One of the threats that has a significant impact on the conservation status and on the preservation of non-forest Natura 2000 habitats, is secondary succession, which is currently analyzed using airborne laser scanning (ALS) data. However, learning about the dynamics of this phenomenon in the past is only possible by using archival aerial photographs, which are often the only source of information about the past state of land cover. Algorithms of dense image matching developed in the last decade have provided a new quality of digital surface modeling. The aim of this study was to determine the extent of trees and shrubs, using dense image matching of aerial images. As part of a comprehensive research study, the testing of two software programs with different settings of image matching was carried out. An important step in this investigation was the quality assessment of digital surface models (DSM), derived from point clouds based on reference data for individual trees growing singly and in groups with high canopy closure. It was found that the detection of single trees provided worse results. The final part of the experiment was testing the impact of the height threshold value in elevation models on the accuracy of determining the extent of the trees and shrubs. It was concluded that the best results were achieved for the threshold value of 1.25–1.75 m (depending on the analyzed archival photos) with 10 to 30% error rate in determining the trees and shrubs cover.

Open areas, along with their non-forest vegetation, are often threatened by secondary succession, which causes deterioration of biodiversity and the habitat’s conservation status. The knowledge about characteristics and dynamics of the secondary succession process is very important in the context of management and proper planning of active protection of the Natura 2000 habitats. This paper presents research on the evaluation of the possibility of using selected methods of textural analysis to determine the spatial extent of trees and shrubs based on archival aerial photographs, and consequently on the investigation of the secondary succession process. The research was carried out on imagery from six different dates, from 1971 to 2015. The images differed from each other in spectral resolution (panchromatic, in natural colors, color infrared), in original spatial resolution, as well as in radiometric quality. Two methods of textural analysis were chosen for the analysis: Gray level co-occurrence matrix (GLCM) and granulometric analysis, in a number of variants, depending on the selected parameters of these transformations. The choice of methods has been challenged by their reliability and ease of implementation in practice. The accuracy assessment was carried out using the results of visual photo interpretation of orthophotomaps from particular years as reference data. As a result of the conducted analyses, significant efficacy of the analyzed methods has been proved, with granulometric analysis as the method of generally better suitability and greater stability. The obtained results show the impact of individual image features on the classification efficiency. They also show greater stability and reliability of texture analysis based on granulometric/morphological operations.

In this study, we compared two sets of antenna phase center corrections for groups of the same type of antenna mounted at the continuously operating global navigation satellite system (GNSS) reference stations. The first set involved type mean models provided by the International GNSS Service (release igs08), while the second set involved individual models developed by Geo++. Our goal was to check which set gave better results in the case of height estimation. The paper presents the differences between models and their impact on resulting height. Analyses showed that, in terms of the stability of the determined height, as well as its variability caused by increasing the facade mask, both models gave very similar results. Finally, we present a method for how to estimate the impact of differences in phase center corrections on height changes.

The timing of ice freeze‐up and break‐up in the Arctic may be responding to climate change. Passive microwave remote sensing is a powerful technique for monitoring this timing. We processed low‐frequency microwave time series from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission for a set of 31 satellite gauging reaches (SGRs) above 65°N between 2010 and 2020 to determine timing of freeze‐up and break‐up and annual river ice durations. We found indication of progressive ice cover reduction over more than half of the monitored river reaches, with possibly the fastest rate occurring over northeast Russia. Some rivers in high‐latitude North America experienced a slight increase in ice cover. Across the data set, we observed an average 2.2 days shift toward later ice freeze‐up in autumn and an average 0.6 days shift toward earlier ice break‐up in spring, resulting in an average decrease of 3.4 days in ice duration between 2010 and 2020. River reaches with the longest duration of ice cover appeared to have experienced the fastest rate of decrease. A possible reduction of the time lag between air temperature rise or fall and corresponding river ice break‐up and freeze‐up was also observed. Yet results on variability are carefuly interpreted given the short length of the time series (2010–2020) and the low statistical confidence rates calculated for the decadal tendency. Still outcomes are consistent with increases in global and Arctic surface air temperature. Following these time series over the next decade using passive microwave satellite sensors can monitor ice cover duration in the Arctic and will further determine temporal and regional trends.

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