AUTHOR=Ri Ana , An Huijun 

TITLE=Health assessment of natural larch forest in arxan guided by forestry remote sensing integrated with canopy feature analysis

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1171660

DOI=10.3389/fenvs.2023.1171660

ISSN=2296-665X

ABSTRACT=This work intends to explore the health status and evaluation methods of natural larch forests in the Greater Khingan Mountains under the guidance of forestry remote sensing combined with the analysis of crown characteristics. Here, the natural larch forest in the Gurban Forest Farm of the Arshan Forestry Bureau is taken as the research object. The 2A sentinel standard reflectance image with a spatial resolution of 10m is obtained and divided into A1, A2, A3, and A4 sample areas through data processing. At the same time, the data of the second-class forest survey in the same period are obtained. After treating the obtained second-class forest survey data, the health of natural larch pine is evaluated by quantitative and qualitative indicators. Furthermore, a feature extraction and classification model based on Spectral-Gabor spatial discriminant analysis is proposed to analyze the acquired Sentinel-2A multispectral remote sensing image features. The analysis of quantitative indicators and qualitative indicators showed that the health of natural larch forests in various plots was the best in plot A3, followed by plots A1 and A2, while the health status of natural larch forests in plot A4 was the worst. In the health rating results, A1 plot is the sub-health level; A2 plot is the general health level; A3 plot is the healthy level; A4 plot is the unhealthy level. In addition, the classification accuracy of the health assessment model suggested that the maximum statistical values of average classification accuracy, average classification effectiveness, overall classification accuracy, and Kappa were 74.19%, 61.91%, 63.18%, and 57.63%, respectively. This demonstrates that the model can accurately identify the health status of natural larch forests. This work can effectively assess the health status of the natural larch forest in the Greater Khingan Mountains and provide relevant suggestions based on the assessment results to offer a reference for the sustainable development of the forest system.