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 |
| DOI: | 10.57237/j.cst.2023.04.005 |
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Received: 13 October 2023
Accepted: 9 November 2023
Published Online: 27 November 2023
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