Remote Sensing Applications for Wildland Urban Interfaces (WUI) Fire

Dear Colleagues,

Wildfires are one of the most important ecological factors worldwide and are recognized as a key driver for many ecosystems. However, wildfires are also considered a serious threat due to their detrimental effects on natural resources and dangerous impact on human life, especially when occurring in wildland urban interfaces (WUIs) (Elia et al. 2019). The expansion of urban settlements into rural and forest areas has led to the creation of landscapes where fires can occur and recur, frequently encroaching on cities. For example, recent newspapers have reported that northern areas of Europe (e.g., Scandinavia and Siberia) are experiencing extraordinary WUI wildfires. In 2018, extreme wildfires occurred in the United Kingdom, Ireland and Latvia, landscapes in which fires have never represented a hazard for natural resources and/or risk to human lives. At the same time, southern Europe continues to record the annual burned area, which in many cases involves the urban and peri-urban areas of many cities.

In this regard, remote sensing represents a cost-effective tool for the study of a large number of fire-related processes. For example, multispectral satellite sensors have been largely applied to estimate the extension of burned areas or to understand how forests recover in relation to burn severity. More recently, active remote sensors such as LiDAR, have been used to understand post-fire modifications in forest stand structure and composition (Giannico et al., 2016). In addition, increasing computational power, has allowed for the development of wildfire event predictive models in terms of occurrence and frequency.

This Special Issue of Remote Sensing hosts studies focusing on the fire and post-fire dynamics in WUIs. The papers will attempt to integrate multi-sensor remote sensing technology and derived products in a streamlined spatial and temporal framework.

Contributions concerning the following topics are invited.

1. Comparison and evaluation of different remote sensing (RS) techniques for monitoring wildfires in WUIs.
2. Analysis of WUI areas and the assessment of wildfire risk using RS approaches.
3. RS techniques for evaluating the post-fire recovery processes of WUI areas.
4. Application of RS-derived tools for the planning of fire and fuel management.
5. Wildfire sensitivity in the intermingling of global warming and the urban interfaces.
6. RS techniques to model wildfire probability and susceptibility in WUIs.
7. Modeling of wildfire causes in WUIs using RS data.
8. Review articles covering one or more of the aforementioned topics.

References

Elia, M.; Giannico, V.; Lafortezza, R. and Sanesi, G. Modeling fire ignition patterns in Mediterranean urban interfaces. Stochastic Environmental Research and Risk Assessment. 2019, 33, 169-181.

Giannico, V.; Lafortezza, R.; John, R.; Sanesi, G.; Pesola, L. and Chen, J. Estimating stand volume and above-ground biomass of urban forests using LiDAR. Remote Sensing. 2016, 8, 339.

Dr. Mario Elia
Dr. Vincenzo Giannico
Guest Editors

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