Burn Area Processing to Generate False Alarm Data for Hotspot Prediction Models

Developing hotspot prediction models using decision tree algorithms require target classes to which objects in a dataset are classified.  In modeling hotspots occurrence, target classes are the true class representing hotspots occurrence and the false class indicating non hotspots occurrence.  This paper presents the results of satellite image processing in order to determine the radius of a hotspot such that random points are generated outside a hotspot buffer as false alarm data.  Clustering and majority filtering were performed on the Landsat TM image to extract burn scars in the study area i.e. Rokan Hilir, Riau Province Indonesia.  Calculation on burn areas and FIRMS MODIS fire/hotspots in 2006 results the radius of a hotspot 0.90737 km.  Therefore, non-hotspots were randomly generated in areas that are located 0.90737 km away from a hotspot. Three decision tree algorithms i.e. ID3, C4.5 and extended spatial ID3 have been applied on a dataset containing 235 objects that have the true class and 326 objects that have the false class. The results are decision trees for modeling hotspots occurrence which have the accuracy of 49.02% for the ID3 decision tree, 65.24% for the C4.5 decision tree, and 71.66% for the extended spatial ID3 decision tree

A Decision Tree Based on Spatial Relationships for Predicting Hotspots in Peatlands

Predicting hotspot occurrence as an indicator of forest and land fires is essential in developing an early warning system for fire prevention.  This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. Spatial data consist of a target layer and ten explanatory layers representing physical, weather, socio-economic and peatland characteristics in the study area Rokan Hilir District, Indonesia. Target objects are hotspots of 2008 and non-hotspot points.  The result is a pruned spatial decision tree with 122 leaves and the accuracy of 71.66%.  The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4.5 algorithm. The ID3 decision tree has accuracy of 49.02% while the accuracy of C4.5 decision tree reaches 65.24%

Drivers of soil carbon dioxide efflux in a 70 years mixed trees species of tropical lowland forest, Peninsular Malaysia

Forest biomass is a major component in carbon sequestration and a driver of heterotrophy and autotrophy soil CO2 efflux, as it accumulation increases carbon organic nutrients, root growth and microbial activity. Understanding forest biomass rational to ascertain the forest ecosystems productivity is important. A study has been conducted in a 70-years-old forest of mixed tree species, Sungai Menyala Forest, Port Dickson, Peninsular Malaysia, measuring the total above ground biomass (TAGB), below ground biomass (BGB), total forest carbon (SOCs), soil organic carbon stock (SOCstoc) and soil CO2 efflux from 1 February to 30 June 2013. The aim was to determine the effect of forest biomass, litter fall and influence of environmental factors on soil CO2 efflux. Multiple regression analysis has been conducted on the relationship between the variables and the soil CO2 efflux. Soil CO2 efflux was found to range from 92.09-619.67 mg m-2 h-1, with the amount of the tropical forest biomass estimated at 1.9×106, 7.7×106 and 9.2×105 kg for TAGB, BGB and SOCs, respectively. The analysis showed a strong correlation between soil CO2 efflux and soil temperature, soil moisture, water potential and forest carbon input with R2 more than 0.89 at p<0.01. The findings showed a strong contribution from forest biomass as drivers of heterotrophy and autotrophy soil CO2 efflux. We can conclude that the forest biomass and environmental factors are responsible for the remarkable variation in soil CO2 efflux, as climate change can cause increase in temperature as well as deforestation decreases forest biomass

Post forest fire management at tropical peat swamp forest: a review of Malaysian experience on rehabilitation and risk mitigation

Malaysian Peat swamp forests constitute a significant component with an estimated 1.54 million hectares remaining. More than 70% of these peat swamp forests are in Sarawak, less than 10% Sabah in and the remainder 20% in Peninsular Malaysia (UNDP, 2006). Peat swamp forest is the fragile unique forest ecosystem type that usually found in the lowland of tropical forest areas. Peat forest is exposed to the fire even especially during the dry season. The impact of forest fires at the peat swamp area not only destroys the above ground biomass but also penetrates the underlying peat, resulting in undesirable environmental impacts, including high atmospheric emissions of carbon gases. Therefore, undertaking the rehabilitation and fire risk mitigation activities at burned peat land is very tough and challenges due to the massive destruction and changes in the ecosystem. This paper will emphasize more on restoration and rehabilitation as well as fire risk mitigation efforts on burn peat swamp forest in Malaysia. The issues and challenges encountered in order to restore the burn peat swamp forest area will also be addressed

Tropical Soil Bacterial Communities in Malaysia: pH Dominates in the Equatorial Tropics Too

The dominant factors controlling soil bacterial community variation within the tropics are poorly known. We sampled soils across a range of land use types-primary (unlogged) and logged forests and crop and pasture lands in Malaysia. PCR-amplified soil DNA for the bacterial 16S rRNA gene targeting the V1-V3 region was pyrosequenced using the 454 Roche machine. We found that land use in itself has a weak but significant effect on the bacterial community composition. However, bacterial community composition and diversity was strongly correlated with soil properties, especially soil pH, total carbon, and C/N ratio. Soil pH was the best predictor of bacterial community composition and diversity across the various land use types, with the highest diversity close to neutral pH values. In addition, variation in phylogenetic structure of dominant lineages (Alphaproteobacteria, Beta/Gammaproteobacteria, Acidobacteria, and Actinobacteria) is also significantly correlated with soil pH. Together, these results confirm the importance of soil pH in structuring soil bacterial communities in Southeast Asia. Our results also suggest that unlike the general diversity pattern found for larger organisms, primary tropical forest is no richer in operational taxonomic units of soil bacteria than logged forest, and agricultural land (crop and pasture) is actually richer than primary forest, partly due to selection of more fertile soils that have higher pH for agriculture and the effects of soil liming raising pH.