Remote sensing for disaster risk management: Advances and limitations

N. Kerle; M. van den Homberg

doi:10.1016/B978-0-443-13220-9.00044-5

Remote sensing for disaster risk management: Advances and limitations

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

1 Citation (Scopus)

86 Downloads (Pure)

Abstract

This chapter explains how the disaster domain has been a key application area for remote sensing since its pioneering days, with a strong development from opportunistic post-event image acquisition to concerted and regular monitoring of socionatural hazards and comprehensive disaster risk management. It highlights recent technical and organizational advances, such as those related to dynamic, multi-hazard risk assessment, as well as large satellite constellations and sophisticated drones, with machine learning and semantic scene analysis playing increasing roles in data processing. Finally, the chapter discusses the limitations of remote sensing data and gaps in how they are used.

Original language	English
Title of host publication	Comprehensive Remote Sensing-
Editors	Shunlin Liang
Publisher	Elsevier
Chapter	9.11
Pages	257-279
Number of pages	23
Edition	2
ISBN (Electronic)	978-0-443-23949-6
ISBN (Print)	978-0-443-13220-9
DOIs	https://doi.org/10.1016/B978-0-443-13220-9.00044-5
Publication status	Published - 2026

Publication series

Name	Comprehensive Remote Sensing
Publisher	Elsevier
Volume	9

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Disaster
Remote sensing
Early action
Anticipatory action
Damage
Geomatics
Disaster risk management
2025 OA procedure

Access to Document

10.1016/B978-0-443-13220-9.00044-5

ArticleFinal published version, 3.08 MBLicence: Taverne

Cite this

@inbook{2932e376ecb14108bbc934d089207165,

title = "Remote sensing for disaster risk management: Advances and limitations",

abstract = "This chapter explains how the disaster domain has been a key application area for remote sensing since its pioneering days, with a strong development from opportunistic post-event image acquisition to concerted and regular monitoring of socionatural hazards and comprehensive disaster risk management. It highlights recent technical and organizational advances, such as those related to dynamic, multi-hazard risk assessment, as well as large satellite constellations and sophisticated drones, with machine learning and semantic scene analysis playing increasing roles in data processing. Finally, the chapter discusses the limitations of remote sensing data and gaps in how they are used.",

keywords = "Disaster, Remote sensing, Early action, Anticipatory action, Damage, Geomatics, Disaster risk management, 2025 OA procedure",

author = "N. Kerle and \{van den Homberg\}, M.",

year = "2026",

doi = "10.1016/B978-0-443-13220-9.00044-5",

language = "English",

isbn = "978-0-443-13220-9",

series = "Comprehensive Remote Sensing",

publisher = "Elsevier",

pages = "257--279",

editor = "Shunlin Liang",

booktitle = "Comprehensive Remote Sensing-",

address = "Spain",

edition = "2",

}

TY - CHAP

T1 - Remote sensing for disaster risk management

T2 - Advances and limitations

AU - Kerle, N.

AU - van den Homberg, M.

PY - 2026

Y1 - 2026

N2 - This chapter explains how the disaster domain has been a key application area for remote sensing since its pioneering days, with a strong development from opportunistic post-event image acquisition to concerted and regular monitoring of socionatural hazards and comprehensive disaster risk management. It highlights recent technical and organizational advances, such as those related to dynamic, multi-hazard risk assessment, as well as large satellite constellations and sophisticated drones, with machine learning and semantic scene analysis playing increasing roles in data processing. Finally, the chapter discusses the limitations of remote sensing data and gaps in how they are used.

AB - This chapter explains how the disaster domain has been a key application area for remote sensing since its pioneering days, with a strong development from opportunistic post-event image acquisition to concerted and regular monitoring of socionatural hazards and comprehensive disaster risk management. It highlights recent technical and organizational advances, such as those related to dynamic, multi-hazard risk assessment, as well as large satellite constellations and sophisticated drones, with machine learning and semantic scene analysis playing increasing roles in data processing. Finally, the chapter discusses the limitations of remote sensing data and gaps in how they are used.

KW - Disaster

KW - Remote sensing

KW - Early action

KW - Anticipatory action

KW - Damage

KW - Geomatics

KW - Disaster risk management

KW - 2025 OA procedure

UR - https://www.scopus.com/pages/publications/105028810972

U2 - 10.1016/B978-0-443-13220-9.00044-5

DO - 10.1016/B978-0-443-13220-9.00044-5

M3 - Chapter

SN - 978-0-443-13220-9

T3 - Comprehensive Remote Sensing

SP - 257

EP - 279

BT - Comprehensive Remote Sensing-

A2 - Liang, Shunlin

PB - Elsevier

ER -

Remote sensing for disaster risk management: Advances and limitations

Abstract

Publication series

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this