Fractional vegetation cover estimation based on an improved selective endmember spectral mixture model

Area of Science:

• Remote Sensing
• Ecology
• Geospatial Analysis

Background:

• Fractional vegetation cover estimation is crucial for monitoring ecosystem health and vegetation growth.
• Traditional methods face challenges in accurately unmixing mixed pixels, impacting vegetation cover assessments.

Purpose of the Study:

• To introduce and evaluate an improved selective endmember linear spectral mixture model (SELSMM) for estimating fractional vegetation cover.
• To compare the performance of SELSMM against the linear spectral mixture model (LSMM) using Landsat TM and HJ-1B imagery.

Main Methods:

• Utilized Landsat TM and HJ-1B satellite imagery as data sources.
• Developed and applied an improved selective endmember linear spectral mixture model (SELSMM).
• Compared SELSMM with the standard linear spectral mixture model (LSMM) and validated results with field survey data.

Main Results:

• SELSMM using TM images achieved the lowest RMSE (0.044) and highest R² (0.668), indicating superior accuracy.
• SELSMM demonstrated higher accuracy and better performance in unmixing mixed pixels compared to LSMM for both TM and HJ-1B images.
• Landsat TM imagery yielded more accurate vegetation cover estimations than HJ-1B imagery when using SELSMM.

Conclusions:

• The SELSMM, particularly when applied to Landsat TM imagery, is a highly accurate and reliable method for regional fractional vegetation cover estimation.
• SELSMM offers significant advantages over LSMM for vegetation cover monitoring.
• This research validates the effectiveness of advanced spectral mixture models in ecological assessments.