Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs

The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg) onboard a DJI Phantom 4 UAV (~1.4 kg) during dry and rainy seasons over inlet areas of both reservoirs. Field measurements of total suspended solids (TSS) and water clarity were made jointly with the airborne survey campaigns. Field hyperspectral radiometry data were also collected during two field surveys. Bio-optical models for TSS were tested for all spectral bands of the Sequoia camera. The near-infrared single band was found to perform the best (R2: 0.94; RMSE: 7.8 mg L−1) for a 0–180 mg L−1 TSS range and was used to produce time series of TSS concentration maps of the study areas. This flexible platform enabled monitoring of the increase of TSS concentration at a ~13 cm spatial resolution in urban and rural drainages in the rainy season. Aerial surveys allowed us to map TSS load fluctuations in a 1 week period during which no satellite images were available due to continuous cloud coverage in the rainy season. This work demonstrates that a low-cost configuration allows dense TSS monitoring at the inlet areas of reservoirs and thus enables mapping of the sources of sediment inputs, supporting the definition of mitigation plans to limit the siltation process.

[1]  Can Huang,et al.  Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery , 2019, Remote. Sens..

[2]  B. Nechad,et al.  Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters , 2010 .

[3]  Tung-Ching Su,et al.  A study of a matching pixel by pixel (MPP) algorithm to establish an empirical model of water quality mapping, as based on unmanned aerial vehicle (UAV) images , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Assefa M. Melesse,et al.  A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques , 2016, Sensors.

[5]  K. Ruddick,et al.  Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters , 2006 .

[6]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[7]  M. Bauer,et al.  Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota. , 2013 .

[8]  M. Présing,et al.  Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations , 2005 .

[9]  Igor Ogashawara,et al.  Terminology and classification of bio-optical algorithms , 2015 .

[10]  Martti Hallikainen,et al.  A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland , 2007 .

[11]  G. Boaventura,et al.  Biogeochemical mechanisms controlling trophic state and micropollutant concentrations in a tropical artificial lake , 2016, Environmental Earth Sciences.

[12]  J. Pulliainen,et al.  Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons. , 2001, The Science of the total environment.

[13]  Stephanie C. J. Palmer,et al.  Remote sensing of inland waters: Challenges, progress and future directions , 2015 .

[14]  Lino Augusto Sander de Carvalho,et al.  Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes , 2017, Remote. Sens..

[15]  Eija Honkavaara,et al.  Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows , 2018, Remote. Sens..

[16]  H. Roig,et al.  Monitoring cyanobacteria occurrence in freshwater reservoirs using semi-analytical algorithms and orbital remote sensing , 2020 .

[17]  Tiit Kutser,et al.  Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters , 2016, Remote. Sens..

[18]  Carl J. Legleiter,et al.  Removing sun glint from optical remote sensing images of shallow rivers , 2017 .

[19]  R. Doerffer,et al.  Imaging Spectrometry of Inland and Coastal Waters: State of the Art, Achievements and Perspectives , 2018, Surveys in Geophysics.

[20]  David Doxaran,et al.  Monitoring the maximum turbidity zone and detecting fine‐scale turbidity features in the Gironde estuary using high spatial resolution satellite sensor (SPOT HRV, Landsat ETM+) data , 2006 .

[21]  Xavier Pons,et al.  Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery. , 2019, The Science of the total environment.

[22]  Jean-Michel Martinez,et al.  A study of sediment transport in the Madeira River, Brazil, using MODIS remote-sensing images , 2013 .

[23]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[24]  N. Oppelt,et al.  Remote sensing for lake research and monitoring – Recent advances , 2016 .

[25]  Arnold G. Dekker,et al.  Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data , 2002 .

[26]  Hiroshi Nakano,et al.  Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs , 2019, Remote. Sens..

[27]  Tung-Ching Su,et al.  Application of Multispectral Sensors Carried on Unmanned Aerial Vehicle (UAV) to Trophic State Mapping of Small Reservoirs: A Case Study of Tain-Pu Reservoir in Kinmen, Taiwan , 2015, Remote. Sens..

[28]  Hyuk Lee,et al.  High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery , 2018, Remote. Sens..

[29]  M. Matthews.  A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters , 2011 .

[30]  Raul Espinoza-Villar,et al.  The optical properties of river and floodplain waters in the Amazon River Basin: Implications for satellite‐based measurements of suspended particulate matter , 2015 .

[31]  M. Schaepman,et al.  Review of constituent retrieval in optically deep and complex waters from satellite imagery , 2012 .

[32]  F. F. Sodré,et al.  Development and application of a SPE-LC-QTOF method for the quantification of micropollutants of emerging concern in drinking waters from the Brazilian capital , 2020 .

[33]  M. Kelly,et al.  UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities , 2018, Drones.

[34]  Daniel Clewley,et al.  Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover , 2016, Remote. Sens..

[35]  Philippe Vauchel,et al.  Spatio-temporal monitoring of suspended sediments in the Solimões River (2000–2014) , 2017 .