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Research article Mapping of the Spatio-Spectral Dynamics of Mangrove Chlorophyll Concentrations via Sentinel-2 Satellite Imagery Basheer Ahammed1,2.
I Wayan Gede Astawa Karang1,*.
I Wayan Nuarsa1.
I Gede Surya Indrawan1.
I Gede Hendrawan1.
Ni Made Nia Bunga Surya Dewi3.
Arvind Chandra Pandey4 Department of Marine Science.
Faculty of Marine Science and Fisheries.
Udayana University.
Jimbaran.
Bali 80361 Indonesia International Union for the Conservation of Nature (IUCN).
Commission on Ecosystem Management (CEM).
South Asia Doctor of Environmental Science.
Post Graduate Program.
Udayana University.
Jimbaran.
Bali 80361 Indonesia Department of Geoinformatics.
School of Natural Resource Management.
Central University of Jharkhand.
Cheri Manatu Rachi, 835 222 India Correspondence: gedekarang@unud.
Citation:
Abstract Ahammed.
Karang.
Mangrove ecosystems play a critical role in maintaining coastal health.
however, they are increasingly threatened by anthropogenic activities and climate change.
Health assessment is essential for effective conservation efforts.
However, traditional remote sensing techniques such as the normalised difference vegetation index (NDVI) may not fully capture the complex physiological processes influencing vegetation health.
Therefore, this study investigated chlorophyll (Ch.
dynamics in mangroves using remote sensing techniques, including the NDVI and a novel method, the normalised area over reflectance curve (NAOC), via Sentinel-2 satellite imagery during October 2023, and analysed spatial variations in Chl content (CC) via the Google Earth Engine API.
NDVI and NAOC-Chl were weakly correlated .
, highlighting their complementary roles.
The average NDVI and NOAC-Chl values for different species were analysed, and Rhizophora mucronata presented the highest value (NDVI: 0.
86 A 0.
NOAC: 20.
48 A 4.
49 yuNyci/ycayco.
, whereas Sonneratia alba presented the lowest average CC (NDVI: 0.
73 A 0.
NOAC: 13.
45 A 3.
02 yuNyci/ycayco.
The study also revealed spatial variations in CC, with potential stress near urban and water body areas and healthier vegetation in core than in the other regions.
The disparities between northern and southern China suggest the impacts of human activity and climate change.
We observed potential negative effects of unregulated tourism.
This study provides insights for informed conservation strategies by demonstrating the value of combining remote sensing techniques and highlighting the importance of sustainable tourism practices.
Nuarsa.
Indrawan.
Dewi.
, & Pandey.
Mapping of the Spatio-Spectral Dynamics of Mangrove Chlorophyll Con-centrations via Sentinel-2 Satellite Imagery.
Forum Geografi.
, 244-246.
Article history:
Received: 11 March 2024 Revised: 27 July 2024 Accepted: 28 July 2024 Published: 27 August 2024 Keywords: Google Earth Engine.
Sentinel 2.
Mangroves.
Canopy chlorophyll.
Vegetation health.
Introduction The global mangrove ecosystem spans approximately 13.
7 million hectares across 118 countries (Pastor-Guzman et al.
, 2015.
Wang et al.
, 2.
, whereas in Southeast Asia, mangroves extend over 4,000,000 ha and constitute 32.
2% of the global mangrove area (Fauzi et al.
, 2.
Mangrove forests serve as indispensable ecosystems, offering a plethora of direct and indirect services, such as acting as natural protection barriers and nursery habitats for marine organisms (Barbier & Strand, 1998.
Ewel et al.
, 1998.
Pastor-Guzman et al.
, 2.
The potential of mangroves as carbon sinks has garnered increasing attention recently, with estimates suggesting that mangrove carbon storage ranges from 160 to 1,000 MgAhaOe1, contingent upon factors such as location, species composition, height, and canopy closure (Lagomasino et al.
, 2019.
Matsui, 1998.
PastorGuzman et al.
, 2.
Despite their ecological importance and role in climate change mitigation, mangroves face significant threats including deforestation and land-use conversion (Akram et al.
