干旱区生态环境知识资源中心(Arid): 基于Google Earth Engine与机器学习的大尺度30m分辨率沙地灌木覆盖度估算

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	基于Google Earth Engine与机器学习的大尺度30m分辨率沙地灌木覆盖度估算
其他题名	Large scale shrub coverage mapping of sandy land at 30m resolution based on Google Earth Engine and machine learning
	陈黔; 李晓松; 修晓敏; 杨广斌
来源期刊	生态学报
ISSN	1000-0933
出版年	2019
卷号	39 期号:11 页码:4056-4069
中文摘要	相较于降雨充沛的南方,中国北方沙地植被呈现覆盖整体偏低、空间异质性强的特点.灌木作为该区域的优势植被,对于风沙固定、食品/木材供给起着极为重要的作用.针对当前大尺度、中高分辨率干旱地区灌木覆盖度遥感产品缺失的现状,研究提出了一套通过Collect Earth样本收集器进行样本采集、利用Google Earth Engine遥感云平台的数据与计算优势开展大尺度灌木覆盖度估算的方法,并选取中国北方四大沙地之一的毛乌素沙地开展了示范应用.研究结果表明:(1)CollectEarth样本收集器可以有效地获取地面灌木覆盖度样本数据集,可以将灌木与高大乔木与草本植被进行有效区分,为灌木覆盖度估算样本集的建立打下了基础;(2)利用Landsat数据与其他辅助数据,机器学习算法可以较好地实现灌木覆盖度的估算,CART模型确定性系数 R2为0.73,均方根误差(Root Mean Square Error, RMSE)为13.66% ,预测精度(Estimated Accuracy, EA)为61.8% ,SVM模型R2为0.72,RMSE为13.73%,EA为61.6% ;(3)提出的基于GEE的灌木覆盖度估算体系可为我国乃至全球尺度干旱地区沙地灌木覆盖度信息提取提供有效支撑,具有较大的应用潜力.
英文摘要	Compared with the humid region in southern China, the sandy land vegetation is characterized by low coverage and a scattered spatial distribution in the north. Shrub is a dominant vegetation type in this region and plays an important role in sand control, food/timber product provision, etc. In view of the current lack of the medium- and high-resolution remote sensing products for shrub coverage at large-scale in arid regions, we proposed a new approach to estimate shrub coverage at large scale based on Collect Earth sample collector and Google Earth Engine (GEE) platform. Then this approach was applied to Mu Us sandy land,one of the four major sandy lands in Northern China. The results showed that:(1) Collect Earth sample collector could effectively obtain the ground shrub coverage sample data set for distinguishing shrubs from tall trees and herbaceous vegetation,which laid a foundation for the establishment of shrub coverage estimation ; (2) Using Landsat data,other ancillary data,and the machine learning algorithm in GEE,the shrub coverage could be estimated effectively. The CART model had a deterministic coefficient R2 of 0.73 and a Root Mean Square Error (RMSE) of 13.66% with the estimated Accuracy (EA) of 61.8%. For SVM model, R2,RMSE, and EA were 0.72, 13.73%, and 61.6%, respectively. (3) The GEE-based approach proposed in this study could provide support to shrub coverage estimation in sandy land at the national and even the global scale with a potential application.
中文关键词	沙地 ; 灌木覆盖度 ; 机器学习
英文关键词	Collect Earth Google Earth Engine sandy land shrub coverage Collect Earth Google Earth Engine machine learning
类型	Article
语种	中文
国家	中国
收录类别	CSCD
WOS类目	Life Sciences & Biomedicine - Other Topics
WOS研究方向	Life Sciences & Biomedicine - Other Topics
CSCD记录号	CSCD:6512947
来源机构	贵州师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/239447
作者单位	陈黔, 贵州师范大学地理与环境科学学院;;中国科学院遥感与数字地球研究所, ;;中国科学院数字地球重点实验室, 贵阳;;, ;;北京 550001;;100094.; 修晓敏, 贵州师范大学地理与环境科学学院;;中国科学院遥感与数字地球研究所, ;;中国科学院数字地球重点实验室, 贵阳;;, ;;北京 550001;;100094.; 李晓松, 中国科学院遥感与数字地球研究所, 中国科学院数字地球重点实验室, 北京 100094, 中国.; 杨广斌, 贵州师范大学地理与环境科学学院, 贵阳, 贵州 550001, 中国.
推荐引用方式 GB/T 7714	陈黔,李晓松,修晓敏,等. 基于Google Earth Engine与机器学习的大尺度30m分辨率沙地灌木覆盖度估算[J]. 贵州师范大学,2019,39(11):4056-4069.
APA	陈黔,李晓松,修晓敏,&杨广斌.(2019).基于Google Earth Engine与机器学习的大尺度30m分辨率沙地灌木覆盖度估算.生态学报,39(11),4056-4069.
MLA	陈黔,et al."基于Google Earth Engine与机器学习的大尺度30m分辨率沙地灌木覆盖度估算".生态学报 39.11(2019):4056-4069.