scholarly journals Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics

2019 ◽  
Vol 11 (8) ◽  
pp. 943 ◽  
Author(s):  
Alessio Domeneghetti ◽  
Guy J.-P. Schumann ◽  
Angelica Tarpanelli
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Flood Mapping ◽  
Special Issue ◽  
Sensing Data ◽  
Wide Range ◽  
Flood Monitoring ◽  
Novel Processing ◽  
Flood Dynamics ◽  
Processing Techniques

This Special Issue is a collection of papers that focus on the use of remote sensing data and describe methods for flood monitoring and mapping. These articles span a wide range of topics; present novel processing techniques and review methods; and discuss limitations and challenges. This preface provides a brief overview of the content.

scholarly journals Editorial for Special Issue: “Remote Sensing based Building Extraction”

2020 ◽  
Vol 12 (3) ◽  
pp. 549
Author(s):  
Mohammad Awrangjeb ◽  
Xiangyun Hu ◽  
Bisheng Yang ◽  
Jiaojiao Tian
Keyword(s):  
Remote Sensing ◽  
Urban Planning ◽  
Disaster Management ◽  
Remote Sensing Data ◽  
Building Extraction ◽  
Special Issue ◽  
Sensing Data ◽  
Geographic Databases

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [...]

Automatic procedures analyzing remote sensing data to minimize flood response time: a step towards National flood mapping service

2017 ◽  
Vol 25 (5) ◽  
pp. 657-663 ◽  
Author(s):  
Vinod Kumar Sharma ◽  
G. Srinivasa Rao ◽  
C. M. Bhatt ◽  
Abhinav Kumar Shukla ◽  
Ashish Kumar Mishra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Response Time ◽  
Remote Sensing Data ◽  
Flood Mapping ◽  
Sensing Data ◽  
Flood Response

scholarly journals Towards a continuous inland excess water flood monitoring system based on remote sensing data

2017 ◽  
Vol 10 (3-4) ◽  
pp. 9-15 ◽  
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács ◽  
György Sipos
Keyword(s):  
Remote Sensing ◽  
Agricultural Sector ◽  
Remote Sensing Data ◽  
Large Area ◽  
Sensing Data ◽  
Excess Water ◽  
Sufficient Detail ◽  
Flood Monitoring ◽  
First Results ◽  
Hungarian Plain

Abstract Inland excess water (IEW) is a type of flood where large flat inland areas are covered with water during a period of several weeks to months. The monitoring of these floods is needed to understand the extent and direction of development of the inundations and to mitigate their damage to the agricultural sector and build up infrastructure. Since IEW affects large areas, remote sensing data and methods are promising technologies to map these floods. This study presents the first results of a system that can monitor inland excess water over a large area with sufficient detail at a high interval and in a timely matter. The methodology is developed in such a way that only freely available satellite imagery is required and a map with known water bodies is needed to train the method to identify inundations. Minimal human interference is needed to generate the IEW maps. We will present a method describing three parallel workflows, each generating separate maps. The maps are combined to one weekly IEW map. At this moment, the method is capable of generating IEW maps for a region of over 8000 km2, but it will be extended to cover the whole Great Hungarian Plain, and in the future, it can be extended to any area where a training water map can be created.

scholarly journals Editorial to Special Issue “Remote Sensing Data Compression”

2021 ◽  
Vol 13 (18) ◽  
pp. 3727
Author(s):  
Benoit Vozel ◽  
Vladimir Lukin ◽  
Joan Serra-Sagristà
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Data Compression ◽  
Remote Sensing Data ◽  
Low Complexity ◽  
Special Issue ◽  
Huge Amount ◽  
Sensing Data ◽  
The Impact ◽  
Lossless And Lossy Compression

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

scholarly journals Editorial for the Special Issue: Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

2021 ◽  
Vol 13 (3) ◽  
pp. 519
Author(s):  
Amin Beiranvand Pour ◽  
Basem Zoheir ◽  
Biswajeet Pradhan ◽  
Mazlan Hashim
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
Mineral Exploration ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Environmental Hazard ◽  
Special Issue ◽  
Sensing Data

In recent decades, multispectral and hyperspectral remote sensing data provide unprecedented opportunities for the initial stages of mineral exploration and environmental hazard monitoring [...]

Applications of Active Remote Sensing Technologies for Natural Disaster Damage Assessments

2019 ◽  
pp. 1014-1025
Author(s):  
Muhammad Tauhidur Rahman
Keyword(s):  
Remote Sensing ◽  
Natural Disaster ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Active Sensors ◽  
Sensing Data ◽  
Passive Remote Sensing ◽  
Relief Operations ◽  
Rescue And Relief ◽  
Processing Techniques

Immediately following a natural disaster, it is imperative to accurately assess the damages caused by the disaster for effective rescue and relief operations. Passive remote sensing imageries have been analyzed and used for over four decades for such assessments. However, they do have their limitations including inability to collect data during violent weather conditions, medium to low spatial resolution, and assessing areas and pixels on a damages/no damage basis. Recent advances in active remote sensing data collection methods can resolve some of these limitations. In this chapter, the basic theories and processing techniques of active remote sensing data is first discussed. It then provides some of the advantages and limitations of using active remote sensing data for disaster damage assessments. Finally, the chapter concludes by discussing how data from active sensors are used to assess damages from various types of natural disasters.

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