九寨沟生物圈保护区大场景植被健康遥感精细监测与诠析——以长海为例

doi:10.6046/zrzyyg.2023057

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在自然过程、地质灾害和人为扰动的交织影响下,生物圈保护区植被健康风险提升,如何从复杂大场景精准提取与识别植被健康信息面临技术挑战。文章充分利用遥感技术宏观、客观与定量的优势,选取九寨沟生物圈保护区长海试验区为例,提出了一种集特征提取和随机森林的大场景植被健康遥感精细监测方法,实现了典型生物圈保护区不健康树木的信息提取与目标识别。结果表明: 应用光谱特征和纹理特征相结合的随机森林分类方法,在高分辨率遥感影像中可以精细提取森林中零散分布的不健康树木; 红绿指数、归一化植被指数、红边波段、红光波段相关性和修正的土壤调整植被指数是遥感植被健康信息提取的典型特征; 长海实验区植被健康状况总体较好,不健康树木占比0.23%,同时地质灾害对不健康树木空间分布有正向作用。研究不仅为九寨沟生物圈保护区植被健康诊断提供了第一手科学数据,而且对我国其他生物圈保护区的生态安全遥感监测具有推广价值。

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	高昇
	陈富龙
	时丕龙
	周伟
	朱猛
	骆艳松
	杨青霞
	王琴

关键词 ：遥感, 植被健康, 特征提取, 特征重要性, WorldView-2

Abstract：

Under the intertwined effects of natural processes, geological disasters, and human disturbances, the health risks of vegetation in biosphere reserves have increased. Accurately extracting and identifying vegetation health information from complex large scenes faces technical challenges. This study investigated the Changhai pilot zone of the Jiuzhai Valley biosphere reserve by leveraging the macro, objective, and quantitative advantages of remote sensing technology. It proposed a fine-scale remote sensing monitoring method integrated with feature extraction and random forest for large-scene vegetation health, achieving the information extraction and target identification of unhealthy trees in typical biosphere reserves. The results show that: ① The random forest classification method combined with spectral and texture features can accurately extract unhealthy trees scattered in forests from high-resolution remote sensing images; ② The red-green ratio index, normalized difference vegetation index, correlation between red-edge and red bands, and corrected soil-adjusted vegetation index constitute typical features for extracting vegetation health information from remote sensing images; ③ The Changhai pilot zone exhibits a generally fair vegetation health status, with unhealthy trees accounting for 0.23%, and geological disasters exert positive effects on the spatial distribution of unhealthy trees. This study provides primary scientific data for vegetation health diagnosis of the Jiuzhai Valley biosphere reserve while showing generalization value for the remote sensing monitoring of ecological security in other biosphere reserves of China.

Key words： remote sensing vegetation health feature extraction feature importance WorldView-2

收稿日期: 2023-03-08 出版日期: 2024-06-14

ZTFLH:

TP79

基金资助:九寨沟风景名胜区管理局项目“基于遥感技术的森林植被分类与健康研究”(2021KJCY0486)

通讯作者: 陈富龙(1980-),男,研究员,研究方向为文化遗产遥感智能感知与可持续保护。Email: chenfl@aircas.ac.cn。

作者简介: 高昇(1999-),男,硕士研究生,研究方向为植被光学遥感。Email: gaosheng21@mails.ucas.edu.cn。

引用本文:

高昇, 陈富龙, 时丕龙, 周伟, 朱猛, 骆艳松, 杨青霞, 王琴. 九寨沟生物圈保护区大场景植被健康遥感精细监测与诠析——以长海为例[J]. 自然资源遥感, 2024, 36(2): 188-197.
GAO Sheng, CHEN Fulong, SHI Pilong, ZHOU Wei, ZHU Meng, LUO Yansong, YANG Qingxia, WANG Qin. Fine-scale remote sensing monitoring and interpretation of large-scene vegetation health in the Jiuzhai Valley biosphere reserve: A case study of the Changhai pilot zone. Remote Sensing for Natural Resources, 2024, 36(2): 188-197.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023057 或 https://www.gtzyyg.com/CN/Y2024/V36/I2/188

