A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data

Lu, Lirong; Luo, Juhua; Xin, Yihao; Xu, Ying; Sun, Zhe; Duan, Hongtao; Xiao, Qitao; Qiu, Yinguo; Huang, Linsheng; Zhao, Jinling

doi:10.1016/j.scitotenv.2023.169404

Cited by 6 publications

(1 citation statement)

References 85 publications

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“…This applies also in the cases of either sparse submerged vegetation plots or mixed-species plots, which presented multiple differences in their spectral response within parts of the same lake. The contribution of very-high-resolution (VHR) data or UAV-acquired data would be a worthwhile endeavor in this matter [111,112]. However, such approach was beyond the scope of our study.…”

Section: Discussionmentioning

confidence: 99%

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Tompoulidou,

Karadimou,

Apostolakis

et al. 2024

Remote Sensing

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Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.

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Section: Discussionmentioning

confidence: 99%

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Tompoulidou,

Karadimou,

Apostolakis

et al. 2024

Remote Sensing

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Precise extraction of targeted apple tree canopy with YOLO-Fi model for advanced UAV spraying plans

Wei,

Yan,

Yan

et al. 2024

Computers and Electronics in Agriculture

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AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV

Istiak,

Khan,

Rony

et al. 2024

Sci Data

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Aquatic vegetation species are declining gradually, posing a threat to the stability of aquatic ecosystems. The decline can be controlled with proper monitoring and mapping of the species for effective conservation and management. The Unmanned Ariel Vehicle (UAV) aka Drone can be deployed to comprehensively capture large area of water bodies for effective mapping and monitoring. This study developed the AqUavplant dataset consisting of 197 high resolution (3840px × 2160px, 4K) images of 31 aquatic plant species collected from nine different sites in Bangladesh. The DJI Mavic 3 Pro triple-camera professional drone is used with a ground sampling distance (GSD) value of 0.04-0.05 cm/px for optimal image collection without losing detail. The dataset is complemented with binary and multiclass semantic segmentation mask to facilitate ML based model development for automatic plant mapping. The dataset can be used to detect the diversity of indigenous and invasive species, monitor plant growth and diseases, measure the growth ratio to preserve biodiversity, and prevent extinction.

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A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data

Cited by 6 publications

References 85 publications

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Precise extraction of targeted apple tree canopy with YOLO-Fi model for advanced UAV spraying plans

AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV

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