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The demand of renewable energy had created a foundation for biofuels to grow. Bio oil as secondary biofuels is produced from pyrolysis process using biomass as feedstock. The quality of bio oil mainly calorific value (high heating value) affected by the process temperature and solid residence time during pyrolysis. In this study, the effect of pyrolysis process temperature and solid residence time on the bio oil quality (calorific value) and yield was evaluated on the empty fruit bunch and oil palm trunk pyrolysis process. The huge amount of these biomass creates interesting and potential feedstock to be used in pyrolysis. Pyrolysis process was performed using the home made air tight stainless steel barrel with capacity of 4 L with every batch of process using 300 g of biomass fines. The fines were subjected to 4 different process temperatures; 300, 400 and 500 and 600°C for 30, 60 and 90 min of solid residence time respectively to perform the pyrolysis. Result shows that the highest bio oil yield recorded for the EFB pyrolysis process was 36.49%, while 41.35% for OPT pyrolysis process. Significant interaction among the process variable on calorific value of bio oil was observed. High calorific value of bio oil associated with higher process temperature. Conclusively, yield and calorific value of bio oil is dependent of process temperature and solid residence time.

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Detection of land cover (LC) changes allows policymakers to recognize the complexities of environmental modification and change to achieve sustainability of economic growth. As a result, recognition of LC features has appeared as an essential research dimension and, consequently, an appropriate and reliable methodology for classifying LC is occasionally required. In this research, Landsat 8 satellite data captured by Operational Land Imager (OLI) and Thermal Infrared Scanner (TIRS) were utilized for the LC classification using the Support Vector Machine (SVM) classifier algorithm. The aim of the study is to enhance classification accuracy by integrating the use of data from satellite thermal and spectral imaging. Land Surface Temperature (LST) is sensitive to the soil surface characteristics, therefore, it may be used to gather LC feature information. The classification accuracy was designed to enhance the integration of thermal information from Landsat 8’s thermal band TIRS and Landsat 8 OLI’s spectral data. In this study, Advanced Thermal Integrated Vegetation Index (ATLIVI) and Thermal Integrated Vegetation Index (TLIVI) established and revealed fairly strong correlations with the related surface temperature (Ts) by R2=0,7 and 0,65 respectively. The relationship between Ts and the other vegetation indices based on the empirical parameterization demonstrate that these two indices showed an improvement of almost 6% in the overall accuracy of the LC classification results compared to the Landsat 8 Standard False Colour Composite image as an input data using SVM algorithm.