基于谷歌Earth Engine （GEE）和ArcGIS的中国南方低山丘陵区地表水面积提取研究

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-06-30 DOI:10.1016/j.rsase.2025.101650

Kindeneh Bekele Emiru , Yin Ren , Shudi Zuo , Abiot Molla , Ayalkibet Mekonnen Seka , Jiaheng Ju

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引用次数: 0

摘要

监测和评估地表水动态对于应对气候变化和促进各个部门的增长至关重要。光谱水指数法是地表水制图和监测的主要方法。该研究是在中国南部低山区和丘陵地区进行的。本文提出了一种提高地表水制图提取精度的综合方法。5种不同的水和植被指数在不同的波段上使用。与其他组合相比，改进的归一化差水指数（mNDWI）与近红外（NIR）波段相结合，在不同类型的水中显示出更高的提取精度。利用联合研究中心（JRC）产品全球地表水（GSW）数据集和现有地表径流数据验证了（mNDWI_NIR）的组合。使用混淆矩阵评估组合方法的准确性，其总体准确率为96.90%，kappa值为0.868。与地表径流分布的R2值分别为0.933和0.926，与GSW和土地利用和土地覆盖（LULC）的R2值分别为0.946和0.946。多年来，该方法在各种环境条件下有效地提取细水，证明了其综合方法、稳定性和通用性。通过分析中国南方低山区和丘陵地区的时空动态，表明了其实用性，突出了其扩展到更大区域的能力，这支持了政府和水管理部门恢复和恢复水资源的努力。

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Combining spectral water index with band for surface water area extraction by using Google Earth Engine (GEE) and ArcGIS in the southern low mountain and hilly areas of China

Monitoring and evaluating surface water dynamics is crucial for addressing climate change and fostering growth in various sectors. The spectral water index method is a predominant approach for mapping and monitoring surface water. The study was conducted in the southern low mountain and hilly areas of China.

This study presents a combined approach to enhancing extraction accuracy in surface water mapping. Five distinct water and vegetation indices were employed alongside various bands. The modified normalized difference water index (mNDWI), combined with the near-infrared (NIR) band, has demonstrated superior extraction accuracy across different types of water compared to the other combinations. The combination of (mNDWI_NIR) was validated with the Joint Research Center (JRC) product Global Surface Water (GSW) dataset and the available surface runoff data. The accuracy of the combined method was assessed using a confusion matrix, which yielded an overall accuracy of 96.90 % and a kappa value of 0.868. It also shows a strong linear correlation with areal surface runoff distributions, with an R² value of 0.946, compared to GSW and land use and land cover (LULC) values of 0.933 and 0.926, respectively.

The method demonstrated a comprehensive approach, stability, and versatility across various environmental conditions over the years in efficiently extracting slender waters. Its usefulness is shown by analyzing spatiotemporal dynamics in the Southern low mountain and hilly areas of China, highlighting its capacity to expand to larger regions, which supports the efforts of the government and water management authorities to recover and restore water resources.

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems