基于高光谱图像的草原地表微斑块识别研究
内蒙古农业大学, 机电工程学院, 内蒙古呼和浩特 010018
| 摘 要： | 荒漠化草原地表微斑块的识别分类是草原退化动态监测的重要指标。目前，传统监测方法存在成本大、分类难度大、适用面积小等问题，而卫星遥感分辨率较低，难以满足对草原荒漠化的动态监测。本研究基于荒漠草原地表微斑块的光谱特性，结合现有植被指数模型特点提出比值调节植被指数（Ratio Adjusted Vegetation Index, RAVI）。研究结果表明，总体识别精度为99.6%，Kappa系数为0.994，相对于DNVI、SAVI、RVI、DVI四种植被指数模型总体精度分别提高了39.5%、37.8%、39.3%、27.3%，Kappa系数分别提高0.49、0.472、0.489、0.356。鼠洞、阴影、裸土、植被四类地物的识别阈值分别为（1, 1.0112）、（1.0112, 1.0437）、（1.0437, 1.1409）、（1.1409, ∞），各地物之间总体识别精度均大于90%。RAVI法的提出，适用于草原荒漠化地表微斑块的识别分类提取，能达到同一植被指数模型识别多种地物的要求，为荒漠草原地表微斑块的识别与分类提供了一种理论方法，为大面积草原退化监测提供了可能。 |
| 关 键 词： | 微斑块; 高光谱遥感; 荒漠化草原; 识别与分类; RAVI |
| DOI： | 10.57237/j.cst.2023.04.005 |
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收稿日期: 2023-10-13
接受日期: 2023-11-09
在线出版日期: 2023-11-27
Identification of Grassland Surface Micro-patch Based on Hyperspectral Imagery
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
| Abstract: | The identification and classification of surface micro-patches in desertified grassland are crucial for dynamically monitoring grassland degradation. However, current traditional monitoring methods have several limitations such as high cost, difficult classification, and a small applicable area. In addition, the low resolution of satellite remote sensing makes it difficult to meet the precision requirements for dynamic monitoring of grassland desertification. In this study, we propose a ratio-adjusted vegetation index incorporating the spectral characteristics of surface micro-patches in desert steppe and the features of existing vegetation index models. The findings reveal an impressive overall recognition accuracy of 99.6% and a Kappa coefficient of 0.994. Notably, the proposed ratio-adjusted vegetation index outperforms four other vegetation index models, namely DNVI, SAVI, RVI, and DVI, by improving the overall accuracy by 39.5%, 37.8%, 39.3%, and 27.3%, respectively, with corresponding Kappa coefficients of 0.49, 0.472, 0.489, and 0.356. Additionally, the detection thresholds for rat hole, shadow, bare soil, and vegetation are determined to be (1, 1.0112), (1.0112, 1.0437), (1.0437, 1.1409), and (1.1409, ∞), respectively, while vegetation recognition accuracy remains consistently above 90%. Consequently, the RAVI method is found to be suitable for identifying, classifying, and extracting surface micro-patches of grassland desertification. Moreover, it can support the identification of various ground objects with the same vegetation index model, thereby providing a theoretical framework for classifying surface micro-patches in the desert steppe. Importantly, it opens up possibilities for monitoring the degradation of vast grassland areas. |
| Keywords: | Micro-patch; Hyperspectral Remote Sensing; Desert Steppe; Recognition and Classification; RAVI |
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