Spatio-temporal variability of heat exposure in Peninsular Malaysia using land surface temperature

Nurfatin Izzati Ahmad Kamal, Zulfa Hanan Ash’aari and Ahmad Makmom Abdullah

Page No. 1-9

The increasing of extreme heat event across the world has become a new threat that was caused by the changing climate. It is important to understand the spatiotemporal dynamics of extreme heat and suggest feasible adaptation strategies to reduce the heat exposure. In this study, daytime land surface temperature (LST) has been retrieved from MODIS Aqua Earth observation satellite from NASA to characterize the latest spatio-temporal variability of heat exposure in Peninsular Malaysia with the reference of short-term mean calculated from year 2003 until 2012. It was found that the LST is increasing by 0.0477°C per year during the period. Trend analysis using Mann-Kendall test shows that the LST increases significantly during annually especially during southwest monsoon.

Based on the z-score of mean LST for each district from year 2003 until 2018, heat exposure index (HEI) was obtained and exhibited the high HEI in mostly northern and urban areas. The HEI value will be one of the inputs for heat vulnerability assessment in the future research. Through cluster analysis, it was found that the northern part of Peninsular Malaysia is considered as the hot spot of extreme heat while cold spot is located in centre part of the region.

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Sentinel-1 SAR Data Preparation for Extraction of Flood Footprints- A Case Study

Kuntla Sai Kiran, Panchagnula Manjusree and M. Viswanadham

Page No. 10-20

Floods are one of the most commonly occurring and destructive natural disasters throughout the globe. Microwave Synthetic Aperture Radar (SAR) satellite data is mostly used for mapping and monitoring flood extents due to its capability of acquiring data in day and night even in adverse weather conditions as it can penetrate through the haze, rainfall, clouds and dust which are mostly found during the floods. Since SAR data is complex and coherent in nature, it requires extensive data preparation before analysing the data.

This study describes the basic steps for SAR data preparation namely orbit file application, thermal noise removal, calibration, terrain flattening, speckle filtering, terrain correction and linear to decibels conversion using SNAP tool. Further, automation of this process is also discussed so that the final product can be used for near real-time applications.

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Modelling and Mapping of Landslide Susceptibility in South Sikkim Himalaya, India using Binary Logistic Regression (BLR) and Geoinformatics

Sarkar Kallol and Mandal Sujit

Page No. 21-29

Landslide Susceptibility map is very much necessary for landslide preventive and mitigation measures. In this study Landslide Susceptibility map of landslide prone South Sikkim Himalaya has been generated using a binary logistic regression model. Fourteen important causative factors of slope instability i.e. slope, slope aspect, earthquake proximity, elevation, geology, geomorphology, lineament density, distance from lineament, drainage density, distance from drainage, land use/cover (LULC), rainfall, road density, distance from road have been considered and corresponding thematic data layers have also been extracted from Arc GIS (10.1) and Geomatica (2016) software environments. To construct the landslide susceptibility map of the study area, the coefficients of the causative factors have been kept by the BLR model along with the constant.

Again, the map has been categorized into four landslide susceptibility zones from low to severe. With the help of receiver operator characteristic (ROC) curve, the resultant landslide susceptibility map has been validated. The ROC curve analysis is showing an accuracy of 69% for an independent set of test samples. The result has shown conformity between distribution of existing landslides and predicted landslide susceptibility zones.

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Characteristic of the morphometric parameters and landcover interaction under climate change scenario in Ganga headwater, Garhwal Himalaya, India: A remote sensing and GIS approach

Ahmad S. and Mulhim M.

Page No. 30-39

The morphometric parameters of the sub-basins have been determined using SRTM DEM in the Alaknanda River basin, Garhwal Himalaya. Relationships among morphometric parameters indicated that linear parameters (stream order and stream length) are in negative relationships with shape index, basin relief ratio and positively related to compact coefficient and drainage texture. Basin relief is highly positive with maximum elevation. Relief Peakedness (Rp) and Hypsometric integral (HI) are positively related to a minimum elevation of the sub-basins. The geology and structural framework largely control the shape of the basin.

Inter-relationships between Land covers and morphometric parameters suggested that barren/permanent snow and ice cover is positive to hypsometric integral, elevation and relief peakedness. Grass land cover area is showing a positive correlation with slope, basin relief and ratio, elevation and negative correlation with bifurcation ratio. Low values of HI and Rp support the development of forest in the areas of lesser physical erosion. Correlation coefficients derived from permanent snow/ice cover, mixed forest and numbers of morphometric parameters are showing the linear progression through time.

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Application of Geospatial technologies in flood vulnerability analysis

Mathur Dhruvesh K.

Page No. 40-45

Flood is a natural disaster occurring in every part of the world every year. Floods are causing huge damage to property and human life. Remote Sensing and GIS are important tools for managing the effects of disasters. Remote Sensing and GIS both are used in pre and post-disaster planning. Using remote sensing, more vulnerable areas are easily identified. GIS helps for managing rescue planning and post-disaster phase operations.

In the present study, villages of Anand district affected by the flood of Mahi River are studied. Factors like elevation, population density, resources for post-disaster and rescue operations of villages of Anand district are studied. Based on analysis, villages near to Mahi are classified in three different vulnerable classes. Sample rescue plan for villages which are highly affected is demonstrated.

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An overview and comparison on recent landslide susceptibility mapping methods

Han Gao, Pei Shan Fam, Heng Chin Low, Lea Tien Tay and Habibah Lateh

Page No. 46-64

Landslide can be considered as a global challenge for all human beings in the world. It causes huge damage to properties and even claims lives. Landslide susceptibility mapping technique, as the most popular landslide prediction tool, plays an essential role in landslide management and assessment. There are various statistical models applied in landslide susceptibility map production which can be classified into three types: bivariate, multivariate and machine learning methods based on statistical theories. In this overview, we singled out six frequently-used methods such as frequency ratio (FR), fuzzy logic (FL), logistic regression (LR), discriminant analysis (DA), artificial neural network (ANN) and support vector machine (SVM) from the literature to analyze and compare the prediction power in landslide susceptibility research.

Although there is not a consistent result in landslide susceptibility mapping using any single method, the multivariate models perform better than the bivariate models. The machine learning models have yet to be developed for their potential prediction power. A worldwide shared landslide database is needed to provide raw data generated from geographic information system (GIS) technique in research.

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