Forest Growth and Tree Structure Detection Based on Remote Sensing

Dear Colleagues,

Sustainable forest management requires complex and wide ranging information particularly related to forest inventory quantitative assessments and monitoring. Such information should characterize forest growth attributes at tree and stand level, forest structure and forest composition, and it should be accurate, up to date and spatially referenced. Remote Sensing technology, such as airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), high spatial resolution (HSR) and very high spatial resolution (VHSR) satellite optical imagery is gradually replacing traditional inventory approaches in terms of data acquisition both through direct measurements or indirect through modeling approaches. Several challenges are being faced by researchers and analysts related to accuracy, scale, cost, technical capacity and data processing methods for sustainable forest management decision making at all levels; operational, tactical and strategic.

This Special Issue calls for high quality updated research papers focused on the use of remote sensing technology for multi-scale data collection, data processing and predictive modeling approaches on forest growth and yield attributes, tree structure detection, forest structure and forest composition related to all kind of forest species.

Dr. Dimitrios I. Raptis
Prof. Dr. Vassiliki Kazana
Dr. Panteleimon Xofis
Guest Editors

Manuscript Submission Information

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