The objective of this research is to determine the suitability of Worldview-2 high resolution multispectral data in classifying and mapping benthic habitats, specifically seagrass. Worldview-2 offers an increased number of spectral bands for high-resolution image, from the traditional 4bands to 8 bands. It boasts of the ability to enhance mapping and monitoring of benthic habitats with the addition of the Coastal Band. This was investigated in this research using a Worldview-2 image of Bolinao, Pangasinan acquired on March 2010. The study site, Bolinao, has the highest single concentration of seagrass in the northern part of the Philippines. To achieve more accurate results, geometric, atmospheric and water column corrections were applied to the images. For geometric correction, a Differential Global Positioning System Topcon Hiper Ga model receiver was used to obtain highly accurate ground control points. Atmospheric correction was performed in ENVI using the Fast Line-of-Sight Atmospheric Analysis (FLAASH) model. Three water column correction models were applied and compared in this research, Lyzenga's Optical Model, Stumpf's Ratio Model and Simple Radiative Transfer Model. A spectral library was created using in situ reflected spectral radiances on both submerged and emerged vegetation to aid in image classification. Different benthic covers, seagrass, sand, corals and rocks are significantly separable spectrally based on spectral signatures obtained on field using a USB 4000 Fiber Optic Spectrometer. Maximum likelihood supervised classification in ENVI 4.8 is utilized for mapping. Using Worldview 2's coastal, green, yellow and red bands and applying the Simple Radiative Transfer Model produced the highest overall accuracy (88.3%) among the classification results. Using the same bands, Stumpf's Ratio Model produced 87.84% overall accuracy while Lyzenga's optical model achieved 75.54%. Moran's I spatial autocorrelation was implemented to increase the classification accuracy. Using lag 1 slightly increased Stumpf's Model's overall accuracy, from 87.84% to 88.08% while using lags 5 and 10 decreased the overall accuracy with 83.91% and 84.25% respectively.

Abstract. Fertilizer application is a crucial farming operation for regulating crop health thus crop yield. Optimal fertilizing doubles agricultural production subsequently raising farmers’ income, food security and economic agriproducts. To optimize the application of fertilizers, initial monitoring of the current nutrient status of the crops is required. This research will focus on Nitrogen (N), the most extensive fertilizer nutrient in crop cultivation. Conventional N monitoring involves the use of Leaf Color Charts (LCC) wherein leaf color intensity is associated with the N content of the crops. Despite its ability to quantify the optimal amount of needed fertilizers, the LCC method requires extensive on-site labor and lacks accuracy. This study developed a method that incorporates capabilities of Unmanned Aerial Vehicles (UAVs) equipped with a multispectral sensor in N monitoring specifically in rice crops, a major agricultural product in the Philippines. In situ N level information collected through LCC was correlated with remote sensing data, particularly vegetation indices (VIs) extracted from UAV multispectral imagery of a rice plantation in San Rafael, Bulacan. Several VIs sensitive to crop N content were tested to determine which has the highest correlation with the LCC data. Through Pearson correlation and regression analysis, NDVIRed Edge was found to be the most strongly correlated with LCC data suggesting its potential in mapping variability in fertilizer requirements. An equation modelling LCC observations and NDVIRed Edge values that estimates the N levels of an entire rice plantation was generated along with the N concentration map of the study area.

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<p><strong>Abstract.</strong> As the unmanned aerial vehicle (UAV) technology has gained popularity over the years, it has been introduced for air quality monitoring. This study demonstrates the feasibility of customized UAV with mobile monitoring devices as an effective, flexible, and alternative means to collect three-dimensional air pollutant concentration data. This also shows the vertical distribution of PM concentration and the relationship between the PM_2.5 vertical distribution and the meteorological parameters within 500<span class="thinspace"></span>m altitude on a single flight in UP Diliman, Quezon City. Measurement and mapping of the vertical distribution of particulate matter (PM)_2.5 concentration is demonstrated in this research using integrated air quality sensors and customized Unmanned Aerial Vehicle. The flight covers an area with a radius of 80 meters, following a cylindrical path with 40-meter interval vertically. The PM_2.5 concentration values are analyzed relative to the meteorological parameters including air speed, pressure, temperature, and relative humidity up to a 500<span class="thinspace"></span>meter-flying height in a single flight in Barangay UP Campus, UP Diliman, Quezon City. The study shows that generally, the PM_2.5 concentration decreases as the height increases with an exception in the 200&amp;ndash;280<span class="thinspace"></span>m above ground height interval due to a sudden change of atmospheric conditions at the time of the flight. Using correlation and regression analysis, the statistics shows that PM_2.5 concentration has a positive relationship with temperature and a negative relationship with relative humidity and wind speed. As relative humidity and wind speed increases, PM_2.5 decreases, while as temperature increases, PM_2.5 also increases.</p>

ABSTRACT:The Philippines embarked on a nationwide mapping endeavour through the Disaster Risk and Exposure Assessment for Mitigation (DREAM) Program of the University of the Philippines and the Department of Science and Technology (DOST). The derived accurate digital terrain models (DTMs) are used in flood models to generate risk maps and early warning system. With the availability of LiDAR data sets, the Phil-LiDAR 2 program was conceptualized as complementary to existing programs of various national government agencies and to assist local government units. Phil-LiDAR 2 aims to provide an updated natural resource inventory as detailed as possible using LiDAR point clouds, LiDAR derivative products, orthoimages and other RS data. The program assesses the following natural resources over a period of three years from July 2014: agricultural, forest, coastal, water, and renewable energy. To date, methodologies for extracting features from LiDAR data sets have been developed. The methodologies are based on a combination of object-based image analysis, pixel-based image analysis, optimization of feature selection and parameter values, and field surveys. One of the features of the Phil-LiDAR 2 program is the involvement of fifteen (15) universities throughout the country. Most of these do not have prior experience in remote sensing and mapping. With such, the program has embarked on a massive training and mentoring program. The program is producing more than 200 young RS specialists who are protecting the environment through RS and other geospatial technologies. This paper presents the program, the methodologies so far developed, and the sample outputs.