Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams

Defining stream channels in a watershed is important for assessing freshwater habitat availability, complexity, and quality. However, mapping channels of small tributary streams becomes challenging due to frequent channel change and dense vegetation coverage. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to obtain a 3D Digital Surface Model (DSM) to estimate the total in-stream channel and channel width within grazed riparian pastures. We used two methods to predict the stream channel boundary: the Slope Gradient (SG) and Vertical Slope Position (VSP). As a comparison, the same methods were also applied using low-resolution DEM, obtained with traditional photogrammetry (coarse resolution) and two more LiDAR-derived DEMs with different resolution. When using the SG method, the higher-resolution, UAV-derived DEM provided the best agreement with the field-validated area followed by the high-resolution LiDAR DEM, with Mean Squared Errors (MSE) of 1.81 m and 1.91 m, respectively. The LiDAR DEM collected at low resolution was able to predict the stream channel with a MSE of 3.33 m. Finally, the coarse DEM did not perform accurately and the MSE obtained was 26.76 m. On the other hand, when the VSP method was used we found that low-resolution LiDAR DEM performed the best followed by high-resolution LiDAR, with MSE values of 9.70 and 11.45 m, respectively. The MSE for the UAV-derived DEM was 15.12 m and for the coarse DEM was 20.78 m. We found that the UAV-derived DEM could be used to identify steep bank which could be used for mapping the hydrogeomorphology of lower order streams. Therefore, UAVs could be applied to efficiently map small stream channels in order to monitor the dynamic changes occurring in these ecosystems at a local scale. However, the VSP method should be used to map stream channels in small watersheds when high resolution DEM data is not available.

[1]  Garry R. Willgoose,et al.  Effects of vertical resolution and map scale of digital elevation models on geomorphological parameters used in hydrology , 1995 .

[2]  Theodora Lendzioch,et al.  Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning , 2021, Remote. Sens..

[3]  Tomislav Hengl,et al.  Finding the right pixel size , 2006, Comput. Geosci..

[4]  Fan-Rui Meng,et al.  Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method , 2021, Remote. Sens..

[5]  Aaron A. Berg,et al.  Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction , 2016 .

[6]  Qian Zhang,et al.  Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile , 2015, Remote. Sens..

[7]  H. Kling,et al.  Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios , 2012 .

[8]  Chen Shi,et al.  Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Gary Thompson,et al.  Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina , 2018, Sensors.

[10]  A. Brzank.  Classification of lidar data into water and land points in coastal areas , 2006 .

[11]  D. Hannah,et al.  What causes cooling water temperature gradients in a forested stream reach , 2014 .

[12]  Guy J.-P. Schumann,et al.  Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory , 2019, Remote. Sens..

[13]  D. Caissie.  The thermal regime of rivers : a review , 2006 .

[14]  David R. Montgomery,et al.  A process-based model for colluvial soil depth and shallow landsliding using digital elevation data , 1995 .

[15]  Luis Iglesias Martínez,et al.  DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations , 2019, Sensors.

[16]  G. Hancock.  The use of digital elevation models in the identification and characterization of catchments over different grid scales , 2005 .

[17]  Giorgos Mallinis,et al.  On the Use of Unmanned Aerial Systems for Environmental Monitoring , 2018, Remote. Sens..

[18]  Mark A. Fonstad,et al.  Topographic structure from motion: a new development in photogrammetric measurement , 2013 .

[19]  Lawrence W. Martz,et al.  Grid size dependency of parameters extracted from digital elevation models , 1994 .

[20]  K. Emanuel.  Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century , 2013, Proceedings of the National Academy of Sciences.

[21]  S. Grimaldi,et al.  Riparian vegetation as a marker for bankfull and management discharge evaluation: The case study of Rio Torbido river basin (central Italy) , 2021 .

[22]  Helena Mitasova,et al.  Fusion of high-resolution DEMs for water flow modeling , 2017, Open Geospatial Data, Software and Standards.

[23]  M. Walter,et al.  Evaluating topographic wetness indices across central New York agricultural landscapes , 2013 .

[24]  K. Brooks,et al.  Grazed Riparian Management and Stream Channel Response in Southeastern Minnesota (USA) Streams , 2008, Environmental management.

[25]  R. Foy,et al.  Approaches to the implementation of the Water Framework Directive: targeting mitigation measures at critical source areas of diffuse phosphorus in Irish catchments. , 2012, Journal of environmental management.