Arifanti et al.
, 2.
Climate change has become the focus of discussions surrounding the fate of mangrove ecosystems as a combination of factors, including escalating temperatures, rising sea levels, greenhouse gas concentrations, shifts in ocean circulation and precipitation patterns, and an increase in extreme weather events, exert considerable pressure on these coastal habitats (Akram et al.
, 2023.
Alongi.
The responses of mangroves to these environmental challenges hinge on the pivotal question of whether critical thresholds have been breached (Saenger et al.
, 1.
Copyright: A 2024 by the authors.
Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license .
ttps://creativecommons.
org/licenses/by/4.
0/).
Ahammed et al.
The productivity and carbon uptake of mangroves are closely related to photosynthesis, a process that depends on the availability of leaf pigments, with chlorophyll (Ch.
being the main pigment (De et al.
, 2021.
Grove, 2.
Variations in leaf pigments serve as indicators of physiological status, senescence, and stress, and mangroves display pigment differences among species and health conditions (Hati et al.
, 2021.
Ku et al.
, 2023.
Pineda & Baryn, 2.
Given their sensitivity to environmental gradients, particularly those induced by seasonal variations, such as water availability, understanding the Chl content (CC) is crucial for monitoring mangrove health and productivity (Cavalcanti et al.
, 2022.
Minu et al.
, 2020.
Wang & Gu, 2.
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Because of their unique adaptation to coastal environments, mangroves are exposed to an array of stressors induced by climate change (Kumari & Rathore, 2.
Elevated temperatures not only directly impact mangrove physiology but also influence sea levels through thermal expansion and the melting of polar ice, leading to altered inundation patterns (Beck Eichler & Barker, 2020.
yNelekli & Zariy, 2023.
Rythig et al.
, 2.
Changes in precipitation patterns and ocean circulation further contribute to shifts in salinity levels, potentially exceeding the tolerance limits for mangrove species .
e Lacerda et al.
, 2022.
Perri et al.
, 2.
Anthropogenic pressure driven by urbanisation and industrialisation, alongside land use change for agriculture and aquaculture, as well as the impact of tourism, collectively pose severe threats to the health of mangrove ecosystems (Basheer Ahammed & Pandey, 2021.
Kumar et al.
, 2022.
Moorhouse et al.
, 2021.
Numbere, 2021.
Umprasoet et al.
, 2.
Pollution from industrial discharges, habitat destruction, and alterations in hydrological regimes disrupt the delicate balance of these coastal habitats.
The conversion of mangrove areas to shrimp and fishponds contributes to habitat loss and fragmentation, and reduces resilience (Ballut-Dajud et al.
, 2022.
Chakraborty.
Steven et al.
, 2.
Unregulated tourism exacerbates this issue through habitat disturbance and pollution (Chandel, 2.
Mitigating these challenges requires integrated coastal zone management, community engagement, strengthened regulations, and educational efforts to strike a balance between the development and preservation of critical ecosystems.
CC estimation involves labour-intensive laboratory methods, including leaf pigment extraction and spectrophotometric determination (Croft et al.
, 2015.
Croft & Chen, 2017.
Pastor-Guzman et , 2.
Portable Chl metres, such as SPAD and Apogee metres, offer a more practical alternative and have been successfully employed in precision agriculture in comparison to the lab measurement (Kamarianakis & Panagiotakis, 2023.
Musacchi et al.
, 2023.
Odabas et al.
, 2.
the context of mangroves, the use of portable Chl metres has shown promise, as it provides correlations between CCs and metre readings.
However, calibration equations are required to convert these readings to CCs (Dou et al.
, 2018.
Jiang et al.
, 2008.
Li et al.
, 2023.
Neres et al.
, 2.
Remote sensing has emerged as a powerful tool for estimating the CC, offering radiative transfer models and vegetation indices (VI.
(Ahmad et al.
, 2020.
Carmona et al.
, 2015.
Dou et al.
, 2018.
Liang et al.
, 2.
Although radiative transfer models require computational resources.
VIs represent a computationally simpler and accurate approach.
