Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations.

Spatial prioritization in systematic conservation planning has traditionally been developed for several to many species and/or habitats, and single-species applications are rare. We developed a novel spatial prioritization model based on accurate estimates of remotely-sensed data and maps of threats potentially affecting long-term species persistence. We used this approach to identify priority areas for the conservation of the Endangered Greek meadow viper Vipera graeca, a cold-adapted species inhabiting mountaintops in the Pindos Mountains of Greece and Albania. We transformed the mapped threats into nine variables to estimate conservation value: habitat suitability (climate suitability, habitat size, occupancy, vegetation suitability), climate change (future persistence, potential for altitudinal range shift) and land-use impact (habitat alteration, degradation, disturbance). We applied the Zonation systematic conservation planning tool with these conservation value variables as biodiversity features to rank the areas currently occupied by the species and to identify priority areas where the chances for population persistence are highest. We found that 90% of current habitats will become unsuitable by the 2080s and that conservation actions need to be implemented to avoid extinction as this is already a threatened species with a narrow ecological niche. If threats are appropriately quantified and translated into variables of conservation value, spatial conservation planning tools can successfully identify priority areas for the conservation of single species. Our study demonstrates that spatial prioritization for single umbrella, flagship or keystone species is a promising approach for the conservation of species for which few data are available.

A number of clear issues are pertinent when considering whether, or not, to use a remotely sensed dataset. We evaluate these issues here by comparing an aerial hyperspectral image at 1.5 m geometric resolution that comprises 128 narrow bands within a spectral range between 400 nm and 1,000 nm as well as a nine-band Landsat 8 image at 30.0 m geometric resolution. We therefore applied Random Forest (RF) and Support Vector Machine (SVM) classifiers utilizing different input data sets to determine the best thematic accuracy for both types of images by involving all possible bands and then minimized them using variable selection and dimension reduction via Minimum Noise Fraction (MNF). We then compared Landsat images to an aerial hyperspectral one. The results of this analysis revealed that band selections based on variable importance and MNF-transformation improved thematic accuracy assessed as Overall Accuracy (OA). Results reveal a 1.00% improvement in OA via variable selection as 59 bands instead of 128 bands and a 1.50% via MNF-transformation of the hyperspectral image. This improvement was 4.52% in the Landsat image when using a MNF-transformation compared to the best performances without transformation or variable selection. Data also showed that application of Landsat spectral range on hyperspectral bands resulted in different outcomes; specifically, SVM resulted in a 91.50% OA while RF resulted in 95.50% OA. Landscape ecology results show that use of the Landsat image provided fewer land cover patches and that differences encompassed 6.30% of the whole area. We therefore conclude that Landsat data can be used with a number of limitations for accurate ecological mapping.

In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.

Black locust (Robinia pseudoacacia L.) breeding is an important component of plantation forestry in Central and Eastern Europe; however, clone trials are still mainly evaluated using conventional field surveys, and the application of high-resolution red-edge satellite indices at the intraspecific level remains rarely applied. As a result, less information is available on the phenological status of black locust clones derived from red-edge satellite data. This study evaluates a clone trial established in Eastern Hungary on slightly acidic Arenosol soil, assessing the growth performance and seasonal spectral dynamics of newly bred black locust clones during their fifth growing season by integrating field measurements with PlanetScope-derived Normalized Difference Red-Edge Index (NDRE) time series. Clone NK2 exhibited the most vigorous growth, reaching a mean height of 11.1 ± 0.15 m and a diameter at breast height (DBH) of 11.21 ± 0.19 cm, which were 35.4% greater in height and 19.0% larger in DBH than those (8.2 ± 0.12 m height, and 9.42 ± 0.23 cm diameter) of the control (‘Üllői’ cultivar). Clone PL251 also exceeded the control by 25.6% in height and 19.2% in DBH. Spectral analysis (NDRE value ± standard error) revealed marked differences in phenological development: in the early stage (April 15), NK1 and PL040 had the highest NDRE values (0.472 ± 0.020 and 0.461 ± 0.019), whereas NK2 showed delayed leaf emergence (0.398 ± 0.019). By June 21, PL251 had reached an NDRE value of 0.692 ± 0.013, which was higher than that of the control (0.673 ± 0.016). In mid-July, NDRE peaked for NK2 and NK1 (0.732 ± 0.012 and 0.731 ± 0.013), with ‘Üllői’ showing consistently lower values across the season. In the final stage, NK2 maintained the highest NDRE values (October 22: 0.618 ± 0.015; November 9: 0.466 ± 0.021), indicating prolonged photosynthetic activity, while NK1 and ‘Üllői’ declined earlier (e.g., November 9: 0.354 ± 0.018 and 0.390 ± 0.027, respectively). These findings highlight NK2 and PL251 as superior candidates for high-yield, climate-resilient tree plantations because of their strong growth and extended physiological activity.

Satellite images are important information sources of land cover analysis or land cover change monitoring. We used the sensors of four different spacecraft: TM, ETM+, OLI and ALI. We classified the study area using the Maximum Likelihood algorithm and used segmentation techniques for training area selection. We validated the results of all sensors to reveal which one produced the most accurate data. According to our study Landsat 8’s OLI performed the best (96.9%) followed by TM on Landsat 5 (96.2%) and ALI on EO-1 (94.8%) while Landsat 7’s ETM+ had the worst accuracy (86.3%).