Remote Sensing and GIS Methods in Urban Disaster Monitoring and Management – An Overview

Twumasi, Nana Yaw Danquah; Shao, Zhenfeng; Altan, Orhan; Mapping,

doi:10.31142/ijtsrd23976

Cited by 7 publications

(6 citation statements)

References 35 publications

(44 reference statements)

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“…The data help to develop protocols for flood management and gives immense information for environmental science research. Remote sensing measures the accuracy of water up to decimeter level and shows real-time transmission [36]. Meng et al [37] presented an approach to predict snowmelt floods in the Juntanghu watershed in China.…”

Section: Multispectralmentioning

confidence: 99%

Remote Sensing Methods for Flood Prediction: A Review

Munawar

Hammad

Waller

2022

Sensors

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Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.

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Section: Multispectralmentioning

confidence: 99%

Remote Sensing Methods for Flood Prediction: A Review

Munawar

Hammad

Waller

2022

Sensors

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“…Land cover identification is the cornerstone for tasks like thematic mapping and change detection analysis. Natural resource management [8], wildlife habitat protection [9], urban expansion/encroachment [10], natural catastrophe (tornadoes, flooding, volcanic, earthquake, fire) [11], and target detection [12] are all covered by LULC maps.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Jain

Varghese²,

Joshi

et al. 2023

Preprint

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Both spatial and non-spatial data are critical to a country's social and economic development. Land use and land cover (LULC) maps are data that can be used to retrieve land information and detect environmental changes. Several recent studies have used deep learning approaches to generate LULC maps from various remote-sensing image formats. They have correctly recognized several land cover elements such as built-up areas, forests, water, etc. On the other hand, spaceborne radar images must be used to test deep learning methods because radar sensors operate in all weather and lighting circumstances and have diverse image characteristics depending on the wavelength. Although many researchers have applied deep learning techniques to classify the land cover features, most of them need to do more by taking less or more several land cover features and taking proper evaluation metrics to justify the classification techniques. The current research shows segmentation on Sentinel-1 C-band synthetic aperture radar (SAR) images of Gujarat's Ahmedabad, Anand, and Kheda regions using four deep-learning models. U-Net, SegNet, FCN, and DeepLabV3+ have primarily been employed in prior research to classify SAR and other satellite image formats and showed excellent results in identifying different land cover features. Training samples of five primary classes for ground-truth data were obtained using Google Earth and Sentinel-2 images as reference maps. For segmenting the Sentinel-1 image, above mentioned four deep-learning semantic segmentation models were chosen and fine-tuned. All models did exceptionally well, with overall accuracy ranging from 76% to 90%. U-Net fared well, followed by DeepLabV3+ in classifying all five land cover features, with mean intersection over union (IoU) of 0.67 and 0.55, respectively. The results reveal that using a self-generated reference map, the Sentinel-1 dual-pol images can categorize the primary land cover features.

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“…preparedness, response, and recovery [21]. GIS and RS can be used to record data for a large area in a shortest time possible, and hence, they have been identified to be fit for disaster management [21,22]. GIS and RS, have been applied to help and obtain early warnings, predict, prepare, prevent, and evacuate the vulnerable members of community in time and avoid risks [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…GIS and RS can be used to record data for a large area in a shortest time possible, and hence, they have been identified to be fit for disaster management [21,22]. GIS and RS, have been applied to help and obtain early warnings, predict, prepare, prevent, and evacuate the vulnerable members of community in time and avoid risks [21,22]. The statements are in agreement with the results from a study conducted by Twumasi et al, [22], who postulated that GIS and RS, have for several years been used towards the risk reduction and management of several disasters, including, Landslides, earth quakes, floods, tsunamis, and droughts.…”

Section: Introductionmentioning

confidence: 99%

“…GIS and RS, have been applied to help and obtain early warnings, predict, prepare, prevent, and evacuate the vulnerable members of community in time and avoid risks [21,22]. The statements are in agreement with the results from a study conducted by Twumasi et al, [22], who postulated that GIS and RS, have for several years been used towards the risk reduction and management of several disasters, including, Landslides, earth quakes, floods, tsunamis, and droughts. This study therefore, aims at documenting how GIS and RS can be used to generate information which may be essential for mitigating risks from landslides.…”

Section: Introductionmentioning

confidence: 99%

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The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review

Nabukonde

Barakagira

Akwango

2023

ACRI

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Landslides, whether induced by nature, or human activities, are one of the most prominent disasters which are of great concern in the world. They cause a lot of havoc to the environment hence a necessity to avoid them whenever possible. This literature review is basically aimed at finding out how Geographical Information System (GIS) and Remote Sensing (RS) technologies are used in avoiding landslides and risks associated with them at different levels; Challenges encountered while using GIS and RS techniques for landslide disaster risk reduction are identified; and therefore, dealing with them requires the involvement of developed countries who have the capacity to provide the necessary equipment to the developing countries that are faced with disasters. This study discusses the use of GIS and RS in mitigation of risks from landslides, and mainly points out how these techniques are applied to avoid disaster risks. Secondary data was reviewed from journal articles, institutional reports, and online publications from similar studies. GIS and RS tools are important in predicting, monitoring and managing landslide disasters. It was concluded that GIS and RS tools provide cheaper, reliable, and faster techniques of accessing spatial data in a given area, therefore regarded as essential technologies that may be necessary for predicting landslide occurrences, these technologies need to be considered in communities which are prone to landslides.

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Remote Sensing and GIS Methods in Urban Disaster Monitoring and Management – An Overview

Cited by 7 publications

References 35 publications

Remote Sensing Methods for Flood Prediction: A Review

Remote Sensing Methods for Flood Prediction: A Review

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review

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