Scholarly Papers of Awardees

Collection of papers of awardees published in various journals.

Castillo, Alan - 2017 Soil C quantities of mangrove forests, their competing land uses, and their spatial distribution in the coast of Honda Bay, Philippines

Mangrove forests provide many ecosystem goods and services and they contain large amount of carbon (C) especially in their soil. Yet, their global area is still declining owing to conversion to non-forest land uses. While studies have been conducted on mangrove soil C stocks, our knowledge on how C stocks of mangrove forests compare with those of non-forest land uses that replaced them is still limited. This knowledge is crucial in land use planning and decision-making in the coastal zone. Site-scale mapping and assessments of mangrove soil C stocks and the land uses that replaced them are also limited. The aim of this study was to quantify and compare the soil C stocks in mangrove forests and their competing non-forest land uses (represented by aquaculture pond, coconut plantation, salt pond and cleared mangrove), estimate soil C loss arising from conversion, and model the soil C stock distribution in the entire study site. On the average, the soil C stock of mangrove forests was 851.9 ± 87 MgC ha−1 while that of their non-forest competing land uses was less than half at 365.1 ± 31 MgC ha−1. Closed canopy mangrove was highest at 1040 ± 104 MgC ha−1, followed by open canopy mangrove (640 ± 131 MgC ha−1) while aquaculture, salt pond and cleared mangrove had comparable C stocks (454 ± 32, 401 ± 9, 413 ± 25 MgC ha−1, respectively) and coconut plantation had the least (42 ± 0.7 MgC ha−1). Overall, the reduction in soil C stock (soil C loss) due to land use conversion in mangrove ranged from 398 to 809 MgC ha−1 (mean: 486.8 MgC ha −1 ) or a decline of 57% in soil C stock, on the average. It was possible to model the site-scale spatial distribution of soil C stocks and predict their values with 85% overall certainty using the Ordinary Kriging approach. Results from this study could help inform current discussions on Blue Carbon and REDD+as well as policy and program development that advance research on soil C conservation and ecosystem services in coastal forested wetlands.

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Castillo, Alan - The rate, extent and spatial predictors of forest loss (2000-2012) in the terrestrial protected areas of the Philippines

While studies on deforestation of protected areas (PAs) have been conducted in many parts of the world, no comparative study has been done over an entire country in the tropics. Thus, we conducted a countrywide assessment of forest cover loss in all terrestrial protected areas of the Philippines, covering 198 PAs with a total area of 4.68 million ha. This study utilised Hansen's Landsat-derived global maps of forest cover change from 2000 to 2012, with tree canopy cover data for 2000 as the base year. Correlation and logistic regression analyses were employed to determine the significance and magnitude of the relationships between forest cover and 11 predictor variables. The assessment of forest loss reveals that the terrestrial protected areas are generally effective in reducing forest loss. Over the 12-year period, the average rate (2.59%) of forest clearing in protected areas is marginally lower by 0.1% than the entire country (2.69%). Within the same duration, the average forest loss rate within the 2-km buffer zones of selected protected areas is 1.4 times of those inside PAs. However, there was a significant number of PAs with phenomenal forest cover loss in terms of extent (48,583 ha over 12 years) and rate (up to 21%). We found that spatial predictor variables included in this study have weak or no relationships with forest cover, and hence they are not reliable inputs for predictive modelling. Comprehensive assessments of deforestation are needed at the micro-scale (e.g. single PA level) level and relatively shorter historical timeframe (e.g. less than a decade), to generate useful information for policy formulation, planning, and management.

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Castillo, Alan - Castillo et-al - 2017 Soil greenhouse gas fluxes in tropical mangrove forests and in land uses on deforested mangrove lands

Mangrove forests are important carbon sinks in the tropics, yet tropical mangrove deforestation and land use conversion still persists. Reporting of greenhouse gas (GHG) emissions from natural and anthropogenic sources in wetlands are important in regional and national emissions inventories. However, very few studies have been conducted to measure on the GHG fluxes in coastal wetlands, particularly in mangrove forest and non-forest land uses in deforested mangroves. We investigated the soil fluxes of CO2, CH4 and NO in mangrove forest and nonforest land uses on deforested mangrove areas (i.e. abandoned aquaculture ponds, coconut plantations, abandoned salt ponds, and cleared mangroves) in the coasts of Honda Bay, Philippines. Results showed that the emissions of CO −12 and CH, respectively) while N42 were higher by 2.6 and 6.6 times in mangrove forests (110 and 0.6 kg CO O emissions were lower by 34 times compared to the average of non-forest land uses (1.3 kg CO 2e ha−1 day 2−1). CH4 and NO emissions accounted for 0.59% and 0.04% of the total emissions in mangrove forest as compared to 0.23% and 3.07% for non-forest land uses, respectively. Site-scale soil GHG flux distribution could be mapped with 75% to 83% accuracy using Ordinary Kriging. Unlike mangroves that can offset all GHG emissions through CO2 uptake from photosynthesis, the non-forest land uses cannot offset their emissions on-site as they are usually devoid of vegetation. Our results could be utilised in higher tier national GHG inventories, to refine regional and global estimates of GHG emissions in mangrove wetlands, and improve policy on coastal wetlands conservation.

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Castillo, Alan - Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery

The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.

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