BACKGROUND Earth system models describe the physical, chemical and biological processes that govern our global climate. While it is difficult to single out one component as being more important than another in these sophisticated models, terrestrial vegetation is a critical player in the biogeochemical and biophysical dynamics of the Earth system. There is much debate, however, as to how plant ...

An analytical form of relationship between spectral vegetation indices (VI) is derived in the context of cross calibration and translation of vegetation index products from different sensors. The derivation has been carried out based on vegetation isoline equations that relate two reflectance values observed at different wavelength ranges often represented by spectral band passes. The derivatio...

The integration between vegetation data, human disturbance factors, and geo-spatial data (Digital Elevation Model (DEM) and image data) is a particular challenge for vegetation mapping in mountainous areas. The present study aimed to incorporate the relationships between species distribution (or vegetation spatial distribution pattern) and topography and human disturbance factors with remote se...

Gross primary productivity (GPP) is a key component of ecosystem carbon fluxes and the carbon balance between the biosphere and the atmosphere. Accurate estimation of GPP is essential for quantifying plant production and carbon balance for grasslands. Satellite-derived vegetation indices (VIs) are often used to approximate GPP. The widely used VIs include atmospherically resistant vegetation in...

Coexistence between species within plant communities is a key issue in the practice of revegetation, forest management, and biodiversity conservation. Vegetation restoration critical to control soil erosion improve ecological environment on Loess Plateau. Here, we investigate interspecific relationships dominant plants during natural vegetation succession The results suggest that, under process...

Remotely sensed reflectance spectra may be biased by several intervening factors, and the biases are propagated into estimations of the fraction of vegetation cover (FVC) by algorithms based on a linear mixture model (LMM). The errors propagated in FVCs depend on the retrieval algorithm used, due to differences in the assumptions of the model as well as constraints employed in the algorithm. Th...