In the presented study, the Sentinel-2 vegetation indices (VIs) were evaluated in context of estimating defoliation of Scots pine stands in western Poland. Regression and classification models were built based on reference data from 50 field plots and Sentinel-2 satellite images from three acquisition dates. Three machine-learning (ML) methods were tested: k-nearest neighbors (kNN), random forest (RF), and support vector machines (SVM). Regression models predicted stands defoliation with moderate accuracy. R 2 values for regression models amounted to 0.53, 0.57, 0.57 for kNN, RF and SVM, accordingly. Analogically, the following values of normalized root mean squared error were obtained: 12.2%, 11.9% and 11.6%. Overall accuracies for two-class classification models were 78%, 75%, 78% for kNN, RF and SVM methods. The Green Normalized Difference Vegetation Index and MERIS Terrestrial Chlorophyll Index VIs were found to be most robust defoliation predictors regardless of the ML method. We conclude that Sentinel-2 satellite images provide useful information about forest defoliation and may contribute to forest monitoring systems.

The study was performed for the part of the administrative district Milicz. The authors analysed the parcels where the changes in land use, compared to the cadastral data, were found. The areas of interest were the parcels, where agricultural use was abandoned and the forest succession progressed. This paper investigates the possibility of applying satellite images Sentinel-2A for the automation of land use/land cover change detection, mainly in the aspect of monitoring uncontrolled forest succession. The results of the supervised classification of images Sentinel-2A were referred to the results of the traditionally applied manual vectorization of aerial orthophotomap. The difference for area covered by trees or shrub was 3.85% of the analysed parcels area. Analysing the results for each parcel in which the process of succession occurred, the mean difference is on average 2.25% for one parcel. The mean difference in the absolute value of the total area of participation in individual land use plots was about 1.54% of the analysed area.ARTICLE HISTORY

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The aim of this study was to investigate the possible use of geoinformatics tools and generally available geodata for mapping land cover/use on the reclaimed areas. The choice of subject was dictated by the growing number of such areas and the related problem of their restoration. Modern technology, including GIS, photogrammetry and remote sensing are relevant in assessing the reclamation effects and monitoring of changes taking place on such sites. The LULC classes mapping, supported with thorough knowledge of the operator, is useful tool for the proper reclamation process evaluation.The study was performed for two post-mine sites: reclaimed external spoil heap of the sulfur mine Machów and areas after exploitation of sulfur mine Jeziórko, which are located in the Tarnobrzeski district. The research materials consisted of aerial orthophotos, which were the basis of on-screen vectorization; LANDSAT satellite images, which were used in the pixel and object based classifi cation; and the CORINE Land Cover database as a general reference to the global maps of land cover and land use.

szostAk M., wężyk P., HAwryło P., PucHAłA M., 2016. Monitoring the secondary forest succession and land cover/use changes of the Błędów Desert (Poland) using geospatial analyses. Quaestiones Geographicae 35(3), Bogucki Wydawnictwo Naukowe, Poznań, pp. 5-13, 5 figs, 1 table.ABstrAct: The role of image classification based on multi-source, multi-temporal and multi-resolution remote sensed data is on the rise in the environmental studies due to the availability of new satellite sensors, easier access to aerial orthoimages and the automation of image analysis algorithms. The remote sensing technology provides accurate information on the spatial and temporal distribution of land use and land cover (LULC) classes. The presented study focuses on LULC change dynamics (especially secondary forest succession) that occurred between 1974 and 2010 in the Błędów Desert (an area of approx. 1210 ha; a unique refuge habitat -NATURA 2000; South Poland). The methods included: photointerpretation and on-screen digitalization of KH-9 CORONA (1974), aerial orthoimages (2009) and satellite images (LANDSAT 7 ETM + , 1999 and BlackBridge -RapidEye, 2010) and GIS spatial analyses. The results of the study have confirmed the high dynamic of the overgrowth process of the Błędów Desert by secondary forest and shrub vegetation. The bare soils covered 19.3% of the desert area in 1974, the initial vegetation and bush correspondingly 23.1% and 30.5%. In the years 2009/2010 the mentioned classes contained: the bare soils approx. 1.1%, the initial vegetation -8.7% and bush -15.8%. The performed classifications and GIS analyses confirmed a continuous increase in the area covered by forests, from 11.6% (KH-9) up to 24.2%, about 25 years later (LANDSAT 7) and in the following 11 years, has shown an increase up to 35.7% (RapidEye 2010).

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Site productivity remains a fundamental concern in forestry as a significant driver of resource availability for tree growth. The site index (SI) reflects the overall impact of all environmental factors that determine tree height growth and is the most commonly used indirect proxy for forest site productivity estimated using stand age and height. The SI concept challenges are local variations in climate, soil, and genotype-environmental interactions that lead to variable height growth patterns among ecoregions and cause inappropriate estimation of site productivity. Developing regional models allow us to determine forest growth and SI more appropriately. This study aimed to develop height growth models for the Scots pine in Poland, considering the natural forest region effect. For height growth modelling, we used the growth trajectory data of 855 sample trees, representing the Scots pine entire range of geographic locations and site conditions in Poland. We compared the development of regional height growth models using nonlinear-fixed-effects (NFE) and nonlinear-mixed-effects (NME) modelling approaches. Our results indicate a slightly better fit to the data of the model built using NFE approach. The results showed significant differences between Scots pine growth in natural forest regions I, II, and III located in northern Poland and natural forest regions IV, V, and VI in southern Poland. We compared the development of regional height growth models using NFE and NME modelling approaches. Our results indicate a slightly better fit to the data of the model built using the NFE approach. The developed models show differences in height growth patterns of Scots pines in Poland and revealed that acknowledgement of region as the independent variable could improve the growth prediction and quality of the SI estimation. Differences in climate and soil conditions that distinguish natural forest regions affect Scots pine height growth patterns. Therefore, extending this research to models that directly describe height growth interactions with site variables, such as climate, soil properties, and topography, can provide valuable forest management information.

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.