Abstract
The physical‐based geometric‐optical Li–Strahler model can be inverted to retrieve forest canopy structural variables. One of the main input variables of the inverted model is the fractional component of sunlit background (K g). K g is calculated by using pure reflectance spectra (endmembers) of the viewed surface components. In this paper, the feasibility of up‐scaling from high (Quickbird) to medium (Hyperion) spatial resolution data for extracting the required endmembers is demonstrated. Furthermore, the sensitivity of the endmembers used as input for inverting Li–Strahler model is evaluated. After validating the inverted model results, namely spatially explicit forest mean crown closure and crown diameter using field measurements, it can be concluded that the regional scaling‐based endmembers derived from the linear unmixing model are the best ones to be used in combination with the inverted Li–Strahler model for quantitatively monitoring disturbance in forest canopy structure.
Highlights
At local to regional and global scales, remote sensing has facilitated extraordinary advances in the modelling, mapping and understanding of ecosystems and their functioning
After validating the inverted model results, namely spatially explicit forest mean crown closure and crown diameter using field measurements, it can be concluded that the regional scaling-based endmembers derived from the linear unmixing model are the best ones to be used in combination with the inverted Li-Strahler model for quantitatively monitoring disturbance in forest canopy structure
After validating the inverted model results, namely spatially explicit forest mean crown closure and crown diameter using field measurements, it can be concluded that the regional scaling-based endmembers derived from the linear unmixing model are the best ones to be used in combination with the inverted Li–Strahler model for quantitatively monitoring disturbance in forest canopy structure
Summary
At local to regional and global scales, remote sensing has facilitated extraordinary advances in the modelling, mapping and understanding of ecosystems and their functioning. One basic characteristic of remote sensing in the twenty-first century is the extensive use of quantitative methods for estimating earth surface variables (Liang 2004). Forests, being one of the most important natural resources worldwide, regulate the global atmospheric cycles, but are increasingly being used in dynamic global vegetation models for terrestrial CO2 estimations (Sitch et al 2003). Forest parameters, such as leaf area index (LAI), species diversity, canopy structural attributes (such as crown closure (CC) and crown diameter (CD)), etc. Monitoring forest structure by quantitatively deriving the canopy structural variables over large areas improves our understanding of several environmental processes and allows accurate estimates of relevant disturbance processes
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