VIs derived from hyperspectral and multispectral data have been applied to estimate vegetation CC in various settings.
However, their application in mangrove forests remains relatively understudied (Connelly, 1997.
Flores-de-Santiago et al.
, 2013b, 2013.
This study sought to bridge this gap by assessing the performance of selected broadband VIs in predicting CC at the canopy level.
Furthermore, it aimed to establish a link between the CCs of different species on the ground and Sentinel-2 (S.
data, paving the way for mapping the spatial distribution and variability of mangrove CCs at the landscape level.
strikes a good balance between high spatial resolution, multispectral capabilities, free access, and revisit frequency (Sentinel 2.
, making it a valuable tool for studying Chl dynamics in mangroves.
This study not only contributes to our understanding of mangrove ecosystems but also introduces the potential use of S2 data in mapping CCs, presenting valuable advancements in mangrove research.
Mangrove ecosystems are critical for coastal health.
however, anthropogenic activities and climate change threaten their well-being.
Traditional methods for assessing mangrove health do not provide a comprehensive picture, particularly at the species level.
The present study addresses this gap by developing a method to invert canopy CCs in mangroves using high-resolution multispectral S2 satellite data.
We employed both the established normalised difference vegetation index (NDVI) and novel normalised area over reflectance curve (NOAC) index.
Although NDVI is widely used for vegetation monitoring.
NOAC have undergone limited exploration, particularly in the context of mangroves.
This research aimed not only to enhance our understanding of mangrove health but also to provide practical guidance for future monitoring and conservation efforts in the Tahura Ngurah Rai mangrove area (Benoa Ba.
in Bali.
Indonesia.
Materials and methods Study area The present study was carried out in the Tahura Ngurah Rai mangrove area (Benoa Ba.
, located in the Kuta and South Kuta Districts.
Badung Regency, and Serangan Island.
South Denpasar District.
Denpasar City of Bali.
Indonesia, with coordinates 08A41AAe08A47A South Latitude and 115A10AAe115A15A East Latitude (Figure .
Bali has distinct tropical, wet, and dry climates.
The wet season (NovemberAeApri.
is characterised by monsoon rains, with an average monthly rainfall of 341.
62 mm in January.
Conversely, the dry season (MayAeOctobe.
is cooler and less humid.
Ahammed et al.
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with average monthly rainfall dropping to a minimum of 39.
93 mm in August.
The annual average rainfall is 234Ae287 mm.
BaliAos topography is dominated by Gunung Agung, a 3,148 m stratovolcano, and the islandAos total coastline stretches for approximately 530 km, encompassing diverse Coastal areas include mangroves, beaches, and cliffs.
Bali, renowned for its vibrant tourism industry, faces significant challenges in the preservation of mangroves and other shallow-water ecosystems, resulting in the loss and deterioration of their health (Mahasani et al.
, 2015.
Sugiana et al.
, 2022.
Karang et al.
, 2.
Mangrove areas are particularly vulnerable to these changes and experience direct impacts from land reclamation on tourist infrastructure and associated pollution from increased human activities (Newton et al.
Steven et al.
, 2.
Coastal development can disrupt the natural hydrological patterns and affect the balance between salinity and water flow, which is crucial for mangrove health (Montagna et al.
, 2012.
Newton et al.
, 2020.
White & Kaplan, 2.
Figure 1.
Location map of the study area: .
administrative area of Indonesia.
Administrative area of Bali Province.
Study area showing the locations of mangrove sample sites with dominant species.
Data acquisition To determine the leaf canopy CC in various mangrove species, multiple field sampling surveys were conducted between October and November, 2023.
The Global Positioning System (GPS) locations of the centre points from the 10 y 10 m field plots and the most dominant tree species in the field plot corresponding to the GPS location were collected (Figure .
The sample points were strategically selected based on the accessibility and diversity of the species and were equally distributed across the study area.
These sampling points were used for canopy CC mapping.
This study focused on six mangrove stand types: Avicennia marina.
Bruguiera gymnorrhiza.
Ceriops tagal.
Rhizophora apiculata.
