Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing

Hsu, Astrid; Kumagai, Joy; Favoretto, Fabio; Dorian, John; Martinez, Benigno Guerrero; Aburto‐Oropeza, Octavio

doi:10.3390/rs12233986

Cited by 27 publications

(13 citation statements)

References 36 publications

(31 reference statements)

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“…Li, et al [180] improved the mangrove distribution of the USGS 2000 dataset and included the detection of submerged mangrove recognition index (SMRI) and normalized difference vegetation index (NDVI). In addition, Hsu, et al [181] used drones to enhance satellite imagery, and correct the GMW dataset. Other than that, aboveground biomass (AGB) could be accurately estimated using light detection and ranging (LiDAR) techniques [182].…”

Section: ) Dataset Availabilitymentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

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Section: ) Dataset Availabilitymentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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“…The UAV used in this study is the Phantom 4 Pro V2.0 manufactured by DJ-Innovations (DJI), which was a reliable low-cost consumer-level drone and widely used in UAV-based studies (e.g., Che et al, 2020;Hsu et al, 2020;Pearce et al, 2020;Fraser et al, 2021). It carries a 24 mm/F2.8, 20 megapixel singlelens digital camera, corresponding to 2.5-3 cm ground resolution at 50 m flight height.…”

Section: Uav Used and Its Camera Settingsmentioning

confidence: 99%

Ablation Patterns of the Debris Covered Tongue of Halong Glacier Revealed by Short Term Unmanned Aerial Vehicle Surveys

Guo

Liu

et al. 2021

Front. Earth Sci.

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Debris-covered glaciers are an important glacier type and have attracted more and more attention. This study presents the results of ablation patterns of debris-covered tongue of the Halong Glacier in the northeastern Tibetan Plateau, by using two repeated unmanned aerial vehicle (UAV) surveys performed on August 11 and September 15, 2019. The results show that the tongue of Halong Glacier has experienced strong ablation during the surveyed period, with an overall ice loss amount to 4.17 × 105 metric tons. Among all the briefly classified surface types, supraglacial debris has the largest area (80.9%) and also mass losses (58.6%) comparing to others. However, ice cliffs show the strongest and the most significant ablation rates (averagely 1.36 and 1.22 m w.e. for supraglacial and lateral ice cliffs, respectively), followed by clean ice regions (1.01 m w.e.). The backwastes of ice cliffs also resulted in up to 7.8 m horizontal back-off at different parts of Halong Glacier, lead to fast terminal retreat and narrowing down of the glacier tongue, and may result in the break off of Halong Glacier tongue into separated parts in the future. The surface ablation rates show a clear negative exponential relationship with the measured debris thicknesses, well in accordance with previous studies. Regions in cutting and flushing by supraglacial and lateral rivers have the largest surface elevation decreases but are not significant due to their limited area and the relatively lower quality of UAV digital surface models (DSMs) in those covered regions.

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“…Satellite imagery enables the identification and monitoring of mangrove forests at regional and global scales, facilitating comprehensive assessments of carbon stock estimates [16,17]. Integrating satellite imagery with Machine Learning (ML) algorithms allows for advanced analysis of spectral bands to predict mangrove species, aiding in species-specific carbon stock calculations [18].…”

Section: Introductionmentioning

confidence: 99%

Satellite-based Mangrove species abundance estimate using Machine Learning ensemble

Harsa,

Winarso,

Setiawan

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The mangrove ecosystem is a vital feature in a coastal area, playing a critical role in carbon sequestration beneath the soil. Carbon preservation capacity varies among different species of mangrove. Thus, by quantifying the number of mangrove species in a given area, the volume of carbon sequestered can be estimated. Satellite imagery is highly effective for gathering such data across vast territories. In this study, we present an evaluation of mangrove species abundance across a large coastal area using Landsat satellite imagery. We employed machine learning algorithms to classify species based on spectral field observation data to achieve this. These algorithms were trained individually and ensembled to enhance prediction performance. There are 466 models generated in a two-hour training phase. After assessing these models, we identified that a stacked ensemble consisting of Deep Learning, two Distributed Random Forests, a Generalized Boosting Model, a Generalized Linear Model, and Extreme Gradient Boosting algorithms has the most superior predictive accuracy. The model achieved a mean accuracy value of 95% when tested on observation data. After applying the best model to the satellite data, our results indicate that Rhizophora Apiculata and Excoecaria Agallocha are the two most abundant mangrove species in the study area, covering 17.71% (19502.37 Ha) and 10.49% (11549.79 Ha), respectively.

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Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing

Cited by 27 publications

References 36 publications

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Ablation Patterns of the Debris Covered Tongue of Halong Glacier Revealed by Short Term Unmanned Aerial Vehicle Surveys

Satellite-based Mangrove species abundance estimate using Machine Learning ensemble

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