文献类型：期刊文献

英文题名：Forest Canopy Cover Inversion Exploration Using Multi-Source Optical Data and Combined Methods

作者：Guan, Yuan[1] Tian, Xin[2] Zhang, Wangfei[1] Marino, Armando[3] Huang, Jimao[4] Mao, Yingwu[1] Zhao, Han[1]

第一作者：Guan, Yuan

通信作者：Zhang, WF[1]

机构：[1]Southwest Forestry Univ, Coll Forestry, Kunming 650224, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Univ Stirling, Biol & Environm Sci, Stirling FK9 4LA, Scotland;[4]Aerosp Xinde Zhitu Beijing Technol Co Ltd, Beijing 100000, Peoples R China

年份：2023

卷号：14

期号：8

外文期刊名：FORESTS

收录：;EI(收录号：20233514644295);Scopus(收录号：2-s2.0-85168804984);WOS:【SCI-EXPANDED(收录号:WOS:001056648000001)】；

基金：The research was supported by the National Natural Science Foundation of China (41871279and 31860240), the High Resolution Earth Observation System Major Project "Application Sub-System for High Resolution Forest Resources Survey (21-Y30B02-9001-19/22-1)", and the High Resolution Earth Observation System Major Special Project "Preparation of Standards and Specifications for Authenticity Testing of High Resolution Common Products" (21-Y20B01-9001-19/22-1).

语种：英文

外文关键词：forest canopy cover; feature filter; Landsat8 OLI; Sentinel-2A; KNN-FIFS

摘要：An accurate estimation of canopy cover can provide an important basis for forest ecological management by understanding the forest status and change patterns. The aim of this paper is to investigate the four methods of the random forest (RF), support vector regression (SVR), k-nearest neighbor (KNN), and k-nearest neighbor with fast iterative features selection (KNN-FIFS) for modeling forest canopy cover, and to evaluate three mainstream optical data sources-Landsat8 OLI, Sentinel-2A, Gaofen-1 (GF-1)-and three types of data combined comparatively by selecting the optimal modeling method. The paper uses the Daxinganling Ecological Station of Genhe City, Inner Mongolia, as the research area, and is based on three types of multispectral remote sensing data, extracting spectral characteristics, textural characteristics, terrain characteristics; the Kauth-Thomas transform (K-T transform); and color transformation characteristics (HIS). The optimal combination of features was selected using three feature screening methods, namely stepwise regression, RF, and KNN-FIFS, and the four methods: RF, SVR KNN, and KNN-FIFS, were combined to carry out the evaluation analysis regarding the accuracy of forest canopy cover modeling: (1) In this study, a variety of remote sensing features were introduced, and the feature variables were selected by different parameter preference methods and then employed in modeling. Based on the four modeling inversion methods, the KNN-FIFS model achieves the best accuracy: the Landsat8 OLI with R-2 = 0.60, RMSE = 0.11, and RMSEr = 14.64% in the KNN-FIFS model; the Sentinel-2A with R-2 = 0.80, RMSE = 0.08, and RMSEr = 11.63% in the KNN-FIFS model; the GF-1 with R-2 = 0.55, RMSE = 0.12, and RMSEr = 15.04% in the KNN-FIFS model; and the federated data with R-2 = 0.82, RMSE = 0.08, and RMSEr = 10.40% in the KNN-FIFS model; (2) the three multispectral datasets have the ability to estimate forest canopy cover, and the modeling accuracy superior under the combination of multi-source data features; (3) under different optical data, KNN- FIFS achieves the best accuracy in the established nonparametric model, and its feature optimization method is better than that of the random forest optimization method. For the same model, the estimation result of the joint data is better than the single optical data; thus, the KNN-FIFS model, with specific parameters, can significantly improve the inversion accuracy and efficiency of forest canopy cover evaluation from different data sources.