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# Fuel Break Monitoring with Sentinel-2 Imagery and GEDI Validation

João E. Pereira-Pires, Valentine Aubard, G. Baldassarre, José M. Fonseca, João Silva, André Mora

► To cite this version:

João E. Pereira-Pires, Valentine Aubard, G. Baldassarre, José M. Fonseca, João Silva, et al., Fuel Break Monitoring with Sentinel-2 Imagery and GEDI Validation. 4th IFIP International Internet of Things Conference (IFIPIoT), Nov 2021, Virtual, Netherlands. pp.67-85, 10.1007/978-3-030-96466-5_5. [hal-04471535](https://hal-inriaHalId:hal-04471535)

HAL Id: hal-04471535

https://inria.hal.science/hal-04471535v1

Submitted on 21 Feb 2024

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# Fuel break Monitoring with Sentinel-2 Imagery and GEDI validation

João E. Pereira-Pires¹, Valentine Aubard², G. Baldassarre², José M. Fonseca¹, João M. N. Silva², André Mora¹

¹ Centre of Technology and Systems/UNINOVA, School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
je.pires@campus.fct.unl.pt, {atm,jmf}@uninova.pt

² Forest Research Centre, School of Agriculture, University of Lisbon, 1349-017 Lisboa, Portugal
{vaubard,isa124928,joaosilva}@isa.ulisboa.pt

**Abstract.** Mediterranean Europe is strongly affected by wildfires. In Portugal, the Portuguese Institute for Nature Conservation and Forests (ICNF) implemented the national fuel break (FB) network responsible for fire control and suppression. FBs are regions where vegetation is reduced to break up the fuel continuity and create pathways for the firefighting vehicles. The efficiency of this strategy relies on the correct implementation of FBs and on periodic fuel treatments. Multispectral imagery from Sentinel-2 (with high temporal and spatial resolution) facilitates the monitoring of FBs and the implementation of methodologies for their management. In this paper a two stages methodology is proposed for monitoring FBs. The first stage consists in detecting fuel treatments in FBs, to understand if those were correctly executed. This is done through a change detection methodology with resource to an Artificial Neural Network. The second stage monitors the vegetation recovery after a fuel treatment, to aid the scheduling of new treatments, ensuring the efficiency of FBs during the fire season. Both methodologies resort to reflectance bands and spectral indices from Sentinel-2; and timeseries and objects, exploiting the temporal and spatial information. The two stages were tested in different regions across the Portuguese territory, demonstrating their usability for all the national fuel break network. The detection of treatments achieved a relative error lower than 4%, and the vegetation recovery cycle estimated by the second stage match the expectations from ICNF.

**Keywords:** Sentinel-2, Change Detection, Fuel Breaks, Remote Sensing, Wild- fires, Vegetation Recovery, Fuel Treatments, GEDI, Artificial Neural Network, Spectral Indices

## List of Abbreviations

**ANN** Artificial Neural Network **FH** Forest Height

**EO** Earth Observation **FL** Fuel Load

**ExG** Excess of Green **FT** Fuel Treatment
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# 1 Introduction

Wildfires are destructive events that occur recurrently during summer seasons. These disasters are common in several regions of the world, of which can be highlighted: the western states of the United States [1]; south-western Canada [2]; Mediterranean Europe (Portugal, Spain, France, Italy, Greece) [3]; and south-eastern Australia [4], [5]. None of these countries had found yet a solution for preventing and efficient suppression of wildfires. Since the current strategies for the suppression and available resources are not sufficient, proactive measures had been applied during the preparation of summer seasons.

In Portugal, the Portuguese Institute for Nature Conservation and Forests (ICNF) defined the Fuel Break Network (FBN), as a firefighting strategy. A Fuel Break (FB) consists of a strip of land which was artificially modified to reduce its Fuel Load (FL) [6]. This is achieved by decreasing and/or removing the vegetation within the FB through Fuel Treatments (FT). An image of an implemented FB is shown in Fig. 1. The technical specifications of a FB defined by ICNF are the following [7]:

* Composed by three parts: Fuel Interruption, Fuel Reduction, and Road Network. The FB has a minimum width of 125 m;

* Fuel Interruption: all the vegetation is removed and have minimum width of 10 m;

* Fuel Reduction: reduction of the vegetation by imposing a minimum distance between trees tops and a pruning height, and a minimum width of 60 m;

* Road Network: a pathway for the firefighting vehicles, with a minimum width of 5 m. It is in the middle of the FB, having a Fuel Interruption and a Fuel Reduction area to each side.

