Assisting mitigation of bushfire threat in regional Australia through MODIS imagery based media GIS

Bushfires have been part of the Australian environment since before human settlement of the continent. Some Australian flora and fauna has evolved to coexist with bushfires, and in the case of eucalypt forest, fire forms an integral part of its regeneration cycle. Today, bushfires have become the dominant phenomenon in Australian natural hazards. According to the Australian bureau of meteorology, the whole southern half of Australia is at fire risk in summer and autumn months. Australian bureau of Criminology published a bushfire damage recorded from 1967 to 1999, and estimated the cost as about $2.5 billion excluding forestry losses. The public attention to bushfire disasters reached to a new peak, after the disastrous Black Saturday bushfire in Victoria. The Black Saturday bushfires in 2009 killed 173 injured 500 more and cause over$2.5 billion in damages. Annually, fire authorities respond to an average 54,000 bushfires in Australia where up to 50% of these fires are deliberately lit or start in suspicious circumstances. This grave situation of bushfire damage encourages researches to explore various bushfire mitigation scenarios. The present study focuses on educating the rural communities by awakening their participation in fire mitigation efforts using semi-real time fire information. In Australia, fire prevention related agencies work extensively to make available various data sources for public and schools. However, the flow of information to rural communities is not smooth due to various technical and social reasons, though their participation is vital. 'I could see the real value of us educating the locals,' said Glenn O’Rourke, Deputy Captain and Community Safety Officer at the Wollombi Rural Fire Brigade. This study discusses an approach to educate rural communities through Media GIS contents based on daily MODIS imagery. These bushfire contents can be uploaded daily to local newspapers, TV, and to mobile subscribers to establish a participatory user cohort. Google functions such as placemarks will be used in KML environment to deliver media GIS contents as spot/image information. Collected Participatory GIS inputs will be used to enrich the GIS database to further enhancements of the communication process on bushfire developments

Assessing land use of lower Mekong basin using multi-temporal MODIS imagery

Among rice producing regions of the world, Lower Mekong Basin (LMB) can be ranked as the most important region due to the huge population it feeds. About 60 million people are engaged in agriculture and freshwater fishing activates in LMB where produce enough food for over 300 million people annually. Food consumption in LMB is also increasing and studies have found it to be doubled by 2050. This socio-economic background has attracted many researchers to work on various aspects of the LMB. To collaborate with these research interests, the present study is designed to assess the land use conditions of the massive LMB, using multitemporal MODIS imagery. The authors have previously produced the land cover map of LMB in 2005 (edited in 2008) using MODIS data at 250m spatial resolution. The present land use assessment will be used the old map and a new map produced in 2014 using the same land cover classification. The investigation on land use conditions is based on the trends on land cover changes, with a focus on food production aspects of the basin, in order to supply a GIS database for food production assessment studies. Annual devastating floods in south and frequent droughts in central regions are also counted in the assessment. The expected results of the study will be GIS data layers of the basin in raster format comprising old and new land cover data, natural disaster hotspots, together with an assessment of the land use

A Combined Approach of Remote Sensing, GIS, and Social Media to Create and Disseminate Bushfire Warning Contents to Rural Australia

Bushfires are an integral part of the forest regeneration cycle in Australia. However, from the perspective of a natural disaster, the impact of bushfires on human settlements and the environment is massive. In Australia, bushfires are the most disastrous natural hazards. According to the records of the Parliament of Australia, the recent catastrophic bushfires in NSW and Victoria burnt out over 10 million hectares of land, a figure more significant than any previous bushfire damage on record. After the deadly 2009 Black Saturday bushfires, which killed 173 people in Victoria, public attention to bushfires reached a new peak. Due to the disastrous consequences of bushfires, scientists have explored various methods to mitigate or even avoid bushfire damage, including the use of bushfire alerts. The present study adds satellite imagery and GIS-based semi-real-time bushfire contents to various bushfire warnings issued by government authorities. The new product will disseminate graphical bushfire contents to rural Australians through social media, using Google Maps. This low-cost Media GIS content can be delivered through highly popular smartphone networks in Australia through social media (Facebook and Twitter). We expect its success to encourage people to participate in disaster mitigation efforts as contributors in a participatory GIS network. This paper presents a case study to demonstrate the production process and the quality of media GIS content and further discusses the potential of using social media through the mobile network of Australia while paying attention to mobile blackspots. Media GIS content has the potential to link with the public information systems of local fire management services, disseminate contents through a mobile app, and develop into a fully automated media GIS content system to expand the service beyond bushfires

