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Special Issue "UAV Photogrammetry for Environmental Monitoring"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 21649

Special Issue Editors

Dr. Javier Cardenal Escarcena
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Guest Editor
Department of Cartographic, Geodetic and Photogrammetric Engineering, University of Jaén, 23071 Jaén, Spain
Interests: photogrammetry; close range; environmental monitoring; landslides; cultural and natural heritage; UAV
Prof. Dr. Jorge Delgado García
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Guest Editor
Department of Cartographic, Geodetic and Photogrammetric Engineering, University of Jaén, 23071 Jaén, Spain
Interests: geomatics; photogrammetry; remote sensing; geostatistics; LiDAR; RPAS; 3D modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Joaquim João Sousa
E-Mail Website
Guest Editor
Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: UAV; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental monitoring refers to processes and actions conducted to describe and monitor the status of an environment. It is a crucial aspect in any analysis of environmental impact assessment. Monitoring involves different methods of data acquisition and processing, for which it may be challenging to optimize the workflow design, the accuracy, effectiveness, cost, etc. Although geomatic techniques have been extensively used in environmental monitoring with success, unmanned aerial vehicle (UAV) photogrammetry is becoming an optimum solution for small–medium-sized areas where very high resolution, both spatial and temporal, is needed. UAV photogrammetry is filling a gap in the spatial data collection methods ranging from space remote sensing to conventional airborne and terrestrial techniques.

At present, unmanned aerial systems have evolved into a mature technology. The miniaturization of components (GNSS/INS), developments in carrier platforms, ground control stations and communication data links, autopilot systems, new sensors (RGB, multi- and hyperspectral, thermal, LiDAR, etc.), falling prices, as well as new developments in data processing (mainly SfM/MVS), have opened a broad range of new applications in environmental monitoring.

This Special Issue seeks innovative and well-documented articles where UAV-based data/photogrammetry are/is used in the field of environmental monitoring. Submitted manuscripts may cover, although not limited to, topics related to: novel systems and methods for data acquisition (passive and active sensors, multi-sensor approaches); georeferencing (indirect vs. direct orientation); point cloud generation and processing; DSM/DTM analysis; orthoimagery and 4D modelling; and spatial–time evolution for environmental applications (catastrophes, hazards, erosion, floods, landslides, coastal monitoring, glaciology, change detection, forestry, natural heritage preservation, fauna and flora monitoring and identification).

Dr. Javier Cardenal Escarcena
Dr. Jorge Delgado García
Dr. Joaquim João Moreira de Sousa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV photogrammetry
  • Environmental mapping and monitoring
  • Environmental applications
  • Georeferencing
  • Environmental research
  • Natural hazards
  • Natural heritage
  • 4D modelling
  • Change detection

Published Papers (10 papers)