A graphical scheme of a FB is presented on Fig. 2 for an easier understanding. The FBN is responsible for compartmentalize the forest, which allows a better control of wildfires and slows down its propagation. Adding to its proactive effects, the road network creates better conditions for the firefighters.

A key factor for the effectiveness of the FBN in preventing the occurrence of wildfires is the correct implementation and maintenance of its FBs. The management and

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Ground observation of a Fuel Break. (Source: http://www2.icnf.pt/).</figcaption></figure>
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monitoring of the FBN is responsibility of ICNF. To accomplish it they need to peri-
odically schedule FTs for the FBs and the implementation of new FBs (from now on
both will be referred as FT), which are executed by Local Authorities. Concretely the
ICNF needs to guarantee that FTs are correctly performed and to periodically assess
the FL of FBs scheduling the necessary FTs. Currently, both activities are done by
inspection of earth observation satellite imagery and ground observation of the FBs.
The first approach is a time-consuming process and prone to errors, the latter is also
time-consuming (due the extension of the FBN) and it is an expensive process.

The increasing number of Earth Observation (EO) missions, the quality of the data acquired from remote sensing platforms, and sensors specifically developed for vegetation monitoring, allows the implementation of new methodologies for forest management. Within the EO platforms, Sentinel-2 (S2, from the Copernicus mission) presents a set of attracting features. Briefly, it carries a multispectral sensor acquiring the reflectance in 13 spectral bands (including 3 on the red edge spectrum, which is recommended for vegetation monitoring), a revisit period of 5 days (suitable for monitoring), and a spatial resolution of 10 m and 20 m for majority of the bands (which gives sufficient information for analyzing FBs). In addition to S2, Global Ecosystem Dynamics Investigation (GEDI) is a Spaceborne Light Detection and Ranging (LiDAR) sensor optimized to measure the vertical structure of vegetation [8], [9]. GEDI acquires several variables of the forest, including the Forest Height (FH), with a spatial resolution of 25 m. Despite its measurement's suitability for the problems that ICNF needs to address, it is a sampling mission which compromises its usage for monitoring.

In this paper is proposed a semi-automatic methodology to support the FBN man-
agement that fulfils the following requirements:

* Detect the month when a FT was executed on a FB from predefined shapefile;

* Avoid detecting incomplete FTs (that cover less than 75% of the FB);

* Estimate FL growth in a FB after a FT;

* Must be a generic methodology that works for regions with different conditions (land cover, weather, geographic location, etc).

This methodology is divided in two stages: the detection of FTs and estimation of the
FL growth since the last FT. The proposed methodology performs an object analysis,
instead of pixel analysis, which enhance the spatial information, and generate
timeseries from the S2 reflectances and the derived spectral indices, to enhance the
temporal information. The detection is ensured by an Artificial Neural Network

<figure><img src="image_4.png" /><figcaption>Fig. 2. Technical specifications of a Fuel Break in the Portuguese Fuel Break Network [7].</figcaption></figure>
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(ANN) that outputs and the FL estimation is based on the analysis of the Normalized Difference Vegetation Index (NDVI) timeseries.

ICNF provided the FBN's shapefile, the information of where and when FTs have been executed, which was used during the ANN training, validation, and error estimation. The select error metrics were relative error, recall (producer's accuracy), precision (consumer's accuracy), and F1-Score, as suggested in [10]. Next a dataset of incomplete FTs (with three different levels of coverage) was tested. Relatively to the FL estimation, the results were validated by ICNF and observation of S2 images. Finally, the pair NDVI/GEDI's FH was evaluated by the Pearson's Correlation, which provides a more precise validation of the usage of NDVI for assessing the FL increase in a FB.

There are no significant studies in the literature regarding change detection related to FTs or FBs. However, there are many published works about change detection methodologies. In [11] space-time features were exploited to detect forest disturbances using two consecutive observations and in [12] annual composites were used for detecting changes. In [13]–[15] methodologies to identify changes on the land and their type are proposed. In all these works Landsat imagery was used, which has a revisiting period of 16 days (three times the revisiting period of S2) and relied in more than one previous observation of the change event.

