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Sammanfattning

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 k-NN method to some types of forest, while using traditional field inventories in other, more valuable, stands.

Nyckelord

carrier phase GPS; forest inventory; prediction difference distance; reference sample plot method; remote sensing

Publicerad i

Scandinavian Journal of Forest Research
2001, volym: 16, nummer: 1, sidor: 67-78
Utgivare: TAYLOR & FRANCIS AS

SLU författare

  • Holmström, Hampus

    • Institutionen för skoglig resurshushållning och geomatik, Sveriges lantbruksuniversitet

  • Nilsson, Mats

    • Institutionen för skoglig resurshushållning och geomatik, Sveriges lantbruksuniversitet

  • Ståhl, Göran

    • Institutionen för skoglig resurshushållning och geomatik, Sveriges lantbruksuniversitet

Associerade SLU-program

Remningstorp

UKÄ forskningsämne

Skogsvetenskap

Publikationens identifierare

  • DOI: https://doi.org/10.1080/028275801300004424

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/41910