アッサアイデ バヤン

<jats:p>This paper offers a comprehensive synthesis of 9 research papers from the Asia-Pacific Network for Global Change Research (APN) project titled "Enhancing Capacities of Local Stakeholders in Coral Triangle in Managing Blue Carbon Ecosystems for Climate Mitigation and Adaptation." These papers are organised into four key thematic areas: (1) assessing the status of mangrove degradation and its underlying factors, (2) exploring community perceptions of seagrass ecosystems and their associated services, (3) analysing local perspectives on sustainable tourism and its influence on blue carbon (BC) ecosystem services, and (4) discerning trends in research and coastal management strategies for BC ecosystems. The findings presented within these papers illuminate the intricate challenges surrounding BC ecosystems in the Philippines and Indonesia, underscoring a range of human-induced pressures and natural vulnerabilities. These studies emphasise the significance of incorporating community perceptions and socio-economic dynamics into the BC ecosystems' conservation and management strategies framework. The comparative insights derived from these papers hold vital implications for local stakeholders and policymakers. Practical training in Geographic Information Systems (GIS) can empower local communities to enhance their capacity-building efforts in the future. This is valuable guidance for shaping future BC ecosystem management plans and programs, particularly in a rapidly changing climate.</jats:p>

<jats:p>Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets asone of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset – GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes,which are urban, mangrove, wetland, snow/ice, and waterwere independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.</jats:p>

<jats:p>Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess spatial association. Our findings showed 70.59% overall accuracy with a 0.51 Kappa coefficient of the roof classification images. Results show that DDPs were found in hotspot, random, and dispersed patterns. Smaller PR size and larger FR size showed some association with increasing DDP into more clusters (OLS: PR value = −0.27; FR = 0.04; R2 = 0.076; GWR: R2 = 0.76). The associations in hotspot patterns are stronger than in other patterns (GWR: R2 in hotspot = 0.39, random = 0.37, dispersed = 0.23).</jats:p>