基于GF-1数据多尺度遥感特征的森林蓄积量估测研究  

Estimation of Forest Volume Based on Multi-Scale Remote Sensing Features of GF-1

作　　者：黄冰倩 岳彩荣[1] 朱泊东 HUANG Bingqian;YUE Cairong;ZHU Bodong(College of Forestry,Southwest Forestry University,Kunming 650224,China;Forestry Survey&Planning Institute of Guizhou Province,Guiyang 550003,China)

机构地区：[1]西南林业大学,昆明650224 [2]贵州省林业调查规划院,贵阳550003

出　　处：《林业资源管理》2022年第3期54-59,共6页Forest Resources Management

基　　金：云南省科技厅重大科技专项(202002AA100007-015);贵州省森林保护“六个严禁”执法专项行动案件管理信息系统研发(黔林科合J [2018]14号)。

摘　　要：基于贵州省观山湖区高分1号(GF-1)遥感数据提取的光谱信息、植被指数及纹理特征,结合实测马尾松样地数据,通过多元逐步回归、随机森林算法构建不同窗口遥感特征的森林蓄积量估测模型。结果表明:多元逐步回归、随机森林估测模型最佳窗口均为13×13窗口;选取DI2,B3,EN2,SM2,CO_(3)作为建模特征变量,以最佳窗口建立蓄积估测模型,随机森林模型拟合效果优于多元逐步回归模型。根据遥感影像分辨率选取适宜窗口提取特征变量,可进一步提高森林蓄积估测的建模精度。Based on spectral information, vegetation index and texture characteristics extracted from GF-1 remote sensing data in Guanshanhu, Guizhou Province, combined with the measured masson pine plot data, multiple stepwise regression and random forest algorithm were used to construct forest stock estimation models with different windows of remote sensing features.The results indicated that: the optimal windows of multiple stepwise regression and random forest estimation models were both 13×13.When feature variables were extracted as DI2,B3,EN2,SM2,CO_(3),and the accumulation estimation model was established with the optimal window, the fitting effect of random forest model was better than that of multiple stepwise regression model.According to the resolution of remote sensing image, selecting suitable window to extract characteristic variables can further improve the modeling accuracy of forest stock estimation.

关 键 词：高分1号 纹理特征 多元逐步回归 随机森林算法 森林蓄积量 

分 类 号：S758.51[农业科学—森林经理学] S771.8[农业科学—林学]