Aerial and Near-Field Remote Sensing Developments in Forestry
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 81770
Special Issue Editors
Interests: forest resource monitoring; forest phenotyping; biodiversity; LiDAR; UAV; satellite images
Special Issues, Collections and Topics in MDPI journals
Interests: active and passive remote sensing technologies for biodiversity assessment; forest structure; species diversity and richness; long time series satellite data; dynamic habitat index
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The past decade has seen an explosion in the availability of highly detailed, remotely sensed information on forestry structure and function. This data revolution has resulted from the widespread use of unmanned aerial vehicles, or drone technologies, the miniaturization of computing and sensor equipment, advances in digital photogrammetric techniques and an improved understanding of how changing spectra and 3D structure can inform our understanding of key forest attributes such as tree dimensions, growth and stand conditions, and characteristics.
Accommodating these acquisition advancements, open source software, cloud computing, and big data allow these datasets to be innovatively processed and linked to other airborne and satellite datasets, which, when integrated intelligently, have the potential to address many of the environmental issues of our time.
This special issue addresses the advancement of these technologies, specifically for forestry applications, be it with forestry production or conservation foci. We encourage papers in the application of 3D technologies such as LiDAR and Photogrammetric Point Clouds (PPS) from UAV/drones from above, or, within the canopy, hand-held or ground-based devices. We encourage papers on the integration of these data with other complementary datasets such as conventional ALS or satellite observations.
Assoc. Prof. Dr. Lin Cao
Prof. Dr. Nicholas Coops
Mr. Tristan Goodbody
Guest Editors
Manuscript Submission Information
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Keywords
- forestry attributes
- tree and stand volume
- unmanned aerial vehicles
- digital aerial photogrammetry
- photogrammetric point clouds
- airborne laser scanning
- terrestrial LiDAR
- species assessment
- tree condition
- plantation forestry
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