban surface heat effect through more effective urban greenspace planning, vegetation community design, and maintenance, thereby enhancing cities’ potential resilience to climate change. Full article