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Open AccessEditorial

Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects

1
Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, P.O Box 15875-4416 Tehran, Iran
2
Department of Remote Sensing, University of Würzburg, Oswald Külpe Weg 86, 97074 Würzburg, Germany
3
Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, 94481 Grafenau, Germany
4
Chair of Wildlife Ecology and Management Faculty of Environment and Natural Resources, University of Freiburg, D-79106 Freiburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1260; https://doi.org/10.3390/rs11111260
Received: 7 May 2019 / Accepted: 24 May 2019 / Published: 28 May 2019
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Abstract

Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide. View Full-Text
Keywords: remote sensing; forest resources inventory; spatial scale remote sensing; forest resources inventory; spatial scale
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Latifi, H.; Heurich, M. Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects. Remote Sens. 2019, 11, 1260.

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