BA, D., LANGHAMMER, J., MACA, P., BODIAN, A., 2024.  Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin.  J. Hydrol. Hydromech., Vol. 72, No. 1, 2024, p. 131 - 147, doi: 10.2478/johh-2023-0044. (IF 2.7)

This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin (Senegal) over a 30-year period employing a 7-year sliding window under different climatic conditions. The results revealed differences in overall performance and variable sensitivity of the models to hydrological conditions and calibration period lengths, stemming from their different structure and complexity. The study emphasized the importance of the length of the calibration period on model performance and on the reduction of uncertainty in the results. 

LANGHAMMER, J., 2023. Flood Simulations Using a Sensor Network and Support Vector Machine Model. Water, 15(11), 2004; https://doi.org/10.3390/w15112004.  (IF 3.4)

This study couples the support vector machine (SVM) model with a hydrometeorological wireless sensor network to simulate different types of flood events in a montane basin. The model was tested in the mid-latitude montane basin of Vydra in the Šumava Mountains, Central Europe, featuring complex physiography, high dynamics of hydrometeorological processes, and the occurrence of different types of floods. Sensitivity analysis was performed to determine the optimal configuration of the SVR model parameters (C, N, and E). Simulation results for different flood scenarios showed the reliability of the model in reconstructing different types of floods. The model accurately captured trend fitting, event timing, peaks, and flood volumes without significant errors. 

LENDZIOCH, T., LANGHAMMER, J., SHESHADRIVASAN, V. K., 2023. Automated mapping of the mean particle diameter characteristics from UAV-imagery using the CNN-based GRAINet model. Journal of Hydroinformatics 2023; jh2023079. doi: https://doi.org/10.2166/hydro.2023.079. (IF 2.7)

This study uses the GRAINet CNN approach on UAV optical aerial imagery to analyze and predict grain size characteristics, specifically mean diameter (dm), along a gravel river point bar in Šumava National Park (Šumava NP), Czechia. By employing a digital line sampling technique and manual annotations as ground truth, GRAINet offers an innovative solution for particle size analysis.  Eight UAV overflights were conducted between 2014 and 2022 to monitor changes in grain size dm across the river point bar. The study highlights the importance of a large manually labeled training dataset for the GRAINet approach, eliminating the need for user-parameter tuning and improving its suitability for large-scale applications.

LANGHAMMER, J., LENDZIOCH, T., ŠOLC, J., 2023. Use of UAV Monitoring to Identify Factors Limiting the Sustainability of Stream Restoration Projects. Hydrology, 10(2), 48; https://doi.org/10.3390/hydrology10020048. (IF 3.2)

This study present the results of long-term, optical RGB UAV monitoring of stream restoration projects to identify the positive and negative features that affect their sustainability. We determined quantitative and qualitative aspects of restoration, such as the restoration effect, the dynamics of fluvial processes, hydrological connectivity, and riparian vegetation. The study was based on six years of UAV monitoring in three restored streams in Prague, Czech Republic. The study pointed to the significant discrepancies in channel geometry between the planned restorations and realized restorations in all assessed projects and issues in qualitative aspects that limit restoration sustainability, such as water overuse, extensive eutrophication, or inefficient riparian shading.

LENDZIOCH, T., LANGHAMMER, J., VLČEK, L., MINAŘÍK, R., 2021. Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning. Remote Sensing, 13(5):907. (IF 5.0)

This paper presents a novel approach for using UAV thermal and multispectral imagery to enhance the spatio-temporal predictions of groundwater level and top-layer soil moisture of montane peat bogs using a machine learning model. Our research aimed to obtain information and predict the dynamic properties of groundwater level (GWL) and top-layer soil moisture (SM) in 2 dimensions and time based on data from digital surface models (DSMs), RGB, multispectral, and thermal data from drone imagery.We used a CAST spatiotemporal Machine Learning (ML) prediction model. For two seasons, we have launched UAV monitoring campaigns and field sampling of GWL and SM ground truth data at the Rokytka Peat bog within the Sumava Mountains, Czechia. Results showed that the ML model, using the UAV-derived datasets, delivers accurate predictions of GWL and SM spatial distribution.

MINAŘÍK, R., LANGHAMMER, J., LENDZIOCH, T., 2020.   Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests. Remote Sensing. 2020, 12, 4081. (IF 5.0)

In this study, we propose a method for individual tree crown delineation and feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud and a multispectral orthomosaic. An excess green index threshold mask was applied to separate targeted coniferous trees from deciduous trees and backgrounds and different algorithms for automated delineation of tree crowns were tested and statistically compared.  We found that for the accuracy of delineation, the density of the photogrammetric point cloud is more significant than the algorithm used. We conclude that the proposed automatic delineation workflow can substitute the time-consuming manual tree crown delineation, used for the detection and sanitation of individual infested trees in forests undergoing bark-beetle disturbance.

LANGHAMMER, J., BERNSTEINOVÁ, J., 2020. Which Aspects of Hydrological Regime in Mid-Latitude Montane Basins Are Affected by Climate Change?. Water, 12(8), 2279. (IF 3.4)

This study analyzed the long-term alterations in runoff regime, seasonality and variability in headwater montane basins in Central Europe in response to the manifestations of climate change. The analyses were done on eight montane catchments in four mid-latitude mountain ranges in Central Europe, based on the uninterrupted time series of daily discharge observations from 1952 to 2018.  We have proved the significant shifts in runoff seasonality, coinciding with the timing of the air temperature rise, marked by earlier snowmelt, followed by a decline in spring flows and a prolonged period of low flows. There was detected a rise in the baseflow index across the mountain ranges. Unlike the common hypotheses, the expected rise of runoff variability and frequency of peak flows was not demonstrated. However, we have identified a significant change of the flood hydrographs, tending to steeper shape with shorter recessing limbs as a sign of rising inner dynamics of flood events in montane catchments.

