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040			_cNVSU
100			_aSaballa, Alminador V.
245			_aEstimating aboveground biomass of capisan landscape using landsat 8 and gis ancillary data/ _cAlmindor V. Saballa
260			_aBayombong: _bNueva Vizcaya State University, _c2016.
300			_axiv, 110 leaves : _bcolored illustration , photos ; _c28 cm ;
500			_a The study employed the combination of remote sensing data, field measurement and topographic variables in estimating the aboveground biomass (AGB) of the Capisaan landscape, a landscape that features many different lands uses and land cover types. Its land cover is predominantly dipterocarp and karst forests interspersed by citrus orchards and patches of farms. AGB densities were estimated using regression models whose independent variables consist of Landsat 8 bands, vegetation indices and band ratios, and topographic data derived from digital elevation model. Two Landsat 8 data were used because of cloud contamination; hence, two sets of models were derived. The best two models, based on RMSE, have either Band 1 and Band 6 or Band 1 and Band 3 as predictors. None of the topographic variables came out as predictor. The landscape contains around 285,140 tons of aboveground biomass translating to 200 ton/ha. The bulk of this total AGB is found in dipterocarp forest. The large proportion of low-biomass land uses and land cover types such as citrus orchard, cultivated area, betel pepper gardens, and grassland, pulled down the average AGB relative to that of the dipterocarp and limestone or karst forests. Tree crown closure is significantly correlated to most of the Landsat 8 bands, vegetation indices and band ratios, implying that Landsat 8 can be used to estimate the tree crown closure of varying land cover types. On the other hand, percent ground cover is significantly correlated to a smaller number of spectral variables. Except for elevation, AGB estimate seems to be independent of GIS ancillary data, namely, slope, aspect and hill shade. The discrepancy between the projected and actual value increases as elevation increases. The opportunity to model the local context of rainfall in Bayombong is presented by the Weather Monitoring System (WMS) of NVSU. The study looked into modeling the periodic component of the monthly precipitation time series of Bayombong from the WMS rainfall data from 2012 to 2015. Fourier series harmonic analysis was employed through backward elimination stepwise Multiple Linear Regression (MLR) with the method of least squares in mathematical modeling. The four models obtained explained 90 percent variation in the observed data. The model with the first and second harmonics and the cosine term of the fifth harmonics is the best fit model and is parsimonious accounting for the 12-, 6- and 2.4-months periods in the observed data. The best fit model has the highest adjusted coefficient of determination (R) of 0.9043, the least root means square error (RMSE) or magnitude of error of 55.99 mm and the least Akaike information (AIC) of 530.23. The model captured the long term periodic monthly rainfall of Bayombong described as Type III in Corona's climate classification which is relatively dry from November to April and wet the rest of the year. The model revealed that periodic rainfall peaks in July and August and is least in January. Periodic rainfall considerably increases in May and considerably decreases in November. Hence, the model can aid the WMS in providing local farmers practical knowledge about climatic rainfall conditions conducive to farming and can also be used in modeling the rainfall of Bayombong at different time scales. Keywords: Best Fit Model, Fourier Series Harmonic Analysis, Least Squares, Mathematical Modeling. Multiple Linear Regression, Rainfall, Weather Monitoring System.
500			_aThesis (Master of Science in Forestry - Forest Resource Management)
500			_aIncludes appendices and bibliographical references.
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