**1. Overview of the Special Issue**

Each year, natural hazards, such as earthquakes, landslides, avalanches, tsunamis, floods, wildfires, severe storms, and drought, globally affect humans through deaths, suffering, and economic losses. According to the insurance broker Aon, 2010–2019 was the worst decade on record for economic losses due to disasters triggered by natural hazards, amounting to \$3 trillion: a \$ trillion more than the 2000–2009 decade. In 2019, economic losses from disasters caused by natural hazards were estimated to be over \$200 billion (UNDRR Annual Report, 2019).

In this context, remote sensing demonstrates a high potential to provide valuable information, at various spatial and temporal scales, concerning natural processes and their associated risks. Recent advances in remote sensing technologies and analysis, in terms of sensors, platforms, and techniques, have strongly contributed to the development of natural hazards research.

In this Special Issue titled "Remote Sensing for Natural Hazards Assessment and Control", we propose state-of-the-art research that specifically addresses multiple aspects of the use of remote sensing (RS) for natural hazards (NH). The aim was to collect innovative methodologies, expertise, and capabilities to detect, assess monitor, and model natural hazards.

The present Special Issue of the *Remote Sensing* journal encompasses 18 open-access papers that present scientific studies based on the exploitation of a broad range of RS data and techniques, as well as a well-assorted sample of NH types (Figure 1). Table 1 summarizes the RS data, the processing techniques used in each paper, and the general purpose of the presented works.

**Citation:** Mazzanti, P.; Romeo, S. Introduction to a Thematic Set of Papers on Remote Sensing for Natural Hazards Assessment and Control. *Remote Sens.* **2023**, *15*, 1048. https://doi.org/10.3390/rs15041048

Received: 7 February 2023 Accepted: 13 February 2023 Published: 15 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** Pie charts of general purpose, natural hazard types, data, and RS technique of published papers.

**Table 1.** Overview of RS data, techniques, purposes, and NH types that are presented in the papers comprising the SI. Access links to each paper are also provided together with DOI numbers.


on 6 February 2023)


**Table 1.** *Cont.*
