气候变化领域集成服务门户(CCPortal): Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover

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DOI	10.1016/j.jag.2021.102573
	Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover
	Guo Zi-chen; Wang Tao; Liu Shu-lin; Kang Wen-ping; Chen Xiang; Feng Kun; Zhi Ying
通讯作者	Liu, SL (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Desert & Desertificat, Donggang West Rd 320, Lanzhou 730000, Gansu, Peoples R China.
发表日期	2021
ISSN	1569-8432
EISSN	1872-826X
卷号	104
英文摘要	Non-photosynthetic vegetation (NPV) plays a crucial role in arid and semi-arid ecosystems. Remote sensing methods can extract NPV information accurately and quantitatively, which helps in studying the water use, community health, and climate response of vegetation communities. This study used the backpropagation network (BP) and random forest (RF) methods to test NPV cover extraction from Landsat 8-OLI images in Mu Us Sandy Land. Pixel-level NPV cover, photosynthetic vegetation (PV) cover, and bare soil (BS) cover from unmanned aerial vehicle (UAV) field sampling data were used to model the BP and RF. After the generalisation ability of the NPV detection model of BP and RF was evaluated using ten-fold cross-validation, the influence of the distribution of sampling data on BP and RF fitting results was also evaluated. The results were as follows: 1. Considering the selection of appropriate parameters and input layers, both BP and RF exhibited high accuracy in detecting NPV, and the detection accuracy of the RF algorithm for PV and BS was slightly higher than that of the BP algorithm (R-RF-NPV(2) = 0.8426, R-BP-NPV(2) = 0.8277, R-RF-PV(2) = 0.8606, R-BP-PV(2) = 0.8514, R-RF-BS(2) = 0.8123, R-BP-BS(2) = 0.7396). 2. When the BP and RF algorithms were used for geospatial continuous value prediction, the distribution of samples affected the final prediction results. The RF algorithm is less sensitive to the sample data distribution. 3. The random sampling method is the best method for collecting training samples. Even with uniform sampling, when there was a large difference between the distribution of the sampling value and the distribution of the real value, the fitting result would have a large deviation. This paper provides suggestions for the fitting of nonphotosynthetic vegetation in arid and semi-arid regions and provides a new method for evaluating the results of remote sensing regression fitting.
关键词	PHOTOSYNTHETIC VEGETATION FRACTIONAL COVER LOESS PLATEAU MODIS DATA BARE SOIL INDEX RESIDUE IMAGERY NDVI CROP
英文关键词	UAV; Nonphotosynthetic vegetation cover; Mu Us sandy land; Random forests; Backpropagation neural network; The sampling distribution
语种	英语
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:000711068700001
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/253773
作者单位	[Guo Zi-chen; Wang Tao; Liu Shu-lin; Kang Wen-ping; Feng Kun; Zhi Ying] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Desert & Desertificat, Donggang West Rd 320, Lanzhou 730000, Gansu, Peoples R China; [Guo Zi-chen; Feng Kun; Zhi Ying] Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China; [Chen Xiang] Northwest Normal Univ, Coll Geog & Environm Sci, Lanzhou 730070, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Guo Zi-chen,Wang Tao,Liu Shu-lin,et al. Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover[J]. 中国科学院西北生态环境资源研究院,2021,104.
APA	Guo Zi-chen.,Wang Tao.,Liu Shu-lin.,Kang Wen-ping.,Chen Xiang.,...&Zhi Ying.(2021).Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,104.
MLA	Guo Zi-chen,et al."Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 104(2021).