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Fluctuations in weather conditions, particularly precipitation and water availability, may strongly affect growth rate patterns and lead to interannual height growth variation. Consequently, height growth models developed using airborne laser scanning (ALS) data collected at short time intervals may over- or underestimate long-term height growth trends and finally result in different growth forecasts. The objective of this study was to develop height growth models for Norway spruce, including the effect of weather conditions. We used ALS-derived top height (TH) estimates and meteorological data from the research area collected for 2007-2012 and 2013-2018 to develop a weather-sensitive height growth model. The top height (TH) growth of Norway spruce was affected by the mean annual precipitation sum (APS) in the studied periods, and a higher APS resulted in faster TH growth. This study demonstrates the high potential of repeated ALS for detecting short-term variation in the tree height increment and the development of weather-sensitive height growth models.
Luiza Tymińska- Czabańska; Jarosław Socha; Paweł Hawryło; Radomir Bałazy; Mariusz Ciesielski; Ewa Grabska-Szwagrzyk; Paweł Netzel. Weather-sensitive height growth modelling of Norway spruce using repeated airborne laser scanning data. Agricultural and Forest Meteorology 2021, 308-309, 108568 .
AMA StyleLuiza Tymińska- Czabańska, Jarosław Socha, Paweł Hawryło, Radomir Bałazy, Mariusz Ciesielski, Ewa Grabska-Szwagrzyk, Paweł Netzel. Weather-sensitive height growth modelling of Norway spruce using repeated airborne laser scanning data. Agricultural and Forest Meteorology. 2021; 308-309 ():108568.
Chicago/Turabian StyleLuiza Tymińska- Czabańska; Jarosław Socha; Paweł Hawryło; Radomir Bałazy; Mariusz Ciesielski; Ewa Grabska-Szwagrzyk; Paweł Netzel. 2021. "Weather-sensitive height growth modelling of Norway spruce using repeated airborne laser scanning data." Agricultural and Forest Meteorology 308-309, no. : 108568.
In the paper, we review selected existing solutions of raster map calculators and propose a new approach for map calculation tools. The main criteria to select raster maps calculators was the ability to run them in batch mode and to use them in external scripts. Such a working method is common in the processing and modeling of massive datasets. We compared the following solutions: r.mapcalc from GRASS GIS, Grid Calculator module in SAGA, gdal_calc.py from GDAL library, and ’calc’ function from R raster package. Moreover, we propose another solution — plMapcalc. The solution has new features, such as multiple outputs, multi-pass processing, and a memory buffer to store temporary values. All raster calculators were compared according to their processing efficiency and precision. Two datasets of different sizes were used in the testing procedure, which started with GeoTIFF input files and produced GeoTIFF resultant files. The results of the test show that the precision of the calculations is comparable. We also compared the processing times of all the calculators using a ranking procedure. The new solution for introducing extra functionalities is the best ranked raster map calculator.
P. Netzel; J. Slopek. Comparison of different implementations of a raster map calculator. Computers & Geosciences 2021, 154, 104824 .
AMA StyleP. Netzel, J. Slopek. Comparison of different implementations of a raster map calculator. Computers & Geosciences. 2021; 154 ():104824.
Chicago/Turabian StyleP. Netzel; J. Slopek. 2021. "Comparison of different implementations of a raster map calculator." Computers & Geosciences 154, no. : 104824.
Variation in tree stem form depends on species, age, site conditions, etc. Stem taper models that estimate stem diameter at any height and volume should comply with this complexity. In the paper, we propose new methods taking into account both unbiased estimates and stem variability: (i) an expert model based on an artificial neural network (ANN) and (ii) a statistical model built using a regression tree (REG). We used the variable-exponent taper equation (STE) as a reference for these two models. Input data contain information about 2856 trees representing eight dominant forest-forming tree species in Poland (birch, beech, oak, fir, larch, alder, pine, and spruce). The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built ANN and REG models and calculated both stem taper and tree volumes. The results show that ANN is a universal approach that offers the most precise estimation of stem diameter at a particular stem height for different tree species. The results for alder are an exception. In this case, the REG model performs slightly better than ANN. In terms of volume prediction, the ANN model provides the most accurate predictions for coniferous and beech. In general, flexibility and predictive performance of the ANN are better than REG and reference the STE equation.
Jaroslaw Socha; Pawel Netzel; Dominika Cywicka. Stem Taper Approximation by Artificial Neural Network and a Regression Set Models. Forests 2020, 11, 79 .
AMA StyleJaroslaw Socha, Pawel Netzel, Dominika Cywicka. Stem Taper Approximation by Artificial Neural Network and a Regression Set Models. Forests. 2020; 11 (1):79.
Chicago/Turabian StyleJaroslaw Socha; Pawel Netzel; Dominika Cywicka. 2020. "Stem Taper Approximation by Artificial Neural Network and a Regression Set Models." Forests 11, no. 1: 79.