基于时序遥感指数的中国竹林植被信息提取

doi:10.11707/j.1001-7488.LYKX20250100

Abstract

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. R_c, RE1_c, and SWIR_c) and two multi-band TSIs (MVI_c and NDWI_c) 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.

Key words: time-series remote sensing indices, bamboo forest, importance order, google earth engine, random forest classification

CLC Number:

S718.5

Jiaheng Hao,Yichao Guo,Hao Li,Aiqing Zhu,Lei Shi. Vegetation Cover Extraction of Bamboo Forest in China Based on Time-Series Remote Sensing Indices[J]. Scientia Silvae Sinicae, 2025, 61(9): 1-11.

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特征类别 Feature category	特征名称 Feature name	计算公式 Calculation formula
传统指数 Traditional index	归一化植被指数 Normalized difference vegetation index （NDVI）	$ \mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=\dfrac{\mathrm{N}\mathrm{I}\mathrm{R}-R}{\mathrm{N}\mathrm{I}\mathrm{R}+R} $
	归一化水体指数 Normalized difference water index （NDWI）	$ \mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}=\dfrac{G-\mathrm{N}\mathrm{I}\mathrm{R}}{G+\mathrm{N}\mathrm{I}\mathrm{R}} $
	增强植被指数 Enhanced vegetation index （EVI）	$ \mathrm{E}\mathrm{V}\mathrm{I}=\dfrac{2.5(\mathrm{N}\mathrm{I}\mathrm{R}-R)}{\mathrm{N}\mathrm{I}\mathrm{R}+6R-7.5B+1} $

红边指数 Red-edge index	红边比值植被指数 Red-edge ratio vegetation index （RVI）	$ \mathrm{R}\mathrm{V}\mathrm{I}=\dfrac{\mathrm{N}\mathrm{I}\mathrm{R}}{\mathrm{R}\mathrm{E}1} $
	红边综合效应指数 Red-edge integrated vegetation index （MVI）	$ \mathrm{M}\mathrm{V}\mathrm{I}=\dfrac{\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{E}1-\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}2}{\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{E}1+\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}2} $
	红边竹林指数1 Red-edge bamboo forest index （BI1）	$ \mathrm{B}\mathrm{I}1=\dfrac{\mathrm{R}\mathrm{V}{\mathrm{I}}_{\mathrm{r}\mathrm{e}1}-\mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}}{\mathrm{R}\mathrm{V}{\mathrm{I}}_{\mathrm{r}\mathrm{e}1}+\mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}} $
	红边竹林指数2 Red-edge bamboo forest index （BI2）	$ \mathrm{B}\mathrm{I}2=\dfrac{\mathrm{M}\mathrm{V}{\mathrm{I}}_{\mathrm{r}\mathrm{e}1}-\mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}}{\mathrm{M}\mathrm{V}{\mathrm{I}}_{\mathrm{r}\mathrm{e}1}+\mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}} $

编号 No.	特征组合 Feature set	特征变量个数 Number of feature variables
FS1	原始波段+传统指数 Original bands + traditional indices	6+3
FS2	原始波段+传统指数+红边指数 Original bands + traditional indices + red-edge indices	6+3+4
FS3	原始波段+传统指数+时序遥感指数 Original bands + traditional indices + time-series indices	6+3+5
FS4	原始波段+传统指数+红边指数+时序遥感指数 Original bands + traditional indices + red-edge indices + time-series indices	6+3+4+5

分类组合 Feature set	总体精度 Overall accuracy	Kappa系数 Kappa coefficient	竹林 Bamboo forest		常绿林 Evergreen forest		落叶林 Deciduous forest		非林地 Non forest land
分类组合 Feature set	总体精度 Overall accuracy	Kappa系数 Kappa coefficient	PA	UA	PA	UA	PA	UA	PA	UA
FS1	0.75	0.73	0.72	0.61	0.60	0.68	0.68	0.77	0.87	0.86
FS2	0.81	0.77	0.79	0.75	0.74	0.80	0.82	0.77	0.89	0.92
FS 3	0.87	0.80	0.89	0.83	0.79	0.83	0.85	0.88	0.91	0.93
FS4	0.92	0.88	0.95	0.85	0.84	0.92	0.92	0.88	0.91	0.97

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Vegetation Cover Extraction of Bamboo Forest in China Based on Time-Series Remote Sensing Indices

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