Long-term mapping of land use and cover changes using Landsat images on the Google Earth Engine Cloud Platform in bay area - A case study of Hangzhou Bay, China

Jintao Liang , Chao Chen , Yongze Song , Weiwei Sun , Gang Yang
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引用次数: 9

Abstract

Large-scale, long-term series, and high-precision land use and cover change (LUCC) mapping is the basic support for territorial spatial planning and sustainable development in the Bay Area. In response to the sustainable development agenda, for characteristics of high landscape fragmentation, strong surface heterogeneity and frequent land use type conversion in the Bay Area, this study developed a random forest (RF) algorithm that considers spectral bands, remote sensing indices and components of a principal component analysis, and the mapping and monitoring of LUCC in Hangzhou Bay from 1985 to 2020 based on Google Earth Engine (GEE) and Digital Shoreline Analysis System (DSAS) were carried out. The results are as follows. (1) The overall accuracy (OA) and kappa coefficient were 92.83% and 0.91, respectively. (2) During the study period, the areas of the construction land, water area, and bare land increased, while the areas of the wood land, cultivated fields, and tidal flats decreased. (3) During the study period, the total area of the tidal flats decreased from 181.65 km2 to 161.50 km2, with an average annual decrease of 0.58 km2, and the tidal flats were primarily concentrated on the south shore of Hangzhou Bay. (4) During the study period, the transfer of cultivated fields to construction land was the most significant (2268.05 km2). (5) During the study period, the length of the coastline decreased from 383.73 km to 362.80 km, with an average annual decrease of 0.60 km. According to the DSAS statistics, the net shoreline movement (NSM) of the coastline on the north shore of Hangzhou Bay was 773.58 m, the end point rate (EPR) and the linear regression rate (LRR) were 22.10 m/a and 27.00 m/a, respectively. The NSM of the south shore was 4109.57 m, and the EPR and LRR were 117.42 m/a and 132.22 m/a, respectively. The proposed methods improve the accuracy of land use classification of the RF algorithm in the complex environment of the Bay Area, and it can provide technical support for natural resource survey and regional sustainable development in the Bay Area.

谷歌地球引擎云平台上基于Landsat影像的湾区土地利用与覆被变化长期制图——以杭州湾为例
大规模、长期、高精度的土地利用和覆盖变化(LUCC)测绘是湾区国土空间规划和可持续发展的基础支撑。为了响应可持续发展议程,针对湾区景观高度碎片化、地表异质性强和土地利用类型转换频繁的特点,本研究开发了一种随机森林(RF)算法,该算法考虑了光谱带、遥感指数和主成分分析的组成部分,并基于谷歌地球引擎(GEE)和数字海岸线分析系统(DSAS)对1985-2020年杭州湾LUCC进行了测绘和监测。结果如下。(1) 总准确度(OA)和kappa系数分别为92.83%和0.91。(2) 在研究期间,建设用地、水域和裸地的面积增加,而林地、耕地和滩涂的面积减少。(3) 研究期间,滩涂总面积从181.65平方公里减少到161.50平方公里,年均减少0.58平方公里,滩涂主要集中在杭州湾南岸。(4) 在研究期间,耕地向建设用地的转移最为显著(2268.05平方公里)。(5) 研究期间,海岸线长度从383.73公里减少到362.80公里,年均减少0.60公里。根据DSAS统计,杭州湾北岸海岸线的净海岸线移动(NSM)为773.58米,终点速率(EPR)和线性回归速率(LRR)分别为22.10米/年和27.00米/年。南岸的NSM为4109.57 m,EPR和LRR分别为117.42 m/a和132.22 m/a。所提出的方法提高了RF算法在湾区复杂环境中土地利用分类的准确性,可以为湾区自然资源调查和区域可持续发展提供技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
6.60
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