The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems

Navarro, Alejandro; Young, Mary; Allan, Blake M.; Carnell, Paul E.; Macreadie, Peter I.; Ierodiaconou, Daniel

doi:10.1016/j.rse.2020.111747

Cited by 130 publications

(95 citation statements)

References 55 publications

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“…Three-dimensional point clouds derived from SfM photogrammetry produced conservative and realistic measures of tree heights [12]. Alongside with point clouds, a DSM, Digital Terrain Model (DTM), and an orthomosaic were also generated from these processes [13].…”

Section: Data Processingmentioning

confidence: 99%

“…Mangrove forests with their harsh physical conditions hinder surveyors from carrying out direct measurements in the field. In principle, a number of remote sensing approaches have been developed as supplement or substitute for ground-based inventory [12][13][14][15]. A Canopy Height Model (CHM)-that is, a three-dimensional representation of the forest-can be derived by using airborne or terrestrial LiDAR, Synthetic Aperture Radar (SAR), and high-resolution optical imagery [16].…”

Section: Introductionmentioning

confidence: 99%

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Eleven Years of Mangrove–Mudflat Dynamics on the Mud Volcano-Induced Prograding Delta in East Java, Indonesia: Integrating UAV and Satellite Imagery

et al. 2021

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This article presents a novel approach to explore mangrove dynamics on a prograding delta by integrating unmanned aerial vehicle (UAV) and satellite imagery. The Porong Delta in Indonesia has a unique geographical setting with rapid delta development and expansion of the mangrove belt. This is due to an unprecedented mud load from the LUSI mud volcanic eruption. The mangrove dynamics analysis combines UAV-based Structure from Motion (SfM) photogrammetry and 11 years (2009–2019) satellite imagery cloud computing analysis by Google Earth Engine (GEE). Our analysis shows unique, high-spatiotemporal-resolution mangrove extent maps. The SfM photogrammetry analysis leads to a 3D representation of the mangrove canopy and an estimate of mangrove biophysical properties with accurate height and individual position of the mangroves stand. GEE derived vegetation indices resulted in high (three-monthly) resolution mangrove coverage dynamics over 11 years (2009–2019), yielding a value of more than 98% for the overall, producer and consumer accuracy. Combining the satellite-derived age maps and the UAV-derived spatial tree structure allowed us to monitor the mangrove dynamics on a rapidly prograding delta along with its structural attributes. This analysis is of essential value to ecologists, coastal managers, and policymakers.

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Section: Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eleven Years of Mangrove–Mudflat Dynamics on the Mud Volcano-Induced Prograding Delta in East Java, Indonesia: Integrating UAV and Satellite Imagery

et al. 2021

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“…By combining remote sensing data with artificial intelligence techniques, it is possible to properly map tree species and improve accuracy in applications regarding vegetation monitoring. In recent years, multiple frameworks have been implemented to assess the performance of such algorithms to accomplish this task (2,5,15,(35)(36)(37). During the past years, the detection and extraction of trees in high-resolution imagery were performed with more traditional machine learning algorithms, like support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and others (38)(39)(40)(41).…”

Section: P R E P R I N Tmentioning

confidence: 99%

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

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et al. 2021

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Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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“…Due to their cost-effectiveness, UAVs are revolutionizing conservation management by providing high spatio-temporal observations, especially in small, inaccessible, or highly sensitive areas (Jiménez López and Mulero-Pázmány, 2019). Although the cost is variable, employing UAVs is often cheaper and more efficient than using manned aircrafts or conducting on-the-ground mangrove surveys (Otero et al, 2018;Ruwaimana et al, 2018;Navarro et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Challenges and Recommendations for Equitable Use of Aerial Tools for Mangrove Research

et al. 2021

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As the use of aerial tools such as unmanned aerial vehicles (UAVs) for mangrove monitoring gains in popularity, understanding who leads this research and where is critical for expanding efficient monitoring methods and achieving international commitments to sustainable development, technology transfer and reduced inequality. Between 2000 and 2019, mangrove research using aerial tools was largely conducted in and led by institutions in higher income countries, despite High-income countries accounting for only 9% of global mangrove coverage. Of studies where the country of the lead institution differed from that of the study site, only 38% of the studies included local co-authors. These results echo historical patterns of research conducted by researchers from higher income countries on biodiversity concentrated in lower income countries, frequently with limited involvement of local scientists—known as “helicopter research.” The disconnect between where mangroves are located and where aerial research is conducted may result from barriers such as government restrictions, limited financial and technical resources, language barriers hindering UAV deployment, or hampered findability of local research. Our findings suggest that expertise for aerial surveys currently lies in “High-income, Annex II” and “Upper-middle-income, Non-Annex” countries, and both groups could invest time and resources in building local, long-term technological capacity in Upper-middle, Lower-middle and Low-income countries. We identify strategic partnerships to expand aerial tools for mangrove research that also address commitments under the United Nations Framework Convention on Climate Change and potential international collaborations under the framework proposed by the UN Decade of Ocean Science for Sustainable Development.

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The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems

Cited by 130 publications

References 55 publications

Eleven Years of Mangrove–Mudflat Dynamics on the Mud Volcano-Induced Prograding Delta in East Java, Indonesia: Integrating UAV and Satellite Imagery

Eleven Years of Mangrove–Mudflat Dynamics on the Mud Volcano-Induced Prograding Delta in East Java, Indonesia: Integrating UAV and Satellite Imagery

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

Challenges and Recommendations for Equitable Use of Aerial Tools for Mangrove Research

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