Unmanned aerial vehicles and low-cost sensor as tools for monitoring freshwater chlorophyll-a in mesocosms with different trophic state

被引:5
作者
Cobelo, I. [1 ]
Machado, K. B. [1 ]
David, A. C. M. [1 ]
Carvalho, P. [2 ]
Ferreira, M. E. [2 ]
Nabout, J. C. [1 ]
机构
[1] Univ Estadual de Goias UEG, Campus Cent,BR 153, BR-75132903 Anapolis, Go, Brazil
[2] Univ Fed de Goias UFG, Campus Samambaia,Av Esperanca,s n, BR-74690900 Goiania, Go, Brazil
关键词
Eutrophication; Phytoplankton; Vegetation indices; Remote sensing; UAV; UAV; EUTROPHICATION; ECOSYSTEMS; VEGETATION; INDEXES; COASTAL; BLOOMS; COLOR;
D O I
10.1007/s13762-022-04386-3
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Eutrophication is one of the leading causes of compromising the quality of freshwater and marine ecosystems, where the concentration of chlorophyll-a is an essential variable to monitoring the water quality. Moreover, monitoring in situ chlorophyll-a require constants samplings, high laboratory, and logistics costs, and sometimes in regions not accessible. Therefore, new technology can be important to increase the monitoring the water quality. In this sense, this work aimed to evaluate remote sensing techniques, from unmanned aerial vehicles (UAV), onboard low-cost sensors (RGB), to determine chlorophyll-a in aquatic environments. The experiment consisted of 26 mesocosms, where phytoplankton samples were inserted, simulating small shallow lakes, with gradual additions of nitrogen and phosphorus, until a trophic gradient was obtained. Subsequently, in situ concentrations of chlorophyll-a and aerial images with the UAV were obtained. The images were processed to generate orthorectified mosaics and calculate eight vegetation indices (NGBDI, SI, NGRDI, SCI, VWRI, GLI, EXG, and VARI) by which simple linear regressions were adjusted as a function of chlorophyll-a concentrations. All indexes were able to detect the gradient of chlorophyll-a, and the best index was NGBDI (R-2 = 0.88). The vegetation indices already used in aquatic environments showed greater efficiency in detecting chlorophyll-a in situ. Therefore, our results indicated that monitoring water quality for the evaluated parameters could be carried out by remotely piloted aircraft, onboard with standard RGB cameras, with faster, simpler, and lower cost protocols.
引用
收藏
页码:5925 / 5936
页数:12
相关论文
共 61 条
[41]  
RAMADAS M, 2017, WATER REMEDIATION
[42]  
Reynolds CS, 2006, ECOL BIODIVERS CONS, P1
[43]  
Rice E, 2017, Standard methods for the Examination of Water and Wastewater
[44]   Remote sensing techniques to assess water quality [J].
Ritchie, JC ;
Zimba, PV ;
Everitt, JH .
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2003, 69 (06) :695-704
[45]  
Sanseverino I., 2016, Algal Bloom and its Economic Impact, DOI [10.2788/660478, DOI 10.2788/660478]
[46]  
Schalles J., 2006, Remote Sensing of Aquatic Coastal Ecosystem Processes, DOI DOI 10.1007/1-4020-3968-9_3
[47]  
Shiraishi Haruhiro., 2018, INT J ENG TECHNOLOGY, V18, P10
[48]   Eutrophication of freshwater and coastal marine ecosystems - A global problem [J].
Smith, VH .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2003, 10 (02) :126-139
[49]   Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index [J].
Song, Bonggeun ;
Park, Kyunghun .
REMOTE SENSING, 2020, 12 (03)
[50]   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 [J].
Su, Tung-Ching ;
Chou, Hung-Ta .
REMOTE SENSING, 2015, 7 (08) :10078-10097