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Detecting Multi-layered Forest Stands Using High Density Airborne LiDAR Data

In this study, we investigate and discuss the potential of full waveform high density airborne LiDAR data (ALS) for detecting, classifying, and stratifying discrete vegetation layers at forest stand level, based on 0.1 ha investigation plots. Full waveform high density ALS data on each 5th percentile level was used in combination with binary logistic regressions to discover the structural layer type of multi-layered forest stands from normalized discrete ALS pulses. The results of the descriptive statistics of ALS point clouds and binary logistic regression models produce particular forest layer profile indices of understorey vegetation and canopy layer. Such parameters can further be used as variables for forest structure analysis algorithms, and can be empirically tested against stand characteristics. The validation of ALS data and model results is tested against empirical forest mensuration data of the “Datenspeicher Wald 2 (DSW 2-Forest inventory data)” and field survey reference points using error matrices. We demonstrate that binary logistic regression analyses are functional for establishing a prediction model. The model was applied successfully on larger forest stands and forest areas, and can become useful for identifying and separating single from multi-layered forest stands using percentiles of total amounts of ALS return pulses on a 10x10m raster size with a high overall accuracy of 90%. The established model has the potential for a broad range of forest management applications, such as timber inventory evaluation, forest growth modelling, monitoring of vegetation dynamic and succession, as well as ecological classifications and the detection of deadwood in forest stands.

Autor / Author: Mund, Jan-Peter; Wilke, Robert; Körner, Michael; Schultz, Alfred
Institution / Institution: Eberswalde University for Sustainable Development, Germany
Seitenzahl / Pages: 11
Sprache / Language: Englisch
Veröffentlichung / Publication: GI_Forum - Journal for Geographic Information Science, 1-2015
Tagung / Conference: GI_Forum 2015 – Symposium and Exhibit GIScience & Technology/Learning with GI
Veranstaltungsort, -datum / Venue, Date: Salzburg, Austria 07-07-15 - 10-07-15
Schlüsselwörter (de):
Keywords (en): LiDAR data, binary logistic regression, multi-structured forest layers
Paper review type: Full Paper Review
DOI:
1644 -