文献类型：期刊文献

英文题名：A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

作者：Jiang, Fugen[1,2,3] Smith, Andrew R.[4] Kutia, Mykola[5] Wang, Guangxing[1,6] Liu, Hua[7] Sun, Hua[1,2,3]

第一作者：Jiang, Fugen

通信作者：Sun, H[1];Sun, H[2];Sun, H[3]

机构：[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

年份：2020

卷号：12

期号：11

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20202508842255);Scopus(收录号：2-s2.0-85086469004);WOS:【SCI-EXPANDED(收录号:WOS:000543397000189)】；

基金：This research was funded by the project of ecological benefits monitoring and evaluation of key ecological engineering in the construction of three North Shelterbelt System funded by the National Key R&D Program of China (N#: 2017YFC0506502); National Natural Science Foundation of China (N#: 31971578); Scientific Research Fund of Hunan Provincial Education Department (N#: 17A225); the National Bureau to Combat Desertification, State Forestry Administration of China (N#: 101-9899); Forestry Administration of Hunan Province (N#: XLK201986); Training Fund of Young Professors from Hunan Provincial Education Department (N#: 90102-7070220090001) and Scientific Innovation Fund for Post-graduates of Hunan Province (N#: CX20190622).

语种：英文

外文关键词：Leaf area index; medium-resolution images; characteristic variable selection; modified kNN; dry regions

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.