从森林到城市：高光谱成像技术如何实现树种识别？

From Forests to Cities: How Does Hyperspectral Imaging Enable Tree Species Identification?

高光谱成像技术在树种识别领域的应用日益广泛，它通过捕捉树木在多个窄波段上的光谱信息，实现对树种的精确分类和识别，在森林资源管理、城市绿化规划和生态环境保护等方面具有重要意义，为相关工作提供了关键技术支撑。

下面是高光谱成像技术在树种识别的应用场景。

Hyperspectral imaging technology is increasingly being applied in the field of tree species identification. By capturing spectral information from trees across multiple narrow bands, it enables accurate classification and identification of species. This technology plays a significant role in forest resource management, urban greening planning, and ecological environment protection, providing critical technical support for related tasks.

Below are the application scenarios of hyperspectral imaging technology in tree species identification.

1. 森林资源调查与监测 / Forest Resource Inventory and Monitoring

·树种分类与分布：高光谱数据可以用于识别和分类森林中的不同树种，生成树种分布图，为森林资源管理提供基础数据。

在巴西大西洋森林的研究中，研究者结合无人机高光谱数据与激光雷达（LiDAR）数据，对8种上层树冠树种进行分类，通过主成分分析（PCA）处理所有特征后，分类总体精度达到76%，为该退化森林的物种分布监测提供了有效数据支撑。

·Tree Species Classification and Distribution: Hyperspectral data can be used to identify and classify different tree species in forests, generating species distribution maps that serve as foundational data for forest resource management.

In a study of the Atlantic Forest in Brazil, researchers combined UAV-based hyperspectral data with LiDAR data to classify eight canopy tree species. After processing all features using Principal Component Analysis (PCA), an overall classification accuracy of 76% was achieved, providing effective data support for monitoring species distribution in this degraded forest.

各种树的平均光谱 / Mean spectra for each tree species

·森林健康评估：通过分析树木的光谱特征，可以评估树木的生长状况和健康程度，及时发现病虫害和环境胁迫，为森林保护提供预警信息。

中国地质调查局在湖北宜城的研究中，采用无人机高光谱数据（400~1000nm，270个光谱波段），结合归一化植被指数（NDVI）、类胡萝卜素反射指数（CRI）和水波段指数（WBI），构建“宽带绿度指数-叶绿素指数-冠层含水量/光合能力指数"的综合评估体系，实现了森林树木健康状况的定性与定量评估，结果与实地观测及假彩色合成图像特征高度一致。

·Forest Health Assessment: By analyzing the spectral characteristics of trees, their growth conditions and health status can be evaluated, enabling timely detection of pests, diseases, and environmental stressors, thereby offering early warning information for forest protection.

In a study conducted by the China Geological Survey in Yicheng, Hubei, UAV-based hyperspectral data (400-1000nm, 270 spectral bands) was used in combination with vegetation indices such as NDVI, CRI, and WBI to construct a comprehensive evaluation system based on "broadband greenness index–chlorophyll index–canopy water content/photosynthetic capacity index." This system achieved both qualitative and quantitative assessments of forest tree health, with results highly consistent with field observations and false-color composite imagery.

(a) 真彩色影像与树种识别分类；(b) 假彩色影像与健康评估

(a) True color image and tree species recognition class; (b) False color image and health assessment

·生物多样性研究：高光谱数据可以用于研究森林生态系统的生物多样性，了解不同树种的生态功能和相互关系，为生态保护提供科学依据。

·Biodiversity Research: Hyperspectral data can be applied to study biodiversity in forest ecosystems, helping to understand the ecological functions and interrelationships of different tree species, thereby providing a scientific basis for ecological conservation.

·林木生长参数反演：利用高光谱数据可以反演林木的叶面积指数、生物量等生长参数，为林木生长模型的建立和优化提供数据支持。

在东北针阔混交林研究中，研究者通过高光谱数据提取植被指数，结合LiDAR获取的树高、冠幅等结构参数，实现了林木叶面积指数和生物量的精准反演，为该区域精准林业中林木生长模型优化提供了关键数据。值得注意的是，在这项研究中，高光谱成像仪和LiDAR是分别挂载在不同的无人机上的。

·Inversion of Tree Growth Parameters: Hyperspectral data can be used to invert growth parameters such as leaf area index and biomass, supporting the establishment and optimization of tree growth models.

