{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T03:41:13Z","timestamp":1768534873528,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u201cRemote Sensing-Based Forest Inventories from Landscape to Global Scale\u201d, 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.<\/jats:p>","DOI":"10.3390\/rs11111260","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T11:18:09Z","timestamp":1559042289000},"page":"1260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1054-889X","authenticated-orcid":false,"given":"Hooman","family":"Latifi","sequence":"first","affiliation":[{"name":"Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, P.O Box 15875-4416 Tehran, Iran"},{"name":"Department of Remote Sensing, University of W\u00fcrzburg, Oswald K\u00fclpe Weg 86, 97074 W\u00fcrzburg, Germany"}]},{"given":"Marco","family":"Heurich","sequence":"additional","affiliation":[{"name":"Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, 94481 Grafenau, Germany"},{"name":"Chair of Wildlife Ecology and Management Faculty of Environment and Natural Resources, University of Freiburg, D-79106 Freiburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1139\/cjfr-2018-0196","article-title":"Applications of the United States Forest Inventory and Analysis dataset: A review and future directions","volume":"48","author":"Tinkham","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_2","unstructured":"Latifi, H. (2017). 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Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"St-Onge, B., and Grandin, S. (2019). Estimating the Height and Basal Area at Individual Tree and Plot Levels in Canadian Subarctic Lichen Woodlands Using Stereo WorldView-3 Images. Remote Sens., 11.","DOI":"10.3390\/rs11030248"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fankhauser, K.E., Strigul, N.S., and Gatyiolis, D. (2018). Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring. Remote Sens., 10.","DOI":"10.3390\/rs10101562"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hogland, J., and Affleck, D.L.R. (2019). 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