The present study demonstrates the use of a new approach for delineating the accurate flood hazard footprint in the urban regions. The methodology involves transformation of Landsat Thematic Mapper (TM) imagery to a three-dimensional feature space, i.e. brightness, wetness and greenness, then a change detection technique is used to identify the areas affected by the flood. Efficient thresholding of the normalized difference image generated during change detection has shown promising results in identifying the flood extents which include standing water due to flood, sediment-laden water and wetness caused by the flood. Prior to wetness transformations, dark object subtraction has been used in lower wavelengths to avoid errors due to scattering in urban areas. The study shows promising results in eliminating most of the problems associated with urban flooding, such as misclassification due to presence of asphalt, scattering in lower wavelengths and delineating mud surges. The present methodology was tested on the 2010 Memphis flood event and validated on Queensland floods in 2011. The comparative analysis was carried out with the widely-used technique of delineating flood extents using thresholding of near infrared imagery. The comparison demonstrated that the present approach is more robust towards the error of omission in flood mapping. Moreover, the present approach involves less manual effort and is simpler to use.
Forest loss is spatial and temporal issue of concern for any governance. Due to limitation of man power, money or resources the issue requires first to point out most critical area i.e. Geospatiotemporal hotspot and then prioritize it so the most needed area always concern first.Geo-spatiotemporal hotspot detection is a scan statistical process that identifies a region exhibiting characteristics of interest (unusual, anomaly, outbreak, critical resources area) over the period. The hotspot detection is a statistical process. It requires geospatial dataset as an input which includes two variables for hotspot detection, Size or Population and Response or Cases. In deforestation hotspot detection, size variable is a total area of forest while response variable is a deforested area. In this paper we figure out deforestation hotspot using Normalized Difference Vegetation Index (NDVI) as a response variable. The paper presents and elaborates algorithm that causes use of NDVI as a response variable. This paper discusses the multi-criteria or multi-indicator ranking methods using Partially Order Set (POSET) which uses as a prioritization tool.
The present paper aims to investigating the impact of flood on the socio economic lifes of the people in Pothukallu panchayath with special reference to Kavalappara at Malappuram districts ,Kerala. Kerala is one of the most eligible aspirants for achieving a developed economy status among Indian state. Unfortunately , the unprecedented flood in Kerala in 2018 and 2019 caused extensive damage to houses, public infrastructure, agriculture crops and livestock, livelihood, businesses, eco-system and bio-diversity. The study focus on the impact of flood , effectiveness government support,intensity of flood etc. therefore through convenient sampling method used to data collection through predesigned questionnaire.
KEYWORDS ; Socio-economic Impact, Climate change, livelihood, Flash floods
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