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This paper explores the potential of Unmanned Aerial Systems (UASs) for the analysis of variations in the fluvial dynamics of a mid-mountain stream. The UAS photogrammetry was employed to acquire a multitemporal set of high-precision digital terrain models (DTMs) and orthoimages, thereby enabling the reconstruction of variations in riverbed and quantitative analysis of volumetric changes. A hexacopter UAS platform was used for the repeated acquisition of data for the photogrammetric analysis of a stretch of mid-mountain streams with elevated fluvial dynamics. Photogrammetric reconstruction enabled the development of accurate DTMs and orthoimages with spatial resolutions of 2 cm per pixel. These were identified and used to quantitatively assess the segments of channels with active lateral erosion. The UAS-derived data facilitated an analysis of the shifts of stream banks and the calculation of the areal extent of changes and volumetric extent of bank erosion. Comparison of UAS-derived point clouds with aerial LiDAR scanning data demonstrated the high spatial accuracy and precision of the UAS data. The accuracy and high operability of the imaging provide spatial data of a new qualitative level and the potential for the detailed analysis of experimental areas where spatial information is of limited availability. OPEN ACCESSRemote Sens. 2015, 7 8587

No abstract

A headwater basin in the Sumava Mountains (Czech Republic), the upper Vydra basin, has undergone forest disturbance as a result of repeated windstorms, a bark beetle outbreak, and forest management. This study analyzed the long-term hydro-climatic changes by using a combination of statistical analyses, including Mann-Kendall tests, CUSUM analysis, Buishand's and Petitt's homogeneity tests, and Kriging. Although the runoff balance over the study period experienced no apparent changes due to climate warming and forest disturbance, significant changes were detected in the share of direct runoff and baseflow, intra-annual variability of the runoff regime, seasonal runoff patterns, and the distribution of peak and low flow events. The seasonal runoff substantially shifted from summers (decreased from 40% to 28%) to springs (increased by 10%). The occurrence of peak flow events has doubled since the 1980s, with a seasonal shift from late spring towards the early spring, while the occurrence of low-flow days decreased by two-thirds. By 1990, these changes were followed by a seasonal shift in runoff from autumn to mid-winter. The changes in hydrological regime in the mid-mountain basin indicate the sensitivity of its hydrological system and the complexity of its feedback with the changing environment. OPEN ACCESSWater 2015, 7 3321

Multispectral imaging using unmanned aerial systems (UAS) enables rapid and accurate detection of pest insect infestations, which are an increasing threat to midlatitude natural forests. Pest detection at the level of an individual tree is of particular importance in mixed forests, where it enables a sensible forest management approach. In this study, we propose a method for individual tree crown delineation (ITCD) followed by feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud (PPC) and a multispectral orthomosaic. An excess green index (ExG) threshold mask was applied before the ITCD to separate targeted coniferous trees from deciduous trees and backgrounds. The individual crowns of conifer trees were automatically delineated as (i) a full tree crown using marker-controlled watershed segmentation (MCWS), Dalponte2016 (DAL), and Li 2012 (LI) region growing algorithms or (ii) a buffer (BUFFER) around a treetop from the masked PPC. We statistically compared selected spectral and elevation features extracted from automatically delineated crowns (ADCs) of each method to reference tree crowns (RTC) to distinguish between the forest disturbance classes and two tree species. Moreover, the effect of PPC density on the ITCD accuracy and feature extraction was investigated. The ExG threshold mask application resulted in the excellent separability of targeted conifer trees and the increasing shape similarity of ADCs compared to RTC. The results revealed a strong effect of PPC density on treetop detection and ITCD. If the PPC density is sufficient (>10 points/m2), the ADCs produced by DAL, MCWS, and LI methods are comparable, and the extracted feature statistics of ADCs insignificantly differ from RTC. The BUFFER method is less suitable for detecting a bark beetle disturbance in the mixed forest because of the simplicity of crown delineation. It caused significant differences in extracted feature statistics compared to RTC. Therefore, the point density was found to be more significant than the algorithm used. We conclude that automatic ITCD methods may constitute a substitute for the time-consuming manual tree crown delineation in tree-based bark beetle disturbance detection and sanitation of individual infested trees using the suggested methodology and high-density (>20 points/m2, 10 points/m2 minimum) PPC.