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Research

Article
The Influence of Image Properties on High-Detail SfM Photogrammetric Surveys of Complex Geometric Landforms: The Application of a Consumer-Grade UAV Camera in a Rock Glacier Survey
Remote Sens. 2022, 14(15), 3528; https://doi.org/10.3390/rs14153528 - 23 Jul 2022
Viewed by 1104
Abstract
The detailed description of processing workflows in Structure from Motion (SfM) surveys using unmanned aerial vehicles (UAVs) is not common in geomorphological research. One of the aspects frequently overlooked in photogrammetric reconstruction is image characteristics. In this context, the present study aims to [...] Read more.
The detailed description of processing workflows in Structure from Motion (SfM) surveys using unmanned aerial vehicles (UAVs) is not common in geomorphological research. One of the aspects frequently overlooked in photogrammetric reconstruction is image characteristics. In this context, the present study aims to determine whether the format or properties (e.g., exposure, sharpening, lens corrections) of the images used in the SfM process can affect high-detail surveys of complex geometric landforms such as rock glaciers. For this purpose, images generated (DNG and JPEG) and derived (TIFF) from low-cost UAV systems widely used by the scientific community are applied. The case study is carried out through a comprehensive flight plan with ground control and differences among surveys are assessed visually and geometrically. Thus, geometric evaluation is based on 2.5D and 3D perspectives and a ground-based LiDAR benchmark. The results show that the lens profiles applied by some low-cost UAV cameras to the images can significantly alter the geometry among photo-reconstructions, to the extent that they can influence monitoring activities with variations of around ±5 cm in areas with close control and over ±20 cm (10 times the ground sample distance) on surfaces outside the ground control surroundings. The terrestrial position of the laser scanner measurements and the scene changing topography results in uneven surface sampling, which makes it challenging to determine which set of images best fit the LiDAR benchmark. Other effects of the image properties are found in minor variations scattered throughout the survey or modifications to the RGB values of the point clouds or orthomosaics, with no critical impact on geomorphological studies. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Monitoring Light Pollution with an Unmanned Aerial Vehicle: A Case Study Comparing RGB Images and Night Ground Brightness
Remote Sens. 2022, 14(9), 2052; https://doi.org/10.3390/rs14092052 - 25 Apr 2022
Viewed by 817
Abstract
There are several tools and methods to quantify light pollution due to direct or reflected light emitted towards the sky. Unmanned aerial vehicles (UAV) are still rarely used in light pollution studies. In this study, a digital camera and a sky quality meter [...] Read more.
There are several tools and methods to quantify light pollution due to direct or reflected light emitted towards the sky. Unmanned aerial vehicles (UAV) are still rarely used in light pollution studies. In this study, a digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken simultaneously during flights at an altitude of 70 and 100 m, and with varying exposure time. NGB correlated significantly both with the brightness index (−0.49 ÷ −0.56) and with red (−0.52 ÷ −0.58) and green band indices (−0.42 ÷ −0.58). A linear regression model based on the luminous intensity index was able to estimate observed NGB with an RMSE varying between 0.21 and 0.46 mpsas. Multispectral analysis applied to images taken at 70 m showed that increasing exposure time might cause a saturation of the colors of the image, especially in the red band, that worsens the correlation between image indices and NGB. Our study suggests that the combined use of low cost devices such as UAV and a sky quality meter can be used for assessing hotspot areas of light pollution originating from the surface. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images
Remote Sens. 2022, 14(6), 1337; https://doi.org/10.3390/rs14061337 - 09 Mar 2022
Cited by 5 | Viewed by 1549
Abstract
Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a [...] Read more.
Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier
Remote Sens. 2022, 14(1), 28; https://doi.org/10.3390/rs14010028 - 22 Dec 2021
Cited by 3 | Viewed by 1909
Abstract
Recently, Unmanned Aerial Vehicles (UAV) have opened up unparalleled opportunities for alpine glacier monitoring, as they allow for reconstructing extensive and high-resolution 3D models. In order to evaluate annual ice flow velocities and volume variations, six yearly measurements were carried out between 2015 [...] Read more.
Recently, Unmanned Aerial Vehicles (UAV) have opened up unparalleled opportunities for alpine glacier monitoring, as they allow for reconstructing extensive and high-resolution 3D models. In order to evaluate annual ice flow velocities and volume variations, six yearly measurements were carried out between 2015 and 2020 on the debris-covered Belvedere Glacier (Anzasca Valley, Italian Alps) with low-cost fixed-wing UAVs and quadcopters. Every year, ground control points and check points were measured with GNSS. Images acquired from UAV were processed with Structure-from-Motion and Multi-View Stereo algorithms to build photogrammetric models, orthophotos and digital surface models, with decimetric accuracy. Annual glacier velocities were derived by combining manually-tracked features on orthophotos with GNSS measurements. Velocities ranging between 17 m y−1 and 22 m y−1 were found in the central part of the glacier, whereas values between 2 m y−1 and 7 m y−1 were found in the accumulation area and at the glacier terminus. Between 2 × 106 m3 and 3.5 × 106 m3 of ice volume were lost every year. A pair of intra-year measurements (October 2017–July 2018) highlighted that winter and spring volume reduction was ∼1/4 of the average annual ice loss. The Belvedere monitoring activity proved that decimetric-accurate glacier models can be derived with low-cost UAVs and photogrammetry, limiting in-situ operations. Moreover, UAVs require minimal data acquisition costs and allow for great surveying flexibility, compared to traditional techniques. Information about annual flow velocities and ice volume variations of the Belvedere Glacier may have great value for further understanding glacier dynamics, compute mass balances, or it might be used as input for glacier flow modelling. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets
Remote Sens. 2021, 13(16), 3238; https://doi.org/10.3390/rs13163238 - 15 Aug 2021
Cited by 5 | Viewed by 1427
Abstract
The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various [...] Read more.
The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Comparing 3D Point Cloud Data from Laser Scanning and Digital Aerial Photogrammetry for Height Estimation of Small Trees and Other Vegetation in a Boreal–Alpine Ecotone
Remote Sens. 2021, 13(13), 2469; https://doi.org/10.3390/rs13132469 - 24 Jun 2021