Rhizophora mucronata, and Sonneratia alba.
S2 imagery with a resolution of 10 m guided the selection of 89 plots that were strategically positioned to encompass extensive patches dominated by individual species.
This approach was followed to ensure the acquisition of pure pixels, each representing a distinct mangrove species.
Image acquisition and processing S2 provides 13 spectral bands with varying spatial resolutions of 10Ae60 m (Table .
and three red-edge bands .
Ae760 n.
, which have proven successful in retrieving vegetation parameters.
In this study, cloudless S2 level-2A surface reflectance products with radiometric and geometric corrections were acquired and processed via the Google Earth engine API, and their acquisition times ranged from 01Ae10Ae2023 to 20Ae11Ae2023 as the fieldwork was conducted between October and November.
Twenty cloud-free images were acquired during this period.
The means of all Ahammed et al.
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available images were selected for further analysis.
Averaging filters can help reduce noise and potentially improve the accuracy of CC estimates, particularly in areas with low inherent spatial variability (Xue et al.
, 2.
All bands were resampled to a 10 m spatial resolution.
Furthermore, 10 m spatial resolution bands were utilised for further analysis (Figure .
Table 1.
General description of the Sentinel 2A and 2B satellite images.
Band name Pixel size .
6/492.
560/559
5/665
9/703.
Description Aerosols Blue Green Red Red edge 1 2/739.
5/779.
Red edge 2
Red edge 3
1/833
8/864
945/943.
1,613.
7/1,610.
2,202.
4/2,185.
B8A
B11
B12
Wavelength S2A/S2B .
9/442.
NIR
Red edge 4
Water vapour
SWIR 1
SWIR 2
Estimating canopy Chl value with VIs VIs are crucial tools for estimating CC and leaf area index (LAI) in scientific research (Broge & Leblanc, 2001.
Haboudane et al.
, 2002.
Liang et al.
, 2016.
Sun et al.
, 2.
These indices simplify complex multispectral data and convert them into single variables for predicting and assessing various vegetation characteristics (Sun et al.
, 2.
Among these indices, the NDVI (Weier & Herring, 2.
is a widely recognised and commonly employed metric.
The NDVI is calculated using Equation 1.
ycAyaycOya = yuU833 yuU833 yuU665 yuU665 Here, yuUycn represents the reflectance at the band centred at a specific wavelength i .
n n.
CC,
green biomass, nitrogen content, and LAI have been widely proven useful in diverse studies on plant development (Fava et al.
, 2009.
Pu et al.
, 2.
In addition, numerous alternative indices sensitive to LAI have been proposed, often utilising bands in the red-edge regions (Daughtry et , 2000.
Glenn et al.
, 2008.
Haboudane et al.
, 2.
The typical spectra of vegetation present a plateau above 750Ae800 nm, which is determined by the structure and composition of leaves (Asner et al.
, 1.
the soil response, excluding the Chl, is relatively featureless.
The influence of Chl induces a minimum reflectance at approximately 665 nm, such that greater the presence of Chl.
To estimate the amount of Chl in plants from multispectral data, the normalised area over the reflectance curve (NAOC) yields the best results among the other VIs, and thus, the NOAC was employed (Delegido et al.
, 2.
Because S2 had many .
bands available, it was possible to derive vegetation characteristics using a more continuous approach instead of using only two Notably, certain characteristics of the S2 instrument, such as spatial size and signal-tonoise ratio, were not explicitly considered in the analysis.
The S2 band configuration features three spectral bands situated in the red-edge region: bands B5 and B6 are positioned at the sharp edge, and band B7 is situated at the shoulder of the near-infrared (NIR) plateau.
Importantly, these three bands, along with the B4 band, were aligned precisely within the integration limits of NAOC.
The NAOC index was used to estimate the CC and was defined as described previously (Delegido et al.
, 2.
Using the available bands in S2, the NAOC for estimating the CC was obtained with integration limits from a = 665 nm to b = 783 nm, resulting in a final expression for NAOC given by Equation 2.
ycAyaycCya O665 yuU yccyuI = 1Oe 118 yuU783 Furthermore, the CC estimated using NAOC can be expressed as follows (Equation .
yaEayco .
uNyci/ycayco2 ) Oe3.
