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CARBON EMISSION ESTIMATION DUE TO LAND COVER CHANGE IN THE TROPICAL FOREST LANDSCAPE IN JAMBI PROVINCE Melati, Dian Nuraini
Jurnal Sains dan Teknologi Mitigasi Bencana Vol 14, No 1 (2019): Jurnal Sains dan Teknologi Mitigasi Bencana
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

Land use land cover change and forestry play an important role in the global environmental change. Anthropogenic activities in changing the land have caused earth surface change. This change has a role to increase the change of global greenhouse gases in the atmosphere which also causes the increase greenhouse gases emission. Land cover change and forestry are sectors which cause high carbon emission. Therefore, a study in land cover change and estimation of carbon emission becomes important. This study took place in Jambi Province where deforestation has been in a high pace. In 2009 and 2011, the dominant area is dryland agriculture mixed with bush followed by secondary forest, i.e. 25% and 18.6%, respectively (in 2009); and 37.1% and 18.9%, respectively (in 2011). For the secondary forest, the gain was caused by the conversion of dryland agriculture mixed with bush and shrub into secondary forest. The loss of secondary forest is the highest among other forest cover at around 87,765 Ha due to the conversion into bare land and dryland agriculture mixed with bush. Due to land cover change in Jambi Province, the estimation of nett emission in the period of 2009-2011 is 4.8 Mt CO2-eq/year.

MULTI TEMPORAL REMOTELY SENSED IMAGE MODELLING FOR DEFORESTATION MONITORING Melati, Dian Nuraini
Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana Vol 3, No 1 (2019): Jurnal Alami
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

Tropical rainforest in Indonesia faces critical issue related to deforestation. Human activities which convert forest cover into non-forest cover has been a major issue. In order to sustain the forest resources, monitoring on deforestation and forest cover prediction is necessary to be done. Remotely sensed data, Landsat images, with acquisition in 1996, 2000, and 2005 are used in this study. In this study area, forest cover decreased around 6 % in the period of 1996 - 2005. For the purpose of forest cover modelling, three model (i.e. Stochastic Markov Model, Cellullar Automata Markov (CA_Markov) Model, dan GEOMOD) were tested. Based upon the Kappa index, GEOMOD performed better with the highest Kappa index. Therefore, GEOMOD is recommended to forecast forest cover.

DISRUPTIVE TECHNOLOGY THROUGH SATELLITE IMAGERY BIG DATA IN DISASTER RISK REDUCTION: OPPORTUNITY AND CHALLENGE Melati, Dian Nuraini
Jurnal Sains dan Teknologi Mitigasi Bencana Vol 14, No 2 (2019): Jurnal Sains dan Teknologi Mitigasi Bencana
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

The development of current massive technology plays an important role in the field of disaster risk reduction and disaster management. In particular, the issue of disruptive technology in which the emerging of new technologies comes with big disruption in many fields. For instance, the development on using satellite imagery big data for disaster management to reduce the disaster risk.The unique characteristics of big data i.e. volume, velocity, and variety stimulate this study to obtain more information on the big data processing and analyze the opportunities and challenges of satellite imagery in the case of disaster risk reduction through literature reviews. The huge amount of data and the ability for real-time analysis provide capabilities on time series analysis and possitive impact on disaster detection, monitoring, and prediction. However, such technology creates disruption due to the change on using and anlysing the data based on cloud environment. Issues on the technology uses, data security might arise. Nonetheless, the use of satellite imagery big data will remain vastly developed and is able to promote the development of satellite imagery technology.

CARBON EMISSION ESTIMATION DUE TO LAND COVER CHANGE IN THE TROPICAL FOREST LANDSCAPE IN JAMBI PROVINCE Melati, Dian Nuraini
Jurnal Sains dan Teknologi Mitigasi Bencana Vol 14, No 1 (2019): Jurnal Sains dan Teknologi Mitigasi Bencana
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

Land use land cover change and forestry play an important role in the global environmental change. Anthropogenic activities in changing the land have caused earth surface change. This change has a role to increase the change of global greenhouse gases in the atmosphere which also causes the increase greenhouse gases emission. Land cover change and forestry are sectors which cause high carbon emission. Therefore, a study in land cover change and estimation of carbon emission becomes important. This study took place in Jambi Province where deforestation has been in a high pace. In 2009 and 2011, the dominant area is dryland agriculture mixed with bush followed by secondary forest, i.e. 25% and 18.6%, respectively (in 2009); and 37.1% and 18.9%, respectively (in 2011). For the secondary forest, the gain was caused by the conversion of dryland agriculture mixed with bush and shrub into secondary forest. The loss of secondary forest is the highest among other forest cover at around 87,765 Ha due to the conversion into bare land and dryland agriculture mixed with bush. Due to land cover change in Jambi Province, the estimation of nett emission in the period of 2009-2011 is 4.8 Mt CO2-eq/year.

PERAN SISTEM VOLUNTEERED GEOGRAPHIC INFORMATION (VGI) SISTEM DALAM PENGURANGAN RISIKO BENCANA: KONSEP DAN IMPLEMENTASI Melati, Dian Nuraini
Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana Vol 4, No 1 (2020): Jurnal Alami
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

There have been a lot of geospatial technologies implemented to support disaster management into a more effective way and achieve disaster risk reduction. One of these technologies is the use Volunteered Geographic Information (VGI). VGI refers to the volunteered activities by anyone to create geographic information. The recent development of VGI is obviously supported by the development technology itself such as social media, Global Positioning System (GPS) with acceptable accuracy. In addition, it is also supported by mostly unlimited cloud-based storage as well as smartphones. In the phenomena of natural disater such as flood, landslide, earth quake, tsunami, and other phenomena, the need of geospatial data and the availability in timely manner becomes important and crucial at all disaster management aspects. The availability of geographic information is very much critical at the time the disaster occurs compared to normal situation. Therefore, VGI is necessary in supporting near real time information. In this case, VGI has a key role in disaster management particularly to reduce disaster risk.

MACHINE LEARNING APPLICATION IN RESPONSE TO DISASTER RISK REDUCTION OF FOREST AND PEATLAND FIRE: Impact-Based Learning of DRR for Forest, Land Fire and Peat Smouldering Riza, Hammam; Santoso, Eko Widi; Kristijono, Agus; Melati, Dian Nuraini; Prawiradisastra, Firman
Majalah Ilmiah Pengkajian Industri Vol. 14 No. 3 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : Deputi TIRBR-BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/mipi.v14i3.4426

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that has not been decomposed. Once it burns, smoldering peat fires consume huge biomass. Peat smoldering fires are challenging to extinguish. These will continuously occur for weeks to months. Experts and practitioners of peat smoldering fires are the most recommended effort to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology.' Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example, monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses, and soon such methods will be replaced by the Machine Learning method as it is developing recently. What about the potential application of Machine Learning in the forest and land fires, particularly smoldering peat fires in Indonesia? This paper tries to answer this question. This paper recommends a conceptual design: impact-based Learning for Disaster Risk Reduction (DRR) of Forest, Land Fire, and Peat Smouldering. Keywords: Artificial Intelligence; Machine Learning; Wildfire; Peat Smouldering; DRR impact-based

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