Cited by 1 | Viewed by 1117
Abstract
Changes in vegetation height in the boreal-alpine ecotone are expected over the coming decades due to climate change. Previous studies have shown that subtle changes in vegetation height (<0.2 m) can be estimated with great precision over short time periods (~5 yrs) for [...] Read more.
Changes in vegetation height in the boreal-alpine ecotone are expected over the coming decades due to climate change. Previous studies have shown that subtle changes in vegetation height (<0.2 m) can be estimated with great precision over short time periods (~5 yrs) for small spatial units (~1 ha) utilizing bi-temporal airborne laser scanning (ALS) data, which is promising for operation vegetation monitoring. However, ALS data may not always be available for multi-temporal analysis and other tree-dimensional (3D) data such as those produced by digital aerial photogrammetry (DAP) using imagery acquired from aircrafts and unmanned aerial systems (UAS) may add flexibility to an operational monitoring program. There is little existing evidence on the performance of DAP for height estimation of alpine pioneer trees and vegetation in the boreal-alpine ecotone. The current study assessed and compared the performance of 3D data extracted from ALS and from UAS DAP for prediction of tree height of small pioneer trees and evaluated how tree size and tree species affected the predictive ability of data from the two 3D data sources. Further, precision of vegetation height estimates (trees and other vegetation) across a 12 ha study area using 3D data from ALS and from UAS DAP were compared. Major findings showed smaller regression model residuals for vegetation height when using ALS data and that small and solitary trees tended to be smoothed out in DAP data. Surprisingly, the overall vegetation height estimates using ALS (0.64 m) and DAP data (0.76 m), respectively, differed significantly, despite the use of the same ground observations for model calibration. It was concluded that more in-depth understanding of the behavior of DAP algorithms for small scattered trees and low ground vegetation in the boreal-alpine ecotone is needed as even small systematic effects of a particular technology on height estimates may compromise the validity of a monitoring system since change processes encountered in the boreal-alpine ecotone often are subtle and slow. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops
Remote Sens. 2021, 13(9), 1704; https://doi.org/10.3390/rs13091704 - 28 Apr 2021
Cited by 8 | Viewed by 2213
Abstract
Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the [...] Read more.
Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h−1 area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing
Remote Sens. 2021, 13(8), 1597; https://doi.org/10.3390/rs13081597 - 20 Apr 2021
Cited by 14 | Viewed by 5448
Abstract
The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different [...] Read more.
The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
An Infrared Thermography Approach to Evaluate the Strength of a Rock Cliff
Remote Sens. 2021, 13(7), 1265; https://doi.org/10.3390/rs13071265 - 26 Mar 2021
Cited by 9 | Viewed by 2154
Abstract
The mechanical strength is a fundamental characteristic of rock masses that can be empirically related to a number of properties and to the likelihood of instability phenomena. Direct field acquisition of mechanical information on tall cliffs, however, is challenging, particularly in coastal and [...] Read more.
The mechanical strength is a fundamental characteristic of rock masses that can be empirically related to a number of properties and to the likelihood of instability phenomena. Direct field acquisition of mechanical information on tall cliffs, however, is challenging, particularly in coastal and alpine environments. Here, we propose a method to evaluate the compressive strength of rock blocks by monitoring their thermal behaviour over a 24-h period by infrared thermography. Using a drone-mounted thermal camera and a Schmidt (rebound) hammer, we surveyed granitoid and aphanitic blocks in a coastal cliff in south-east Sardinia, Italy. We observed a strong correlation between a simple cooling index, evaluated in the hours succeeding the temperature peak, and strength values estimated from rebound hammer test results. We also noticed different heating-cooling patterns in relation to the nature and structure of the rock blocks and to the size of the fractures. Although further validation is warranted in different morpho-lithological settings, we believe the proposed method may prove a valid tool for the characterisation of non-directly accessible rock faces, and may serve as a basis for the formulation, calibration, and validation of thermo-hydro-mechanical constitutive models. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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Article
Combining UAV-Based SfM-MVS Photogrammetry with Conventional Monitoring to Set Environmental Flows: Modifying Dam Flushing Flows to Improve Alpine Stream Habitat
Remote Sens. 2020, 12(23), 3868; https://doi.org/10.3390/rs12233868 - 25 Nov 2020
Cited by 5 | Viewed by 2389
Abstract
Setting environmental flows downstream of hydropower dams is widely recognized as important, particularly in Alpine regions. However, the required flows are strongly influenced by the effects of the physical environment of the downstream river. Here, we show how unmanned aerial vehicle (UAV)-based structure-from-motion [...] Read more.
Setting environmental flows downstream of hydropower dams is widely recognized as important, particularly in Alpine regions. However, the required flows are strongly influenced by the effects of the physical environment of the downstream river. Here, we show how unmanned aerial vehicle (UAV)-based structure-from-motion multiview stereo (SfM-MVS) photogrammetry allows for incorporation of such effects through determination of spatially distributed patterns of key physical parameters (e.g., bed shear stress, bed grain size) and how they condition available stream habitat. This is illustrated for a dam-impacted Alpine stream, testing whether modification of the dam’s annual flushing flow could achieve the desired downstream environmental improvement. In detail, we found that (1) flood peaks in the pilot study were larger than needed, (2) only a single flood peak was necessary, (3) sediment coarsening was likely being impacted by supply from nonregulated tributaries, often overlooked, and (4) a lower-magnitude but longer-duration rinsing flow after flushing is valuable for the system. These findings were enabled by the spatially rich geospatial datasets produced by UAV-based SfM-MVS photogrammetry. Both modeling of river erosion and deposition and river habitat may be revolutionized by these developments in remote sensing. However, it is combination with more traditional and temporarily rich monitoring that allows their full potential to be realized. Full article
(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)
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