Identification of Constructive Species and Degraded Plant Species in the Temperate Typical Grassland of Inner Mongolia Based on Hyperspectral Data

Haining Liu, Hong Wang (), Xiaobing Li, Tengfei Qu, Yao Zhang, Yuting Lu, Yalei Yang, Jiahao Liu, Xili Zhao, Jingru Su and Dingsheng Luo
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Haining Liu: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Hong Wang: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Xiaobing Li: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Tengfei Qu: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yao Zhang: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Yuting Lu: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Yalei Yang: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiahao Liu: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Xili Zhao: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Jingru Su: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Dingsheng Luo: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

Agriculture, 2023, vol. 13, issue 2, 1-20

Abstract: In recent years, grassland degradation has become a global ecological problem. The identification of degraded grassland species is of great significance for monitoring grassland ecological environments and accelerating grassland ecological restoration. In this study, a ground spectral measurement experiment of typical grass species in the typical temperate grassland of Inner Mongolia was performed. An SVC XHR-1024i spectrometer was used to obtain field measurements of the spectra of grass species in the typical grassland areas of the study region from 6–29 July 2021. The parametric characteristics of the grass species’ spectral data were extracted and analyzed. Then, the spectral characteristic parameters + vegetation index, first-order derivative (FD) and continuum removal (CR) datasets were constructed by using principal component analysis (PCA). Finally, the RF, SVM, BP, CNN and the improved CNN model were established to identify Stipa grandis (SG), Cleistogenes squarrosa (CS), Caragana microphylla Lam. (CL), Leymus chinensis (LC), Artemisia frigida (AF), Allium ramosum L. (AL) and Artemisia capillaris Thunb. (AT). This study aims to determine a high-precision identification method based on the measured spectrum and to lay a foundation for related research. The obtained research results show that in the identification results based on ground-measured spectral data, the overall accuracy of the RF model and SVM model identification for different input datasets is low, but the identification accuracies of the SVM model for AF and AL are more than 85%. The recognition result of the CNN model is generally worse than that of the BP neural network model, but its recognition accuracy for AL is higher, while the recognition effect of the BP neural network model for CL is better. The overall accuracy and average accuracy of the improved CNN model are all the highest, and the recognition accuracy of AF and CL is stable above 98%, but the recognition accuracy of CS needs to be improved. The improved CNN model in this study shows a relatively significant grass species recognition performance and has certain recognition advantages. The identification of degraded grassland species can provide important scientific references for the realization of normal functions of grassland ecosystems, the maintenance of grassland biodiversity richness, and the management and planning of grassland production and life.

Keywords: hyperspectral remote sensing; typical grassland; degraded plant species; neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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