The use of remotely sensed data and polish NFI plots for prediction of growing stock volume using different predictive methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons

Using point clouds from laser scanning for revising particular objects in the forest digital map database

Celem pracy było sprawdzenie możliwości wykorzystania danych z lotniczego skanowania laserowego do detekcji budynków na terenach leśnych. Ponadto sprawdzono możliwość wykorzystania tych danych do aktualizacji wybranych warstw z leśnej mapy numerycznej. W pracy przeanalizowano obszar leśny wraz z buforem 100 m wokół wydzieleń na terenie dwunastu nadleśnictw górskich, położonych na obszarach badawczych w Sudetach i Beskidach. Przy wykorzystaniu danych z lotniczego skanowania laserowego wykryto 515 budynków co stanowiło 89,2% wszystkich budynków znajdujących się w wektorowej warstwie wydzieleń leśnych. Na poszczególnych obszarach badawczych osiągnięto dokładność odpowiednio 80,5%, 94,2% i 91,2%. Podsumowując, lotnicze skanowanie laserowe może być wykorzystywane do aktualizacji wybranych warstw w leśnej mapie numerycznej, zawierających informacje o budynkach oraz obiektach budowlanych. Istniejące algorytmy detekcji budynków nie są bezbłędne, więc przyszłe prace powinny skupić się na poprawie dokładność analiz.The aim of the presented studies was to determine the ability to detect buildings in forest areas on the basis of airborne laser scanning data. Moreover, the usefulness of this data for updating selected items of the FDM has been evaluated. In this study forest areas with a 100 m buffer zone have been analyzed, including twelve mountain forest districts, grouped in three research areas located in the Sudety and the Beskidy Mountains. Using LiDAR data 515 buildings have been detected which represents 89.2% of all buildings in the vector layer of the digital forest map. In particular research areas the detection accuracy reached to 80.5%, 92.4%, 91.2%. As a result of the study it can be concluded that the airborne laser scanning data may be helpful in updating the selected layers of the digital maps of forest, containing information of forest engineering. Existing building detection algorithms are not error-free, so further research should be conducted to improve the accuracy of analyzes