Feature extraction and selection in remote sensing-aided forest inventory
Haapanen R. (2014). Feature extraction and selection in remote sensing-aided forest inventory. https://doi.org/10.14214/df.181
Abstract
This dissertation explored the potential of image features derived from remotely sensed data in the context of large-area forest inventory. The study areas were located in Finnish boreal forests, with one exception in Northern Minnesota, USA. Estimation of forest variables was carried out at pixel (or an equidistant grid) level. The non-parametric k nearest neighbour estimation method was applied throughout the study. The used remotely sensed data included Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite images, colour infra-red aerial photographs, TerraSAR-X radar and airborne laser scanning (ALS) data. An indicative suitability order of these image types for estimation of forest variables was ALS, TerraSAR-X, aerial photographs and Landsat 7 ETM+. Special emphasis was placed on combining features extracted from individual remotely sensed data sources and searching for sets of image features that led to the best performance for estimation of forest variables. Selection of the image features was mainly carried out using a genetic algorithm. The resulting relative root mean square errors (RMSEs) ranged from 23% to 77% in the case of estimating mean volume of growing stock. The best results were obtained employing ALS and aerial photograph-based feature combinations. These combinations led to relative RMSEs of 23–30% when estimating mean volume of growing stock, depending on the landscape complexity. Combining image types with complementary properties typically improved the estimation accuracy. Automatic selection of image feature sets greatly reduced noise and dimensionality of the large feature sets used as input data and resulted in better performance in terms of estimation error. In studies employing ALS data, the ALS observations describing the vertical structure of forest stands played a critical role in decreasing the estimation error.
Keywords
ALS;
Landsat satellite image;
aerial photograph;
TerraSAR-X;
k nearest neighbour;
genetic algorithm
Published 7 November 2014
Views 4230
Available at https://doi.org/10.14214/df.181 | Download PDF
Original articles
Haapanen R., Ek A.R., Bauer M.E., Finley A.O. (2004). Delineation of forest/nonforest land use classes using nearest neighbor methods. Remote Sensing of Environment 89: 265–271.
https://doi.org/10.1016/j.rse.2003.10.002
Haapanen R., Tuominen S. (2008). Data combination and feature selection for multi-source forest inventory. PE&RS 74(7): 869–880.
http://asprs.org/a/publications/pers/2008journal/july/2008_jul_869-880.pdf
Holopainen M., Haapanen R., Karjalainen M., Vastaranta M., Hyyppä J., Yu X., Tuominen S., Hyyppä H. (2010). Comparing accuracy of airborne laser scanning and TerraSAR-X radar images in the estimation of plot-level forest variables. Remote Sensing 2010(2): 432–445.
https://doi.org/10.3390/rs2020432
Tuominen S., Haapanen R. (2011). Comparison of grid-based and segment-based estimation of forest attributes using airborne laser scanning and digital aerial imagery. Remote Sensing 2011(3): 945–961.
https://doi.org/10.3390/rs3050945
Tuominen S., Haapanen R. (2013). Estimation of forest biomass by airborne laser scanning and digital aerial photographs. Silva Fennica 47(1), article id 902. 20 p.