Estimation of carbon emission from peatland fires using Landsat-8 OLI imagery in Siak District, Riau Province

Indonesia’s peatland condition is getting worse because of peatland fire. Peatland fire causes many negative impacts, so early detection is needed. Data mining is one of approach that can be used for finding sequential pattern from hotspot data as one of indicators for peatland fire. This study aims to find sequential patterns on hotspot data in Riau province Indonesia. The Douglas-Peucker algorithm and substring tree structure concept were used for finding the patterns. The experiment results three types of sequential patterns, namely sequences of date, day, and location of hotspot data in 2014. The most interesting frequent pattern of hotspot occurrence is 11 March 2014 -1 13 March 2014 meaning that the hotspot occurrences on 11 March 2014 was followed by the occurrences in the same location on 13 March 2014. This pattern was found in 9 of 12 districts in Riau Province. Another interesting frequent pattern based on day of occurrence is Friday -1 Saturday -1 Sunday meaning that there was hotspot in Friday, Saturday, and Sunday in the same location. The experiment results show that about 22.77% hotspots in 2014 are considered as strong indicator for peatland fires because it occurred in sequence patterns.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Traffic carbon emissions have a non-negligible impact on global climate change. Effective estimation and control of carbon emissions from tourism transport will contribute to the reduction in the amount of global carbon emissions. Based on the panel data of Dunhuang in western China from 2010 to 2019, the process analysis method was used to estimate the carbon emissions from tourism traffic of Dunhuang. By establishing the Kaya identity of tourism traffic carbon emissions, the LMDI decomposition method was used to reveal the contribution of different factors to the change in tourism traffic carbon emissions. The results showed that the impact of tourism traffic carbon emissions was diversified; we found three main factors of promoting carbon emissions, namely the number of tourists, tourism expenditure per capita, and energy consumption per unit of passenger turnover. However, the contribution of tourism activities to GDP, passenger turnover per unit of GDP, and energy structure largely inhibited the increase in carbon emissions.

Statement of problem: Shade selection remains as a challenge for any restorative dental procedures. There is a need to check for the reliability of methods used in shade selection for a successful restorative treatment. Aim: The aim of this study was to compare the reliability of two different methods used for shade selection, i.e.; visual method and digital photography method. Materials and methods: Fifty-two student participants of age group 18-25 years were selected for the study from our institution after obtaining informed consent. The only inclusion criteria for the study were the presence of an unrestored maxillary right central incisor. The shade of the right maxillary central incisor was determined by visual and digital photography method. In visual method, the shade was selected by the clinician using shade guide (vita classical shade guide) and the shade was manually entered. In digital photography method, the digital camera was used (eye special c iii dental camera). Standardized digital photographic images were taken and sent to a dental technician. The reliability of the perception of the clinician selected shade by visual method and the technician selected shade by digital method were compared and evaluated after being subjected to appropriate statistical analysis. Results: On comparison of the two different methods of shade selection, there was fair agreement between the two methods of shade selection. Conclusion: Digital camera can be used for shade selection as it improves communication between the dentist and dental technician and with further advancements in digitalisation, it may replace the visual method in the near future.

We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to ~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C∙ha−1 for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR—albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires.

Extensive tropical peatlands are located in the Malaysian and Indonesian lowlands, particularly in Borneo, Sumatra, West Papua, and Peninsular Malaysia. In an undisturbed condition, these peatlands make a significant contribution to terrestrial carbon storage, both in terms of their aboveground biomass (peat swamp forest) and thick deposits of peat. Occasional forest fires, including peatland fires, have occurred in Southeast Asia over several millennia but, in recent years, they have become a more regular feature. The most severe fires have been linked with the El Nino phase of ENSO which causes extended periods of drought, particularly across the peatland areas of southern Sumatra and southern Kalimantan. During the last 20 years, rapid land use change, exacerbated by climatic variability, has led to an increase in fire frequency, as the remaining peat swamp forests come under pressure from increased illegal logging, development for plantations and agriculture-based settlement, and, where economic development has failed, land abandonment. A case study of fire occurrence in Borneo illustrates that peat swamp forests are much more prone to fire than any other forest type, largely as a result of the high pressure being put on these last remaining forested lands. From studies in central Kalimantan (southern Borneo), we demonstrate the relationships between peat drainage, vegetation change, and increased fire frequency, including the role that peat combustion and subsidence play in an increased incidence of surface flooding. Tropical peatland fires, and the changes in vegetation that they bring about, have significant impacts on the atmosphere, the carbon cycle, and various ecosystem services; they also cause wide-ranging social and economic impacts. Fires on peatlands usually affect both the surface vegetation and the underlying peat layer and, as a result, they release much larger amounts of C02 into the atmosphere than forest fires on mineral soils. In 1997, peatland fires in Indonesia resulted in the release of between 0.81 Gt and 2.57Gt of carbon into the atmosphere, equivalent to 13% to 40% of mean annual global carbon emissions from fossil fuels, and over the last ten years a conservative estimate of total carbon emissions from peatland fires in Southeast Asia is of the order of 2Gt to 3Gt. Future climate changes may place further pressure on the tropical peatland ecosystem and are likely to lead to enhanced carbon emissions from both peat degradation and fire.

