No abstract

Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

The Philippines being an archipelagic country has one of the most diverse mangrove ecosystems.  Species composition and diversity assessment are vital for sustainable management and conservation activities of mangrove resources. The diversity of mangroves was assessed to determine the status of the mangrove ecosystem in the central part of Zambales, Philippines. Data were gathered using a random quadrat sampling method in the twelve pre-selected stations. Each quadrat measures 1  meters and the quantitative description of each plant species was determined. There were a total of 15 mangrove species surveyed in the area, under seven families, which family Rhizophoraceae was the most represented, with six species, and families Euphorbiaceae, Meliaceae, and Myrsinaceae were only represented with one species each. Shannon’s Diversity Index showed that the mangrove ecosystems in Botolan had “very low” diversity (SDI=1.71) and Iba had “low” diversity (SDI=2.11). Anthropogenic sources such as agri-aqua and encroachment served as the major driving forces in the diversity of mangroves. Other threats include solid waste disposal, quarrying, oil spill, organic fertilizer run-off, and soil erosion among others. These may threaten species that could eventually cause greater biodiversity and economic loss. Hence, protection, conservation, and sustainable management be given priority.