Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Titre : Identification and Inversion of Plants in Seriphidium Transiliense Desert Grassland Based on Multi-Source Remote Sensing Data

Auteur : 马建

Grade : Master 2022

Université : Xinjiang Agricultural University

Résumé partiel
It is the basis for classifying grassland types,clarifying grassland degradation and restoration,and protecting grassland plant diversity to effectively understand the changes and fluctuations of grassland plant population.In this study,the main plants of the Seriphidium transiliense desert grassland concentrated in Xinjiang were studied.In April,June and September,the multi-source information of ground hyperspectral imager,UAV remote sensing and satellite remote sensing is used to analyze the variation law and trend of plant canopy spectral reflectance,extract characteristic bands,screen and identify sensitive bands,and identify the identified objects,so as to realize the inversion of plant coverage and biomass,and clarify the differences of plant canopy spectral characteristics under different spatial resolution remote sensing data under time sequence changes ;The identification accuracy of different identification and inversion models is discussed in order to provide technical basis for the identification of main species of this kind of grassland and their effective monitoring and protection.The main results are as follows :（1）Using ground hyperspectral data to observe the two main plants of S.transiliense desert grassland,it generally shows the typical spectral characteristics,but the reflection amplitude in 400 ～ 1000 nm band are different in different periods ;The spectrum of bare land tends to be linear,which is significantly different from that of vegetation.The identification parameters selected in different months are the same,and the OIF value is the largest in 638.64,789.49 and923.79 nm bands.The recognition accuracy between classifiers is SVM ＞ CNN ;The months are April ＞ September ＞ June,and the recognition objects are land ＞ S.transiliense ＞ C.arenarius.The overall recognition accuracy in April is 86.16%for CNN and 92.12% for SVM ;Using the identified spatial position to extract the coverage of single species,the inversion accuracy is consistent with the identification accuracy ;Using vegetation index to retrieve the biomass of Seriphidium and Chenopodium respectively,the best fitting vegetation index obtained has no difference among plants,but it is different between months.S.transiliense had the best fitting effect with NDVI in September,C.arenarius had the best fitting effect with DVI in June,and aboveground biomass had the highest fitting effect with NDVI in September.

Mots clés : Seriphidium transiliense desert grassland ;Spectral characteristics ;Parameter selection ;Identification;Inversion ;

Présentation (CNKI)