Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches

Dear Colleagues,

The structure, species composition (occurrence and abundance) and productivity of forests and how they are explicit in time and space are key pieces of information for forest management.

Recent advances in remote sensing technologies allow us to capture large datasets on species-specific tree and stand attributes from multiple measurement systems. The new ways to analyze and process these datasets (e.g., novel machine learning algorithms) provide new insights necessary to generate spatially explicit information. This information has great value for nature conservationists as well as for forest managers that frequently require it to be displayed for large spatial extents.

In this Special Issue, the guest editors encourage the submission of current research that use data acquired with a variety of remote sensing technologies (airborne and terrestrial laser scanning (ALS/TLS), digital aerial photogrammetry (DAP), and high/very high spatial resolution (HSR/VHSR) satellite optical imagery) under different inventory approaches—the area-based approach (ABA) and the individual tree detection (ITD) approach—designed to characterize forest resource information for strategic, tactical, and operational planning. We would particularly welcome submissions on multi-sensor data fusion.

Dr. Marcos Barrio-Anta
Dr. Carlos A. Lopez-Sanchez
Guest Editors

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