Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)

Gerardo, Romeu; Lima, Isabel P. de

doi:10.3390/w14152284

Cited by 12 publications

(7 citation statements)

References 66 publications

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“…The GRVI is sensitive to leaf color changes (chlorophyll and fall coloration) and can be used to differentiate green vegetation, water, and soil [ 60 ]. The GRVI uses the high reflectivity of plants in green (about 540 nm) and the absorption of the red and blue parts of the visible spectrum (400–700 nm) by plant chlorophyll to identify vegetation [ 61 ].…”

Section: Methodsmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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Background Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features. Results In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes. Conclusions In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

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Section: Methodsmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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“…Lemna sp. grows quickly, is easy to harvest, and many scientists favor it for phytoremediation research [37][38][39]. Among aquatic macrophytes, it is the most efficient plant for eliminating metals and pesticides due to its rapid growth and ability to float on water [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

“…Among aquatic macrophytes, it is the most efficient plant for eliminating metals and pesticides due to its rapid growth and ability to float on water [40][41][42]. It also grows well in various climates and has a long storage capacity, quick reproduction rate, low cost, and little volume of biological and chemical sludge [39,[42][43][44]. According to Khataee et al [43], the ideal temperature and pH ranges for Lemna sp.…”

Section: Introductionmentioning

confidence: 99%

Accumulation Assessment of Mo4+, Pb++, and Cu++ in the Acidic Water of Copper Mines with Lemna minor and Lemna gibba

Konakci

2024

Water

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Pollutants accumulate in aquatic habitats due to mining activities. The duckweed family includes water plants such as Lemna gibba and Lemna minor, which are tiny, delicate, free-floating aquatic plants. L. minor and L. gibba were used in this study to examine the accumulation capacities of Mo4+, Pb++, and Cu++ in acidic fluids from copper mining. Two reactors were assigned to L. gibba and L. minor, respectively. These plants and the reactor water were gathered daily for 8 days. Acid mine water pH, temperature, and electric conductivity were also tested daily. L. gibba and L. minor were cleaned, dehydrated, and burned in a drying oven for a whole day at 300 °C. ICP-MS (inductively coupled plasma mass spectroscopy) was used to determine the Mo4+, Pb++, and Cu++ content of the plant and water samples. The Mo, Pb, and Cu concentrations in the copper mining acidic fluids were 30 ± 4, 260 ± 12, and 15,535 ± 322 μg L−1, respectively. Regarding Mo, Pb, and Cu extraction from copper mining acidic fluids, L. gibba and L. minor performed more efficiently than control samples, gathering 29 and 177 times more Mo, 30 and 109 times more Pb, and 495 and 1150 times more Cu, respectively. Considering these findings, L. gibba and L. minor are good plants for rehabilitating polluted waters and can efficiently remove Mo, Pb, and Cu from acid mine fluids.

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“…Because Lemna sp. grows more quickly and is easier to harvest, many scientists favor it for phytoremediation research [25][26][27]. Among aquatic macrophytes, it is the most efficient plant in eliminating metals and pesticides due to its rapid growth and ability to float on the water [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Among aquatic macrophytes, it is the most efficient plant in eliminating metals and pesticides due to its rapid growth and ability to float on the water [28][29][30]. It also grows well in a variety of climates, has a long storage capacity, quick reproduction rates, low cost, and little volume of biological and chemical sludge [27,[30][31][32]. The ideal temperature and pH ranges for Lemna sp.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Accumulation Performance of Mo, Pb, and Cu in the Acidic Water of Copper Mine with Lemna minor and Lemna gibba

Konakcı

2024

Preprint

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Pollutants accumulate in aquatic habitats as a result of mining activities. Lemna gibba and Lemna minor were used in this study to examine the accumulation capacities of Mo, Pb, and Cu in the acid fluids of the copper mining. Duckweed family includes water plants like L. minor and L. gibba. These are tiny, delicate, free-floating aquatic plants. Two reactors were assigned to L. gibba and L. minor, respectively. Throughout the course of eight days, these plants and reactor water were gathered every day. The acid mine water's pH, temperature, and electric conductivity were also tested every day. The L. gibba and L. minor were cleaned, dehydrated, and burned in a drying oven for a whole day at 300 oC. ICP-MS (Inductively Coupled Plasma Mass Spectroscopy) was used to determine the Mo, Pb, and Cu content of the plant and water samples. The concentrations of Mo, Pb, and Cu in the acid mine fluids of the copper mining were 30±4, 260±12, and 15535±322 μg L−1, in that order. When it came to extracting Mo, Pb, and Cu from the acidic fluids of the copper mining, L. gibba and L. minor performed well and efficiently compared to control samples; they did so 29 and 177 times for Mo, 30 and 109 times for Pb, and 495 and 1150 times for Cu. All things considered, the findings point to the possible application of L. gibba and L. minor in environmental rehabilitation by demonstrating their ability to efficiently extract Mo, Pb, and Cu from acid mine fluids.

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Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)

Cited by 12 publications

References 66 publications

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

Accumulation Assessment of Mo4+, Pb++, and Cu++ in the Acidic Water of Copper Mines with Lemna minor and Lemna gibba

Assessment of the Accumulation Performance of Mo, Pb, and Cu in the Acidic Water of Copper Mine with Lemna minor and Lemna gibba

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