scholarly journals Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques

2020 ◽  
Vol 12 (4) ◽  
pp. 714 ◽  
Author(s):  
Asif Sajjad ◽  
Jianzhong Lu ◽  
Xiaoling Chen ◽  
Chikondi Chisenga ◽  
Nayyer Saleem ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Damage Assessment ◽  
Response Assessment ◽  
Geographical Information ◽  
Landsat 8 ◽  
Water Index ◽  
Flood Duration ◽  
Gis Techniques ◽  
Normalized Difference Water Index ◽  
Future Events

In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.

scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

scholarly journals Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4333 ◽  
Author(s):  
Poliyapram Vinayaraj ◽  
Nevrez Imamoglu ◽  
Ryosuke Nakamura ◽  
Atsushi Oda
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Water Index ◽  
Tuning Parameters ◽  
Water Classification ◽  
Water Region ◽  
Normalized Difference Water Index ◽  
Perceptron Learning

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

scholarly journals Delineating surface and sub surface waterlogged area using RS & GIS: a case study of Rachna Doab

2017 ◽  
Vol 5 (2) ◽  
pp. 81
Author(s):  
Hannan Mehmood ◽  
Mobushir Riaz Khan ◽  
Muhammad Amin ◽  
Rizwan Ali
Keyword(s):  
Remote Sensing ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Landsat 8 ◽  
Monsoon Period ◽  
Groundwater Levels ◽  
Water Index ◽  
Post Monsoon ◽  
Waterlogged Area ◽  
Gis Environment

Remote sensing (RS) combined with Geographical Information Systems (GIS) offers fabulous contrasting option to routine mapping strategies in observing and mapping of surface and sub-surface waterlogged areas. In the present study, a pre-monsoon and post-monsoon surface waterlogged area was delineated in the four districts of Rachna doab, using Landsat 8 data acquired for the year 2014. Modified Normalized Difference Water Index (MNDWI) was used mainly to delineate surface waterlogged areas. Perennial surface waterlogged areas were assessed for the study area by incorporating the waterlogged areas derived for both the pre-monsoon and post-monsoon seasons under GIS environment. Result shows that the total surface waterlogged area in pre-monsoon is 5,861 ha, which is 0.51 % of study area and for post-monsoon the surface waterlogging is 8,661 ha, which is 0.75% of study area respectively. Perennial surface waterlogging is 3,573 ha, which is 0.30% of the study area. Maximum waterlogged area was observed in Gujranwala district followed by Hafizabad, Sheikhupura and Nankana Sahib respectively. Further, waterlogged areas caused by rise in groundwater level were also assessed spatially under ArcGIS environment using the piezometric data pertaining of pre-monsoon and post-monsoon seasons for the year 2014 which were spread all over the study area. The analysis of both the seasons of groundwater levels indicates that the area under critical category during pre-monsoon period was 47,309 ha, which is 4% of the total area. Area under most critical category during post-monsoon period increased from 47,309 to 131,070 ha, which is 11% of the total. The study shows utility of remote sensing and GIS for evaluation of waterlogging areas especially where waterlogging situations occurs because of excessive irrigation and accumulation of rain and floodwater.

scholarly journals Contributions of remote sensing in the diachronic study of the spatial and temporal evolution of the Ahmed El Hansali dam water reservoir from 2002-03 to 2018-19

2020 ◽  
Vol 183 ◽  
pp. 02004
Author(s):  
Tarik El Orfi ◽  
Mohamed El Ghachi ◽  
Sébastien Lebaut
Keyword(s):  
Remote Sensing ◽  
Negative Impact ◽  
Water Reservoir ◽  
Water Levels ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Diachronic Study ◽  
Flooded Area