Fig.1 研究区位置

Fig.2 健康和不健康云杉与冷杉的光谱曲线对比

Fig.3 卫星影像和无人机影像呈现的病害树木

Fig.4 植被健康监测与分析技术流程

类型	光谱指数	计算公式^①	描述
归一化植被指数	NDVI57	NDVI57 $= (N I R 1 - R e d) / (N I R 1 + R e d)$	传统用来识别植被的指数,与叶绿素浓度相关
归一化植被指数	NDVI58	NDVI58 $= (N I R 2 - R e d) / (N I R 2 + R e d)$	传统用来识别植被的指数,与叶绿素浓度相关
红绿指数	RGI34	RGI34 $= Y e l l o w / G r e e n$	凸显叶片变黄趋势
	RGI35	RGI35 $= R e d / G r e e n$
	RGI45	RGI45 $= R e d / Y e l l o w$
修正的土壤调节植被指数	MSAVI57	MSAVI57 $= (2 N I R 1 + 1) - (2 N I R 1 + 1) 2 - 8 (N I R 1 - R e d) 2$	弱化土壤对植被的影响
修正的土壤调节植被指数	MSAVI58	MSAVI58 $= (2 N I R 2 + 1) - (2 N I R 2 + 1) 2 - 8 (N I R 2 - R e d) 2$	弱化土壤对植被的影响
绿色归一化植被指数	GNDVI37	GNDVI37 $= (N I R 1 - G r e e n) / (N I R 1 + G r e e n)$	与叶绿素浓度相关,且对于叶绿素a浓度相比于NDVI更为敏感
绿色归一化植被指数	GNDVI38	GNDVI38 $= (N I R 2 - G r e e n) / (N I R 2 + G r e e n)$	与叶绿素浓度相关,且对于叶绿素a浓度相比于NDVI更为敏感
归一化差异红边指数	NDRE67	NDRE67 $= (N I R 1 - R e d E d g e) / (N I R 1 + R e d E d g e)$	反映植被受胁迫时早期的红边异常
归一化差异红边指数	NDRE68	NDRE68 $= (N I R 2 - R e d E d g e) / (N I R 2 + R e d E d g e)$	反映植被受胁迫时早期的红边异常

Tab.1 WorldView-2计算的光谱指数

纹理度量	计算公式^①	描述
均值MEA	$M E A = ∑ i, j = 0 N - 1 i p (i, j)$	灰度共生矩阵窗口的灰度均值,反映图像的明暗深浅
相异性DIS	$D I S = ∑ i, j = 0 N - 1 p (i, j) i - j$	反映图像灰度的相异性
角二阶矩 $A S M$	$A S M = ∑ i, j = 0 N - 1 p (i, j) 2$	反映图像灰度分布均匀程度和纹理粗细度
对比度CON	$C O N = ∑ i, j = 0 N - 1 p (i, j) (i - j) 2$	反映图像的清晰度和纹理的强度深浅
相关性COR	$C O R = ∑ i, j = 0 N - 1 p (i, j) (i - u i) (j - u j) σ i 2 σ j 2$	度量图像的灰度级在行或列方向上的相似程度
反差VAR	$V A R = ∑ i, j = 0 N - 1 p (i, j) (i - u i) 2$	反映图像的局部差异性
同质性HOM	$H O M = ∑ i, j = 0 N - 1 p (i, j) 1 + (i - j) 2$	反映图像纹理的同质性
熵ENT	$E N T = ∑ i, j = 0 N - 1 p (i, j) (- l n p (i, j))$	图像包含信息量的随机性度量

Tab.2 灰度共生矩阵计算的纹路度量

Tab.3 不同特征加入分类器的识别精度

Fig.5 不同灰度共生矩阵窗口大小下的识别精度

Fig.6 聚类树状图

Tab.4 新特征集的相对分类能力

Fig.7 置换重要性大于0.02的特征

Fig.8 不健康树木识别结果

Fig.9 不同高程不健康树木发生概率

Fig.10 3种不同类型的不健康树木成因

Tab.5 3种成因的不健康树木发生概率

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