Keywords: Aboveground Biomass, RapidEye, Sentinel 2, PlanetScope, Mangroves Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r²) values were obtained using multispectral band predictors for Sentinel-2 (r² = 0.89) and Planetscope (r² = 0.80); and vegetation indices for RapidEye (r² = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r² ranging from 0.62 to 0.92. Based on the r² and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r² = 0.92) and RapidEye data (r² = 0.91).

Urbanization can be observed through the occurrence of land-use changes as more land is being transformed and developed for urban use. One of the Philippine cities with high rate of urbanization is Baguio City, known for having a subtropical highland climate. To understand the spatiotemporal relationship between urbanization and temperature, this study aims to analyze the correlation of urban extent with land surface and air temperature in Baguio City using satellite-based built-up extents, land surface temperature (LST) maps, and weather station-recorded air temperature data. Built-up extent layers were derived from three satellite images: Landsat, RapidEye and PlanetScope. Land-use land cover (LULC) maps were generated from Landsat images using biophysical indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI); while RapidEye and PlanetScope built-up extent maps were generated by applying the visible green-based built-up index (VgNIR-BI). Mean LST values from 1988 to 2018 during the dry and wet seasons were calculated from the Landsat-retrieved surface temperature layers. The result of the study shows that the increase in the built-up extent significantly intensified the LST during the dry season which was observed in all satellite data-derived built-up maps: RapidEye+PlanetScope (2012–2018; r = 0.88), Landsat 8 (2012–2018; r = 0.63) and Landsat 5,7,8 (1988–2018; r = 0.61). The main LST hotspots were detected inside the Central Business District where it expanded gradually from year 1998 (43 ha) to 2011 (83 ha), but have increased extensively within the years 2014 to 2019 (305 ha). On average, 98.5% of the hotspots detected from 1995 to 2019 are within the equivalent built-up area.

This study produced spatiotemporal hot and cold spot occurrence maps for Davao City for the period 1994-2019 using land surface temperature (LST) images. Urban heat is theorized to have been affected by some, if not all, of the following impact factors: air pollutant concentrations/particulate matter (PM10), vegetation “abundance” (using EVI), building “density” (NDBI), albedo, topography, and population density. A mobile traverse sampling was performed in the morning and afternoon of 15 April 2019 to measure PM10 in the city’s identified hot spots. The remaining factors were generated from imagery (i.e., Landsat 8, Synthetic Aperture Radar) and obtained from the Philippine Statistics Authority. These factors were analyzed against the LST which was obtained through Project GUHeat’s methodology. The relationships between the factors and LST were studied through multiple and quantile regression models (MRM & QRM). Results showed that variable PM10 does not have any significance in the MRM. Meanwhile, QRM were fitted to different quantile values, namely: 10th, 25th, 50th, 75th, and 90th. It is only at the 90th quantile where all the independent variables are good predictors for the LST. Albedo is the most important predictor for the LST at 10th quantile whereas Elev for the 25th quantile. However, when LST is at the 50th, 75th, and 90th quantiles NDBI is the most significant variable at predicting LST. Reliable spatiotemporal assessment and modelling of surface temperature are essential for urban planning and management to formulate sustainable strategies for the welfare of people and environment.

The Philippines has a booming sugarcane industry contributing about PHP 70 billion annually to the local economy through raw sugar, molasses and bioethanol production (SRA, 2012). Sugarcane planters adapt different farm practices in cultivating sugarcane, one of which is cane burning to eliminate unwanted plant material and facilitate easier harvest. Information on burned sugarcane extent is significant in yield estimation models to calculate total sugar lost during harvest. Pre-harvest burning can lessen sucrose by 2.7% - 5% of the potential yield (Gomez, et al 2006; Hiranyavasit, 2016). This study employs a method for detecting burn sugarcane area and determining burn severity through Differenced Normalized Burn Ratio (dNBR) using Landsat 8 Images acquired during the late milling season in Tarlac, Philippines. Total burned area was computed per burn severity based on pre-fire and post-fire images. Results show that 75.38% of the total sugarcane fields in Tarlac were burned with post-fire regrowth; 16.61% were recently burned; and only 8.01% were unburned. The monthly dNBR for February to March generated the largest area with low severity burn (1,436 ha) and high severity burn (31.14 ha) due to pre-harvest burning. Post-fire regrowth is highest in April to May when previously burned areas were already replanted with sugarcane. The maximum dNBR of the entire late milling season (February to May) recorded larger extent of areas with high and low post-fire regrowth compared to areas with low, moderate and high burn severity. Normalized Difference Vegetation Index (NDVI) was used to analyse vegetation dynamics between the burn severity classes. Significant positive correlation, rho = 0.99, was observed between dNBR and dNDVI at 5% level (p = 0.004). An accuracy of 89.03% was calculated for the Landsat-derived NBR validated using actual mill data for crop year 2015-2016.