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Using remotely sensed data to construct and assess forest attribute maps and related spatial productsAuthor(s): Ronald E. McRoberts; Warren B. Cohen; Erik Naesset; Stephen V. Stehman; Erkki O. Tomppo
Source: Scandinavian Journal of Forest Research. 25: 340-367.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionTremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and model-based inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.
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CitationMcRoberts, Ronald E.; Cohen, Warren B.; Naesset, Erik; Stehman, Stephen V.; Tomppo, Erkki O. 2010. Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scandinavian Journal of Forest Research. 25: 340-367.
Keywordsaccuracy assessment, change detection, inference, lidar, prediction
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