The studies of vegetation monitoring, FL accumulation, usually rely on the estimation of FH and aboveground biomass. Several methodologies are already implemented using different sensors' types (LiDAR [16], [17], Optical [18]–[20], SAR [21]), but the estimation models rely in in-situ measurements (which are spatially limited, time-consuming, and may be destructive), and are not generalizable for different regions.

This paper is written through five sections: this section concerning the introduction and state of art; the second section dedicated to the material and methods, all the data used in this article is presented, the used methods are exposed, and the explanation of how the methodology was validated. Next in section 3 the results and a brief description of them is provided; and the final two sections are dedicated to the discussion and conclusions of the developed work.

# 2 Materials and Methods

## 2.1 Study Areas

The implementation and validation of the proposed methodology relied in 19 study areas across the national territory, described in **Table 1**. As is shown, they vary in terms of land cover (including 6 different types, and areas with multiple types) and geographic location, creating richer datasets.

For the FT detection training and validation four of the 19 study areas were used. In the selected areas FT had been executed between 2017 and 2018. The available samples from this period were divided into the training and validation dataset, and the defined error metrics computed. Next, the detection was tested in real-case scenarios: 14 regions treated in the first semester of 2020; and a FB outside the FBN that be-
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longs to the National Power Network treated in 2018 (Marisol). Also, a specific test regarding the performance of the methodology for incomplete FTs was performed.

The FL growth estimation was tested in four areas treated during 2017 and 2018. ICNF validated the obtained results. Finally, the usage of the NDVI for the estimation was evaluated data other four study areas (due the fact the GEDI mission only provides data after April 2019).

## 2.2 Data and Tools

The main data sources used in development and validation of the proposed methodology were:

* FBN shapefile provided by ICNF;

* S2 imagery from mid-2015 to mid-2020;

* FH measurements from samples acquired in 2020 by GEDI.

The FBN shapefile is composed by the polygons corresponding to the FBs under authority of the ICNF (note that not all FBs are already implemented). Additionally, a polygon for Marisol was designed by the authors based on S2 imagery.

<table><caption>**Table 1.** Study areas description. Usage: 1 – Fuel Treatment training & validation; 2 - Fuel Treatment test; 3 – Fuel Load growth test; 4 - NDVI validation.</caption><thead><tr><td>Study Area</td><td>Relative Location in Country</td><td>Land Cover</td><td>Usage</td></tr></thead><tbody><tr><td>Amieria</td><td>Inland Center</td><td>Eucalyptus and cork tree forest</td><td>2</td></tr><tr><td>Besteiros</td><td>Inland Center</td><td>Eucalyptus, pine, and cork tree forests</td><td>2</td></tr><tr><td>Cabro-Baixo</td><td>Inland Center</td><td>Agriculture, cork oak forests, shrubs</td><td>2</td></tr><tr><td>Canedo</td><td>Inland North</td><td>Pine forests</td><td>2, 4</td></tr><tr><td>Capinha</td><td>Inland North</td><td>Eucalyptus forests</td><td>2, 4</td></tr><tr><td>Casa Nova</td><td>South</td><td>Cork oak forests, shrubs</td><td>2</td></tr><tr><td>Famalicão</td><td>Inland North</td><td>Pine forests</td><td>2</td></tr><tr><td>Fundão</td><td>Inland North</td><td>Eucalyptus and pine forests, shrubs</td><td>1, 3</td></tr><tr><td>Marisol</td><td>Coast Center</td><td>Eucalyptus forests</td><td>2, 3</td></tr><tr><td>Monte Zorra</td><td>Coast South</td><td>Cork oak forests, shrubs</td><td>2, 4</td></tr><tr><td>Monte Velho</td><td>South</td><td>Cork oak forests, shrubs</td><td>2</td></tr><tr><td>Paradelhas</td><td>Inland North</td><td>Shrubs</td><td>2, 4</td></tr><tr><td>Pincho</td><td>South</td><td>Agriculture, shrubs</td><td>2</td></tr><tr><td>Salir</td><td>South</td><td>Cork oak forests</td><td>2</td></tr><tr><td>Seia</td><td>Inland North</td><td>Artificial Territories, shrubs</td><td>1</td></tr><tr><td>Serra dos Candeeiros</td><td>Coast Center</td><td>Agriculture, shrubs</td><td>1, 3, 4</td></tr><tr><td>Sertã</td><td>Inland Center</td><td>Shrubs</td><td>1, 3</td></tr><tr><td>Terras Ordem</td><td>South</td><td>Pine forests</td><td>2</td></tr><tr><td>Vale Grou</td><td>South</td><td>Eucalyptus and cork oak forests, shrubs</td><td>2</td></tr></tbody></table>
S2 is a constellation of two satellites platform (S2-A and S2-B) that carries a multi-spectral sensor measuring the reflectance in 13 spectral bands, covering the visible, vegetation red edge, near infrared, and shortwave infrared spectrum's regions. For this methodology Level-1C products (top-of-atmosphere radiance) due its availability since mid-2015. With a revisiting period of five days and spatial resolutions of 10 m and 20 m (bands with a spatial resolution of 60 m were not used due the minimum width of 125 m of FBs) it is a suitable platform for monitoring the FBN. From the authors' previous works, regarding the FT detection [22], bands 2, 3, and 4 were used to compute the spectral indices Excess of Green (ExG) and Excess of Red (ExR), and band 5 was used directly. Relatively to the FL growth bands 4 and 8 were used to compute the NDVI for the estimation. All used observations were downloaded from Copernicus Open Access Hub.

