Forest Change Detection by Using Point Clouds From Dense Image Matching Together With a LiDAR-Derived Terrain Model

Recent development of image matching algorithms has enabled the generation of highly detailed image point clouds (IPCs) from aerial stereo images. The acquisition of aerial images is typically significantly less expensive than the collection of light detection and ranging (LiDAR) or field data. IPCs thus provide a cost-effective alternative to update forest resource data more frequently than at present. We evaluate the feasibility of IPCs in the detection of forest changes by assessing the accuracy of automatic IPC-based classification of thinnings and clear cuts. IPCs were created by using the semi-global matching and next-generation automatic terrain extraction algorithms. To predict changes over a period of three years, we created difference layers which displayed the difference in height or volume between the initial and subsequent time points. These were constructed by calculating the difference in either IPC-based canopy height models or in IPC-derived height and volume models. In this process, the LiDAR-derived digital terrain model was used to scale heights to above ground level. The values from the difference layers were then used in logistic regression models to classify the study area into the categories ClearCut, Thinning, or NoChange. When the predicted changes were compared with the true changes verified in the field, we obtained a classification accuracy for clear cuts 98.6% at best, but only 24.1% for thinnings. In conclusion, IPCs are applicable for the detection of major changes in forests, but incapable of detecting minor changes. The same seems to be applied to LiDAR data.

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