Search Results (24)

Intensified by climate change and anthropogenic activities, flood disasters necessitate rapid and accurate mapping for effective disaster management. This study develops an integrated framework leveraging synthetic aperture radar (SAR) and cloud computing to enhance flood monitoring, with a focus on a 2024 extreme rainfall event in Liaoning Province, China. Utilizing the Google Earth Engine (GEE) platform, we combine three complementary techniques: (1) Otsu automatic thresholding, for efficient extraction of surface water extent from Sentinel-1 GRD time series (154 scenes, January–October 2024), achieving processing times under 2 min with >85% open-water accuracy; (2) random forest (RF) classification, integrating multi-source features (SAR backscatter, terrain parameters from 30 m SRTM DEM, NDVI phenology) to distinguish permanent water bodies, flooded farmland, and urban areas, attaining an overall accuracy of 92.7%; and (3) Fuzzy C-Means (FCM) clustering, incorporating backscatter ratio and topographic constraints to resolve transitional “mixed-pixel” ambiguities in flood boundaries. The RF-FCM synergy effectively mapped submerged agricultural land and urban spill zones, while the Otsu-derived flood frequency highlighted high-risk corridors (recurrence > 10%) along the riverine zones and reservoir. This multi-algorithm approach provides a scalable, high-resolution (10 m) solution for near-real-time flood assessment, supporting emergency response and sustainable water resource management in affected basins. Full article

Paddy fields are essential for food security and sustaining global dietary needs, yet urban expansion often encroaches on agricultural lands. Analyzing paddy fields and land use/land cover changes over time using satellite images provides critical insights for sustainable food production and balanced urban growth. However, mapping the paddy fields in tropical monsoon areas presents challenges due to persistent weather interference, monsoon-submerged fields, and a lack of training data. To address these challenges, this study proposed a flooding-assisted maximum likelihood classification (F-MLC) method. This approach utilizes accurate training datasets from intersecting flooded paddy field maps from the rainy and dry seasons, combined with the Automated Water Extraction Index (AWEI) to distinguish natural water bodies. The F-MLC method offers a robust solution for accurately mapping paddy fields and land use changes in challenging tropical monsoon climates. The classified images for 1989, 2000, 2013, and 2021 were produced and categorized into the following five major classes: urban areas, vegetation, paddy fields, water bodies, and other lands. The paddy field class derived for each year was validated using samples from various sources, contributing to the overall accuracies ranging from 83.6% to 90.4%, with a Kappa coefficient of between 0.80 and 0.88. The study highlights a significant decrease in paddy fields, while urban areas rapidly increased, replacing 23% of paddy fields between 1989 and 2021 in the watershed. This study demonstrates the potential of the F-MLC method for analyzing paddy fields and other land use changes over time in the tropical watershed. These findings underscore the urgent need for robust policy measures to protect paddy fields by clearly defining urban expansion boundaries, prioritizing paddy field preservation, and integrating these green spaces into urban development plans. Such measures are vital for ensuring a sustainable local food supply, promoting balanced urban growth, and maintaining ecological balance within the watershed. Full article

As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint. Full article

Accurate extraction of water bodies is the basis of remote sensing monitoring of water environments. Due to the complex types of ground objects around urban water bodies, high spectral and spatial resolution are needed to achieve accurate extraction of water bodies. Addressing the limitation that most spectral index methods used for water body extraction are more suitable for open waters such as oceans and lakes, this study proposes a PCA-NDWI accurate extraction model for urban water bodies based on hyperspectral remote sensing, which combines Principal Component Analysis (PCA) with Normalized Difference Water Index (NDWI). Furthermore, aiming at the common water shadow problem in urban hyperspectral remote sensing images, the advantages of the PCA-NDWI model were further verified by experiments. By comparing the accuracy and F1-Measure of the PCA-NDWI, NDWI, HDWI, and K-means models, the results demonstrated that the PCA-NDWI model was better than the other tested methods. The accuracy and F1-Measure of the PCA-NDWI model water extraction data were 0.953 and 0.912, respectively, and the accuracy and F1-Measure of the PCA-NDWI model water shadow extraction data were 0.858 and 0.872, respectively. Therefore, the PCA-NDWI model can effectively separate shadows and the surrounding features of urban water bodies, accurately extract water body information, and has great application potential in water resources management. Full article

