No abstract

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.

No abstract

No abstract