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Simultaneous Estimations of Forest Parameters using Aerial Photograph Interpreted Data and the k Nearest Neighbour Method

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Information about the state of the forest is of vital importance in forest management planning. To enable high-precision modelling, many forest planning systems demand input data at the single-tree level. The conventional strategy for collecting such data is a plot-wise field inventory. This is expensive and, thus, cost-efficient alternatives are of interest. During recent years, the focus has been on remote sensing techniques. The k nearest neighbour (kNN) estimation method is a way to assign plot-wise data to all stands in a forest area, using remotely sensed data in connection with a sparse sample of field reference plots. Plot-wise aerial photograph interpretations combined with information from a stand register were used in this study. Nearness to a reference plot was decided upon using a regression transform distance. Standing stem volume was estimated with a relative root mean square error (RMSE) equal to 20% at the stand level, while age could be estimated with a RMSE equal to 15%. A cost-efficient data-capturing strategy could be to assign plot data with the presented kNN method to some types of forest, while using traditional field inventories in other, more valuable, stands.
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Keywords: CARRIER; DIFFERENCE; DISTANCE; ENTORY; FOREST; GPS; METHOD; PHASE; PLOT; PREDICTION; REFERENCE; REMOTE; SAMPLE; SENSING

Document Type: Research Article

Affiliations: Remote Sensing Laboratory, Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden

Publication date: 2001-01-01

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