Accurately mapping urban built-up areas is critical for monitoring urbanization and development. Previous studies have shown that Night light (NTL) data is effective in characterizing the extent of human activity. But its inherently low spatial resolution and saturation effect limit its application in the construction of urban built-up extraction. In this study, we developed a new index called VNRT (Vegetation, Nighttime Light, Road, and Temperature) to address these challenges and improve the accuracy of built-up area extraction. The VNRT index is the first to fuse the Normalized Difference Vegetation Index (NDVI), NPP-VIIRS Nighttime NTL data, road density data, and land surface temperature (LST) through factor multiplication. To verify the good performance of VNRT in extracting built-up areas, the built-up area ranges of four national central cities in China (Chengdu, Wuhan, Xi’an, and Zhengzhou) in 2019 are extracted by the local optimum thresholding method and compared with the actual validation points. The results show that the spatial distribution of VNRT is highly consistent with the actual built-up area. THE VNRT increases the variability between urban built-up areas and non-built-up areas, and can effectively distinguish some types of land cover that are easily ignored in previous urban indices, such as urban parks and water bodies. The VNRT index had the highest Accuracy (0.97), F1-score (0.94), Kappa coefficient (0.80), and overall accuracy (92%) compared to the two proposed urban indices. Therefore, the VNRT index could improve the identification of urban built-up areas and be an effective tool for long-term monitoring of regional-scale urbanization. Full article

As global warming exacerbates and urbanization accelerates, extreme climatic events occur frequently. Urban waterlogging is seriously spreading in China, resulting in a high level of vulnerability in urban societies and economies. It has been urgent for regional sustainable development to effectively identify and analyze the risk factors behind urban waterlogging. A novel model incorporating satellite image semantic segmentation into extreme gradient boosting (XGBoost) is employed for identifying and forecasting the urban waterlogging risk factors. Ground object features of waterlogging points are extracted by the satellite image semantic segmentation, and XGBoost is employed to predict waterlogging points and identify the primary factors affecting urban waterlogging. This paper selects the coastal cities of Haikou, Xiamen, Shanghai, and Qingdao as research areas, and obtains data from social media. According to the comprehensive performance evaluation of the semantic segmentation and XGBoost models, the semantic segmentation model could effectively identify and extract water bodies, roads, and green spaces in satellite images, and the XGBoost model is more accurate and reliable than other common machine learning methods in prediction performance and precision. Among all waterlogging risk factors, elevation is the main factor affecting waterlogging in the research areas. For Shanghai and Qingdao, the secondary factor affecting waterlogging is roads. Water bodies are the secondary factor affecting urban waterlogging in Haikou. For Xiamen, the four indicators other than the elevation are equally significant, which could all be regarded as secondary factors affecting urban waterlogging. Full article

The accurate monitoring of long-term spatial and temporal changes in open-surface water bodies offers important guidance for water resource security and management. In the middle and lower reaches of the Yangtze River, the monitoring of water body changes is especially critical due to the dense population and drastic climate change. Due to the complexity of the physical environment in which the water bodies are located, the advantages and disadvantages of various water body detection rules can vary in large-scale areas. In this paper, we use Landsat 5/7/8 data to extract the area of water bodies in the study area and analyze their spatial and temporal trends from 1984 to 2020 using the Google Earth Engine (GEE) platform. We propose an improved water body extraction rule based on an existing multi-indicator water body algorithm that combines impervious surface data and digital elevation model data. In this study, the performance of the improved algorithm was cross-validated using seven other water body indicator algorithms, and the results showed the following: (1) the rule accurately retained information about the water body while minimizing the interference of shadows on the extracted water body. (2) On the annual scale from 1984 to 2020, the open-surface water body dataset extracted using this improved rule showed that the turning point for the area of each water body type was 2011, with an overall decreasing trend in area before 2011 and an increasing trend in area after 2011, with the exception of special years, such as 1998. (3) The driving mechanism analysis showed that, overall, precipitation was positively correlated with the water body area and temperature was negatively correlated with the water body area. Additionally, human activities can have an impact on surface water dynamics. The key influencing factors are diverse for each water body type; it was found that seasonal water bodies were correlated with precipitation and paddy fields and permanent water bodies were correlated with temperature and urban construction. The accurate monitoring of the spatial and temporal dynamics of open-surface water performed in this study can shed light on the sustainable development of water resources and the environment. Full article