GEDI is a LiDAR sensor carried by the International Space Station, optimized for forest measurement. The mission started operating in April 2019, it acquires 25 m resolution samples, and has a coverage between the 51.6°N and 51.6S, promising to sample 4% of the Earth's Surface [8], [9]. GEDI Level-2B products were used, concretely the RH100 parameter which corresponds to the sample FH [23]. Since it is a sampling mission, this information is not suitable for monitoring, so it was used to validate the usage of the NDVI in the FL growth estimation. To guarantee the quality of the GEDI data, only samples meet the minimum quality requirements were used (samples with the quality flags l2a_quality_flag and l2b_quality_flag set).

Quantum GIS (QGIS) and several python modules were used for the methodology implementation. QGIS is an open-source geographic information system which includes several useful features for working with Remote Sensing data. Also, it allows the automation of tasks through its python console and by the development of plugins. The main python modules used in this methodology were: GDAL and Scikit-Learn.

## 2.3 Time Series Generation

The time series generation is ensured by the pre-processing stage. The analysis is performed at object level, i.e., within the FB area and is shared by the FT detection and FL growth estimation. In Fig. 4 it is presented an info graph describing the object time series generation. It consists in the following steps:

1. Correction of observations' geolocation errors;

2. Generation of the object values for all required reflectance bands;

3. Computation of the time series with a month timestep;

4. Computation of the spectral indices;

5. Estimation of the month values, when there no available observations.

ESA reported in [24] that S2 observations may suffer of an translational geolocation error up to 1.5 pixels, which corresponds to a maximum of 15 m error for the 10 m resolution bands and 30 m for the 20 m resolution bands. For most applications, this error may be neglected, however due the minimum width of 125 m of a FB, this error can negatively influence the results. The correction algorithm used in step 1 is
presented in [25], and used in [22], [26], and it decreases the maximum error to 1.5 m
and 3 m per resolution, which is negligible for the proposed methodologies.

In step 2 the object value is computed. An object is a group of adjacent pixels, be-
ing used in this methodology the group of pixels within a FB polygon. The objective
of this work is to detect FTs on FBs, so the usage of an object approach is also conse-
quence of the defined requirements. Also, change detection application benefits from
the spatial information obtained using this approach [15] which agrees with the To-
bler's first law of geography. The used metric for the object value were the mean
value of the pixels within the FB.

Step 3 builds up the basis of the proposed methodology. Change detection applica-
tions try to find significant changes in data to understand if it was a real change and
not a pseudo change (phenology, illumination, external effects) [12], and this is not an
exception. FT detection relies on the comparison of consecutive values and FL growth
estimation on the inter-annual comparison of values, being fully dependent of the
generated time series from this step. Time series using all observation are very noisy,
so this step also reduces the noise (as shown in **Fig. 3**), which improves the ability of
the FT detection in avoiding pseudo changes and the quality of the FL growth estima-
tions. In [22] a mean and a median noise filter were tested, exhibiting similar results,
however the mean filter was chosen since it offers a more defensive approach, detect-
ing less FTs but with more ability to discard false FT. This was decided by the authors
because detecting a false FT is worse than not detecting a FT, in this specific case.

