Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

Abstract

The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.

Fully Automated Countrywide Monitoring of Fuel Break Maintenance Operations

“…In a recent study, an object-based approach for FB fuel treatment detection suggested that most discriminant vegetation indices for classification were based on visible bands, using B05, the excess of green and excess of red indices [19]. Our comparison between NDVI and MExG results did not confirm this hypothesis.…”

“…However, this requires a good empirical knowledge of the change signal and duration, which is not feasible in this study due to the diversity of climate, soil, species and vegetation phenology responses and land use types present in FB [18]. Object-based detections have the advantage to use spatial information but require a reliable segmentation, which limits their utility in the case of FB, where fuel treatments may have variable geometries over the years [19]. The Copernicus Sentinel-2 multispectral instrument (S2) from the European Space Agency (ESA) [20] has its first four bands at 10 m resolution and, since the launch of a second satellite in 2017, a 5 day revisit period.…”

Earth Observations as a Tool for Detecting and Monitoring Potential Environmental Violations and Policy Implementation

“…In addition, as it can be observed in Figure 7, errors are introduced in the final change detection product due to the misregistration of Sentinel-2 images (middle row). Several studies have also identified that Sentinel-2 orthoimages may have geolocation offset errors, especially at higher spatial scale (10m bands) (Kukawska et al, 2017;Pereira-Pires et al, 2020;Yan et al, 2018). The effect of the misregistration might be important when employing multi-temporal Sentinel-2A data for applications that require precise sub-pixel registration suggesting the use of additional pre-processing steps (Pereira-Pires et al, 2020;Yan et al, 2018).…”

“…Several studies have also identified that Sentinel-2 orthoimages may have geolocation offset errors, especially at higher spatial scale (10m bands) (Kukawska et al, 2017;Pereira-Pires et al, 2020;Yan et al, 2018). The effect of the misregistration might be important when employing multi-temporal Sentinel-2A data for applications that require precise sub-pixel registration suggesting the use of additional pre-processing steps (Pereira-Pires et al, 2020;Yan et al, 2018). Finally, the Sen2Cor processor, as indicated in previous studies, presents moderate accuracy in cloud/shadow removal, including over-detection of clouds over bright targets and degraded performance for scenes with low clouds (Baetens et al, 2019).…”

“…Moreover, * Corresponding author environmental crimes are rising by 5-7% annually, which is equal to 2 -3 times the corresponding rising rate of the global economy GDP. (Molino, 1995). This difficulty which is a common denominator of most environmental crimes generates serious doubts that undermine the enforcement of environmental law.…”