94 O ycAyaycCya The NAOC has demonstrated its reliability as a predictor of Chl.
In a recent study that compared its predictive efficacy against 32 established indices sensitive to Chl.
NAOC was one of the best Ahammed et al.
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performers, securing a position within the top three indices in terms of accuracy (Verrelst et al.
Figure 2.
Schematic representation of the methodology workflow.
Results and Discussion Spatial variation in CC The assessment of leaf CC via S2 imagery has emerged as a pivotal element in understanding the intricate dynamics of vegetation health and ecological processes within the studied mangrove The observed NOAC-Chl ranged from 33.
64 to 0 (Figure 3.
, signifying a comprehensive depiction of vegetation conditions, with lower values coinciding with areas devoid of The calculated average CC of 2.
34 A 8.
92 provides a baseline metric for evaluating the overall Chl abundance in the study area.
The observed NDVI values ranged between Oe0.
97 (Figure 3.
The spatial distribution of CC offers intriguing insights into the ecological dynamics of the mangrove landscape.
The lowest CC in areas adjacent to water bodies and built-up structures suggested the presence of potential stressors in these regions (Figure .
Vegetation in such areas is more stressed than that in other areas due to pollutants in the air and soil (Czaja et al.
, 2020.
Kannankai et al.
, 2022.
Zhao et al.
, 2.
Conversely, the observation of the highest CC in the core areas of the mangrove patches indicates robust and healthy vegetation in these central regions owing to the absence of pollutants (Hai et al.
, 2.
This spatial heterogeneity in CC reveals the potential impacts of urbanisation and proximity to water bodies on the Chl dynamics of mangrove ecosystems (Wong et al.
, 2021.
Zhao et al.
, 2.
Notably, a distinct pattern was observed, with high-Chl patches predominantly located in the northern part of the study area, whereas areas in the southern part of the study area presented signs of stress.
This spatial variation may be attributed to a combination of factors, including developmental activities, waste disposal, and the influence of climate change scenarios such as rising temperatures and salinity (Arifanti 2020.
Ng & Ong 2.
The suggestion of stress in the Ahammed et al.
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southern areas necessitates further investigation to unravel the intricate interplay between anthropogenic and environmental factors influencing mangrove health.
Figure 3.
Maps showing the spatial variation in mangrove health derived from vegetation indices: .
NDVI and .
NOAC-Chl.
uNyci/ycayco.
Figure 4.
Land use/landcover map of the study area.
These findings highlight the utility of S2 in providing detailed insights into the spatial distribution of Chl, thereby enabling the identification of areas under potential stress and those exhibiting healthier vegetation conditions (Misra et al.
, 2.
This knowledge is crucial for targeted conservation and management strategies, especially in urbanised and water-body-adjacent regions Ahammed et al.
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where mangroves face heightened challenges (Czaja et al.
, 2020.
Kannankai et al.
, 2022.
Zhao et , 2.
Future analyses should delve deeper into the specific drivers of stress and their implications for mangrove resilience to offer a foundation for informed decision-making and sustainable management practices in the face of evolving environmental dynamics.
Canopy CC in mangrove species The results obtained from the estimation of the CC in mangrove plant canopies using S2 imagery provide valuable insights into the ecological dynamics of different mangrove species.
The application of the NDVI revealed distinct variations in vegetation density and health among the studied species (Rugel et al.
, 2.
Ceriops tagal had the highest NDVI .
, indicating robust health, whereas R.
apiculate presented the lowest NDVI .
, suggesting potential variations in vitality.
The observed NDVI range .
3Ae0.
(Table .
emphasised the diverse physiological conditions across the mangrove ecosystem.
Notably, the highest average NDVI values were associated with R.
86 A 0.
, underscoring its elevated CC.
Conversely.
alba presented the lowest average NDVI .
73 A 0.
suggesting potential differences in its CC.
apiculata showed the greatest variation in the NDVIbased CC, highlighting its heterogeneous health conditions.