From the reflectance bands time series, in Step 4 are generated the time series for
the spectral indices. Alternatively, the spectral indices time series may be computed
on a pixel-based approach, where the index is calculated for each pixel and after the
object value obtained in step 2. However, the authors demonstrated in [22] that the
difference relatively to this approach is negligible, with the advantage that it is less
time-consuming. The outputs of this step are the NDVI, ExG, and ExR time series.

Despite the revisiting period of 5 days, the cloud cover lead to the unusability of
several S2 observations. On some winter months, there are no available observations,
so it is needed a step 5 where a value per attribute is estimated for the months without

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** NDVI's time series of a non-treated area in Fundão from 2017 to 2019. The black vertical lines highlight the one-year period.</figcaption></figure>
usable observations. If there are two months without data, it is estimated a value just for the first month. In the evaluated study areas, there were no cases with two consecutive months without data, especially after the launching of S2-B. A very simple approach was used, the values of the previous month were used as values of the month without observations.

Each stage has a specific final pre-processing step. For the FT detection the final step consists in the data set generation for the ANN. For the FL growth estimation, a moving average (with a window size of three months) is applied as another noise reduction technique. Although it is suitable for this stage, for the FT detection it may smooth the data at the point of masking FTs.

## 2.4 Fuel Treatments Detection

The first stage of the proposed methodology is the FT detection. The methodology is prepared to identify three types of FTs:

1. On FBs that not periodically treated;

2. On FBs that periodically treated;

3. That occur in two consecutive months.

Type 1 are the easiest FTs to detect due being the ones where more FL is removed. Therefore, the effect on time series is more significant, also in the observations, as seen in **Fig. 5 a), b), and Fig. 6** (blue curve). In Type 2 when the FTs are executed, the amount of FL is lower than in Type 1, because is not yet a dangerous situation, but it requires maintenance. FTs are not instantaneous events. Usually, they are observed in just one month. However sometimes observations of consecutive months show FL reduction. In those cases, it is just expected to detect a FT on the month with higher FL reduction. Type 2 and 3 have a less impact on the time series and in the observations (as seen in **Fig. 5 c), d) and Fig. 6**, orange curve), being harder to distinguish

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Object time series generation info graph.</figcaption></figure>
from phenology.

The FT detection is guaranteed by an ANN that use as features: reflectance B5, ExG, and ExR for the month being evaluated and the previous month (a total of 6 features). Several ANN structures were tested using one and two hidden layers, and its quality assessed by its accuracy and detection recall. From [22] was concluded that the best structure was an ANN using one hidden layer with 53 neurons. The input layer used a neuron per feature, and since it is a binary classification, the output layer constituted by one neuron. The main challenge of the proposed ANN is to identify Type 2 FTs, distinguishing them from phenology phenoms.

## 2.5 Fuel Load Growth Estimation

FL growth estimation, stage 2, completes the monitoring of the FBN. Vegetation is constantly growing in forests and after a FT is just a matter of time for vegetation to start regenerating, consequently increasing the available FL within the FBs. ICNF is responsible for scheduling regular FTs in the FBN, ensuring its efficiency and low fire risk. Due the flora diversity in Portugal and that not all FBs are already being treated periodically, it is required a generic approach which allows the monitoring of the FBN. The drawback of using this approach is that there are not computed absolute metrics as the FH and Aboveground Biomass, as in [19], [20], [27], which usually are developed for specific areas and conditions. Instead, it yields an estimate of the percentage of the regenerated FL comparing with the state before the FT.

The first feature of this stage is the ability of self-learning, by analyzing data before the FT, of when is supposed to perform a FT in that FB, and of the NDVI's time series behavior relatively to the FB land cover and environmental conditions. Despite

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** TCI images previous and after Fuel Treatments: a), b) - Serra dos Candeeiros; c), d) - Fundão.</figcaption></figure>
this sensibility of the NDVI (as seen in **Fig. 3**), the index reflects the FL growth, increasing its value until reaching the pre-FT values (as seen in **Fig. 7**). This is done through an adaptive reference system, where the maximum (*max*) NDVI of the 6 months before the FT is defined as the max FL NDVI measurement, and the NDVI's local minimum (*min*) after the FT defines the min of the FL's scale. Although only one measurement is needed before the FT to establish the FL's scale *max*, is expected a better behavior of the method if it has the six previous months' values. Usually, the NDVI's time series starts reflecting the vegetation regeneration in the 6 months after the FT. So, for NDVI's measurements acquired after the FT, but before the NDVI's local *min*, the method automatically estimates 0% of FL, since it is not verified in the time series any FL growth.

