Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

doi:10.3390/f11040399

	Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution
	Zhang, Chenchen 1,2; Huang, Chong 1,3; Li, He 1; Liu, Qingsheng 1; Li, Jing 4; Bridhikitti, Arika 5; Liu, Gaohuan 1
刊名	FORESTS
	2020-04-01
卷号	11 期号:4 页码:18
关键词	rubber plantation multiresolution remote sensing images GLCM multiscale window sizes variable importance Random Forest classifier
DOI	10.3390/f11040399
通讯作者	Huang, Chong(huangch@lreis.ac.cn)
英文摘要	The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 x 3 to 31 x 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 x 31, 11 x 11 to 15 x 15, and 3 x 3 to 7 x 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer's accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user's accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.
资助项目	CAS Earth Big Data Science Project[XDA19060302] ; National Science Foundation of China[41561144012] ; National Science Foundation of China[41661144030] ; Innovation Project of LREIS[O88RA303YA]
WOS关键词	MAINLAND SOUTHEAST-ASIA ; TREE SPECIES CLASSIFICATION ; FOREST ; SATELLITE ; IMAGES ; PALSAR ; EXPANSION ; MODIS
WOS研究方向	Forestry
语种	英语
出版者	MDPI
WOS记录号	WOS:000534632500037
资助机构	CAS Earth Big Data Science Project ; National Science Foundation of China ; Innovation Project of LREIS
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159596]
专题	中国科学院地理科学与资源研究所
通讯作者	Huang, Chong
作者单位	1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, CAS Engn Lab Yellow River Delta Modern Agr, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 5.Mahidol Univ, Sch Multidisciplinary, Environm Engn & Disaster Management Program, Kanchanaburi Campus, Sai Yok 71150, Kanchanaburi, Thailand
推荐引用方式 GB/T 7714	Zhang, Chenchen,Huang, Chong,Li, He,et al. Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution[J]. FORESTS,2020,11(4):18.
APA	Zhang, Chenchen.,Huang, Chong.,Li, He.,Liu, Qingsheng.,Li, Jing.,...&Liu, Gaohuan.(2020).Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution.FORESTS,11(4),18.
MLA	Zhang, Chenchen,et al."Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution".FORESTS 11.4(2020):18.