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.

This paper presents a new non-invasive technique of granulometric analysis based on the fusion of two imaging techniques, Unmanned Aerial Vehicles (UAV)-based photogrammetry and optical digital granulometry. This newly proposed technique produces seamless coverage of a study site in order to analyze the granulometric properties of alluvium and observe its spatiotemporal changes. This proposed technique is tested by observing changes along the point bar of a mid-latitude mountain stream. UAV photogrammetry acquired at a low-level flight altitude (at a height of 8 m) is used to acquire ultra-high resolution orthoimages to build high-precision digital terrain models (DTMs). These orthoimages are covered by a regular virtual grid, and the granulometric properties of the grid fields are analyzed using the digital optical granulometric tool BaseGrain. This tested framework demonstrates the applicability of the proposed method for granulometric analysis, which yields accuracy comparable to that of traditional field optical granulometry. The seamless nature of this method further enables researchers to study the spatial distribution of granulometric properties across multiple study sites, as well as to analyze multitemporal changes using repeated imaging.

The topographic signature of a mountain belt depends on the interplay of tectonic, climatic and erosional processes, whose relative importance changes over times, while quantifying these processes and their rates at specific times remains a challenge. The eastern Andes of central Bolivia offer a natural laboratory in which such interplay has been debated.Here, we investigate the Rio Grande catchment which crosses orthogonally the eastern Andes orogen from the Eastern Cordillera into the Subandean Zone, exhibiting a catchment relief of up to 5000 m. Despite an enhanced tectonic activity in the Subandes, local relief, mean and modal slopes and channel steepness indices are largely similar compared to the Eastern Cordillera and the intervening Interandean Zone. Nevertheless, a dataset of 57 new cosmogenic 10 Be and 26 Al catchment wide denudation rates from the Rio Grande catchment reveals up to one order of magnitude higher denudation rates in the Subandean Zone (mean 0.8 mm/yr) compared to the upstream physiographic regions. We infer that tectonic activity in the thrusting dominated Subandean belt causes higher denudation rates based on A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT cumulative rock uplift investigations and due to the absence of a pronounced climate gradient. Furthermore, the lower rock strength of the Subandean sedimentary units correlates with mean slopes similar to the ones of the Eastern Cordillera and Interandean Zone, highlighting the fact, that lithology and rock strength can control high denudation rates at low slopes.Low denudation rates measured at the outlet of the Rio Grande catchment (Abapo) are interpreted to be a result of a biased cosmogenic nuclide mixing that is dominated by headwater signals from the Eastern Cordillera and the Interandean zone and limited catchment sediment connectivity in the lower river reaches. Therefore, comparisons of short-(i.e., sediment yield) and millennial denudation rates require caution when postulating tectonic and/or climatic forcing without detailed studies. Highlights1 -millennial denudation rates across the central eastern Andes are highest in Subandes 2 -common geomorphic metrics cannot explain high denudation rates in the Subandes 3 -cumulative seismic volume uplift provide best spatial match for denudation rates in Subandes 4 -lower rock strength permits higher denudation at lower slopes in Subandes

ABSTRACT:Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proofof-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

This study aimed to examine the potential of convolutional neural networks (CNNs) for the detection of individual trees infested by bark beetles in a multispectral high-resolution dataset acquired by an unmanned aerial system (UAS). We compared the performance of three CNN architectures and the random forest (RF) model to classify the trees into four categories: pines, sbbd (longer infested trees when needles turn yellow), sbbg (trees under green attack) and non-infested trees (sh). The best performance was achieved by the Nez4c3b CNN (kappa 0.80) and Safaugu4c3b CNN (kappa 0.76) using only RGB bands. The main misclassifications were between sbbd and sbbg because of the similar spectral responses. Merging sbbd and sbbg into a more general class of infested trees made the selection of model type less important. All tested model types, including RF, were able to detect infested trees with an F-score of the class over 0.90. Nevertheless, the best overall metrics were achieved again by the Safaugu3c3b model (kappa 0.92) and Nez3cb model (kappa 0.87) using only RGB bands. The performance of both models is comparable, but the Nez model has a higher learning rate for this task. Based on our findings, we conclude that the Nez and Safaugu CNN models are superior to the RF models and transfer learning models for the identification of infested trees and for distinguishing between different infestation stages. Therefore, these models can be used not only for basic identification of infested trees but also for monitoring the development of bark beetle disturbance.