The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping
Open accessAbstract
Abstract Advancement in Remote Sensing allows rapid mangrove mapping without the need for data-intensive methodologies, complex classifiers, and skill-dependent classification techniques. This study proposes a new index, the Mangrove Vegetation Index (MVI), to rapidly and accurately map mangroves extent from remotely-sensed imageries. The MVI utilizes three Sentinel-2 bands green, Near Infrared (NIR) and Shortwave Infrared (SWIR) in the form |NIR-Green|/|SWIR-Green| to discriminate the distinct greenness and moisture of mangroves from terrestrial vegetation and other land cover. Spectral band analysis shows that the |NIR-Green| part of MVI captures the differences of greenness between mangrove forests and terrestrial vegetation. The |SWIR-Green| part of the index expresses the distinct moisture of mangroves without the need for additional intertidal data and water indices. The MVI value increases with the increasing probability of a pixel being classified as mangroves. Eleven mangrove forests in the Philippines and one mangrove park in Japan were then mapped using MVI. Accuracy assessment was done using field inventory data and high-resolution drone orthophotos. MVI have successfully separated the mangroves from other cover especially terrestrial vegetation, with an overall index accuracy of 92%. The MVI was applied to Landsat 8 images using the equivalent bands to test the universality of the index. Comparable MVI mangrove maps were produced between Sentinel-2 and Landsat images, with an optimal minimum threshold of 4.5 and 4.6, respectively. MVI can effectively highlight the greenness and moisture information in mangroves as reflected by its moderate to high correlation value (r = 0.63 and 0.84, α = 0.05) with the Sentinel-derived chlorophyll-a (Ca) and canopy water (Cw) biophysical products. This study developed and implemented two automated platforms: an offline IDL-based ‘MVI Mapper’ and an online Google Earth Engine-based MVI mapping interface. The MVI implemented in Google Earth Engine was used in generating the latest mangrove extent map of the Philippines. Additionally, the application of MVI were tested to four additional mangrove forests in Southeast Asia: Thailand, Vietnam, Indonesia and Cambodia; and to selected mangroves forests in South America, Africa and Australia.
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Cites
A soil-adjusted vegetation index (SAVI)
Huete, A. R. (1988) · Remote Sensing of Environment · doi:10.1016/0034-4257(88)90106-x · Cited by 7,948
Monitoring Vegetation Systems in the Great Plains with Erts
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High-resolution mapping of global surface water and its long-term changes
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016) · Nature · doi:10.1038/nature20584 · Cited by 5,513
A survey of image classification methods and techniques for improving classification performance
Lu, D., & Weng, Q. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160600746456 · Cited by 3,402
Status and distribution of mangrove forests of the world using earth observation satellite data
Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2010.00584.x · Cited by 3,171
PROSPECT: A model of leaf optical properties spectra
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Present state and future of the world's mangrove forests
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Biology of mangroves and mangrove Ecosystems
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The Harmonized Landsat and Sentinel-2 surface reflectance data set
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.09.002 · Cited by 1,497
Metadata
- Authors
- Baloloy, A. B., Blanco, A. C., Sta. Ana, R. R. C., & Nadaoka, K.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 166
- Pages
- 95-117
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W3035067580
- Cited by
- 304
- References
- 38
- FWCI
- 15.86
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Baloloy, A. B., Blanco, A. C., Sta. Ana, R. R. C., & Nadaoka, K. (2020). Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2020.06.001
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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