Application of Modis data to assess the latest forest cover changes of Sri Lanka

Assessing forest cover of Sri Lanka is becoming important to lower the pressure on forest lands as well as man-elephant conflicts. Furthermore, the land access to north-east Sri Lanka after the end of 30 years long civil war has increased the need of regularly updated land cover information for proper planning. This study produced an assessment of the forest cover of Sri Lanka using two satellite data based maps within 23 years of time span. For the old forest cover map, the study used one of the first island-wide digital land cover classification produced by the main author in 1988. The old land cover classification was produced at 80m spatial resolution, using Landsat MSS data. A previously published another study by the author has investigated the application feasibility of MODIS and Landsat MSS imagery for a selected sub-section of Sri Lanka to identify the forest cover changes. Through the light of these two studies, the assessment was conducted to investigate the application possibility of MODIS 250m over a small island like Sri Lanka. The relation between the definition of forest in the study and spatial resolution of the used satellite data sets were considered since the 2012 map was based on MODIS data. The forest cover map of 1988 was interpolated into 250m spatial resolution to integrate with the GIS data base. The results demonstrated the advantages as well as disadvantages of MODIS data in a study at this scale. The successful monitoring of forest is largely depending on the possibility to update the field conditions at regular basis. Freely available MODIS data provides a very valuable set of information of relatively large green patches on the ground at relatively real-time basis. Based on the changes of forest cover from 1988 to 2012, the study recommends the use of MODIS data as a resalable method to forest assessment and to identify hotspots to be re-investigated. It's noteworthy to mention the possibility of uncounted small isolated pockets of forest, or sub-pixel size forest patches when MODIS 250mx250m data used in small regions

Mapping Mekong River Basin land cover to support food production

The Lower Mekong Basin (LMB) where rice cultivation is dominant is one of the very important agro-based regions of the world. Over 60 million people are extensively engage in agriculture and freshwater fishing activates in LMB. But, due to the population increase, heavy commercialization of rice crop, environmental degradation of the river basin, and floods and drought, food production of LMB is facing a threat which even affects the global market. If these risk factors are spatially mapped, a GIS, based on detail land cover mapping can be used to estimate potential risks in food production. This study initially investigates the value of the detailed land cover data of LMB in assessing fluctuations in food production

Detecting tsunami damage from satellite data in Sri Lanka

Satellite technology again proved its strength through the extensive use of tsunami images of damage regions of December 2004 Sumatra earthquake and tsunami disaster. Mainly due to continue earth surface monitoring capability, satellites are collecting a valuable set of data (images), which is increasingly developing together with the developments of the computer. From the beginning of the disastrous earthquake and the killer tsunami that killed about 0.25 million people, quake and tsunami warning platforms of remote sensing observations were in action by continually monitoring seismic activities. Satellites exchanged and recorded those seismic data though warning systems, unfortunately not implemented in Indian Ocean to inform about the tsunami. As the result, the killer tsunami traveled thousands of kilometers, and reached African coast taking 8-10 hours, without any warning. The result was the largest natural disaster in Sri Lankan history by the terms of deaths and economic damage. But, some other satellites also monitored the disaster and left valuable data to study the damage. In this study, we used freely available those satellite images that shows the before and after tsunami disaster of Sri Lanka and compare with some images of Sumatra. See figure 01 for general area affected by the Sumatra earthquake and tsunami, and the death toll of each country by after one month of the hi

Application of remote sensing and social media to mitigate bushfire threat in regional Australia

