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Remote Sens. 2014, 6(5), 4582-4599; doi:10.3390/rs6054582
Article

Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures

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Received: 26 March 2014 / Revised: 12 May 2014 / Accepted: 13 May 2014 / Published: 21 May 2014
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Abstract

The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using laser height and intensity data. The main objective of the present study was to assess a possible improvement in the performance of classifying tree and nontree laser echoes from high-density ALS data. The data were collected along a 1000 km long transect stretching from southern to northern Norway. Different geostatistical and statistical measures derived from laser height and intensity values were used to extent and potentially improve more simple models ignoring the spatial context. Generalised linear models (GLM) and support vector machines (SVM) were employed as classification methods. Total accuracies and Cohen’s kappa coefficients were calculated and compared to those of simpler models from a previous study. For both classification methods, all models revealed total accuracies similar to the results of the simpler models. Concerning classification performance, however, the comparison of the kappa coefficients indicated a significant improvement for some models both using GLM and SVM, with classification accuracies >94%.
Keywords: airborne laser scanning; classification; forest-tundra ecotone airborne laser scanning; classification; forest-tundra ecotone
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Stumberg, N.; Hauglin, M.; Bollandsås, O.M.; Gobakken, T.; Næsset, E. Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures. Remote Sens. 2014, 6, 4582-4599.

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