New spectral metrics for mangrove forest identification

Shi, Tiezhu; Liu, Jue; Hu, Zhongwen; Liu, Huizeng; Wang, Junjie; Wu, Guofeng

doi:10.1080/2150704x.2016.1195935

Cited by 68 publications

(25 citation statements)

References 23 publications

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“…The two newly invented indices were computed from the SWIR-2 and NIR band. Shi et al (2016) [61] also used SWIR-2 and SWIR-1 band-derived indices to enhance the spectral differences of mangrove forests and terrestrial vegetation based on L8 imagery. Chen et al (2017) [18] employed NDVI, EVI, and LSWI (land surface water index, computed from NIR and SWIR bands) to map the total mangrove forests in China.…”

Section: The Importance Of Spectral Bands and Texture Information Formentioning

confidence: 99%

Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

et al. 2018

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Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.

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Section: The Importance Of Spectral Bands and Texture Information Formentioning

confidence: 99%

Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

et al. 2018

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“…3) by using a function that masks out clouds (Giri et al, 2011;Hansen et al, 2013). Seven spectral indices: Normalized difference vegetation index (NDVI), Normalized difference mangrove index (NDMI), Modi ed normalized difference water index (NDWI), Simple ratio (SR), Ratio54, Ratio35, and Green chlorophyll vegetation index (GCVI) were added to the Landsat imageries to provide information on not only vegetation in the pixels but also water content because mangroves are found close to water (Shi et al, 2016;Nathan et al, 2018). The Landsat data were ltered by date and region.…”

Section: Optical Images (Landsat and 8)mentioning

confidence: 99%

Quantification of Mangrove Extent using a Combination of Optical and Radar Images in Google Earth Engine Platform: The case of Anlo Beach Wetland Complex, Shama District, Western Region, Ghana

Aja

Miyittah

Angnuureng

2021

Preprint

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Mangrove Forest classification in tropical coastal zones based on only passive remote sensing methods is hampered by Mangrove complexities, topographic considerations and cloud cover effects among other things. This paper reports on a novel approach that combines Optical Satellite images and Synthetic Aperture Radar alongside their derived parameters to overcome the challenges of distinguishing Mangrove stand in cloud prone regions. Google Earth Engine (GEE) cloud-based geospatial processing platform was used to extract several scenes of Landsat Surface Reflectance Tier1 and synthetic aperture radar (C-band and L-band). The imageries were enhanced by creating a function that masks out clouds from the optical satellite image and by using speckle filter to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Our result show that about 16% of the mangrove extent was lost in the last decade. The accuracy was assessed based on three classification scenarios: classification of optical data only, classification of SAR data only, and combination of both optical and SAR data. The overall accuracies were 99.1% (Kappa Coefficient =0.797), 84.6% (Kappa Coefficient = 0.687) and 98.9% (Kappa Coefficient = 0.828) respectively. This case study demonstrates how mangrove mapping can help focus conservation practices locally in climate change setting, coupled with sea level rise and related threats to coastal ecosystems.

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“…Precision index of LULC classification. [59,60]. This non-parametric test is based on a binary distinction between correct and incorrect class allocations (Table 6).…”

Section: Accuracy Evaluationmentioning

confidence: 99%

“…N ii , N i+ and N +i represent the correctly classified pixel, the sum of the class i in the classified data, and the sum of class i in the validation data, respectively. The statistical significance of the difference between classifications was evaluated using McNemar's test [59,60]. This non-parametric test is based on a binary distinction between correct and incorrect class allocations (Table 6).…”

Section: Number Precision Index Expression Modelmentioning

confidence: 99%

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Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Yang

Ding

et al. 2017

Remote Sensing

Self Cite

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Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

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New spectral metrics for mangrove forest identification

Cited by 68 publications

References 23 publications

Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

Quantification of Mangrove Extent using a Combination of Optical and Radar Images in Google Earth Engine Platform: The case of Anlo Beach Wetland Complex, Shama District, Western Region, Ghana

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

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