In recent years, remote sensing technology has enabled researchers to fill the existing statistics and research gaps on evapotranspiration in different land use classes. Thus, a remotely sensed-based approach was employed to investigate how evapotranspiration rates changed in different land use/cover classes across the Lake Urmia Basin from 2016 to 2020. This was accomplished by applying the Surface Energy Balance System (SEBS) and the maximum likelihood algorithm. Results showed that from 2016 to 2020, grassland, savanna, and wetland decreased by 1%, 0.58%, and 1%, respectively, whereas an increase of 0.4%, 0.4%, 2.5%, and 1.2% occurred in cropland, urban, shrubland, and water bodies, respectively. Based on the model’s results, over 98, 63, 90, 93, and 91% of the studied area, respectively, experienced a value of evapotranspiration between 0–6, 3–8, 0–4, 0–4, and 0–6 mm from 2016 to 2020. It was also found that these values are more closely related to water bodies and wetlands, followed by cropland, urban areas, savanna, non-vegetated, grassland, and shrubland. A strong correlation with R² > 70% was observed between the SEBS and the ground-measured values, while this value is lower than 50% for the MODIS Global Evapotranspiration Project (MOD16A2). The findings suggest that evapotranspiration and land use/cover can be extracted on a large-scale using SEBS and satellite images; thus, their maps can be presented in an accurate manner. Full article

A common task in land-cover classification is water body extraction, wherein each pixel in an image is labelled as either water or background. Water body detection is integral to the field of urban hydrology, with applications ranging from early flood warning to water resource management. Although traditional index-based methods such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) have been used to detect water bodies for decades, deep convolutional neural networks (DCNNs) have recently demonstrated promising results. However, training these networks requires access to large quantities of high-quality and accurately labelled data, which is often lacking in the field of remotely sensed imagery. Another challenge stems from the fact that the category of interest typically occupies only a small portion of an image and is thus grossly underrepresented in the data. We propose a novel approach to data augmentation—pixelwise category transplantation (PCT)—as a potential solution to both of these problems. Experimental results demonstrate PCT’s ability to improve performance on a variety of models and datasets, achieving an average improvement of 0.749 mean intersection over union (mIoU). Moreover, PCT enables us to outperform the previous high score achieved on the same dataset without introducing a new model architecture. We also explore the suitability of several state-of-the-art segmentation models and loss functions on the task of water body extraction. Finally, we address the shortcomings of previous works by assessing each model on RGB, NIR, and multispectral features to ascertain the relative advantages of each approach. In particular, we find a significant benefit to the inclusion of multispectral bands, with such methods outperforming visible-spectrum models by an average of 4.193 mIoU. Full article

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively. Full article

Urban water bodies are critical for sustainable urban ecological and social development. However, the complex compositions of urban land cover and small water bodies pose considerable challenges to urban water surface delineation. Here, we propose a novel urban water extraction algorithm (UWEA) that is efficient in distinguishing water and other low-reflective objects by combining the modified normalized difference water index (mNDWI) and HSV transformation. The spectral properties of urban land covers were analyzed and the separability of objects in different color spaces was compared before applying the HSV transformation. The accuracy and robustness of the UWEA were validated in six highly urbanized subregions of Beijing, Tokyo, and New York, and compared with the mNDWI and HIS methods. The results show that the UWEA had the fewest total errors (sum of omission and commission errors) for all the validation sites, which was approximately 3% fewer errors than those of the mNDWI and 17% fewer errors than those of the HIS method. The UWEA performed best because it was good at identifying small water bodies and suppressing reflective surfaces. The UWEA is effective in urban water monitoring and its thresholds are also robust in various situations. The resulting highly accurate water map could support water-related analyses. This method is also useful for scientists, managers, and planners in water resource management, urban hydrological applications, and sustainable urban development. Full article

