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

Study on Flood Inundation in Pekalongan, Central Java

Hal.76-83: ilus. ; 30 c

Analysis of meteorological drought pattern during different climatic and cropping seasons in Bangladesh

Drought is one of the most frequent natural disasters in Bangladesh which severely affect agro-based economy and people's livelihood in almost every year. Characterization of droughts in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development. In this study, standardized precipitation index is used to understand the spatial distribution of meteorological droughts during various climatic seasons such as premonsoon, monsoon, and winter seasons as well as cropping seasons such as Pre-Kharif (March-May), Kharif (May-October), and Rabi (December-February). Rainfall data collected from 29 rainfall gauge stations located in different parts of the country were used for a period of 50 years (1961-2010). The study reveals that the spatial characteristics of droughts vary widely according to season. Premonsoon droughts are more frequent in the northwest, monsoon droughts mainly occur in the west and northwest, winter droughts in the west, and the Rabi and Kharif droughts are more frequent in the north andnorthwest of Bangladesh. It is expected that the findings of the study will support drought monitoring and mitigation activities in Bangladesh

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity

The false color composite of PALSAR 50-m orthorectified mosaic imagery (R/G/B = HH/HV/HH-HV) in Southeast Asia in 2009.

<p>Country names were labeled as Myanmar (A), Thailand (B), Laos (C), Vietnam (D), Cambodia (E), Malaysia (F), Brunei (G), Indonesia (H), Philippines (I), Singapore (J), and East Timor (K). The inset graphs show forest, cropland, water body, and built-up land, respectively. The PALSAR 50-m mosaic data was unavailable in the West Papua and Papua regions.</p