A new hierarchical multiplication and spectral mixing method for quantification of forest coverage changes using Gaofen (GF)-1 imagery in Zhejiang Province, China

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
• Main Authors: Liu, Haijian, Yu, Zhifeng, Shum, CK, Man, Qixia, Wang, Ben
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Correlation coefficient; Correlation coefficients; Data analysis; Data mining; Data processing; Deforestation; Earth; Ecological effects; forest coverage; Forestry; Forests; GF-1 WFV image; hierarchical multiplication method; Hyperspectral imaging; Indexes; Monitoring; Mountains; Sociology; Spectral bands; spectral mixture analysis; Support vector machines; Vegetation mapping
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3303078

The forest survey is a prerequisite or critical for ecological conservation. Spectral mixture analysis is an effective method to extract the forest coverage and its changes, however it is mostly applied to hyperspectral image data processing due primarily to the limit of the number of spectral bands. Therefore, a hierarchical multiplication (HM) model based on the hierarchical method and stepwise multiplication algorithm is proposed for forest coverage extraction using multispectral images. Within the process, the hierarchical method reduces the complexity of the problem to satisfy the SMA at each level, and the multiplication method transfers forest abundances among levels for the soft classification. Compared with Zhejiang Forest Bureau Reports, the HM method in this study extracted 98.43% of forests using the 16-m resolution Gaofen-1 (GF-1) wide field of view (WFV) data, which has a 95% correlation coefficient with results obtained by the 2-m resolution panchromatic / multispectral (PMS) data using the support vector machine (SVM) method. Zhejiang's overall forest coverage was found to exceed 60% in 2019, with a standard deviation of 0.419 pixels. Densely covered forests are primarily distributed in mountains and hills, and have slightly increased from 2014 to 2019, while sparsely covered forests are mostly located in plains, basins, and valleys, and slightly have declined during the past five years. The forest coverage is mainly affected by topography, population, economy, and policies. The experiment indicates the combined use of HM and multispectral data can accurately extract forest cover changes and achieve similar results compared to more sophisticated classifications using higher precision/spectral band (hyperspectral) data.