The introduction of the NAOC as a novel index for CC estimation further enriches this study.
The CC variation induced by the NOAC reinforced the diversity in CC among the studied species (Figure 5.
Table .
mucronata presented the highest average CC .
48 A 4.
49 yuNyci/ycayco.
, confirming its robust CC, whereas S.
alba presented the lowest average CC .
45 A 3.
02 yuNyci/ycayco.
marina presented the highest CC value .
98 yuNyci/ycayco.
, indicating its distinct Chl richness, whereas R.
apiculata presented the lowest CC value .
42 yuNyci/ycayco.
(Table .
These findings highlight the intricate interplay among species-specific CC in mangrove ecosystems.
The observed variations underscore the importance of employing advanced remote sensing techniques, such as NDVI and NAOC, to capture nuanced ecological dynamics (Gafurov et al.
Su et al.
, 2.
This study not only contributes to the understanding of mangrove health but also provides a foundation for future ecological assessments and conservation strategies tailored to the specific needs of different mangrove species.
It is crucial to consider diverse Chl patterns in mangroves to formulate effective conservation and management practices in the face of environmental challenges.
Figure 5.
Chlorophyll concentrations in different species derived from .
the NDVI and .
NOAC-Chl.
uNyci/ycayco.
The box diagram shows the minimum and maximum values, and the black dots represent the average value of each species.
Table 2.
Statistics of the NDVI values of different species.
Ahammed et al.
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Species No of samples Mean Min Max Avicennia marina Bruguiera gymnorrhiza Ceriops tagal Rhizophora apiculata Rhizophora mucronata Sonneratia alba Table 3.
Statistics of NOAC-Chl values in different species.
Species No of samples Mean Avicennia marina Bruguiera gymnorrhiza Ceriops tagal Rhizophora apiculata Min Max Rhizophora mucronata Sonneratia alba 3 Correlations between the NDVI and NOAC-Chl The observed disparity in canopy CCs estimated via NDVI and NAOC prompted a meticulous exploration of their distinct sensitivities to vegetation health.
The relatively low correlation coefficient .
A OO 0.
(Figure .
between NDVI and NOAC-Chl suggests that these indices provide complementary information, each capturing unique aspects of Chl distribution in the studied mangrove ecosystem.
Figure 6.
Scatter plot showing the correlation between the NDVI and NOAC-Chl.
The divergence in wavelength utilisation becomes evident in this study, with the NDVI relying on red and NIR bands and the NOAC incorporating multiple spectral bands in the red-edge region.
This discrepancy in wavelength sensitivity contributed to the observed variation in CC.
Owing to its broader spectral range.
NDVI can capture different aspects of vegetation health, whereas NOAC, with its emphasis on red-edge bands, tends to offer a more refined perspective of Chl distribution (Cendrero et al.
, 2014.
Misra et al.
, 2020.
Sun et al.
, 2.
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A comparative analysis revealed the superior performance of NOAC in Chl estimation compared to that of NDVI.
This outcome aligns with the understanding that indices focusing on the rededge region can provide more accurate assessments of CC than those focusing on the other regions, owing to their heightened sensitivity to subtle variations in leaf pigments and physiological conditions (Chang-Hua et al.
, 2010.
Cendrero et al.
, 2014.
Guimaryes et al.
, 2.
Figure 7.
Scatter plots showing the correlation between the NDVI and NOAC-Chl.
in different species.
Furthermore, species-specific correlation analyses provide insights on the subtle responses of different mangrove species to NDVI and NOAC.
Notably.
apiculata and B.
gymnorhiza presented greater correlations in the two indices .
A = 0.
39 and 0.
33, respectivel.
(Figure .
, indicating a stronger alignment between the CCs of these species, as estimated by the NDVI and NOAC.
alba displayed a notably lower correlation .
A = 0.
(Figure .
, sugges Ahammed et al.
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Acknowledgements The authors would like to thank ESA for the Sentinel data.
Google for GEE platform.
Special thanks to the Udayana University for the Support.