Global warming occurs due to too many greenhouse gases in the atmosphere, especially carbon dioxide (CO2). One of the causes of the increasing amount of CO2 gas is forest and peatland fires. Peatlands are known to store carbon stocks not only above the ground surface but also below the ground surface which if there is a fire it will turn into carbon emissions. The forest and peatland fires in 2015 were one of the worst fire events in Indonesia (Sumatra and Kalimantan) in recent years. Therefore, many researchers have tried to estimate carbon emissions resulting from fires in several areas. This study estimates the number of carbon emissions (above surface and subsurface carbon emissions) from peatland fires in Banjar Regency in 2015 using remote sensing technology (Landsat 8), imagery data and Geographic Information Systems (GIS). Based on two types of vegetation, namely shrubs and agricultural land (the results of land cover classification), that occupy burned peatlands, the resulting carbon emissions above the surface of 1,718.55 tons. Meanwhile, the amount of subsurface carbon emissions (based on the category of depth and peat maturity) is 1,092.14 tons. So the total carbon emissions resulting from peatland fires in Banjar Regency in 2015 were 2,810.69 tons. Overall, our findings indicate that peat fires in the Banjar district produce significantly higher carbon emissions than currently reported in emission inventories, which has consequences for the predicted impacts of peat burning on air quality.

Emissions from forest fires directly affect the global and regional carbon cycles by increasing atmospheric carbon as well as affecting carbon sequestration by forests. We have estimated the release of total carbon, carbon-based trace gases (CO2, CO, CH4) and non-methane hydrocarbons (NMHC) emitted from forest fires in Japan during a 30-year period from 1979 through 2008. The area burnt varied widely from year to year but has gradually diminished since the 1980s. The mean annual area burnt during the period was 1878 ha. The mean annual estimate of direct carbon emissions from forest fires in Japan was 15.8 Gg C year–1 and ranged between 2.7 and 60.4 Gg C year–1. The mean annual trace gas emissions were 49.4 Gg CO2 year–1, 3.4 Gg CO year–1, 0.15 Gg CH4 year–1 and 0.18 Gg NMHC year–1. Although the carbon emissions varied widely from year to year based on the area burnt, they decreased dramatically from the 1980s onward. The interannual variations in trace gases parallel the total carbon emissions. The direct emissions from forest fires in Japan were substantially lower compared with the mean annual net primary production of Japanese forests or the carbon release in other countries and regions. However, the average annual carbon released per unit area burnt was comparable to that estimated in other regions and rose gradually with the increasing age of plantations.

Buildings' sustainability is one of the crucial parts for achieving urban sustainability. Applied to buildings, life-cycle assessment encompasses the analysis and assessment of the environmental effects of building materials, components and assemblies throughout the entire life of the building construction, use and demolition. Estimate of carbon emissions is essential and crucial for an accurate and reasonable life-cycle assessment. Addressing the need for more research into integrating analysis of real-time and automatic recording of key indicators for a more accurate calculation and comparison, this paper aims to design a real-time recording model of these crucial indicators concerning the calculation and estimation of energy use and carbon emissions of buildings based on a Radio Frequency Identification (RFID)-based system. The architecture of the RFID-based carbon emission recording/tracking system, which contains four functional layers including data record layer, data collection/update layer, data aggregation layer and data sharing/backup layer, is presented. Each of these layers is formed by RFID or network devices and sub-systems that operate at a specific level. In the end, a proof-of-concept system is developed to illustrate the implementation of the proposed architecture and demonstrate the feasibility of the design. This study would provide the technical solution for real-time recording system of building carbon emissions and thus is of great significance and importance to improve urban sustainability.

