Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province

Ran Cao; Yuanqing Zhang; Changbao Yang; Tianyi Chen

doi:10.1117/12.3029281

16 May 2024 Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province

Ran Cao, Yuanqing Zhang, Changbao Yang, Tianyi Chen

Author Affiliations +

Proceedings Volume 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024); 1316607 (2024) https://doi.org/10.1117/12.3029281
Event: International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 2024, Changchun, China

Abstract

Due to rapid economic growth, urbanization, and population expansion, human society is facing increasing pressure on natural resources and the environment. Vegetation cover is closely related to these issues, and as a key indicator of ecological health, it is crucial for maintaining biodiversity, preventing soil erosion, and promoting economic and social well-being. Remote sensing technology, due to its high efficiency and low cost, has become the primary means of monitoring vegetation coverage. In this study, the Songhua Lake Basin in Jilin Province was selected as the research area. Landsat TM/ETM+ data and Normalized Difference Vegetation Index (NDVI) were used to calculate and analyze the vegetation coverage in the Songhua Lake Basin in 2000 and 2006, employing the dimidiate pixel model. The obtained vegetation coverage data provide a scientific basis for targeted conservation measures.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ran Cao, Yuanqing Zhang, Changbao Yang, and Tianyi Chen "Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province", Proc. SPIE 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 1316607 (16 May 2024); https://doi.org/10.1117/12.3029281

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