On 20 July 2021, an extraordinary rainfall event occurred in Henan Province, China, resulting in heavy waterlogging, flooding, and hundreds of fatalities and causing considerable property damage. Because the damaged region was a major grain-producing region of China, assessing crop food production losses following this event is very important. Because the crop rotation production system is utilized in the region to accommodate two crops per year, it is very valuable to accurately identify the types of crops affected by the event and to assess the crop production losses separately; however, the results obtained using these methods are still inadequate. In this study, we used China’s first commercial synthetic aperture radar (SAR) data source, named Hisea-1, together with other open-source and widely used remote sensing data (Sentinel-1 and Sentinel 2), to monitor this catastrophic flood. Both the modified normalized difference water index (MNDWI) and Sentinel-1 dual-polarized water index (SDWI) were calculated, and an unsupervised classification (k-means) method was adopted for rapid water body extraction. Based on time-series datasets synthesized from multiple sources, we obtained four flooding characteristics, including the flooded area, flood duration, and start and end times of flooding. Then, according to these characteristics, we conducted a more precise analysis of the damages to flooded farmlands. We used the Google Earth Engine (GEE) platform to obtain normalized difference vegetation index (NDVI) time-series data for the disaster year and normal years and overlaid the flooded areas to extract the effects of flooding on crop species. According to the statistics from previous years, we calculated the areas and types of damaged crops and the yield reduction amounts. Our results showed that (1) the study area endured two floods in July and September of 2021; (2) the maximum areas affected by these two flooding events were 380.2 km² and 215.6 km², respectively; (3) the floods significantly affected winter wheat and summer grain (maize or soybean), affecting areas of 106.4 km² and 263.3 km², respectively; and (4) the crop production reductions in the affected area were 18,708 t for winter wheat and 160,000 t for maize or soybean. These findings indicate that the temporal-dimension information, as opposed to the traditional use of the affected area and the yield per unit area when estimating food losses, is very important for accurately estimating damaged crop types and yield reductions. Time-series remote sensing data, especially SAR remote sensing data, which have the advantage of penetrating clouds and rain, play an important role in remotely sensed disaster monitoring. Hisea-1 data, with a high spatial resolution and first flood-monitoring capabilities, show their value in this study and have the potential for increased usage in further studies, such as urban flooding research. As such, the approach proposed herein is worth expanding to other applications, such as studies of water resource management and lake/wetland hydrological changes. Full article

One of the most basic classification tasks in remote sensing is to distinguish between water bodies and other surface types. Although there are numerous techniques for extracting surface water from satellite imagery, there is still a need for research to more accurately identify water bodies with a view to efficient water maintenance in the future. Delineation accuracy is limited by varying amounts of suspended matter and different background land covers, especially those with low albedo. Therefore, the objective of this study was to develop an advanced index that improves the accuracy of extracting water bodies characterized by varying amounts of water constituents, especially in mountainous regions with highly rugged terrain, urban areas with cast shadows, and snow- and ice-covered areas. In this context, we propose a triangle water index (TWI) based on Sentinel-2 data. The principle of the TWI is that it first analyzes the reflectance values of water bodies in different wavelength bands to determine specific types. Then, triangles are constructed in a cartesian coordinate system according to the reflectance values of different water bodies in the respective wavelength bands. Finally, the TWI is achieved by using the triangle similarity theorem. We tested the accuracy and robustness of the TWI method using Sentinel-2 data of several water bodies in Mongolia, Canada, Sweden, the United States, and China and determined kappa coefficients and the overall precision. The performance of the classifier was compared with methods such as the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI), the enhanced water index (EWI), the automated water extraction index (AWEI), and the land surface water index (LSWI). The classification accuracy of the TWI for all test sites is significantly higher than that of these indices that are commonly used classification methods. The overall precision of the TWI ranges between 95% and 97%. Moreover, the TWI is also effective in extracting flooded areas. Hence, the TWI can automatically extract different water bodies from Sentinel-2 data with high accuracy, which provides also a favorable analysis method for the study of droughts and flood disasters and for the general maintenance of water bodies in the future. Full article

Lakes are an important component of global water resources. Lake water bodies extraction based on satellite remote sensing mainly utilizes optical or radar data. However, due to the influence of water quality, ground features with low reflectivity, and smooth surface features, it is still challenging to accurately extract water bodies in complex geographic environments. In this work, we proposed a lake water bodies extraction method by fusing Sentinel-1/2 data. Firstly, the proposed method analyzed the difference of the spectral polarization features between water and non-water in complex geographical environment. Then, the spectral polarization and water index were fused to multidimensional features by feature stacking. Finally, support vector machines are used to classify. Six typical lakes (including urban, mountains, and polluted and clean lakes) in China were used to verify the mapping accuracy. The results showed that extracting lake water bodies by fusing Sentinel-1/2 data had a better performance than using optical or radar data solely, all types of lakes achieved better extraction results, the overall accuracy of lake water extraction is improved by 3%, and the error of commission and omission is controlled within 6%. Comparative experiments indicate that combine radar polarization information with spectral information is helpful to improve the accuracy of different types of lakes extraction in complex geographical environment. Full article

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained. Full article