About this Research Topic
Biodiversity monitoring is a key component of protected area management and planning. Vegetation biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), and fractional vegetation cover (FVC) are cited as key biodiversity essential variables (EBVs). Remote sensing provides an alternative method for biodiversity monitoring through EBVs. A comprehensive comparison and integration of retrieval methods is needed to explore their accuracies in the estimation of vegetation biophysical variables over heterogeneous environments, including protected mountainous regions characterized by the diversity of land cover, species diversity, and varying terrain slopes. Various retrieval methods ranging from parametric regression, non-parametric regression, physically-based model inversion, and hybrid regression have been widely used and tested on remotely sensed data.
However, their performance and accuracy are yet to be fully understood in quantifying vegetation biophysical variables in heterogeneous natural environments. In addition, the accurate parameterization of the methods remains a key consideration for modeling the multispecies canopies accurately. This has significant implications for the development of transferrable models and thus a building block toward the development of biodiversity monitoring tools at different spatial scales.
Scope:
• Multi-spectral and hyperspectral remote sensing;
• SAR and Lidar remote sensing;
• Sensors integration;
• Multi-scale remote sensing;
• Parametric and/or Non-parametric retrieval;
• Physical and/or Hybrid retrieval;
• Algorithm development;
• Testing of theoretical models;
• Validation of regional/ global products;
• Natural environment;
• Agricultural applications.
However, their performance and accuracy are yet to be fully understood in quantifying vegetation biophysical variables in heterogeneous natural environments. In addition, the accurate parameterization of the methods remains a key consideration for modeling the multispecies canopies accurately. This has significant implications for the development of transferrable models and thus a building block toward the development of biodiversity monitoring tools at different spatial scales.
Scope:
• Multi-spectral and hyperspectral remote sensing;
• SAR and Lidar remote sensing;
• Sensors integration;
• Multi-scale remote sensing;
• Parametric and/or Non-parametric retrieval;
• Physical and/or Hybrid retrieval;
• Algorithm development;
• Testing of theoretical models;
• Validation of regional/ global products;
• Natural environment;
• Agricultural applications.
Keywords: Biodiveristy, vegetation, remote sensing, biochemical, area management
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