干旱区生态环境知识资源中心(Arid): A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

Arid

DOI	10.3390/rs12111884
	A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China
	Jiang, Fugen 1,2,3; Smith, Andrew R.4; Kutia, Mykola 5; Wang, Guangxing 1,6; Liu, Hua 7; Sun, Hua 1,2,3
通讯作者	Sun, Hua
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:11
英文摘要	As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.
英文关键词	Leaf area index medium-resolution images characteristic variable selection modified kNN dry regions
类型	Article
语种	英语
国家	Peoples R China ; Wales ; USA
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000543397000189
WOS关键词	NEAREST NEIGHBORS TECHNIQUE ; LANDSAT-TM ; VEGETATION INDEXES ; SATELLITE IMAGERY ; FOREST VARIABLES ; INVENTORY DATA ; REFLECTANCE ; INFORMATION ; CLASSIFIER ; INFERENCE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319755
作者单位	1.Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China; 2.Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China; 3.Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China; 4.Bangor Univ, Sch Nat Sci, Bangor LL57 2UW, Gwynedd, Wales; 5.Bangor Univ, Bangor Coll China, 498 Shaoshan Rd, Changsha 410004, Peoples R China; 6.Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA; 7.Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Fugen,Smith, Andrew R.,Kutia, Mykola,et al. A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China[J],2020,12(11).
APA	Jiang, Fugen,Smith, Andrew R.,Kutia, Mykola,Wang, Guangxing,Liu, Hua,&Sun, Hua.(2020).A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China.REMOTE SENSING,12(11).
MLA	Jiang, Fugen,et al."A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China".REMOTE SENSING 12.11(2020).