The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

Anthropogenic lake eutrophication can allow toxic cyanobacteria (blue-green algae) to grow uncontrollably, resulting in harmful algal blooms with potentially serious economic and health-related impacts. Development of monitoring methods for predicting bloom events is an important goal of monitoring programs and is one of the fundamental interests to those examining the ecology of aquatic ecosystems. Implementation of new monitoring methods complies with the provisions of national and community laws for the protection and restoration of good chemical and ecological status of water reservoirs and dependent ecosystems, and allows for the fulfilment of obligations of legislation policy of European Union (WFD 2000/60/EC) and Polish Water Law Acts, and The Environmental Protection Law. Our paper investigates a comparison between aerial remote sensing methods and currently used technology, and presents advantages of the proposed monitoring as an accurate, flexible, cheap, and fast method of detecting and predicting eutrophication and therefore cyanobacteria bloom in water reservoirs, taking into account the complexity and dynamics of an ecosystem. The article also states that aerial remote sensing technology represents an innovative tool strongly supporting traditional methods for continuous monitoring application and an early warning system against algal bloom. Therefore, it is reasonable to continue remote sensing methods development in order to precisely determine the cyanobacteria blooms in lakes. It is also necessary to improve satellite algorithms and the use of both satellite images and those taken from unmanned aerial vehicles (UAVs) and manned planes.

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 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 800 m) is presented. We present remote\ud 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).Przemysław Szmi

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 which 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 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 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 (+0.67 ± 0.08) and Quercus 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.

In this study, we analyzed in situ data from the years 2018–2022 encompassing entire forest plantations in Poland. Based on data regarding stand density and the occurrence of fungal, water-related, climate-related, fire, and insect factors that may intensify with climate changes, we determined the correlation between their occurrence and the decline in wood increments for six tree species: pine, birch, oak, spruce, beech, and alder. Subsequently, we identified age intervals in which the species–factor interaction exhibited statistically significant effects. Next, we developed neural network models for short-term wood increment predictions. Utilizing these models, we estimated a reduction in wood supply harvested in accordance with the plans for the years 2023–2025 assuming a tenfold greater intensity of factors than in 2022. Findings indicate: birch: water-related factors may reduce wood production by 0.1%–0.2%. This aligns with previous research linking drought to birch wood decline, highlighting its sensitivity to water-related issues. Oak: fungal and insect factors could decrease wood production by up to 0.1%. Prior studies emphasize the significant influence of fungal diseases on oak health and regeneration, as well as the impact of insect infestation on wood production. Alder: water-related factors may lead to a slight reduction in wood production, approximately 0.02%. The impact is significant within specific age ranges, indicating potential effects on harvesting. Pine: water- and climate-related factors may result in up to a 0.05% reduction in wood production. Pine, a key forest-forming species in Poland, is notably sensitive to these factors, especially as it nears harvesting age. Spruce: insects, fungi, and climate-related factors could lead to a reduction in wood production of up to 0.2%–0.3%. Analyses demonstrate sensitivity, resulting in a noticeable growth differential compared to the typical rate. Short-term predictions based on neural networks were developed, acknowledging their suitability for short-term forecasts due to uncertainties regarding long-term factor impacts. Additionally, our study discussed modeling wood increments in divisions well below the harvesting time, emphasizing that the influence of current and 2023–2025 factors on wood increments and supply may only manifest several decades from now. These results imply important indications for the economic and financial performance of the wood industry.