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.

Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns, and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning (ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded. Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALS-enhanced stand-level inventory.Results: Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/− 5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m². Only a slight gain was observed when bigger plots were used.Conclusions: ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.

There are many satellite systems acquiring environmental data on the world. Acquired global remote sensing datasets require ground reference data in order to calibrate them and assess their quality. Regarding calibration and validation of these datasets with broad geographical extents, it is essential to register zones which might be considered as Homogeneous Patches (HPs). Such patches enable an optimal calibration of satellite data/sensors, and what is more important is an analysis of components which significantly influence electro-magnetic signals registered by satellite sensors.

This study examines the applicability of the 3-30-300 rule in five medium-sized Polish municipalities. The rule states that residents should be able to see at least three trees from their homes, neighborhoods should have at least 30% tree canopy coverage, and public green spaces should be within 300 m. The method proposed in this study shows that the tree visibility component of the 3-30-300 concept is the most fluctuating index, and it strongly depends on the settings of the algorithm parameter, as well as on the placement of artificially generated observers. This demonstrates the complexity of the issue and the need to further specify the nuances of the 3-30-300 rule. The work shows that all variables of the 3-30-300 rule can be calculated based on publicly available data, such as point clouds, which are increasingly being made available for free for research and implementation purposes. The study concludes that the proposed solution is effective in assessing the availability of green spaces and emphasizes the need for qualitative improvements in the management of urban green spaces. While the 3-30-300 rule can serve as the foundation for future urban planning, complementary strategies are needed to ensure long-term sustainability and better access to green spaces.

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