Global Vegetation Moisture Index Research Articles

Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications

The Global Vegetation Moisture Index (GVMI) was developed to retrieve vegetation water content from local to global scale rapidly and reliably using SPOT-VEGETATION data. This paper validates the GVMI with field measurements of vegetation water content measured over four different ecosystems in Senegal. Two of the sites show exact concordance between GVMI-derived and field-measured water content. The remaining two sites show differences in value but provide identical evolution over time. Comparison between ecosystems illustrates that GVMI-derived water content is consistent with field measurements of water content expressed as a quantity of water per unit area. Additional study shows that GVMI is not related to the vegetation moisture content expressed as a percentage of water per quantity of biomass. Comparison between the GVMI and NDVI methods also illustrates that the NDVI provides different information (vegetation greenness), which is not directly related to the quantity of water in the vegetation. Potential applications of the new GVMI are also discussed.

Discussion

Although remote sensing technology has been used to evaluate regional ecosystem health for a long time, it is still necessary to find a suitable index system to better evaluate ecosystem health. This study aims to improve the ecosystem health measurement ability of remote sensing technology. This research was carried out in Fuzhou under the traditional Vigor-Organization-Resilience (VOR) framework by optimizing and improving the construction method of sub-indexes. Sub-indexes were constructed using spectral index analysis, landscape theoretical ecology model and spatial measurement. Three remote sensing datasets were used (1996, 2008 and 2021) to carry out remote sensing diagnosis of regional ecosystem health in the Fuzhou administrative region. The main research findings and conclusions were as follows. A new comprehensive vigor index (CVI) was developed by the principal component analysis (PCA) based on the four indicators: fractional vegetation cover (FVC), global vegetation moisture index (GVMI), vegetation temperature condition index (VTCI), normalized differential build-up and bare soil index (NDBSI). A new organizational index was constructed based on the landscape index. Four types of indexes, namely landscape heterogeneity (LH), landscape connectivity (LC), the shape characteristics of forest patches (CS) and the connectivity of forest patches (CC) were used as the main factors for calculating the organizational index. A resilience index calculation framework was proposed based on the habitat quality model. The temporal and spatial characteristics of ecosystem health were evaluated and analyzed. The regional ecosystem health value of the whole region reduced gradually, with average values of 0.3521 (1996), 0.3445 (2008) and 0.3345 (2021) respectively. The average reduction rate was 0.0007 per year (1996–2021). The proposed remote sensing diagnosis method provides a complete framework for solving the problems of measuring the dynamic evolution process and characteristics of regional ecosystem health.