[26]  Gregory J. McDermid,et al.  A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles , 2017, Remote. Sens..

[27]  Mohammad Sadegh Taskhiri,et al.  Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement , 2020, Remote. Sens..

[28]  Thibault Mathevet,et al.  Hydrology under change: an evaluation protocol to investigate how hydrological models deal with changing catchments , 2015 .

[29]  Xiaoyu Chen,et al.  Unmanned Aerial Vehicle for Remote Sensing Applications - A Review , 2019, Remote. Sens..

[30]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[31]  A. Moldenke,et al.  Headwater riparian forest-floor invertebrate communities associated with alternative forest management practices. , 2007, Ecological applications : a publication of the Ecological Society of America.

[32]  B. Loučková.  VEGETATION–LANDFORM ASSEMBLAGES ALONG SELECTED RIVERS IN THE CZECH REPUBLIC, A DECADE AFTER A 500‐YEAR FLOOD EVENT , 2012 .

[33]  G. Zaimes,et al.  Riparian Land-Use Impacts on Stream Bank and Gully Erosion in Agricultural Watersheds: What We Have Learned , 2019, Water.

[34]  Anne E. Thessen,et al.  Adoption of Machine Learning Techniques in Ecology and Earth Science , 2016 .

[35]  E. Addink,et al.  Monitoring height and greenness of non-woody floodplain vegetation with UAV time series , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[36]  Abderrahim Nemmaoui,et al.  UAV-Based Digital Terrain Model Generation under Leaf-Off Conditions to Support Teak Plantations Inventories in Tropical Dry Forests. A Case of the Coastal Region of Ecuador , 2019, Sensors.

[37]  C. Gibson,et al.  Sediment loads and sources in heavily modified Irish catchments: A move towards informed management strategies , 2006 .

[38]  Peter Surový,et al.  Mapping Forest Structure Using UAS inside Flight Capabilities , 2018, Sensors.

[39]  P.-E. Mellander,et al.  Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[40]  F. Castelli,et al.  Advances in remote sensing of hydraulic roughness , 2012 .

[41]  J. Barquín,et al.  Quantifying the performance of automated GIS-based geomorphological approaches for riparian zone delineation using digital elevation models , 2012 .

[42]  Shengyang Chen,et al.  A new method to improve the accuracy of remotely sensed data for wetland water balance estimates , 2020, Journal of Hydrology: Regional Studies.

[43]  Z. Easton,et al.  Hydrological impact of roadside ditches in an agricultural watershed in Central New York: implications for non‐point source pollutant transport , 2013 .

[44]  J. Seibert,et al.  Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency , 2018, Hydrological Sciences Journal.

[45]  M. Wipfli.  Terrestrial invertebrates as salmonid prey and nitrogen sources in streams: contrasting old-growth and young-growth riparian forests in southeastern Alaska, U.S.A. , 1997 .

[46]  J. Seibert,et al.  Effects of DEM resolution on the calculation of topographical indices: TWI and its components , 2007 .

[47]  Jeroen M. Schoorl,et al.  Mapping hydrological pathways of phosphorus transfer in apparently homogeneous landscapes using a high - resolution DEM , 2006 .

[48]  Justyna Jeziorska,et al.  UAS for Wetland Mapping and Hydrological Modeling , 2019, Remote. Sens..

[49]  Jae Ogilvie,et al.  Topographic modelling of soil moisture conditions: a comparison and verification of two models , 2009 .

[50]  Swapan Talukdar,et al.  Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh , 2021, Environmental Science and Pollution Research.

[51]  Riparian Land-Use, Stream Morphology and Streambank Erosion within Grazed Pastures in Southern Iowa, USA: A Catchment-Wide Perspective , 2020 .

[52]  R. Chorley,et al.  Cartographic problems in stream channel delineation , 1972 .

[53]  P. Martínez-Santos,et al.  Predictive mapping of aquatic ecosystems by means of support vector machines and random forests , 2021 .

[54]  Siva Kumar Balasundram,et al.  Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Oil Palm Plantations , 2018, Agricultural Robots - Fundamentals and Applications.

[55]  Ian Manning,et al.  Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR , 2020, Remote. Sens..

[56]  H. Rauch,et al.  The influence of riparian vegetation shading on water temperature during low flow conditions in a medium sized river , 2017 .