Haapanen Forest Consulting


Haapanen Forest Consulting

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Aguirre-Salado C.A.,Autonomous University of Nuevo León | Aguirre-Salado C.A.,Autonomous University of San Luis Potosi | Trevino-Garza E.J.,Autonomous University of Nuevo León | Aguirre-Calderon O.A.,Autonomous University of Nuevo León | And 7 more authors.
Journal of Arid Land | Year: 2014

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+). © 2014 Science Press, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

Holopainen M.,University of Helsinki | Vastaranta M.,University of Helsinki | Rasinmaki J.,University of Helsinki | Kalliovirta J.,University of Helsinki | And 6 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

The objective here was to analyse the effects of inventory errors on the prediction of assortment outturn volumes carried out in current airborne laser scanning (ALS) inventory method and forest-planning simulation computing in Finland. Harvested logging machine data of 12 clear-cutting stands (5300 trees) in Evo (southern Finland) study area was used as field reference of the study. Prediction error of assortment outturn volumes contains forest inventory, stem distribution generation, prediction of stem form and simulation of bucking errors. ALS inventory-related bias in estimated timber assortments ranged from-5.1 m3/ha to 20.5 m3/ha and RMSE from 6.0 m3/ha to 46.2 m3/ha. Accuracy of the estimated stem distributions varies in different stands. The results showed that the accuracy of the estimates of timber assortments is considerably poorer than the accuracy of stands mean characteristics.

Holopainen M.,University of Helsinki | Vastaranta M.,University of Helsinki | Rasinmaki J.,University of Helsinki | Kalliovirta J.,University of Helsinki | And 5 more authors.
European Journal of Forest Research | Year: 2010

Uncertainty factors related to inventory methodologies and forest-planning simulation computings in the estimation of logging outturn assortment volumes and values were examined. The uncertainty factors investigated were (1) forest inventory errors, (2) errors in generated stem distribution, (3) effects of generated stem distribution errors on the simulation of thinnings and (iv) errors related to the prediction of stem form and simulation of bucking. Regarding inventory errors, standwise field inventory (SWFI) was compared with area-based airborne laser scanning (ALS) and aerial photography inventorying. Our research area, Evo, is located in southern Finland. In all, 31 logging sites (12 clear-cutting and 19 thinning sites) measured by logging machine in winter 2008 were used as field reference data. The results showed that the most significant source of error in the prediction of clear-cutting assortment outturns was inventory error. Errors related to stem-form prediction and simulated bucking as well as generation of stem distributions also cause uncertainty. The bias and root-mean-squared error (RMSE) of inventory errors varied between -11.4 and 21.6 m3/ha and 6.8 and 40.5 m3/ha, respectively, depending on the assortment and inventory methodology. The effect of forest inventory errors on the value of logging outturn in clear-cuttings was 29.1% (SWFI) and 24.7% (ALS). The respective RMSE values related to thinnings were 41.1 and 42%. The generation of stem distribution series using mean characteristics led to an RMSE of 1.3 to 2.7 m3/ha and a bias of -1.2 to 0.6 m3/ha in the volume of all assortments. Prediction of stem form and simulation of bucking led to a relative bias of -0.28 to 0.00 m3 in predicted sawtimber volume. Errors related to pulpwood volumes were -0.4 m3 to 0.21 m3. © 2010 Springer-Verlag.

Holopainen M.,University of Helsinki | Haapanen R.,Haapanen Forest Consulting | Karjalainen M.,Finnish Geodetic Institute | Vastaranta M.,University of Helsinki | And 3 more authors.
Remote Sensing | Year: 2010

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 12 features, nine of them originating from the ALS data and three from the TerraSAR-X data. The relative RMSEs for the best performing feature set were 34.7% (mean volume), 28.1% (basal area), 14.3% (mean height), 21.4% (mean diameter), 99.9% (mean volume of Scots pine), 61.6% (mean volume of Norway spruce) and 91.6% (mean volume of deciduous tree species). The combined feature set outperformed an ALS-based feature set marginally; in fact, the latter was better in the case of species-specific volumes. Features from TerraSAR-X alone performed poorly. However, due to favorable temporal resolution, satellite-borne radar imaging is a promising data source for updating large-area forest inventories based on low-pulse ALS. © 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.

Haapanen R.,Haapanen Forest Consulting | Aro L.,Finnish Forest Research Institute | Koivunen S.,Water and Environment Research of South - West Finland | Lahdenpera A.-M.,Pöyry | And 4 more authors.
Radioprotection | Year: 2011

In Finland, Olkiluoto Island on the western coast has been selected as a repository site for spent nuclear fuel. Due to the shallow sea areas around the island, the postglacial land uplift is going to change the landscape within the next millennia. For instance, new lakes and mires will develop on the present offshore areas. Concerning radionuclide transport models, the properties of the future ecosystems surrounding Olkiluoto Island can be forecast based on the properties of present lakes and mires. Due to the lack of site-specific data, lakes and mires of various successional stages were selected within a larger geographical area as analogues of the future ones. Here we present an example of a systematic process for selection of appropriate analogue sites. © 2011 EDP Sciences.

Tuominen S.,Finnish Forest Research Institute | Haapanen R.,Haapanen Forest Consulting
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

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 systematic grid, where the grid elements serve as inventory units, for which the laser and aerial image data are extracted and, for which the forest variables are 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 generating the silvicultural treatment and cutting units, since their borderlines should follow the actual stand borders, whereas the grid elements typically cover parts of several forest stands. In this study we carried out an automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating ecologically homogeneous micro-stands. Further, we extracted laser and aerial image features both for systematic grid elements and segments. For both units, the set of features used for estimating the forest attributes were selected using a genetic algorithm, which aims at minimizing the estimation error of the forest variables. The estimation accuracy produced by both approaches was assessed by comparing their estimation results. The preliminary results indicate that despite of 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 estimating forest attributes.

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