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The influence of number and size of sample plots on modelling growing stock volume based on Airborne Laser Scanning

Stereńczak K., Lisańczuk M., Parkitna K., Mitelsztedt K., Mroczek P., Miścicki S.

Drewno : prace naukowe, doniesienia, komunikaty

2018

vol. 61, nr 201

5--22

Current forest growing stock inventory methods used in Poland are based on statistical methods using field measurements of trees on circular sample plots. Such measurements are carried out with traditional equipment, i.e. callipers and range finders. Nowadays, remote sensing based inventory techniques are becoming more popular and have already been applied in North America and some Scandinavian countries. Remote sensing based forest inventories require a certain amount of ground sample plots, which serve either as reference data used for model calibration and/or as a validation dataset for the assessment of the accuracy of modelled variables. Using a set of 900 ground sample plots and Airborne Laser Scanner (ALS) from the Milicz forest district, a statistical model for the estimation of plot growing stock volume was developed. Next, the developed model was once again fitted to different variants of sample plot size and number of sample plots. Each variant was selected from a full 900 sample plot set. The selection started from 800, 700, 600, …, down to 25 plots, respectively, and was carried out in proportion to the dominant tree age range. To account for the area effect, each plot number variant was similarly tested with various sample plot areas, i.e. 500, 400, …, 100 m2. Sampling in each variant was repeated in order to take into account the effect of a single selection. The results showed a strong relationship between obtained modelling errors and the size and number of used sample plots. It has been demonstrated that the number of sample plots has no influence on the accuracy of GSV estimation above about 300-400 sample plots (about 500 sample plots for bias), whereas sample plot size has a visible impact on estimation accuracy, which reduces with decreasing sample plot size, regardless of the number of sample plots. If it is about precision, results showed that the influence of a single selection to be relevant only below 300-400 plots (about 500 for bias) and the same trend can be observed in each sample plot size variant. The results showed it is possible to strongly reduce the number of ground sample plots (minimum 300- 400), while still maintaining decent accuracy and precision levels, at least in similarly investigated forest conditions.

Wykorzystanie chmur punktów z lotniczego skanowania laserowego do weryfikacji stanu aktualności wybranych obiektów w bazie geometrycznej leśnej mapy numerycznej

Ciesielski M., Bałazy R., Mitelsztedt K., Zawiła-Niedźwiecki T.

Roczniki Geomatyki

2015

T. 13, z. 3(69)

217--225

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.