Preparation of digital data sets on land use/land cover, soil and digital elevation model for temperature modelling using Remote Sensing and GIS techniques

Samanta, Sailesh; Pal, Dilip Kumar; Lohar, D.; Pal, Babita

doi:10.17485/ijst/2011/v4i6.22

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…Remote sensing and GIS application are cross-sectoral approaches in all sectors of planning and managements. Remote sensing allows for regional and extensive data collection at any given time saving cost and time with comparative analysis over time (Samanta et al 2011(Samanta et al , 2012. Greenbaum et al (1995) assessed the application of remote sensing and GIS as a rapid landslide mapping tool for Papua New Guinea, using the Kaiapit Landslide (in 1998) as a case study.…”

Section: Introductionmentioning

confidence: 99%

Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques

Michael

Samanta

2016

Model. Earth Syst. Environ.

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Weighted linear combination (WLC) method was used to assess landslides vulnerability of the Simbu Province, Papua New Guinea within the GIS environment of ArcGIS. This multi-criteria evaluation method allows flexibility and tradeoffs amongst all parameters used. Ranks and weights are assigned depending on their influence on the occurrence of landslides. Parameters selected for the study include slope angle, elevation, rainfall, vegetation cover, land use/land cover, landform, proximity to roads, proximity to rivers and proximity to lineaments. Restricted in some sense in terms of data, WLC was appropriate in using existing metadata of the country; Papua New Guinea Resource Information System and Forest Information Management System. The landslide susceptibility map provides valuable information of the risk at hand in the province and district levels to better manage and plan mitigation measures. The slope factor was assigned a weighted of 4 as having greater influence on landslides in the region followed by rainfall weighted of 2 and the other having uniform influence of 1. The study area shows the distribution of the five vulnerability/susceptibility classes ranking from very low (1) to very high (5). Areas with very high landslide vulnerability zones are found in the northern and western parts of Simbu Province. Comparatively, southern parts have very low vulnerability areas. From the calculations done, 6.21 % of area is at very low risk, 20.24 % at low risk, 32.27 % of moderate risk, 26.88 % of high risk and 14.41 % of very high risk area coverage.

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Section: Introductionmentioning

confidence: 99%

Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques

Michael

Samanta

2016

Model. Earth Syst. Environ.

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“…Elevation and slope are indeed crucial parameters in flood risk and vulnerability mapping. Variations in elevation have a definitive impact on climate characteristics (Samanta et al 2011), while slope controls surface runoff, water flow ferocity, and vertical percolation (Youssef et al 2011;Adiat et al 2012). The land use/land cover factor directly or indirectly influences AQUA -Water Infrastructure, Ecosystems and Society Vol 00 No 0, 3 infiltration, evapotranspiration, and surface runoff generation (Samanta et al 2018).…”

Section: Methodsmentioning

confidence: 99%

A novel approach to flood risk zonation: integrating deep learning models with APG in the Aji Chay catchment

Abdollahzadeh Bina,

Fard Moradinia

2024

AQUA — Water Infrastructure, Ecosystems and Society

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Each year, floods, as one of the natural calamities, lead to significant destruction in various regions globally. Consequently, precise flood prediction becomes crucial in mitigating human and financial losses and effectively managing water resources. To achieve this, Convolutional Neural Network and Long Short-Term Memory (LSTM) models were utilized in this study to map flood hazards in the Aji Chay watershed. Flood data points were collected from the study area and subsequently divided into two groups using the Absence Point Generation technique. The first group, comprising 70% of the data, served as the training dataset for model construction, while the remaining 30% formed the testing dataset for validation. Seven key factors influencing floods, namely, precipitation, land use, Normalized Difference Vegetation Index, drainage density, flow direction, topographic wetness index, and terrain ruggedness index, were identified through Leave-One-Feature-Out approach and employed in the modeling process. The LSTM model with a Kolmogorov–Smirnov (KS) statistic value of 88.14 was chosen as the best model based on the KS plot. The results revealed that approximately 37% of the study area fell into high and very high flood risk classes. These research findings can be valuable in the effective management of flood-prone areas and the reduction of flood damages.

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“…LULC affects infiltration, evapotranspiration, and surface runoff generation directly or indirectly. Supervised classification from Landsat-8 OLI imagery was employed to build the LULC map [68] (Figure 6d). Additionally, lithology is essential in causing floods because impermeable rock units increase runoff by decreasing infiltration rates.…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt

Megahed,

Abdo,

AbdelRahman

et al. 2023

Applied Sciences

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The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using a frequency ratio approach. The frequency ratio (FR) model was used to produce flood prediction maps for New Cairo City, Egypt. Using field data and remote sensing data, 143 spatial flooded point sites were mapped to build a flood inventory map. The primary driving criteria for flash floods were determined to be elevation, slope, aspect, Land Use Land Cover (LULC), lithology, stream distance, stream density, topographic wetness index (TWI), surface runoff, and terrain ruggedness index (TRI), in that order of importance. A flood susceptibility map (FSM) has been created using the FR model, which combines geographical flooded sites and environmental variables. Our findings from FSM, roughly a fifth of the city is very highly susceptible to flooding (19.32%), while the remaining 40.09% and 13.14% of the study area rank very low and low risk, respectively. The receiver operating characteristic curve (ROC) technique was also used to validate the FSM, and the resulting results showed an area under the curve (AUC) of 90.11%. In conclusion, decision makers can employ models to extract and generate flood risk maps in order to better understand the effects of flash floods and to create alternative measures to prevent this hazard in similar regions. The results of this study will aid planners and decision makers in developing some likely actions to reduce floods vulnerability in this area.

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Preparation of digital data sets on land use/land cover, soil and digital elevation model for temperature modelling using Remote Sensing and GIS techniques

Cited by 16 publications

References 9 publications

Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques

Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques

A novel approach to flood risk zonation: integrating deep learning models with APG in the Aji Chay catchment

Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt

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