TY - JOUR
T1 - Assessment of interventions in fuel management zones using remote sensing
AU - Afonso, Ricardo
AU - Neves, André
AU - Damásio, Carlos Viegas
AU - Pires, João Moura
AU - Birra, Fernando
AU - Santos, Maribel Yasmina
N1 - UIDB/04516/2020
UIDB/00319/2020
PCIF/MOG/0161/2019
PY - 2020/9/7
Y1 - 2020/9/7
N2 - Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.
AB - Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.
KW - Fuel Management Zones
KW - Machine learning
KW - Remote sensing
KW - Sentinel-1
KW - Sentinel-2
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85090920250&partnerID=8YFLogxK
U2 - 10.3390/ijgi9090533
DO - 10.3390/ijgi9090533
M3 - Article
AN - SCOPUS:85090920250
SN - 2220-9964
VL - 9
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 9
M1 - 9090533
ER -