This work was supported by the UNISERF Project DIPA PNBP of Udayana University FY 2023 ( B/775235/UN14.
A/PT.
03/2023,
dated 26 July 2.
funded by the Udayana University.
Indonesia Author Contributions Conceptualisation: Basheer Ahammed.
Karang.
methodology: Basheer Ahammed .
Karang.
Nuarsa.
Hendrawan.
, & Pandey.
Basheer Ahammed.
Karang, .
Indrawan.
, &
Dewi.
writingAi original draft preparation:
Basheer Ahammed.
, & Karang.
writingAireview and editing: Basheer Ahammed, , & Karang.
visualisation: Basheer Ahammed.
, & Karang.
All authors have read and agreed to the published version of the manuscript.
Conflict of interest All authors declare that they have no conflicts of interest.
Data availability Data is available upon request.
Funding This work was supported by the UNISERF Project DIPA PNBP of Udayana University FY 2023, inaccordance with the Research Implementation Assignement Letter (SP.
Number: B/775235/UN14.
A/PT.
03/2023,
dated 26 July 2023 funded by the Udayana University.
Indonesia.
Ahammed et al.
ting a potential divergence in the response of this species to the two indices.
mucronata presented a unique scenario with a negative correlation .
A = Oe0.
, necessitating further investigation into the underlying factors influencing Chl dynamics.
The red-edge band is particularly valuable for identifying subtle variations in plant health and stress (Li et al.
, 2.
As the CC decreases due to stress factors, such as nutrient deficiencies, diseases, or drought, the reflectance pattern in the rededge region shifts (Chang-Hua et al.
, 2.
The red-edge band can also be helpful in differentiating different vegetation types based on their CC and pigment composition.
For example, healthy broadleaf plants typically exhibit steeper slopes in red-edge regions than do coniferous trees (Fernyndez et al.
, 2.
This study underscores the importance of selecting appropriate indices for accurate Chl estimation considering the unique spectral characteristics of different mangrove species.
The dissimilarities between NDVI and NOAC highlight the need for a comprehensive approach that integrates multiple indices to capture the multifaceted nature of vegetation health in mangrove ecosystems.
This fine understanding is crucial for developing informed conservation and management strategies tailored to the diverse physiological nuances of various mangrove species.
Conclusion This study provides a comprehensive exploration of mangrove health and Chl dynamics in tropical ecosystems, using advanced remote sensing techniques.
The integration of the NDVI and NAOC offers nuanced insights into Chl distribution among different mangrove species.
The observed low correlation between NDVI and NOAC-Chl underscores the distinct scenarios captured by these indices, emphasising the importance of their complementary use for a comprehensive understanding of vegetation health.
Utilising S2 imagery, this study further enriches our understanding of CC, revealing spatial variations indicative of stressors and thriving vegetation patches within mangrove landscapes.
The identification of lower Chl values near urban areas and water bodies, coupled with higher CCs in core mangrove regions, than those in the other areas implies the influence of anthropogenic activities on mangrove health.
The spatial disparity between the northern and southern study areas suggests potential impacts of developmental activities, waste disposal, and climate change scenarios, emphasising the need for further investigation.
Moreover, this study addresses the broader context of anthropogenic pressures on mangrove health, specifically highlighting the detrimental impact of unregulated tourism practices.
Boat traffic and recreational activities associated with tourism can cause physical damage to mangrove ecosystems, thereby contributing to the overall loss of mangrove areas and compromising their The cumulative effects of tourism-related pressures underscore the urgency of implementing sustainable tourism practices, stringent regulations, and community involvement.
These measures are essential for mitigating the negative impacts and ensuring the preservation of BaliAos mangrove ecosystems amid the flourishing tourism industry.
To summarise, this study not only advances our understanding of mangrove Chl dynamics but also underscores the critical need for integrated conservation strategies that consider the intricate interplay of environmental, anthropogenic, and tourism-related factors.
By adopting a holistic approach informed by the findings of this study, we can pave the way for sustainable management and preservation of mangrove ecosystems in the face of ongoing environmental changes and human activities.
References