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.
The purpose of this study was to determine the spatial structure of vegetation on the repository of the mine “Fryderyk” in Tarnowskie Góry. Tested area was located in the Upper Silesian Industrial Region (a large industrial region in Poland). It was a unique refuge habitat – Natura2000; PLH240008. The main aspect of this elaboration was to investigate the possible use of geotechniques and generally available geodata for mapping LULC changes and determining the spatial structure of vegetation. The presented study focuses on the analysis of a spatial structure of vegetation in the research area. This exploration was based on aerial images and orthophotomaps from 1947, 1998, 2003, 2009, 2011 and airborne laser scanning data (2011, ISOK project). Forest succession changes which occurred between 1947 and 2011 were analysed. The selected features of vegetation overgrowing spoil heap “Fryderyk” was determined.
The results demonstrated a gradual succession of greenery on soil heap. In 1947, 84% of this area was covered by low vegetation. Tree expansion was proceeding in the westerly and northwest direction. In 2011 this canopy layer covered almost 50% of the research area. Parameters such as height of vegetation, crowns length and cover density were calculated by an airborne laser scanning data. These analyses indicated significant diversity in vertical and horizontal structures of vegetation. The study presents some capacities to use airborne laser scanning for an impartial evaluation of the structure of vegetation.
Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA).
The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.
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