Highforest - Forest Parameter Estimation from High Resolution Remote Sensing Data

Abstract

The aim of the study was to develop a tool for the estimation of forest variables using high-resolution satellite data. The tool included modular operative software. The image analysis methodology focused on the reduction of the known problems of the previous satellite image based methods, i.e. the saturation of the estimates at higher biomass levels and uncertainty in tree species estimation. Modern contextual image analysis methods were combined with the spectral information of the imagery. In the test application the tool used images from the Ikonos satellite with a ground resolution of one and four meters. The developed Forestime software estimated the forest variables by segmenting the imagery to ‘micro-stands’, by computing standwise image feature vectors for the stands from the input satellite image, and by combining ground reference data with clusters from an unsupervised clustering stage. The estimates are produced as weighted sums of the input sample class probabilities. The target variables in the study were stem volume, average stem diameter, stem number and tree species proportions. The RMSE% for total stem volume was 37.4 % (% of mean), for average stem diameter 23.4 %, for stem number 87 %, for pine percentage 111 %, for spruce percentage 47 %, and for broad-leaved tree percentage 137 %.

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Cite this paper

@inproceedings{Astola2004HighforestF, title={Highforest - Forest Parameter Estimation from High Resolution Remote Sensing Data}, author={Heikki Astola and Catherine Bounsaythip and Jussi Ahola and Tuomas H{\"a}me and Eija Parmes and Laura Sirro and Brita Veikkanen}, year={2004} }