林业科学2025，Vol.61Issue(9)：1-11,11.DOI:10.11707/j.1001-7488.LYKX20250100
基于时序遥感指数的中国竹林植被信息提取
Vegetation Cover Extraction of Bamboo Forest in China Based on Time-Series Remote Sensing Indices
摘要
Abstract
[Objective]Bamboo forest is a unique forest type in China with significant ecological,economic,and social values.Its spectral feature is often confused with those of other forest types in the same distribution region.It is thus challenging to accurately mapping bamboo forest distribution using remote sensing technology.This study aims to improve the accuracy of mapping bamboo forest by developing newly time-series remote sensing indices(TSI)and combining them with the random forest algorithm,and thus provide a new technical approach for bamboo forest resource monitoring.[Method]Training samples for bamboo forest,evergreen forest,deciduous forest,grassland,building,bare land,water body and road were selected through visual interpretation.Based on the Sentinel-2A imagery from 2022-2023,spectral differences between bamboo forests and other cover types were firstly analyzed.Then three single-band(i.e.Rc,RE1c,and SWIRc)and two multi-band TSIs(MVIc and NDWIc)were innovatively developed to distinguish bamboo forests from other forest types,and four feature sets schemes were designed,namely original bands+traditional indices(FS1),original bands+traditional indices+red-edge indices(FS2),original bands+traditional indices+TSIs(FS3),and original bands+traditional indices+red-edge indices+TSIs(FS4).The random forest classification algorithm was used to compare the effect of FS1,FS2,FS3,and FS4 on the accuracy of mapping bamboo forest,and the importance of TSI in mapping bamboo forest distribution was analyzed.The area of bamboo forest derived from interpreted thematic map were validated against statistics from the 2021 China Forest and Grassland Ecological Comprehensive Monitoring and Evaluation Report.[Result]In the four combination schemes,the overall accuracy ranking of land cover classification is as follows:FS4>FS3>FS2>FS1.The producer and user accuracy of bamboo forests are also the highest in FS4,with values of 0.95 and 0.85,respectively.The comparison results of the combination of schemes show that the introduction of TSI significantly improves the accuracy of bamboo forest extraction.The bamboo forest area extracted by FS4 has a better consistency with statistical data,and the root mean square error has decreased from 17.53 in FS2 without using TSI to 7.46.The ranking of feature value importance shows that the five constructed TSIs are all at the top of the importance ranking,and their relative importance is above 75%,indicating that the developed TSIs have important contributions in bamboo forest extraction.[Conclusion]The newly developed TSIs play a significant role in mapping bamboo forest distribution,effectively distinguishing bamboo forests from other forest types.The TSIs based on multi-temporal imagery pose a great application potential in forest cover classification.关键词
时序遥感指数/竹林/重要性排序/Google earth engine/随机森林分类Key words
time-series remote sensing indices/bamboo forest/importance order/google earth engine/random forest classification分类
农业科技引用本文复制引用
郝嘉珩,郭毅超,李浩,朱爱青,石雷..基于时序遥感指数的中国竹林植被信息提取[J].林业科学,2025,61(9):1-11,11.基金项目
国家重点研发计划项目(2023YFF1304401,2023YFC3804902). （2023YFF1304401,2023YFC3804902）