Bushfire behaves as an integral part of forest regeneration cycle, but when it comes to the point of a natural disaster, the impact on human settlements and the environment is massive. In Australia, bushfires have become the most disastrous natural hazards. According to the Australian Bureau of Criminology, bushfire damage recorded from 1967 to 1999 have an estimated cost about $2.5 billion excluding losses to the forest cover and the environment. After the disastrous 2009 Black Saturday bushfire in Victoria, public attention to bushfire took a new peak. The Black Saturday bushfire has killed 173 people and injured about 500 people. However, about 50% of 54,000 average annual Australian bushfires occur due to suspicious and deliberate reasons. Due to this grave situation, scientists are regularly exploring various methods to mitigate the damage from bushfires. This study focuses on a low-cost safety measure that can be powered by widely available free satellite images and social media to mitigate the bushfire disasters, particularly in regional Australia. The prime focus of this study is to educate rural communities about the behaviour of the bushfire using semi real-time MODIS satellite imagery. These satellite imagery based bushfire contents or Media GIS contents will be available for local communities through social media to encourage people in participate of disaster mitigation efforts. MODIS data can be linked with Google high-resolution images and information gathered from participatory GIS to deliver precise and latest bushfire information. Collected Participatory GIS (PGIS) data can be used to enrich the GIS database to improve the safety of rural communities in bushfire hazards. Also, PGIS can be used as a tool to widen the discussions among local communities in natural disasters

Application of Modis 250m images without in situ observations for mapping Mekong River Basin land cover

Mekong River runs from Hengduan Mountains in central-west China to Vietnam covering 805,604 sq km of land by its basin. The Lower Mekong Basin (LMB), the region mapped in this study, covers nearly 3/4 of the entire basin. About 90% of the population and agricultural activities of the Mekong River basin is located in this fertile LMB which faces disastrous floods almost annually. Mapping LMB at moderate resolution gives number of advantages for studies of flood mitigation and land utilization. However, compiling a cloud free mosaic and collecting ground truth data for training samples and map validation make map production process a challenging task. This study utilized MODIS 250m image data of the region obtained in 2005 February. Dry weather in Jan-Apr makes the sky relatively free of clouds and 2005 February also had fewer disturbances coming from smoke of biomass burning. The methodology of the study substantially relied on high resolution images in Google Earth for collection of training sample for supervised classification and accuracy assessment. Arc GIS generated KMZ file of unclassified and classified maps used to overlay image and map on Google Earth for identifying training site and field information extraction for accuracy assessment. Also ground information collected by a different research projects in 2008 were combined with information gathers from Google Earth images. The classified map showed 29.2% of the LMB under forest, 36.5% under Scrubland, when combined its Highland and Lowland subcategories. Three subcategories of paddy cultivated area covered 27.9% of LMB. Accuracy assessment conducted with randomly selected 200 points against high resolution images gave an overall accuracy of 80.7% in major land cover classes. According to the 250m resolution, urban features have not clearly separated though large urban areas like Phnom Penh and Can Tao have accurately classified. The methodology of this study produced a noteworthy success in classifying land cover of large areas like LMB, without expensive data sources and difficult and costly field investigations

Mapping Mekong land cover at 250m resolution without in situ observations

[Abstract]: Mekong River runs from Hengduan Mountains in central-west China to Vietnam covering 805,604 sq km of land by its basin. The Lower Mekong Basin (LMB), the region mapped in this study, covers nearly 3/4 of the entire basin. About 90% of the population and agricultural activities of the Mekong River basin is located in this fertile LMB which faces disastrous floods almost annually. Mapping LMB at moderate resolution gives number of advantages for studies of flood mitigation and land utilization. However, compiling a cloud free mosaic and collecting ground truth data for training samples and map validation make map production process a challenging task. This study utilized MODIS 250m image data of the region obtained in 2005 February. Dry weather in Jan-Apr makes the sky relatively free of clouds and 2005 February also had fewer disturbances coming from smoke of biomass burning. The methodology of the study substantially relied on high resolution images in Google Earth for collection of training sample for supervised classification and accuracy assessment. Arc GIS generated KMZ file of unclassified and classified maps used to overlay image and map on Google Earth for identifying training site and field information extraction for accuracy assessment. Also ground information collected by a different research projects in 2008 were combined with information gathers from Google Earth images. The classified map showed 29.2% of the LMB under forest, 36.5% under Scrubland, when combined its Highland and Lowland subcategories. Three subcategories of paddy cultivated area covered 27.9% of LMB. Accuracy assessment conducted with randomly selected 200 points against high resolution images gave an overall accuracy of 80.7% in major four land cover classes. According to the 250m resolution, urban features have not clearly separated though large urban areas like Phnom Penh and Can Tao have accurately classified. The methodology of this study produced a noteworthy success in classifying land cover of large areas like LMB, without expensive data sources and difficult and costly field investigations