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Mangroves of Barangay Jawili, Tangalan, Aklan province, central Philippines

Mangroves of Barangay Jawili, Tangalan, Aklan province, central Philippines

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Practical mapping methods employing GPS field surveys and manual image analyses with affordable software were used to assess two mangrove sites in Aklan Province, NE Panay Island, central Philippines. The Jawili mangroves, absent from current maps, actually included 21.5ha with 24 true mangrove species. On the other hand, the Batan Estuary mangrove...

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... more surprising is the high diversity of true mangrove species found in this small area. The Philippines has about 40 true species of mangroves (Primavera 2000; Primavera et al. 2004) and the Jawili mangroves has 24 of these (Table 1) true mangroves (Duke 1992;Tomlinson 1986). The Jawili mangroves were mostly secondary growth of Avicennia marina, Sonneratia alba, and Aegiceras corniculatum lining the rivers and creeks. ...

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... Few Philippine studies took advantage of this tool to advance mangrove research. The high acquisition cost of equipment, software, and expertise was the major constraint limiting GIS technology to well-financed institutions (Altamirano et al., 2010). It was suggested that a GIS-based tool leading to implications for mangrove conditions should be developed, given that data inconsistencies were commonly encountered in Southeast Asia (Adeel and Pomeroy, 2002). ...
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The utility of Geographic Information System (GIS) has been adopted in mapping and predicting sites for mangrove suitability in Alabel, Sarangani Province, Province. Biodiversity assessment of coastal mangroves in Alabel, Sarangani Province, Philippines was performed in purposive manner using sampling quadrats. There were 13 observed mangrove species in sand-dominated (51.48%) soil. The sampled field data points are scattered irregularly, showing slight curvature on the east-west trendline but a flatter surface on the north-south trendline. The abundance and soil data were converted into shapefiles and subjected to inverse distance weighted (IDW) interpolation. The rasterized and reclassified thematic map layers were assigned with factor influence consisting of mangrove abundance (40%), land cover (30%), sand composition (20%), and slope (10%) for suitability analysis. The suitable mangrove areas were computed using weighted overlay suitability analysis, and the final score for the suitability index was converted into suitability classes. Prediction of mangrove suitable areas was performed under the ModelBuilder workflow tool in ArcMap. The mangrove suitability classes were extracted using a zonal geometry tool depicting three suitability classes. The predicted mangrove high suitability areas covered 463.37 km 2 , the low suitability areas covered 573.02 km 2 , and the unsuitable areas covered 9,403.77 km 2. The study utilized a combination of field gathering and available online data for GIS analysis which local government units could perform. The results could be used to plan and manage mangrove resources in the area effectively.
... This area has a seasonal climate, with a dry season extending from December to April, and a rainy season from June through October. More than 90 % of the mangrove in Batan Bay Estuary has been converted to aquaculture ponds through 2008 (Altamirano et al. 2010). However, several mangrove restoration stations were established by universities, the government (Department of Environment and Natural Resources), and local communities. ...
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... Profile of mangrove areas are well established in the municipal plans since several mangrove ecosystem assessments have already been done in Eastern Samar-Salcedo and Lawaan (i.e., Mendoza and Alura 2001; Salmo, De la Cruz, and Gianan 2019), and Aklan-Batan, and Kalibo (i.e., Primavera et al. 2004;Walton et al. 2006;Altamirano et al. 2010) that could provide data for the mangroves' location, abundance, distribution, and species present. Mangrove habitat assessments have increased in these provinces in response to the destruction brought by super typhoon Haiyan (i.e., Alura and Alura 2016;Long et al. 2016;Primavera et al. 2016). ...
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... These can be utilized as vector and raster data inputs as which can be manipulated using geospatial techniques. It was utilized to detect mangrove cover changes influenced by typhoons (Buitre et al. 2019), mapping small patches of mangroves (Altamirano et al. 2010), and determining optimal locations for planting (Syahid et al. 2020). The geospatial analysis has been a valuable tool for mangrove rehabilitation and management (Omar et al. 2019). ...