Tropical peatlands are wetland ecosystems formed from the accumulation of organic matter over thousand of years period. Indonesia has an area of about 13.5 million ha and play important roles for society and the environment. The development of drainage canals has caused peatlands to become dry and degraded, rendering them highly susceptible to fires. Peatland restoration through rewetting activities with canal blockings can restore the hydrological function of peatlands. In addition, groundwater level (GWL) also affects carbon emissions from peatlands. This study aims to determine the distance of canal blocking effect on groundwater level so that it can be known which areas have a lower risk of fire and carbon emissions in peatlands. This study compared areas affected by canal blocking with those without canal blocking. The results of this study show the significant effect of canal blocking in increasing the groundwater level in areas with <100 m distance from the canals and with different types of land use. The average GWL of peat during one year of monitoring period was around - 26.67 ± 2.4 cm at the location of the monitoring well close to the canal with canal blocking, equivalent to carbon emissions of 26.5 tCO2eq ha−1 year−1. This is lower than the average GWL of those areas without canal blocking that was - 58.67 ± 3.1 cm, which is equivalent to carbon emissions of 57.8 tCO2eq ha−1 year−1.

During the 1997/98 El Niño-induced drought peatland fires in Indonesia may have released 13-40% of the mean annual global carbon emissions from fossil fuels. One major unknown in current peatland emission estimations is how much peat is combusted by fire. Using a light detection and ranging data set acquired in Central Kalimantan, Borneo, in 2007, one year after the severe peatland fires of 2006, we determined an average burn scar depth of 0.33 +/- 0.18 m. Based on this result and the burned area determined from satellite imagery, we estimate that within the 2.79 million hectare study area 49.15 +/- 26.81 megatons of carbon were released during the 2006 El Niño episode. This represents 10-33% of all carbon emissions from transport for the European Community in the year 2006. These emissions, originating from a comparatively small area (approximately 13% of the Indonesian peatland area), underline the importance of peat fires in the context of green house gas emissions and global warming. In the past decade severe peat fires occurred during El Niño-induced droughts in 1997, 2002, 2004, 2006, and 2009. Currently, this important source of carbon emissions is not included in IPCC carbon accounting or in regional and global carbon emission models. Precise spatial measurements of peat combusted and potential avoided emissions in tropical peat swamp forests will also be required for future emission trading schemes in the framework of Reduced Emissions from Deforestation and Degradation in developing countries.

Climate Change and Health: Strengthening the Evidence Base for Policy

It is an indisputable fact that carbon emissions lead to global warming. Finding a rapid and accurate method for estimating carbon emissions is the prerequisite for making real-time emission reduction measures. In this paper, an estimation method for quick inversion of provincial-level carbon emissions in China is proposed by using night-time light data. This method was based on the corrected night-time light image and combined with the statistical data of the built-up area to extract the total night light value (TDN) in the built-up areas of 30 provinces (Municipalities directly under the Central Government and autonomous regions were collectively referred to as provinces; Tibet, Hong Kong, Macao and Taiwan were not involved here) in Chinese mainland from 1997 to 2017. The regression equation was established by using the TDN of the built-up areas in each province from 1997 to 2014 and the provincial-level carbon emission data released by CEADs (China emission accounts and datasets) in the same period, and then the TDN values from 2015 to 2017 were used as the independent variable to estimate the carbon emission of each province according to the established regression equation. Finally, we used the entropy method and carbon emission allocation model to distribute China’s national-level carbon emission data released by the international authoritative databases to each province and compared them with the provincial-level carbon emissions estimated by the above regression equations from 2015 to 2017. The results show that: (1) There was a significant linear relationship between the established carbon emission estimation models in all provinces, with R2 values greater than 0.8 except Beijing, Hainan and Shanxi. (2) Comparing the difference between the estimated carbon emissions and the carbon emissions allocated to provinces by the database, except for Shandong, Shanxi, Inner Mongolia and Shaanxi provinces, the errors of the other provinces were relatively small, RMSE and MAE were less than 260mt, and the MAPE of most provinces were less than 50%, indicating that the estimation models have high goodness-of-fit and accuracy. (3)The provincial-level carbon emissions allocated by the four international databases from 2015 to 2017 and the carbon emissions estimated by the model were plotted separately, and it is found that the corresponding scatter points of most provinces were distributed near the 1:1 line, which once again showed that the carbon emissions inverted based on night-time light data were close to the carbon emissions allocated to the provinces by each database, especially the provincial-level carbon emissions from CEADs database. The above results demonstrate that this method can provide a faster and more accurate estimation of provincial-level carbon emissions for China.