The second feature is the robustness against the vegetation phenology effects. As seen in **Fig. 7**, the presented NDVI's time series has a growing trend, but due its peaks and valleys behavior, it is not clear how the FL increases along time (especially when there is a decrease on the NDVI's values without any vegetation reduction). As it is observed in **Fig. 3**, phenology in time series behaves like a periodic signal with a one-year period, implying that measurements of the same month in different years are subjected to the same environmental conditions. To take advantage of this, the proposed method estimates the monthly FL growth from the NDVI's inter-annual difference, instead of using the difference of consecutive measurements. In the 12 months after the FL starts growing, it is used the difference of the month being analyzed and the local *min*, and afterward the inter-annual difference is used. This is required since the inter-annual difference before and after the FT, which leads to a wrong estimation of the FL growth. As the reader may observe, it is assumed that yearly FL growth is linear, however it does not mean that the estimation is linear (as it is seen in 0). It is explained due the fact that for each new estimation, new inter-annual measurements are used, leading to new linear regressions whose slopes are constantly changing and tend towards zero with time.

By combining the two identified features (self-learning and robustness to phenolo-

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** ExG's time series: *blue* - Serra dos Candeeiros, type 1; *orange* – Fundão, type 2. The mean value of each time series was subtracted, and the time scale adjusted to enhance the comparison of the Fuel Treatments types.</figcaption></figure>
gy) we modeled the FL growth using equation (1) and (2), where *i* is the month being estimated, *j* the month used for the difference (for the first year it ranges from 1 to 11, after that it is equal to 12), and *k* the number of months after the FT. The first term corresponds monthly FL growth and the second is the scaling term. Each monthly value should be added to the previous month estimation to get the current FL state. Despite being an incremental approach, the method is able to include treatments within the FB. If a vegetation reduction occurs within the FB, there will be a decrease in the FL estimations, which shows the reliability of the method.

## 2.6 Error Estimation

Different validation and error estimation were used in the two stages of the FBN monitoring. For the FT detection the available data for the ANN implementation was divided into training and validation set (with the proportion 67%-33%, respectively). For the training set a cross-validation was applied for the error estimation. After defining all the ANN parameters, the assessment using the validation set was performed. To test the method for incomplete FTs, nine FBs that were incompletely treated were evaluated. Finally, a real case scenario was tested, applying the detection stage for the first semester of 2020 and Marisol (treated in 2018). These results were validated by visualization of the S2 imagery, and by the pixel-based detection proposed by the authors in [26], [28]. The metrics presented are the Relative Error, and to evaluate the ability of the code in detecting FTs and avoiding false detections the Recall, Precision, and F1-Score, as proposed in [10].

The FL growth estimation was validated by visualization of S2 Imagery and by

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** NDVI time series of Fundão between July 2015 to May 2020. The crosses mark points 34 and 46, used to calculate the inter-annual difference</figcaption></figure>

$$Growth_i = \frac{NDVI_i - NDVI_{i-j}}{j} \times \frac{1}{(Reference_{Max} - Reference_{Min})} \quad (1)$$

$$FL Growth = \sum_k Growth_k \quad (2)$$
ICNF who corroborated the obtained results. To have a numerical assessment of the proposed method, an indirect test was executed with resort to GEDI data. The FH is not the same as the FL but are correlated. The test consisted in obtaining 50 sample pairs of FH and NDVI and computing the Pearson's correlation to evaluate if NDVI is an indicator of the FH, and consequently of the FL. The samples were from treated FBs and surrounding areas where no FT was performed.

# 3 Results

## 3.1 Fuel Treatment Detection Results

The error estimation in the FTs detection was performed with the training and validation set, and with the test set where the method was applied to a real case scenario. The training, validation, and test set had 633, 311, and 641 samples respectively, being the results presented in **Table 2**.