The OumErRbiabasin is one of the watersheds with the largest number of hydraulic infrastructures in Morocco. These hydraulic structuressupply water for drinking, industrial and agricultural uses. The Ahmed El Hansali dam is a 740 Mm³ reservoir located near Zaouyat Cheikh andhave an active storage of473 Mm³. The succession of dry years in the OumErRbiabasin has had a negative impact on the water resource and has caused a remarkable decrease in the reservoir of the Ahmed el Hansali dam. In this paper, the MNDWI (Modified Normalized Difference Water Index) from Landsat 5-TM, Landsat 7-ETM, and Landsat 8-OLI satellite images was used to estimate the spatial and temporal fluctuations of the volumes of water stored in the reservoir between hydrological years 2002-03 and 2018-19. Results show that the volumes estimated by remote sensing reasonably match the volumes estimated by the OumErRbia Hydraulic Basin Agency (OERHBA)using recorded water levels and reservoir storage curve for years 2002-03 and 2013-14; the determination coefficient R² exceeds 0.90. The mapping of the extent of the dam’s impoundment has shown a very significant decreasein the flooded area level during dry years.

scholarly journals Crop Pattern Change and Crop Water Requirement Judgment using Remote Sensing and GIS Techniques: a research on Tungabhadra Dam Right Canal

2019 ◽  
Vol 8 (2S3) ◽  
pp. 652-657
Keyword(s):  
Remote Sensing ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Delta Method ◽  
Geographical Information ◽  
Command Area ◽  
Cropping Pattern ◽  
Crop Water ◽  
Irrigation Project ◽  
Gis Techniques

Agriculture is most important resources of any country worldwide which is a major renewable source and is dynamic. The study area selected was command area under Basavanna canal which is one of the canals to Tungabhadra river on right side bank. This selected canal for cropping pattern analysis has a command of 1240.00 hectare and is located at Vallabhpur, Bellary district. Basavanna canal has a designed discharge capacity of 125 cusecs for serving the cropping area. Every irrigation project has planned cropping pattern, the crop water requirement (CWR) for which is calculated based on Duty / Delta method. However due to growing population and increase demand for food products crop violation is found in every command leading to more irrigation. Remote Sensing (RS) and Geographical Information System (GIS) techniques have emerged as powerful tools for crop water management. Remotely sensed land use-land cover data was used for analysing the cropping pattern in the area and also to estimate the change in the cropping pattern. This study was performed using ArcGIS 9.3 and ERDAS 9 software. Crop water requirement was calculated using Modified Penman Equation for present cropping pattern. The study finds that, approximately 50% of water could be saved using modified Penmen method compared to crop water requirement calculated using Duty Delta method as adopted in project report and the same water may be diverted to meet other needs

scholarly journals A study on deforestation in hilly areas of Haryana using Remote Sensing and GIS technique

2020 ◽  
pp. 69-77
Author(s):  
Anju Jangra ◽  
Anurag Airon ◽  
Ram Niwas
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Special Focus ◽  
Landsat 8 ◽  
Deforestation Rate ◽  
Gis Technique ◽  
Prime Concern

Forest is an essential part or backbone of the earth ecological system. In a country like India, the people and the economy of nation is mainly relies on the diversity of natural resources. In today's world degradation of forest resources is a prime concern for many of the scientists and environmentalists because the canvas had been transformed from last few decades to cultivated and non-cultivated land. In India, Haryana state has lowest forest cover i.e. 3.59% followed by Punjab 3.65%. Over the several decades, the advancement of Remote Sensing and Geographical Information System (GIS) technique has emerged as an efficient tool to monitor and analyse deforestation rate in hilly areaor over a variety of location. Remote sensing based vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for assessment and monitoring of tress. The aim of the present study was to analyse the deforestation in hilly areas in Haryana State (India) by remote sensing data with a special focus on Panchkula and Yamunanagar. The information was collected through the LANDSAT 8 satellite of NASA. The result revealed that the deforestation rate is high in Hilly areas of Haryana. The study shows that the forest cover in hilly areas of Haryana in 2013 was 50,879.07 hectares and in 2019 it was 44,445.51 hectares of land. Thereby decrease in forest cover of 6,433.56 hectares had been observed in the study period of 2013-2019 i.e. 6 years. Spatial variations in deforestation were also mapped in GIS for the hilly areas in Panchkula and Yamunanagar districts of Haryana.  

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