Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping

Baloloy, A. B.; Blanco, A. C.; Ana, Raymund Rhommel C. Sta.; Nadaoka, Kazuo

doi:10.1016/j.isprsjprs.2020.06.001

Cited by 155 publications

(117 citation statements)

References 61 publications

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“… Name Equation No. Ref Atmospherically Resistant Vegetation Index (1) 72 Difference Vegetation Index (2) 99 Enhanced Built-Up and Bareness Index (3) 100 Enhanced Vegetation Index (4) 101 Green Chlorophyll Index (5) 102 Mangrove Vegetation Index (6) 80 Normalized Burn Ratio (7) 81 Normalized Different Bareness Index (8) 79 Normalized Difference Built-Up Index (9) 91 Normalised Difference Pond Index (10) 92 Normalized Difference Turbidity Index (11) 92 ...…”

Section: Satellite-based Covariate Calculationmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

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Section: Satellite-based Covariate Calculationmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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“…A mangrove index was recently developed for rapid and accurate mangrove mapping specifically applied in the mangroves of the Philippines. Baloloy, et al (2020) developed an equation that can delineate mangroves using NIR, SWIR, and green reflectance bands without the need for complex classification techniques which can be time-consuming and very user-skill-biased.…”

Section: Mangrove Vegetation Indexmentioning

confidence: 99%

“…Its threshold values were then computed through the use of a nationwide field-acquired mangrove inventory and drone data. According to Baloloy, et al (2020), the optimal minimum threshold values that can discriminate mangroves from non-mangroves areas were about 4.5 and 4.6, respectively with mean MVI varying depending on the type of mangrove forest. The mean MVI threshold value was 6.7 for riverine mangrove forests (forests found in rivers) and 8.9 for fringe mangrove types (forests found in coastal areas).…”

Section: Mangrove Vegetation Indexmentioning

confidence: 99%

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Post-Disaster Assessment of Mangrove Forest Recovery in Lawaan-Balangiga, Eastern Samar Using Ndvi Time Series Analysis

Cabello

Germentil

Blanco

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In 2013, Typhoon Haiyan (Yolanda) struck the Eastern Philippines. Mangrove forests in the area were destroyed and were estimated to have at least 86% of damage. Some studies done on the typhoon-stricken mangroves had collected data such as measurements of mangrove trunk, height, roots, and seedlings to investigate the extent of damage and recovery. While these studies were proven to effectively identify mangrove gains and losses, these methods are only applicable in sites that are relatively accessible. This paper highlights the relevance of effective remote monitoring of mangrove forests that are vulnerable to typhoons including post-typhoon recovery. In this study, a Time Series Analysis using Google Earth Engine (GEE) was applied in assessing the damages and recovery of mangroves struck by Super Typhoon Haiyan in Lawaan and Balangiga, Samar (Eastern Philippines). The changes in mangrove extent followed the changes in NDVI; however, there were significant site-specific differences. Based on NDVI values, it was estimated that 83% of the mangrove area was damaged. After three years, regeneration from 2014–2017 was about 144%. Mangroves steadily developed but with a minimal change of 2.83% from 2017–2019. Most villages followed the general recovery trends in Lawaan and Balangiga. However, based on the time series analysis, some villages have minimal recovery than others. It suggests that the recovery of mangroves may be a function of the pre-typhoon mangrove extent and possibly vegetation condition. Even if there were new spaces for mangroves to colonize, some of the sites may not be conducive for plant regrowth.

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“…Since Sentinel-2 and Sentinel-1 are different products, scaling, aligning, resampling and normalizing were conducted in order for Sentinel-1 and Sentinel-2 images to have the same resolution and pixels. A mangrove mask generated from a Mangrove Vegetation Index (MVI) (Baloloy et al, 2020) was applied to isolate the mangrove patches for both imageries.…”

Section: Imagery Derivatives Data Preparationmentioning

confidence: 99%

Multi-Sensor Approach to Leaf Area Index Estimation Using Statistical Machine Learning Models: A Case on Mangrove Forests

Martinez

Burgos

Blanco

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.

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Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping

Cited by 155 publications

References 61 publications

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Post-Disaster Assessment of Mangrove Forest Recovery in Lawaan-Balangiga, Eastern Samar Using Ndvi Time Series Analysis

Multi-Sensor Approach to Leaf Area Index Estimation Using Statistical Machine Learning Models: A Case on Mangrove Forests

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