From the presented results it can be concluded that the FTs detection method is accurate. From the relative error it is not expected to have misdetections above the 4%. Regarding the recall it was obtained 77% in the validation phase and 85% in the test phase, which shows the ability of the method in detecting FTs. However, the precision values are lower, 64% and 73%, which shows that some false detection will occur. Finally, the F1-Score, which reflect both previous metrics, shows that the method is reasonably good in the FTs detection. Giving a more detailed description of the test set, was composed by 34 FBs where a FT as occurred. There were five non detected FTs, and 11 misdetections. These misdetections do not correspond to incomplete FTs. The confusion matrix is presented in **Table 3**.

To assess the method's robustness against incomplete FTs, nine incomplete FTs were tested. They were divided into three groups of completeness: 0-25%; 25-50%; and 50-75% (above 75% it is considered a complete FT). The results are shown in **Table 4**. There were no misdetections for FTs that covered less than 50% of the FB extension. Only on the two cases tested for the 50-75% completeness one was wrongly detected as FT.

Considering all the tests, the FT detection method has proved to be able to accurately detect FT and is guaranteed and its application to real cases scenarios.
## 3.2 Fuel Load Growth Estimation

In Fig. 8 the results of the FL growth estimation for four different study areas are presented. It should be noted that with this method, phenology effects were strongly reduced. Next, it is seen that the FL growth estimation curves begin with a nearly linear growth and start to decrease after 15 months. This was expected, and can be consequence of NDVI saturation, a characteristic of the spectral indices [29]. The comparison with the S2 imagery is consistent with estimations. In Fig. 9, images before and after the FT are presented. Fig. 9 a) and Fig. 9 d) are visually similar and the last FL estimation was 100%, reinforcing the method's efficiency. FTs are usually scheduled by ICNF to a specific FBs every 2 to 3 years. The FL growth estimations start stabilizing 24 months after the FL started growing, which agrees with the ICNF procedures and validates the presented results. The estimation for Serra dos Candeeiros showed that it can adjust to new FBs implementations. The decrease on FL after month 30 is not an error, it was due to a new FT. Finally, we can divide the results into two groups: Marisol and Serra dos Candeeiros that exceeded the 100%; and Fundão and Sertã that stabilize at the 100%. The FBs on the second group were already being periodically treated, opposing the others, what showed that study areas where the NDVI was not saturated lead to better estimations. The final validation step was the comparison between the FH measurements from GEDI and the NDVI measurements from S2, that are summarized in Fig. 10 and Table 5. It is immediately seen

<table><caption>**Table 2.** Fuel Treatments detection results. Recall, Precision, and F1-Score computed relative to the detection.</caption><thead><tr><th></th><th>Training Set</th><th>Validation Set</th><th>Test Set</th></tr></thead><tbody><tr><th>Recall</th><td>97%</td><td>77%</td><td>85%</td></tr><tr><th>Precision</th><td>89%</td><td>64%</td><td>73%</td></tr><tr><th>F1-Score</th><td>93%</td><td>70%</td><td>78%</td></tr><tr><th>Relative Error</th><td>3.3%</td><td>2.5%</td><td>2.5%</td></tr></tbody></table>

<table><caption>**Table 3.** Confusion matrix for the test set.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Class</th><th colspan="2">Validation</th></tr><tr><th>FT</th><th>No FT</th></tr></thead><tbody><tr><th rowspan="2">Detection</th><td>FT</td><td>29</td><td>11</td></tr><tr><td>No FT</td><td>5</td><td>598</td></tr></tbody></table>

<table><caption>**Table 4.** Results for incomplete FTs.</caption><thead><tr><th>Completeness</th><th>Number of Cases</th><th>Misdetections</th></tr></thead><tbody><tr><td>0-25%</td><td>3</td><td>0</td></tr><tr><td>25-50%</td><td>4</td><td>0</td></tr><tr><td>50-75%</td><td>2</td><td>1</td></tr></tbody></table>
that there is a correlation between both variables, and that correlation is higher if the samples are grouped by study areas. The figures confirm it, being the full dataset Pearson's correlation equal to 0.69, and when grouping by study areas the average correlation increases to 0.84, with values ranging from 0.76 to 0.98, as shown in **Table 5**.