This paper presents the application of the weather modification technology (WMT) for mitigation of peatlands fire in Riau Province, Indonesia. The application of WMT has been conducted to reduce the fire disaster risks which were very rare to be implemented in the world. The province of Riau which is about 56% covered with peatlands, is very vulnerable against haze disaster caused by peatlands fires. Peatlands are fragile ecosystems formed over thousands of years by the accumulation of dense wet plant material. When drained or cleared by fire for commercial plantations, such as for palm oil or pulpwood, the carbon is released into the atmosphere. In 2013-2015, Indonesia experienced its most serious fires in some years, worsened by dry weather caused by an El Nino phenomenon, and cloaked large stretches of Southeast Asia in choking smog for mounts. The history proved that the peatlands fires only can be stopped completely by rainfall. On the other hand, the peatland fires usually occur in the dry season that rainfall is very rare. The technology of artificial precipitation has an important role to solve this kind of natural disaster. The aim of this research is to study the impact of the application of the WMT on increasing of precipitation for mitigation of peatlands fire in Riau Province. The analysis was focused on the Kepulauan Meranti and Siak Regency which are very severe peatland fire in 2014, 2015, and 2016. This research found that the application of weather modification technology could increase precipitation occurrence and significantly reduce peatland fire in Riau. The increasing rainfall value (PCH) in Kepulauan Meranti Regency was 1.019, 1.08, 0.68, and 1.649 during the four times WMT application from 2014 to 2016. However, the PCH value in Siak Regency during the four times WMT application in 2014 to 2016 was 1.127, 0.7, 0.66, and 1.88.

Introduction: Disasters caused by climate change threaten Most of the world's ecosystems, especially developing countries with rich natural resources, including peatland ecosystems such as those found in Riau Province, Indonesia. Climate change causes peatland ecosystems to become drier and more prone to burning. The peatland fires that occurred in Riau Province during the period of 2016-2020 have become a valuable lesson for the government, the private sector, and the local community. During that period, collaboration between the government, the private sector, and the community in addressing climate change-related disasters, especially peatland fires, has been successfully carried out for the mitigation and adaptation of peatland fire disasters. This research reveals the role of social capital and social institutions in the process of empowering communities to address land fire disasters. Using qualitative descriptive methods, this study reveals findings that the strengthening of social capital such as social institutions, trust, social networks, and reciprocal relationships occurs in the actions taken to mitigate peatland fire disasters. In addition, the implementation of mitigation and adaptation strategies for peatland fire disasters is also in line with the Sustainable Development Goals, particularly the goal of Combating Climate Change (SDG 13) and Protecting Terrestrial Ecosystems (SDG 15). Objective: The purpose of this research is to understand the role of social capital and social institutions in the process of community empowerment to address climate change disasters, particularly peatland fires occurring in Bengkalis Regency, Riau Province, Indonesia. Theoretical Framework: The theory used in this research is the Social Capital Theory developed by Putnam (1993) and Woolcock (1998 &amp; 2002). The relevance of Social Capital Theory in this study is to observe the bonds among communities manifested in three forms of social capital, namely social institutions, trust, and social networks formed among communities affected by the climate crisis due to peatland fire disasters. Method: This research was conducted using qualitative methods, which are research procedures to produce descriptive data from the observed research objects. This method examines the experiences of individuals/organizations in their entirety, thus not limiting the statements of individuals/organizations to specific hypotheses. Data collection was carried out through participant observation and Focus Group Discussion (FGD). The research informants were selected based on purposive sampling with the intention that the chosen informants already have a deep understanding of the issues, allowing them to provide comprehensive information. Results and Discussion: This research reveals findings that the strengthening of social capital such as social institutions, trust, social networks, and reciprocal relationships occurs in the actions taken to mitigate peatland fire disasters. Research Implications: The practical implication of this research finding is to develop the strengthening of social institutions in areas with a high risk of climate crisis and peatland fires. Strengthening social institutions can take the form of institutional strengthening, enhancing mutual trust among communities, and strengthening social networks within the community. Originality/Value: The social capital model discovered in communities situated in high-risk regions impacted by climate crises, including peatland fire disasters, is what makes this study unique. This includes the community's social networks, high levels of mutual trust, and the robustness of social institutions, all of which can enhance climate crisis management.