STUDY ON FLOOD INUNDATION IN PEKALONGAN, CENTRAL JAVA

Tidal flood or ‘rob’ is a serious problem in many coastal areas in Indonesia, including Pekalongan in the northern coast of Java island. This study aimed to simulate the flood inundation area for different scenarios of sea level rise, also to investigate the possibility of land subsidence that may further aggravate the problem of flooding in Pekalongan. In this study, the MIKE-21 model was used to simulate and predict the flood inundation area. Tidal data were generated from the Tide Model Drive (TMD). The tidal flood simulations were carried out for three different scenarios of sea level rise: 1) current situation, 2) next 50 years, assuming no sea level rise, and 3) next 50 years, assuming 50 cm of sea level rise. Based on the results, the ranges of water level rise in Pekalongan for each scenario were 0.23-1.27 m, 0.36-1.38 m, and 0.65-1.53 m, respectively. Meanwhile, ground displacement maps were derived from the ALOS/PALSAR data using Differential Interferometric Synthetic Aperture Radar (D-InSAR) technique. Twelve level 1.0 images of ALOS/PALSAR data acquired in ascending mode during 2008 to 2009 were collected and processed in time-series analyses. In total, 11 pairs of interferogram were produced by taking the first image in 2008 as the master image. The results showed that the average of land subsidence rate in Pekalongan city was 3 cm/year, and the subsidence mainly occurred in the western part of the city

Applying the global standard FAO LCCS to map land cover of rural Queensland

Production of land cover maps has developed rapidly with the introduction of satellite images. However, these mapping tasks face a common challenge in adopting an internationally accepted classification scheme. Classification schemes were generally tailored to match local conditions without a flexibility to apply in other parts of the world. Land cover mapping in Australia is also facing the same dilemma, “the lack of standard classification system” to classify its massive land mass and compare internally and internationally. To address this issue, the Food and Agriculture Organization (FAO) produced a widely acceptable land cover classification system (FAO LCCS) in year 2000, based on an a priori (pre-decided) approach to classify the land to match with any region of the world. In this study we classified rural Queensland land cover, using the hierarchical and the a priori method used in FAO LCCS. Under the a priori approach, all classes were determined before the classification start to maintain the standardization of categories. The hierarchical dichotomous approach was (divide into subcategories) applied afterward, to obtain classes without having any conflict between two given land cover types. We classified satellite images of two rural Queensland regions, Hughenden grasslands and semi-arid Mt Isa. After classifying regions into level 1 to level 3 (FAO pre-set classes), classifiers based on spectral values and field investigations were implemented to build the level 4. Primarily, SPOT 10m images were classified for land cover maps, however, all other available information were utilized for the classification process. Field investigations were carried out to verify uncertainties in spectral values and to collect ground truth information. Results of the study rendered well-classified two maps at 10m resolution with over 80% overall accuracy. The most significant outcome of the study is the successful implementation of FAO LCCS approach to local conditions of Queensland, which could serve as a guideline to map other regions in Queensland and other states of Australia

Application of Digital Image Processing Integration with Satellite Remote Sensing and GIS in Land Use Land Cover Change and Soil Erosion

The relationship between land use land cover changes and soil erosion is investigated using digital image processing integrated with Remote Sensing (RS) and Geographic Information System (GIS) in Tonle Sap Watershed, Cambodia. The Universal Soil Loss Equation (USLE –Wischmeier and Smith, 1978) was applied to build a model to estimate the annual soil loss from the watershed in 1976 and 2002. The analysis process on land use land cover change is based on geo-processing of GIS utilizing the raster and vector analysis. The analysis result of land use land cover change between 1976 and 2002 show that the agriculture land was expanded and the forest area was decreased in the study area. A grid based and polygon based GIS were used to comparatively calculated soil loss map. The result shows that grid based method also enables the meaningful use of pixel based remotely sensed land cover information for modeling soil erosion. The result also shows that increasde soil erosion in the agriculture land and suggests that mitigation measure should be taken for prevention of further degradation. High resolution satellite images are very effective tool for not only monitoring the land use land cover change but also estimating soil erosion in the watershed. Similarly, GIS is also an effective tool in analyzing by overlaying various vector maps related to factors affecting in soil erosion. C++ programs were also developed for digital image processing such as solving LS factor and converting raw Digital Number (DN) value to reflectance valves. The main data used in this study are Landsat ETM satellite images

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

Application of Remote Sensing and GIS Technology in Natural Disaster Management and Rehabilitation

Cyclone Nargis was a strong tropical cyclone that caused the deadliest natural disaster in the recorded history of Myanmar. Satellite maps show that the storm's damage was concentrated over an area of about 30,000 sq km, stretching along the Andaman Sea and Gulf of Martaban coastlines. This area is home to nearly a quarter of Myanmar’s 57 million people. Satellite images can be used to identify storm damage from multiple vantage points and can help with the planning of disaster recovery and rebuilding efforts. Images collected before and after Nargis slammed into the Myanmar coastline illustrate just how quickly flood waters rushed and swelled over the network of creeks that meander through the lower Ayeyarwaddy delta. Using pre-impact images and post-impact images, damaged area and risk zone classification can be done integrated with remote sensing and geographic information system (GIS) technique. It is firmly believed that the outcome result will surely contribute to foresee and target medical and other assistance in both ongoing and emergency relief efforts in Myanmar

Study on Flood Inundation in Pekalongan, Central Java

Hal.76-83: ilus. ; 30 c