Assessing rehabilitation of managed mangrove ecosystems using high resolution remote sensing

Oostdijk, Maartje; Santos, Maria J.; Whigham, Dennis F.; Verhoeven, Jos T. A.; Silvestri, S.

doi:10.1016/j.ecss.2018.06.020

Cited by 18 publications

(8 citation statements)

References 44 publications

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“…The performance of these enhancements can be measured using remote sensing-derived data and metrics. For instance, multispectral imagery can be used to monitor erosion control (Osorio et al 2020), vegetation performance (Oostdijk et al 2018), as well as habitat change and biodiversity (Furberg et al 2020); lidar data can be used to assess access, navigable paths, and inspect structure performance over time; and HSI and high-resolution satellite imagery can be used to evaluate sediment transport and water quality parameters (Magnuszewski et al 2018).…”

Section: ® ®mentioning

confidence: 99%

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

Suir¹,

Reif²,

Saltus³

2022

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Engineering With Nature (EWN®) is a US Army Corps of Engineers (USACE) Initiative and Program that promotes more sustainable practices for delivering economic, environmental, and social benefits through collaborative processes. As the number and variety of EWN® projects continue to grow and evolve, there is an increasing opportunity to improve how to quantify their benefits and communicate them to the public. Recent advancements in remote sensing technologies are significant for EWN® because they can provide project-relevant detail across a large areal extent, in which traditional survey methods may be complex due to site access limitations. These technologies encompass a suite of spatial and temporal data collection and processing techniques used to characterize Earth's surface properties and conditions that would otherwise be difficult to assess. This document aims to describe the general underpinnings and utility of remote sensing technologies and applications for use: (1) in specific phases of the EWN® project life cycle; (2) with specific EWN® project types; and (3) in the quantification and assessment of project implementation, performance, and benefits.

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Section: ® ®mentioning

confidence: 99%

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

Suir¹,

Reif²,

Saltus³

2022

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“…Recent studies reported that satellite remote sensing images have been widely used in wetland vegetation mapping [8,9] and obtained good classification accuracy. However, the most often used global open satellite images are usually in the medium and coarse spatial resolutions, which is difficult to effectively distinguish from complex wetland vegetation types [10], while high-resolution satellite images have high costs and find it difficult to achieve continuous monitoring of wetland vegetation due to the cloudy and rainy weather conditions [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Decision Fusions for Classifying Karst Wetland Vegetation Using One-Class and Multi-Class CNN Models with High-Resolution UAV Images

Deng

et al. 2022

Remote Sensing

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Combining deep learning and UAV images to map wetland vegetation distribution has received increasing attention from researchers. However, it is difficult for one multi-classification convolutional neural network (CNN) model to meet the accuracy requirements for the overall classification of multi-object types. To resolve these issues, this paper combined three decision fusion methods (Majority Voting Fusion, Average Probability Fusion, and Optimal Selection Fusion) with four CNNs, including SegNet, PSPNet, DeepLabV3+, and RAUNet, to construct different fusion classification models (FCMs) for mapping wetland vegetations in Huixian Karst National Wetland Park, Guilin, south China. We further evaluated the effect of one-class and multi-class FCMs on wetland vegetation classification using ultra-high-resolution UAV images and compared the performance of one-class classification (OCC) and multi-class classification (MCC) models for karst wetland vegetation. The results highlight that (1) the use of additional multi-dimensional UAV datasets achieved better classification performance for karst wetland vegetation using CNN models. The OCC models produced better classification results than MCC models, and the accuracy (average of IoU) difference between the two model types was 3.24–10.97%. (2) The integration of DSM and texture features improved the performance of FCMs with an increase in accuracy (MIoU) from 0.67% to 8.23% when compared to RGB-based karst wetland vegetation classifications. (3) The PSPNet algorithm achieved the optimal pixel-based classification in the CNN-based FCMs, while the DeepLabV3+ algorithm produced the best attribute-based classification performance. (4) Three decision fusions all improved the identification ability for karst wetland vegetation compared to single CNN models, which achieved the highest IoUs of 81.93% and 98.42% for Eichhornia crassipes and Nelumbo nucifera, respectively. (5) One-class FCMs achieved higher classification accuracy for karst wetland vegetation than multi-class FCMs, and the highest improvement in the IoU for karst herbaceous plants reached 22.09%.

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“…These studies reflect the objectives of RAMSAR. For example, Oostdijk et al, ( 2018 ) applied remote sensing using high-resolution satellite image to determine improvements in mangrove forests under different hydrological condition. Pham et al, ( 2019 ) used remote sensing to evaluate the growth of mangrove forests in Can Gio, Vietnam.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of tropical mangrove forest land use change from 2010 to 2020

Razali

Radzi

Marín

et al. 2021

Environ Dev Sustain

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Assessing rehabilitation of managed mangrove ecosystems using high resolution remote sensing

Cited by 18 publications

References 44 publications

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

Evaluation of Decision Fusions for Classifying Karst Wetland Vegetation Using One-Class and Multi-Class CNN Models with High-Resolution UAV Images

A bibliometric analysis of tropical mangrove forest land use change from 2010 to 2020

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