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

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成果类型:
期刊论文
作者:
Jiang, Fugen;Smith, Andrew R.;Kutia, Mykola;Wang, Guangxing;Liu, Hua;...
通讯作者:
Sun, Hua
作者机构:
[Jiang, Fugen; Wang, Guangxing; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.
[Jiang, Fugen; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.
[Jiang, Fugen; Sun, Hua] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.
[Smith, Andrew R.] Bangor Univ, Sch Nat Sci, Bangor LL57 2UW, Gwynedd, Wales.
[Kutia, Mykola] Bangor Univ, Bangor Coll China, 498 Shaoshan Rd, Changsha 410004, Peoples R China.
通讯机构:
[Sun, Hua] C
[Sun, Hua] K
Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.
Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.
Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.
语种:
英文
关键词:
Leaf area index;medium-resolution images;characteristic variable selection;modified kNN;dry regions
期刊:
Remote Sensing
ISSN:
2072-4292
年:
2020
卷:
12
期:
11
页码:
1884
基金类别:
All authors read the manuscript; conceptualization and methodology, H.S. and G.W.; validation, F.J. and H.L.; formal analysis, F.J.; investigation, F.J., H.S., and H.L.; draft, F.J., H.S., and M.K.; supervision, H.S.; review, editing, and revision, A.R.S. and G.W.; funding acquisition, H.S. and G.W. All authors have read and agreed to the published version of the manuscript. 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).
机构署名:
本校为第一且通讯机构
院系归属:
林学院
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 ...

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