基于无人机可见光及多光谱数据的林草覆盖率提取方法研究

doi:10.16843/j.sswc.2023.05.014

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摘要为了高效、准确地监测生产建设项目林草覆盖率指标，在山东省东营市建立典型样区，采用无人机搭载相同光谱分辨率的可见光和多光谱相机分别获取可见光、多光谱影像，建立16种植被指数（多光谱8种、可见光8种）。采用阈值法、支持向量机法计算林草覆盖率，并用混淆矩阵对结果精度进行对比，筛选出最优植被指数和分类方法。研究表明：1）8种多光谱植被指数和3种可见光植被指数识别精度>90%，Kappa系数>0.90，以上11种植被指数可满足实际水土保持监测需求。2）多光谱植被指数提取植被信息分类方法中，GRVI、SAVI、GNDVI、NDRE可用支持向量机法，RENDVI、EVI2、NDVI、OSAVI可用阈值法，可见光植被指数R、G、EXG可用阈值法。3）在正常状况下，多光谱和可见光植被指数获得较好的植被信息识别效果，但在有阴影存在情况下，单波段可见光R、G有错分现象。4）多光谱植被指数相比于可见光植被指数，具有更好的适用性和稳定性。

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	赵莹
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	杜文贞
	邱浩

关键词 ：可见光, 多光谱, 植被指数, 无人机, 林草覆盖率

Abstract：[Background] The remote sensing image obtained with unmanned aerial vehicle (UAV) has been widely used in soil and water conservation monitoring. However, compared with other industries, there are shortcomings in the UAV remote image, such as inadequate research depth, few application functions, and low monitoring accuracy. Moreover, images shot with different cameras and application of different classification methods will have impact on the monitoring accuracy of forest and grassland coverage in the monitoring process. The objective of our study is to provide a rapid and accurate method to monitor the coverage rate of forest and grass. [Methods] This study conducted a case study in Kenli, Dongying, Shandong province. The forest and grass coverage was calculated based on the multispectral data of UAV, and then the coverage was compared with the vegetation information extracted from the visible light. The visible light and multispectral images with high-resolutions were simultaneously obtained via UAV equipped with visible light and multispectral cameras having 5 multispectral sensors. Each camera was equipped with the same spectral resolution of 2 megapixels. Sixteen vegetation indices, including 8 multispectral vegetation indices and 8 visible light vegetation indices, were established, and the object-oriented threshold method and support vector machine method were used to extract the vegetation information and calculate the forest and grass coverage, respectively. Finally, the optimal vegetation index and classification method were chosen via the confusion matrix. [Results] The accuracy of vegetation index identified by multi-spectrum was over 90%, and the Kappa coefficient was over 0.90. Three types of visible light vegetation indices had reached the above level. The 11 vegetation indices mentioned above met the requirements of practical applications in soil and water conservation monitoring for the production and construction projects. In the classification method of multispectral vegetation indices, the available support vector machine methods were used in green ratio vegetation index (GRVI), soil-adjusted vegetation index (SAVI), green normalized difference vegetation index (GNDVI) and normalized differential index with red edge (NDRE), while the threshold methods were used in normalized difference vegetation index with red edge (RENDVI), enhanced vegetation index2 (EVI2), normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI). The threshold methods were all used in the visible light vegetation indices, including red (R), green (G), and excess green vegetation index (EXG).Confirmatory experiments in three study areas showed that under normal conditions, better effects of vegetation information identification in calculating the forest and grass coverage might be obtained under both of the multispectral and visible light vegetation indices. In the presence of shadows, the single-band visible lights R and G were not well classified. On the contrary, the multispectral vegetation index presented better applicability and stability compared with the visible light covered index. [Conclusions] This study provides a high-precision extraction method to monitor the forestry and grass coverage for soil and water conservation based on UAV visible light data and multispectral data.

Key words： visible light multi-spectrum vegetation index UAV forestry and grass coverage

收稿日期: 2022-05-19

PACS:

S157.1

基金资助:山东省调水工程运行维护中心科技项目"胶东调水工程的水生态系统调查与生态风险对策研究"（2021JDDS005）；山东省重点研发计划项目"流域水土流失阻控及面源污染防治关键技术研究"（2018GSF117001）

作者简介: 赵莹(1979-),女,硕士,高级工程师。主要研究方向:水土保持工程,水生态。E-mail:411980361@qq.com

引用本文:

赵莹, 王光辉, 任建锋, 杜文贞, 邱浩. 基于无人机可见光及多光谱数据的林草覆盖率提取方法研究[J]. 中国水土保持科学, 2023, 21(5): 120-128.
ZHAO Ying, WANG Guanghui, REN Jianfeng, DU Wenzhen, QIU Hao. Extracting methods for forestry and grass coverage based on UAV visible light data and multispectral data. SSWC, 2023, 21(5): 120-128.

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http://journal12.magtechjournal.com/Jweb_stbc/CN/10.16843/j.sswc.2023.05.014 或 http://journal12.magtechjournal.com/Jweb_stbc/CN/Y2023/V21/I5/120

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