Spectral signature analysis to determine mangrove species delineation structured by anthropogenic effects

Zulfa, A.W.; Norizah, K.; Hamdan, O.; Faridah-Hanum, I.; Rhyma, P.P.; Fitrianto, Anwar

doi:10.1016/j.ecolind.2021.108148

Cited by 16 publications

(4 citation statements)

References 40 publications

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“…These are 3 species of mangrove plants common in the tidal flat area of the Dong Rui commune. Almost all mangrove species are distinguishable in the visible and near-infrared infrared because of the dispersion in porous mesenchymal cells in plants (Zulfa et al 2021;Zulfa et al 2020). In addition to the image classification method combined with random forest terminology, the determination of chlorophyll content in plant leaves according to plant indicators based on spectroscopic measurements is also a common method for classifying plants (Meivel et al 2023;Xue et al 2009).…”

Section: Discussionmentioning

confidence: 99%

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

Ngo,

Quoc,

Dang

et al. 2024

GES

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Mangroves are one of the most important types of wetlands in coastal areas and perform many different functions. Assessing the structure and function of mangroves is a premise for the management, monitoring and development of this most diverse and vulnerable ecosystem. In this study, the unmanned aerial vehicle (UAV) Phantom 4 Multispectral was used to analyse the structure of a mangrove forest area of approximately 50 hectares in Dong Rui commune, Tien Yen district, Quang Ninh Province – one of the most diverse wetland ecosystems in northern Vietnam. Based on the visual classification method combined with the results of field taxonomic sampling, a mangrove tree classification map was established for UAV with three species, Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, achieving an overall accuracy = 86.28%, corresponding to a Kappa coefficient =0.84. From the images obtained from the UAV, we estimated and developed maps and assessed the difference in tree height and four vegetation indices, including the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), enhanced vegetation index (EVI), and green chlorophyll index (GCI), for three mangrove plant species in the flying area. Bruguiera gymnorrhiza and Rhizophora stylosa reach an average height of 4 to 5 m and are distributed mainly in high tide areas. Meanwhile, Kandelia obovata has a lower height (ranging from 2 to 4 m), distributed in low-tide areas, near frequent flows. This study confirms the superiority of UAV with red edge and near-infrared wave bands in classifying and studying mangrove structures in small-scale areas.

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Section: Discussionmentioning

confidence: 99%

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

Ngo,

Quoc,

Dang

et al. 2024

GES

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“…Table 2 lists a comparison of the wavelengths used in the Sentinel-2A image and the spectroradiometer. The results of the in situ measurements were used to develop a spectrum library 58 to compare the spectral image to the reference spectrum. The spectrum library was used as the reference to compare the Sentinel-2A image reflectance value to the field reflectance value 59 .…”

Section: Methodsmentioning

confidence: 99%

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Simarmata,

Wikantika,

Darmawan

et al. 2024

J. Appl. Rem. Sens.

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Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.

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“…Kappa overall indices are above 0.70, and Kappa per class indices oscillate between 0.60 and 0.80 (Figure6c). Although the Kappa values have been low for some years, these are consistent with similar studies that classify mangrove species, e.g.,[45,53,54]. In general, AD presents higher values than QD (Figure6d), excluding 2003, where QD (0.07) in Rhizophora and Avicennia classes was higher than AD (0.06).…”

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confidence: 90%

Death and Regeneration of an Amazonian Mangrove Forest by Anthropic and Natural Forces

et al. 2022

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The Amazon Macrotidal Mangrove Coast contains the most extensive and continuous mangrove belt globally, occupying an area of ~6500 km2 and accounting for 4.2% of global mangroves. The tallest and densest mangrove forests in the Amazon occur on the Bragança Peninsula. However, road construction that occurred in 1973 caused significant mangrove degradation in the area. A spatial-temporal analysis (1986–2019) based on optical, Synthetic Aperture Radar (SAR), drone images, and altimetric data obtained by photogrammetry and validated by a topographic survey were carried out to understand how the construction of a road led to the death of mangroves. The topographic data suggested that this road altered the hydrodynamical flow, damming tidal waters. This process killed at least 4.3 km2 of mangrove trees. Nevertheless, due to natural mangrove recolonization, the area exhibiting degraded mangrove health decreased to ~2.8 km2 in 2003 and ~0.73 km2 in 2019. Climatic extreme events such as “El Niño” and “La Niña” had ephemeral control over the mangrove degradation/regeneration. In contrast, the relative sea-level rise during the last several decades caused long-term mangrove recolonization, expanding mangrove areas from lower to higher tidal flats. Permanently flooded depressions in the study area, created by the altered hydrodynamical flow due to the road, are unlikely to be recolonized by mangroves unless connections are re-established between these depressions with drainage on the Caeté estuary through pipes or bridges to prevent water accumulation between the road and depressions. To minimize impacts on mangroves, this road should have initially been designed to cross mangrove areas on the highest tidal flats and to skirt the channel headwaters to avoid interruption of regular tidal flow.

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Spectral signature analysis to determine mangrove species delineation structured by anthropogenic effects

Cited by 16 publications

References 40 publications

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Death and Regeneration of an Amazonian Mangrove Forest by Anthropic and Natural Forces

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