Reija Haapanen

Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables

Abstract: In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k -nearest neighbour ( k -NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained

Data Combination and Feature Selection for Multi-source Forest Inventory

Both satellite images and aerial photographs are now used operationally in Finland’s forestry for different tasks; satellite images are used for national forest inventory purposes and aerial images for forest management planning. Due to the double coverage, it could be advantageous to utilize the strengths of both image types. The aim of this study was to evaluate the potential of

Comparison of Grid-Based and Segment-Based Estimation of Forest Attributes Using Airborne Laser Scanning and Digital Aerial Imagery

Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as inventory units, for which the laser and aerial image data are extracted and the forest variables estimated. As an alternative or a complement to the grid elements, image segments can be used as inventory units. The image segments are particularly useful as the basis for generation of the silvicultural treatment and cutting units since their boundaries should follow the actual stand borders, whereas when using grid elements it is typical that some of them cover parts of several forest stands. The proportion of the so-called mixed cells depends on the size of the grid elements and the average size and shape of the stands. In this study, we carried out automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating micro-stands that are homogeneous in relation to their forest attributes. Further, we extracted laser and aerial image features for both systematic grid elements and segments. For both units, the feature set used for estimating the forest attributes was selected by means of a genetic algorithm. Of the features selected, the majority (61–79%) were based on the airborne laser scanning data. Despite the theoretical advantages of the image segments, the laser and aerial features extracted from grid elements seem to work better than features extracted from image segments in estimation of forest attributes. We conclude that estimation should be carried out at grid level with an area-specific combination of features and estimates for image segments to be derived on the basis of the grid-level estimates.

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k -nearest neighbor strategy in North Central Mexico

As climate change negotiations progress, monitoring biomass and carbon stocks is becoming an important part of the current forest research. Therefore, national governments are interested in developing forest-monitoring strategies using geospatial technology. Among statistical methods for mapping biomass, there is a nonparametric approach called k-nearest neighbor (kNN). We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone. Satellite derived, climatic, and topographic predictor variables were combined with the Mexican National Forest Inventory (NFI) data to accomplish the purpose. Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique. The results indicate that the Most Similar Neighbor (MSN) approach maximizes the correlation between predictor and response variables (r=0.9). Our results are in agreement with those reported in the literature. These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation (REDD+).

Creating a digital treeless peatland map using satellite image interpretation

Abstract In the satellite image-based estimation and classification of forest variables in Finland peatlands are usually processed separately from mineral soil forests, to improve the accuracy of the results. The division into peatlands and mineral soil forests is based on a mask provided by the National Land Survey. It would be advantageous, however, to update the mask with the satellite imagery used for estimating forest variables. The aim here was to compare methods for treeless peatland detection on a Landsat ETM+ satellite image. The area concerned was located within the southern aapa mire zone in Finland. The classification methods tested included sequential maximum a posteriori (SMAP), supervised maximum likelihood (ML) and unsupervised ML with Iso Cluster-based signatures. The unsupervised Iso Cluster ML method performed poorly, while the overall accuracies of SMAP and supervised ML were better and quite similar (88–94% and 89–90% on forestry land, respectively). SMAP produced more usable maps, by forming compact and unspeckled treeless peatland regions. The existing peatland mask was slightly more accurate than SMAP and ML, although it performed less well in the treeless peatland class. The updating of the existing mask by combining it with the best classification result did not succeed. The main conclusion is that a peatland mask can be based on Landsat TM classification, but in areas where a good topographic mask exists the latter is more useful, and cannot easily be updated with help of satellite image data.