The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 1–5, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-1-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 1–5, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-1-2017

  19 Oct 2017

19 Oct 2017

COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION

D. Ali-Sisto and P. Packalen D. Ali-Sisto and P. Packalen
  • School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland

Keywords: Change detection, forestry, image matching, image point cloud, LiDAR, Semi-Global Matching (SGM)

Abstract. This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.