Airborne laser scanning based forest inventory for forest management by applying novel metrics and multiple data source
Pippuri I. (2015). Airborne laser scanning based forest inventory for forest management by applying novel metrics and multiple data source. https://doi.org/10.14214/df.193
Abstract
The aim of this work was to develop airborne laser scanning (ALS) based forest inventory for practical forest management by applying novel horizontal metrics and multiple data sources. In particular, this work examined classification of forest land attributes, prediction of species-specific stand attributes and detection of spatial pattern of trees and need for silvicultural operations, such as first thinning and tending of seedling stand. Multiple data sources, such as ALS data and satellite images, aerial images and existing stand register data were used in area-based prediction. The applicability of field data from national forest inventory of Finland as a training data was also tested. Special interest was to test the applicability of horizontal ALS-based metrics in classification of forest land attributes, spatial pattern of trees and need for first thinning. The results showed that it is possible to identify forest land attributes, spatial pattern of trees and the need for silvicultural operations using ALS-based forest inventory. The classification of land use/land cover classes was highly accurate. Also, classification of site fertility type, peatland type and drainage status succeeded moderately well. The prediction of species-specific stand attributes of several tree species was more accurate when tree species proportions from existing stand register data were used in prediction. The classification accuracies were very high for the spatial pattern of trees and need for first thinning, and moderately high for the need for tending of seedling stands. Horizontal ALS-based metrics were the most applicable predictor variables in classification of land use/land cover, main land type, drainage status, detection of spatial pattern of trees and need for first thinning. To conclude, this work provided valuable methodological know-how on the applicability of horizontal ALS-based metrics and the use of multiple data sources for cost-effective forest inventory and planning. Some of the methods have already been implemented in practical forest inventories in Finland.
Keywords
forest inventory;
ALS;
classification;
horizontal metric;
multiple data source;
silvicultural need
Published 3 June 2015
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Available at https://doi.org/10.14214/df.193 | Download PDF
Original articles
Pippuri I., Suvanto A., Maltamo M., Korhonen K.T., Pitkänen J., Packalen P. 2015. Classification of forest land attributes using multi-source remotely sensed data. International Journal of Earth Observation and Geoinformation 44:11-22.
https://doi.org/10.1016/j.jag.2015.07.002
Pippuri I., Maltamo M., Packalen P., Mäkitalo J. (2013). Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data. European Journal of Forest Research 132 (5-6): 999-1012.
http:dx.doi.org/10.1007/s10342-013-0736-8
Pippuri I., Kallio E., Maltamo M., Peltola H., Packalén P. (2012). Exploring horizontal area-based metrics to discriminate the spatial pattern of trees and need for first thinning using airborne laser scanning. Forestry 85(2): 305-314.
http:dx.doi.org/10.1093/forestry/cps005
Korhonen L., Pippuri I., Packalén P., Heikkinen V., Maltamo M., Heikkilä J. (2013). Detection of the need seedling stands tending using high-resolution remote sensing data. (2013). Silva Fennica 47(2).