Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects
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- 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 forAdvances 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.…
Autor(en): | Hooman Latifi, Marco Heurich |
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URN: | urn:nbn:de:bvb:20-opus-197358 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Remote Sensing |
ISSN: | 2072-4292 |
Erscheinungsjahr: | 2019 |
Band / Jahrgang: | 11 |
Heft / Ausgabe: | 11 |
Aufsatznummer: | 1260 |
Originalveröffentlichung / Quelle: | Remote Sensing (2019) 11:11, 1260. https://doi.org/10.3390/rs11111260 |
DOI: | https://doi.org/10.3390/rs11111260 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie |
5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften | |
Freie Schlagwort(e): | forest resources inventory; remote sensing; spatial scale |
Datum der Freischaltung: | 29.04.2022 |
Datum der Erstveröffentlichung: | 28.05.2019 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |