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Research Highlights: This paper provides an alternative approach to contextualize mangrove forest loss by integrating available environmental and socio-economic data sets and products. Background and Objectives: Mangrove forest ecosystems grow in brackish water especially in areas exposed to accumulation of organic matter and tides. This forest type is widely distributed in tropical and subtropical coastal areas. Recent studies have revealed that the mangrove forest ecosystem had significantly degraded due to Land Use and Cover Changes (LUCC) in the recent past. Therefore, contribution of mangrove deforestation drivers has to be assessed to ensure a comprehensive analysis for ecosystem conservation and restoration and facilitate decision making. Materials and Methods: Firstly, a correlation analysis was conducted between individual data products and mangrove deforestation. Each data product was associated with the Dominant Land Use of Deforested Mangrove Patches data for 2012. Next, calculations were performed for specific data combinations to estimate the contributions of anthropogenic factors to mangrove deforestation. Results: In general, our study revealed that 22.64% of the total deforested area was converted into agriculture, 5.85% was converted into aquaculture, 0.69% was converted into infrastructure, and 16.35% was not converted into any specific land use class but was still affected by other human activities. Conclusions: We discovered that the percentage of land affected by these anthropogenic factors varied between countries and regions. This research can facilitate trade-off analysis for natural resources and environmental sustainability policy studies. Diverse management strategies can be evaluated to assess the trade-offs between preserving mangrove forests for climate change mitigation and transforming them for economic purposes.

The alarming rate of global mangrove forest degradation corroborates the need for providing fast, up-to-date and accurate mangrove maps. Conventional scene by scene image classification approach is inefficient and time consuming. The development of Google Earth Engine (GEE) provides a cloud platform to access and seamlessly process large amount of freely available satellite imagery. The GEE also provides a set of the state-of-the-art classifiers for pixel-based classification that can be used for mangrove mapping. This study is an initial effort which is aimed to combine machine learning and GEE for mapping mangrove extent. We used two Landsat 8 scenes over Agats and Timika Papua area as pilot images for this study; path 102 row 64 (2014/10/19) and path 103 row 63 (2013/05/16). The first image was used to develop local training areas for the machine learning classification, while the second one was used as a test image for GEE on the cloud. A total of 838 points samples were collected representing mangroves (244), non-mangroves (161), water bodies (311), and cloud (122) class. These training areas were used by support vector machine classifier in GEE to classify the first image. The classification result show mangrove objects could be efficiently delineated by this algorithm as confirmed by visual checking. This algorithm was then applied to the second image in GEE to check the consistency of the result. A simultaneous view of both classified images shows a corresponding pattern of mangrove forest, which mean the mangrove object has been consistently delineated by the algorithm.

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Abstract. The objectives of this study were to evaluate the effects of sub-lethal lead concentrations on serum osmolality, Na + and Cl -levels, and hematological parameters in Nile tilapia, Oreochromis niloticus (L.) at different salinity levels. The serum osmolalities (SO) were not significantly different at any of the salinity levels in the control fish, while in Pb-exposed fish the SO increased with increasing salinity. The concentrations of serum Na + and Cl -in both the control and Pb-exposed fish increased with increasing salinity. The levels of red blood cells (RBC), hemoglobin (Hb), and hematocrit (Ht) in the control fish were not significantly different at any of the salinity levels. Meanwhile, the levels of RBC, Hb, and Ht in Pb-exposed fish increased with increasing salinity levels. The levels of RBC (at 0 and 5 ppt) and Ht (at 0, 5 and 10 ppt) in Pb-exposed fish were lower than in the control fish. The levels of Hb in Pb-exposed fish were lower than in the control fish at all salinity levels. The levels of WBC in the control fish increased with increasing salinity, while its levels in the Pb-exposed fish decreased with increasing salinity. The levels of WBC in the Pb-exposed fish were higher than in the control fish at 0 and 5 ppt.

In the present study, we investigated the effects of waterborne copper (Cu) on the levels of metallothionein (MT) and malondialdehyde (MDA), as well as activities of superoxide dismutase (SOD) and catalase (CAT) in gills of cichlid fish Oreochromis niloticus. The Cu concentrations in gills were measured using an atomic absorption spectrometer. The sandwich-ELISA was used to measure MT, SOD, CAT, and MDA. The Cu concentrations in gills of fish that were exposed to 1, 5, and 10 mg Cu/L were significantly increased at day 1 (D1), then gradually decreased starting from D2, and reaches the similar value with the controls at D5. A similar tendency has been observed in the MT levels in the gills. All of the Cu-exposed fish showed the highest level of MT on D1, and then decreased at D3 and a plateau at D4 and D5. The levels of SOD and CAT in gills in all Cu-exposed fish showed a similar pattern: increased significantly at D1, then gradually decreased starting from D2, and increased again at D4 and D5. The levels of MDA in gills of all Cu-exposed fish showed no significant difference. The indifference levels of MDA in gills of all Cu-exposed fish suggested the antioxidant defense systems (SOD and CAT) combined with the induction of MT were able to completely scavenge the increased ROS.