The main purpose of this publication is to present the current progress of the work associated with the use of a lightweight unmanned platforms for various environmental studies. Current development in information technology, electronics and sensors miniaturisation allows mounting multispectral cameras and scanners on unmanned aerial vehicle (UAV) that could only be used on board aircraft and satellites. Remote Sensing Division in the Institute of Aviation carries out innovative researches using multisensory platform and lightweight unmanned vehicle to evaluate the health state of forests in Wielkopolska province. In this paper, applicability of multispectral images analysis acquired several times during the growing season from low altitude (up to 800m) is presented. We present remote sensing indicators computed by our software and common methods for assessing state of trees health. The correctness of applied methods is verified using analysis of satellite scenes acquired by Landsat 8 OLI instrument (Operational Land Imager).

Drought periods have an adverse impact on the condition of oak stands. Research on different types of ecosystems has confirmed a correlation between plant species diversity and the adverse effects of droughts. The purpose of this study was to investigate the changes that occurred in an oak stand (Krotoszyn Plateau, Poland) under the impact of the summer drought in 2015. We used a method based on remote sensing indices from satellite images in order to detect changes in the vegetation in 2014 and 2015. A positive difference was interpreted as an improvement, whereas a negative one was treated as a deterioration of the stand condition. The Shannon-Wiener species diversity was estimated using an iterative principal component analysis (PCA) algorithm based on aerial images. We observed a relationship between the species indices of the individual forest divisions and their response to drought. The highest correlation between the index differences and the Shannon-Wiener indices was found for the Green Normalized Difference Vegetation Index (GNDVI) index (+0.74). In addition, correlations were observed between the mean index difference and the percentage shares in the forest divisions of species such as Pinus sylvestris L. (P. sylvestris) (+0.67 ± 0.08) and Quercus robur L. (Q. robur) (−0.65 ± 0.10). Our results lead us to infer that forest management based on highly diverse habitats is more suitable to meet the challenges in the context of global climatic changes, characterized by increasingly frequent droughts.

The main aim of this research was to determine the impact of drought (in 2015) on forests stand condition using remote sensing and statistical techniques. The study was based on the analysis of vegetation indices calculated from a series of Landsat-8 OLI satellite images covering the 2014 and 2015 growing seasons. Various tree biophysical and physical parameters as well as forest habitat characteristics were tested in order to find the most significant factors affecting drought resistance. Three approaches were used: (i) index differences, (ii) PCA analysis, and (iii) ANOVA statistical analysis. All three approaches used in this study indicate that forest biodiversity is the most important factor determining habitat response to stress conditions. Coniferous and mixed tree habitats were less sensitive than deciduous ones. Statistical analysis revealed the relationship between stress and soil types, as those more permeable were less dependent on rainwater. The highest stress was found for precipitation-dependent gley soils. Undergrowth density and height were also indicated as important factors inducing habitat response to a changing weather situation. All the results confirmed the usefulness of mid-infrared based indices for water shortage monitoring in forests. They confirmed that habitat biodiversity has a positive effect on its resistance to stressful conditions. Also forest type (conifer/deciduous) determines it’s sensitivity. Precipitation and groundwater shortages have different effects on the forest condition depending on soil type.

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In this article, we describe methods for the correction of multispectral aerial images by accounting for atmospheric interference. We also summarize the first correction results for images acquired at flight altitudes and evaluate the suitability of selected methods for the atmospheric correction of these images. Furthermore, processes and phenomena occurring in the atmosphere that potentially affect image quality and interfere with the electromagnetic radiation registered by the imaging sensors are discussed as well. The purpose of atmospheric correction is to reduce or eliminate atmospheric interference during multispectral image processing. Here we present methodology for image correction based on data gathered at various altitudes during the autumn flights conducted as a part of the HESOFF project.