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... These grids were loaded in the handheld Garmin 62CSX, GPS for field investigation. Enumeration of mangrove species was carried out through a 150 m line transect technique [61] with a 50 m interval between each transect within the 1 km 2 grid during the dry season (January-May, 2019 and March-April, 2020). These line transects were laid orthogonal to the coast either ways (land to sea and sea to land). ...
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Andaman and Nicobar Islands (ANI’s) being situated in the Tropical zone is the cradle of multi-disasters viz., cyclones, floods, droughts, land degradation, runoff, soil erosion, shallow landslides, epidemics, earthquakes, volcanism, tsunami and storm surges. Mangroves are one of the first visible reciprocators above land and sea surface to cyclonic storms, storm surges, and tsunamis among the coastal wetlands. The Indian Ocean 2004 tsunami was denoted as one of the most catastrophic ever recorded in humankind’s recent history. A mega-earthquake of Magnitude (9.3) near Indonesia ruptured the Andaman-Sunda plate triggered this tsunami. Physical fury, subsidence, upliftment, and prolonged water logging resulted in the massive loss of mangrove vegetation. A decade and half years after the 2004 tsunami, a study was initiated to assess the secondary ecological succession of mangrove in Tsunami Created Wetlands (TCWs) of south Andaman using Landsat satellite data products. Since natural ecological succession is a rather slow process and demands isotope techniques to establish a sequence of events succession. However, secondary ecological succession occurs in a short frame of time after any catastrophic event like a tsunami exemplifying nature’s resilience. Band-5 (before tsunami, 2003) and Band-6 (after tsunami, 2018) of Landsat 7 and Landsat-8 satellite respectively were harnessed to delineate mangrove patches and TCWs in the focus area using ArcMap 10.5, Geographic Information Systems (GIS) software. From the study, it was understood that Fimbrisstylis littoralis is the pioneering key-stone plant followed by Acrostichum aureum and Acanthus ilicifolius facilitating Avicennia spp/Rhizopara spp for ecological succession in the TCWs.
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Mangroves are coastal ecosystems and a detailed characterization of their spatial metrics is important to understand the underlying processes of mangrove change and to inform future mangrove management. The main objectives of this study were: (1) to determine the past (1940) and current (2014) spatial metrics of mangroves at high latitudes and (2) to assess the relationship between spatial metrics and land-based, climatic and oceanic factors in 2014. Mangroves were mapped into categories of tall and dwarf mangroves, as well as into delta, riverine and estuarine mangroves based on field surveys, satellite images and aerial photographs across 38 estuaries of varying size in the Auckland Region, New Zealand. Fragstats was applied to determine spatial metrics indices related to complexity, connectivity, and configuration of mangrove patches. While the number of mangrove patches remained stable, mangrove patch size increased significantly (75%) suggesting that the increase in total mangrove area in the Auckland Region is mainly driven by an increase in patch size. Tall mangroves became more fragmented, through both reduction in connectivity (Mean Proximity Index in 2014 = 214, decrease of 35%) and their shape becoming more irregular (Mean Shape Index in 2014 = 1.9, an increase of 24%). Catchment related factors (e.g., sediment accumulation rate) had a strong effect on the total mangrove area and number of patches. In contrast, mean patch size and patch connectivity were influenced by nitrogen concentration, mean wave height and tidal amplitude, which in combination explained up to 63% of the variability. These findings can be used to facilitate decision-making concerning the sustainable future management of mangroves.
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... Kamthonkiat et al. (2011) also monitored damage and recovery in Thailand after the Indian Ocean tsunami of 2004 using Aster satellite images. Although they were able to obtain useful information from remote sensing data, high-resolution remote sensing data are very expensive (Altamirano et al. 2010;Hirata et al. 2010). Moreover, in order to analyze satellite images fully utilizing their multiple wavelengths, expensive softwares are required. ...