Analysis of Carbon Dioxide Emission from Forest Fires based on Fire Radiative Power in Riau

Riau is one of the susceptible regions in Indonesia, which faces frequent land and forest fires. Fires occur in various land covers and soil types, both peat and mineral soils, which emitted huge carbon to the atmosphere. Forest fires emit greenhouse gases, including carbon dioxide (CO2). The objective of the research was to quantify CO2 from land and forest fires. The quantification emission was for 2016 – 2018 based on the fire radiant power (FRP) dataset along with the buffer methodology for assessing fire-affected land extents across different land covers. The FRP dataset we used to be only at a confidence level of 70% or higher, which represents hotspots. The results revealed large numbers of FRP focal points (> 1000) that can be identified as fires for 2016 and 2018, whereas only small numbers (121) were identified for 2017. Then we quantified the area burned of 95,396 Ha in Riau for 2016, which was double to the 2018’s area burned. Further, this burning contributed to CO2 emission equal to 313,456 tCO2  for 2016. Emission in 2017 was a relatively low as not many observed fires detected

Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture

The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%