LANGHAMMER, J., 2019. UAV Monitoring of Stream Restorations. Hydrology, 6(2), 29; doi:10.3390/hydrology6020029 (IF 3.2)

This study examines the potential and limits of the unmanned aerial vehicles (UAVs) applicability for the monitoring of stream restoration in an urban environment. UAV imaging was used for long-term post-restoration monitoring of an urban stream. The recurrent imaging campaigns in the restored segment of Hostavicky brook in Prague, The Czech Republic, were undertaken for three years since the restoration. The UAV monitoring revealed that the new stream pattern substantially differs from the proposed restoration plan. Despite this, the new channel has proved stability, supported by intense grassing of the floodplain, resulting in only marginal evolution of the restored channel. The new channel proved the ability to mitigate the course of a significant flood event without significant flood spills outside the riparian zone. The UAV monitoring also revealed intense eutrophication in newly created shallow ponds with insufficient drainage. 

LENDZIOCH, T., LANGHAMMER, J., JENICEK, M.. 2019. Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry. Sensors 2019, 19(5), 1027; doi:10.3390/s19051027.  (IF 3.9)

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime.Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

LANGHAMMER, J., JANSKÝ, B., KOCUM, J., MINAŘÍK, R., 2018. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21.  (IF 4.9)

In this study, we used unmanned aerial vehicles (UAVs) to produce a detailed 3-D reconstruction of an abandoned montane reservoir that was built for timber flowing in the beginning of 19th century and that has not recently been used for any purpose. The UAV imaging and photogrammetric processing provided an ultra-high-resolution 3-D model of the reservoir basin (5?cm per pixel). Bathymetric analyses were performed based on this basin model to calculate the reservoir volume and flooded area for different water levels. The reliability of the UAV-based model was tested by comparing the results with those of elevation models derived from geodetic field survey using a total station and from conventional data sources based on available aerial LiDAR data. The data were compared to the historical estimates of the reservoir parameters found in the literature.Bathymetric reconstruction of the reservoir properties based on high-resolution UAV data revealed significant retention potential of the structure and historical underestimation of its capacity. The highly detailed UAV-based model helped to eliminate inaccuracies, resulting from the use of the generalized conventional elevation data, that affect the volumetric estimates in the flat topography of the reservoir basin.

LANGHAMMER, J., VACKOVÁ, T., 2018. Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry. Pure and Applied Geophysics. 175, 3223–3245 (2018). doi:10.1007/s00024-018-1874-1  (IF 2.0)

In this paper, we present a novel technique for the objective detection of the geomorphological effects of flooding in riverbeds and floodplains using imagery acquired by unmanned aerial vehicles (UAVs, also known as drones) equipped with a panchromatic camera. The proposed method is based on the fusion of the two key data products of UAV photogrammetry, the digital elevation model (DEM), and the orthoimage, as well as derived qualitative information, which together serves as the basis for object-based segmentation and the supervised classification of fluvial forms. The orthoimage is used to calculate textural features, enabling detection of the structural properties of the image area and supporting the differentiation of features with similar spectral responses but different surface structures. The DEM is used to derive a flood depth model and the terrain ruggedness index, supporting the detection of bank erosion. The newly derived information layers are merged into a multi-band data set, which is used for object-based segmentation and the supervised classification of principal fluvial forms resulting from flooding, i.e., fresh and old gravel accumulations, sand accumulations, and bank erosion. The method was tested on the effects of a snowmelt flood that occurred in December 2015 in a montane stream in the Sumava Mountains, Czech Republic, Central Europe.

LANGHAMMER, J., BERNSTEINOVÁ, J., MIŘIJOVSKÝ, J., 2017. Building a High-Precision 2D Hydrodynamic Flood Model Using UAV Photogrammetry and Sensor Network Monitoring. Water, 9, 861. doi:10.3390/w9110861  (IF 3.4)

This paper explores the potential of the joint application of unmanned aerial vehicle (UAV)-based photogrammetry and an automated sensor network for building a hydrodynamic flood model of a montane stream. UAV-based imagery was used for three-dimensional (3D) photogrammetric reconstruction of the stream channel, achieving a resolution of 1.5 cm/pixel. Automated ultrasonic water level gauges, operating with a 10 min interval, were used as a source of hydrological data for the model calibration, and the MIKE 21 hydrodynamic model was used for building the flood model. Three different horizontal schematizations of the channel—an orthogonal grid, curvilinear grid, and flexible mesh—were used to evaluate the effect of spatial discretization on the results. The research was performed on Javori Brook, a montane stream in the Sumava (Bohemian Forest) Mountains, Czech Republic, Central Europe, featuring a fast runoff response to precipitation events and that is located in a core zone of frequent flooding. The studied catchments have been, since 2007, equipped with automated water level gauges and, since 2013, under repeated UAV monitoring. The study revealed the high potential of these data sources for applications in hydrodynamic modeling. In addition to the ultra-high levels of spatial and temporal resolution, the major contribution is in the method’s high operability, enabling the building of highly detailed flood models even in remote areas lacking conventional monitoring.

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