In a study on mixed coniferous-broadleaf forests in Northeast China, researchers extracted vegetation indices from hyperspectral data and combined them with structural parameters (e.g., tree height and crown width) obtained from LiDAR to achieve accurate inversion of leaf area index and biomass. This provided key data for optimizing tree growth models in precision forestry in the region. It is worth noting that in this study, the hyperspectral imager and LiDAR were mounted on different UAVs.

树种专题图 / Thematic map of tree species

2. 城市绿化规划与管理 / Urban Greening Planning and Management

·城市树种识别与分布：高光谱图像可以用于识别城市中的树种，了解城市绿化的树种构成和分布情况，为城市绿化规划提供参考。

·城市树木健康监测：通过分析城市树木的光谱特征，可以评估城市树木的生长状况和健康程度，及时发现病虫害和环境胁迫，为城市树木的养护管理提供指导。

香港理工大学一团队利用高光谱图像对城市树种进行了识别分类，2018年11月至2019年10月期间，在不同季节对19个树种的75棵城市树木进行了图像采集，深度神经网络方法在物种识别中达到了85%~96%的准确率。不同物种对健康状况表现出不同的光谱响应。

·Urban Tree Species Identification and Distribution: Hyperspectral imagery can be used to identify tree species in urban areas, helping to understand the composition and distribution of species in urban greening, thus providing references for urban greening planning.

·Urban Tree Health Monitoring: By analyzing the spectral characteristics of urban trees, their growth conditions and health status can be assessed, enabling timely detection of pests, diseases, and environmental stressors, thereby guiding maintenance and management efforts.

A team at The Hong Kong Polytechnic University used hyperspectral imagery to identify and classify urban tree species. From November 2018 to October 2019, images of 75 urban trees from 19 species were collected across different seasons. Deep neural network methods achieved an accuracy of 85%–96% in species identification. Different species exhibited distinct spectral responses to health conditions.

(a-d)为原始图像；(e-h)为对应的掩蔽后图像

Typified examples of masking canopies and homogenous regions: (a-d) are original images; (e-h) are corresponding masked images.

各轮实地数据采集中的不同树种平均冠层光谱特征，树种分别为：(a) 相思树（样本量N=6）；(b) 大叶合欢（N=3）；(c) 白楸（N=5）；(d) 榕树（N=3）

Mean canopy spectral signature of different species in each round of in-situ data acquisition, the species are: (a) Acacia confuse (N = 6); (b) Albizia lebbeck (N = 3); (c) Mallotus paniculatus; (N = 5); (d) Ficus macrocarpa (N = 3). N indicates the number of tree samples for the corresponding species;

高光谱成像技术为树种识别提供了高效、精确的技术方案，它可以减少人工调查的工作量、获取精细信息、为森林资源管理、城市绿化规划和生态环境保护提供科学依据和决策支持，应用前景广阔。

随着技术发展，它将进一步助力林业与生态领域的可持续发展，持续发挥核心支撑作用。

作为高光谱的供应商，爱博能提供全面的产品线，包括全波段的高光谱相机、无人机载高光谱成像系统、便携式、高光谱实验室和显微高光谱。欢迎垂询！

Hyperspectral imaging technology provides an efficient and accurate technical solution for tree species identification. It reduces the workload of manual surveys, captures detailed information, and offers scientific basis and decision-making support for forest resource management, urban greening planning, and ecological environment protection. Its application prospects are broad.

With technological advancements, it will further contribute to the sustainable development of forestry and ecology, continuing to play a core supporting role.

As a supplier of hyperspectral solutions, ExponentSci provides a comprehensive product line, including full-band hyperspectral cameras, UAV-mounted hyperspectral imaging systems, portable systems, hyperspectral laboratories, and micro-hyperspectral imagers. Welcome to inquire!

案例来源 / Sources：

1. Zhong, H., Lin, W., Liu, H., Ma, N., Liu, K., Cao, R., Wang, T., & Ren, Z. (2022). Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China. Frontiers in Plant Science, 13, 964769.

2. Martins-Neto, R. P., Tommaselli, A., Imai, N., Honkavaara, E., Miltiadou, M., Moriya, E., & David, H. (2023). Tree species classification in a complex Brazilian tropical forest using hyperspectral and LiDAR data. Forests, 14(5), 945.

3. Zeng, G., Xu, J., Zhang, W., & Wang, B. (2023). Tree species identification and health assessment of forest sample plots based on UAV hyperspectral remote sensing technology. Journal of Physics: Conference Series, 2621(1), 012001.

4. Abbas, S., Peng, Q., Wong, M. S., Li, Z., Wang, J., Ng, K. T. K., Kwok, C. Y. T., & Hui, K. K. W. (2021). Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 204–216.