针对岩溶区水体破碎植被与水体重叠的特点提出一种新型混合水体指数对该地区水资源管理与评估有着重要作用。以2017年普者黑的Landsat8 OLI影像数据为基础数据，选择不同流域水系类型，构建新型混合水体指数NEWI，与归一化水体指数（Normalized Difference Water Index，NDWI）和改进的归一化水体指数（Modified Normalized Difference Water Index，MNDWI）进行对比，分析各指数的水体信息提取的准确性和完整性。结果表明：新型水体指数NEWI的提取效果优于NDWI和MNDWI，特别是含有坑塘和水体植被的破碎湖泊区域，NEWI计算水体与阴影的区分度25.3%，大于NDWI（17.9%）和MNDWI（15.6%）；水体与非水体的总体识别精度为88.83%，Kappa系数0.76，也大于NDWI（87.10%，Kappa系数0.74）和MNDWI（81.93%，Kappa系数0.64）。利用NEWI对遥感影像进行水体增强的方法，不仅能够很好地提取出开阔水域的水体信息，边缘清晰可靠；对于破碎水域，NEWI水体提取精度最高，并且相比基于复杂数学理论的分类提取过程，操作相对简单，易于推广，能够较好地提高岩溶破碎区的水系提取和水域实时监测的精度。
Abstract
Water in Karst area is broken and the characteristics of vegetation and water body overlapping put forward a new type of mixed water index of the regional water resources management and evaluation plays an important role. The Puzhehei's Landsat8 OLI is based on the data of image data, building a new hybrid NEWI water index. Results show that the new water body index NEWI extracting effect is better than that of NDWI and MNDWI, especially vegetation breakage lakes contain pits and water area, NEWI calculated 25.3% degree of differentiation of water body and shadow, is greater than the NDWI (17.9%) and MNDWI (15.6%). Water's overall recognition accuracy is 88.83%, the Kappa coefficient 0.76 is greater than the NDWI (87.10%, Kappa coefficient 0.74) and MNDWI (81.93%, Kappa coefficient 0.64). NEWI water was used by the remote sensing image enhancement method, it can not only effectively extract the open water of the water body information, the edge is clear and reliable. For broken waters, NEWI water extract the highest precision, and compared with the classification of extraction process, based on the theory of the complex mathematical operation is relatively simple, easy to promote, can well improve Karst fractured zone, the accuracy of extraction and water drainage system for real-time monitoring.
关键词
破碎水域 /
遥感信息提取 /
NEWI /
普者黑
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Key words
karst area /
broken waters /
remote sensing information extraction /
NEWI /
Puzhehei
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基金
云南农业大学学生科技创新创业行动基金项目;云南省科技惠民专项计划项目
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参考文献
[1]聂亚文, 余明, 蓝婷. 基于MAWEI指数的水体信息提取方法[J]. 地球环境学报, 2019,10(3):281-290.
[2]吴佳平, 张旸, 张杰, 等. 基于MODIS数据的淤泥质海岸水体指数比较与分析：以黄河三角洲海岸为例[J]. 国土资源遥感, 2019,31(3):242-249.
[3]张永永, 刘丽娟, 赵盼盼. 新型水体指数的构建及在滨海湿地水域提取中的有效性验证[J]. 浙江农林大学学报, 2018,35(4):735-742.
[4]SHARMA R, TATEISHI R, HARA K, et al. Developing superfine water index (SWI) for global water cover mapping using MODIS data[J]. Remote Sensing, 2015,7(10):13 807-13 841.
[5]XIE H, LUO X, XU X, et al. Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI imagery[J]. Remote Sensing, 2016,8(7):584-586.
[6]YANG X C, ZHAO S S, QIN X B, et al. Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening[J]. Remote Sensing, 2017,9(6):596-597.
[7]刘云根, 王妍, 张紫霞, 等. 普者黑岩溶湿地景观格局演变及驱动机制研究[J]. 林业科技, 2019,44(1):4-9.
[8]闻国静, 王妍, 刘云根, 等. 典型岩溶湖泊湿地流域土地利用动态及模拟预测[J]. 福建农林大学学报(自然科学版), 2017,46(2):220-227.
[9]闻国静, 刘云根, 王妍, 等. 普者黑湖流域景观格局及生态风险时空演变[J]. 浙江农林大学学报, 2017,34(6):1 095-1 103.
[10]倪大伟, 王妍, 刘云根, 等. 普者黑湿地小流域土壤总磷分布及污染风险评价[J]. 环境污染与防治, 2018,40(12):1 425-1 430.
[11]张紫霞, 刘鹏, 王妍, 等. 普者黑岩溶湿地沉积物氮, 磷, 有机质分布及污染风险评价[J]. 生态环境学报, 2019,28(9):1 835-1 842.
[12]肖骁, 张英, 桑会勇, 等. Landsat8-OLI 深蓝波段用于近海泥沙水质监测的效果[J]. 辽宁工程技术大学学报 (自然科学版), 2016,35(12):1 480-1 484.
[13]MCFEETERS S K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996,17(7):1 425-1 432.
[14]徐涵秋. 利用改进的归一化差异水体指数 (MNDWI) 提取水体信息的研究[J].遥感学报, 2005(5):589-595.
[15]刘双童, 王明孝, 杨树文, 等. 基于 GF-2 高分辨率遥感影像的水体提取方法研究[J]. 全球定位系统, 2018,43(6):37-43.
[16]ZHAO X, WANG P, CHEN C, et al. Water body information extraction from remote-sensing images after disasters based on spectral information and characteristic knowledge[J]. International Journal of Remote Sensing, 2017,38(5):1 404-1 422.
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