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Rainfall Variability and Tidal Inundation Influences on Mangrove Greenness in Karimunjawa National Park, Indonesia. SUSTAINABILITY 2022. [DOI: 10.3390/su14148948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mangroves, which are vulnerable to natural threats and human activities on small islands in the tropics, play an essential role as carbon sinks, helping to mitigate climate change. In this study, we discussed the effect of natural factors on mangrove sustainability by analyzing the impact of rainfall, land surface temperature (LST), and tidal inundation on the greenness of mangroves in Karimunjawa National Park (KNP), Indonesia. We used Sentinel-2 image data to obtain the normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) during the dry season to determine the effect of inundation on mangrove greenness and soil moisture. The tidal inundation area was calculated using topographic data from the KNP and tidal observations from the area adjacent to it. Unmanned autonomous vehicles and topographic data were used to estimate mangrove canopy height. We also calculated mangrove greenness phenology and compared it to rainfall from satellite data from 2019–2021. Results show that the intertidal area is dominated by taller mangroves and has higher NDVI and NDMI values than non-intertidal areas. We also observed that mangroves in intertidal areas are mostly evergreen, and optimum greenness in KNP occurs from February to October, with maximum greenness in July. Cross-correlation analysis suggests that high rainfall affects NDVI, with peak greenness occurring three months after high rainfall. The LST and NDVI cross-correlation showed no time lag. This suggests that LST was not the main factor controlling mangrove greenness, suggesting tides and rainfall influence mangrove greenness. The mangroves are also vulnerable to climate variability and change, which limits rainfall. However, sea-level rise due to climate change might positively impact mangrove greenness.
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Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing. REMOTE SENSING 2022. [DOI: 10.3390/rs14143365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest.
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Celis-Hernandez O, Villoslada-Peciña M, Ward RD, Bergamo TF, Perez-Ceballos R, Girón-García MP. Impacts of environmental pollution on mangrove phenology: Combining remotely sensed data and generalized additive models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 810:152309. [PMID: 34910948 DOI: 10.1016/j.scitotenv.2021.152309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
Mangrove ecosystems worldwide have been affected by anthropogenic activities that modify natural conditions and supply trace elements that affect mangrove health and development. In order to gain a better understanding of these ecosystems, and assess the influence of physicochemical (granulometry, pH, salinity and ORP) and geochemical variables (concentrations of V, Cr, Co, Ni, Cu, Zn, Pb, Rb, Sr and Zr) on mangrove phenology, we combined field and satellite derived remotely sensed data. Phenology metrics in combination with Generalized Additive Models showed that start of the season was strongly influenced by Pb and Cu pollution as well as salinity and pH, with a large percentage of deviance explained (92.10%) by the model. Start of season exhibited non-linear delays as a response to pollution. Other phenology parameters such as the length of season, timing of the peak of season, and growth peak also indicated responses to both trace elements and physicochemical and geochemical variables, with percentages of deviance explained by the models ranging between 33.90% and 97.70%. While the peak of season showed delays as a response to increased pH and decreased salinity, growth peak exhibited a non-linear decrease as a response to increased Sr concentrations. These results suggest that trace element pollution is likely to lead to altered phenological patterns in mangroves.
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Affiliation(s)
- Omar Celis-Hernandez
- Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Estación el Carmen, Campeche, C.P. 24157 Ciudad del Carmen, Mexico; Dirección de Cátedras CONACYT, Av. Insurgentes Sur 1582, Alcaldía Benito Juárez, C.P. 03940 Ciudad de México, Mexico.
| | - Miguel Villoslada-Peciña
- Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51014 Tartu, Estonia; Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
| | - Raymond D Ward
- Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51014 Tartu, Estonia; Centre for Aquatic Environments, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, United Kingdom.
| | - T F Bergamo
- Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51014 Tartu, Estonia
| | - Rosela Perez-Ceballos
- Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Estación el Carmen, Campeche, C.P. 24157 Ciudad del Carmen, Mexico; Dirección de Cátedras CONACYT, Av. Insurgentes Sur 1582, Alcaldía Benito Juárez, C.P. 03940 Ciudad de México, Mexico
| | - María Patricia Girón-García
- Laboratorio de Fluorescencia de Rayos X. LANGEM. Instituto de Geología, Universidad Nacional Autónoma de México, Circuito Exterior, Ciudad Universitaria, Coyoacan, C.P. 04510, Ciudad de México, Mexico
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