Spectral Characteristic Analysis and Spectral Identification of Desert Plants in Yanchi, Ningxia
JI Tong1, 2, WANG Bo1, 2, YANG Jun-ying1, 2, LI Qiang1, 2, HE Guo-xing1, 2, PAN Dong-rong3, LIU Xiao-ni1, 2*
1. College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China
2. Key Laboratory of Grassland Ecosystem, Ministry of Education/Pratacultural Engineering Laboratory of Gansu Province （Gansu Agricultural University）, Lanzhou 730070, China
3. Grassland Technique Extension Station of Gansu Province, Lanzhou 730070, China
Abstract：The desert grassland in Yanchi County of Ningxia belongs to the mid-temperate arid climate. Due to over-utilization, different degrees of degradation has occurred, and the proportion of degradation indicator species has increased, resulting in large differences in the composition of different desert grassland communities. How to distinguish between different desert grassland plants and determine the Dynamic degradation monitoring of indicator species is the key to understanding the degree of desert grassland degradation. At present, random forest (RF), support vector machine (SVM) and K-neighbor (KNN) classification models are widely used in the remote sensing classification of forest plants and crops. The classification and recognition effect is good, but few studies on the classification and recognition of grassland, especially the desert grassland. Therefore, in July, the ASD ground feature spectrometer was used in Ningxia Yanchi Erbukeng, Fengjigou, Gaoshawo and Mahuangshan. In the desert grassland, a total of 442 spectral data of 32 species of plants were collected for spectral feature analysis, and 7 vegetation indexes were selected: normalized vegetation index 705 (NDVI705), green channel vegetation index (GNDVI), photochemical vegetation index (PRI), soil Adjusted vegetation index (OSAVI), visual pressure resistance index (VARI), vegetation attenuation index (PSRI) and normalized water index (NDWI) as random forest model (RF), support vector machine (SVM) model, K-neighbor (KNN) the original variables of the model, classify and identify 32 species of desert grassland plants, and screen the best model by comparing the accuracy of the classification models. The research results show that: (1) The spectral reflectance of different plants is in line with the characteristics of green plants but there are obvious differences between the different bands of the original spectrum of each plant, and the difference in the water absorption bands of the original spectrum of plants is obvious, and there is a red edge blue shift phenomenon; (2) RF The classification accuracy of the three classification models, SVM and KNN for 32 species of plants reached 0.98, 0.94 and 0.98, respectively, and the recognition effect was good. However, the three classification models all made mistakes in the classification of Artemisia spp. Judgment; (3) NDWI and PRI are the key indicators to distinguish desert grassland plants in the importance of random forest model indicators, indicating that desert plant canopy water and carotenoid content are important factors affecting the spectral classification of desert grassland plants. The experiment uses a random forest model (RF), support vector machine (SVM) and K-neighbor (KNN) classification methods to establish a classification model for main plants, laying the foundation for remote sensing monitoring of desert grasslands.
Key words：Hyperspectral; Desert plants; Classification model; Vegetation index
纪 童，王 波，杨军银，李 强，何国兴，潘冬荣，柳小妮. 宁夏盐池荒漠植物光谱特征分析及光谱识别[J]. 光谱学与光谱分析, 2022, 42(03): 678-685.
JI Tong, WANG Bo, YANG Jun-ying, LI Qiang, HE Guo-xing, PAN Dong-rong, LIU Xiao-ni. Spectral Characteristic Analysis and Spectral Identification of Desert Plants in Yanchi, Ningxia. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 678-685.