<figure><img src="image_10.png" /><figcaption>Fig. 8. Fuel Load growth estimations.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** TCI Images from Fundão. a) Before treatment; b) Right after treatment; c) 2-year after treatment; d) 3-year after treatment.</figcaption></figure>
# 4 Discussion

The results presented in section 3 showed that the proposed methodology is capable to monitor the FBN in two stages: detecting FTs and identifying when a FB needs a new FT. Starting with the FT detection a high accuracy on the results was achieved for a diverse dataset (in terms of geography, land cover, and seasonality). Also, the detection was performed comparing two consecutive observations opposing the need of more observations as in the previous works presented in section 0. The method achieved similar results to the aforementioned works, with the advantage of being tested in a more heterogeneous dataset. This lead us to expect that the methodology would work for unknown areas (as occurred with the test set). However, in [13]–[15] the proposed methodologies had the ability to identify the changes typology (land cover, land use).

For the second stage, FL growth estimation, a new robust method was successfully designed, where the phenology effect was strongly reduced, being capable to estimate the FL on a FB and identifying when a FB needs to be treated. The validation with GEDI data showed a high correlation between the FH and NDVI, and it is higher if

<figure><img src="image_12.png" /><figcaption>Fig. 10. NDVI vs. Forest Height per Fuel Break</figcaption></figure>

<table><caption>Table 5. Pearson's Correlations between Forest Height and NDVI.</caption><thead><tr><td>Study Area</td><td>Pearson's Correlation</td></tr></thead><tbody><tr><td>Canedo</td><td>0.92</td></tr><tr><td>Capinha</td><td>0.98</td></tr><tr><td>Monte Zorra</td><td>0.76</td></tr><tr><td>Paradelhas</td><td>0.89</td></tr><tr><td>Serra dos Candeeiros</td><td>0.77</td></tr><tr><td>Full Dataset</td><td>0.69</td></tr></tbody></table>
data is grouped by study areas. These results showed ability to self-adapt to the region where the estimation is being performed. The tradeoff was not calculating absolute metrics as the FH or the aboveground biomass, as in the works presented during the introduction.

# 5 Conclusion and Future Work

In this paper it is proposed a semi-automatic methodology for monitoring FBs subject to different conditions (land cover, weather, geographic location, etc). It is composed by two stages responsible for detecting FTs in the FBN and to estimate the FL growth in the treated FBs. Both stages resort to S2 periodic observations, being exploited the spatial and temporal features of the data by the generation of objects and the respective timeseries.

The detection stage achieved an error below of 4% for the training, validation, and test set; and a minimum F1-Score of 70% for the three sets. Also, it is demonstrated its ability to avoid the detection of incomplete FTs since it has not detected any FT where the treated area is 50% or less of the FB's area. However, in the interval [50%, 75%] it is not guaranteeing the ability to avoid the false detection.

The FL growth stage yields periodic estimation of the FB state in terms of FL accumulation. It relies on the NDVI's timeseries and it is a self-learning method that uses measurements before the FT to establish a FL's scale, so it can adapt to different FBs. Also, the estimation of the growth uses the inter-annual difference of two observations instead the difference of two consecutive observations, so it can reduce the phenology noise present on data. The method was validated by ICNF, the expected cycles between FTs were achieved (2.5 to 3 years), and the comparison between NDVI and GEDI's FH exhibit significant correlation values.

Finally, since the shapefiles relative to the FBs are designed by the user, so it is not yet a fully automatic methodology, and its performance decreases with incorrect designs of shapefiles.

Future work will include the automatization of the generation of the shapefiles of the FBs, using the methodology proposed in [28]; recover observations not used due its atmospheric conditions; test new features (as textural features) to estimate the FL growth; use spectral indices not based on the normalized difference to decrease the saturation effect [29]; and start estimating biophysical parameters as the FH and aboveground biomass.

# Acknowledgements

The authors would like to acknowledge Fundação de Ciências e Tecnologia (FCT) for funding the projects FUELMON (PTDC/CCI-COM/30344/2017) and foRESTER (PCIF/SSI/0102/2017), and the Research Units, Centre of Technology and Systems – Uninova (UIDB/00066/2020) and Forest Research Centre (UIDB/00239/2020). Also, the ICNF deserves an acknowledgment for presenting us with the topic and supplying data regarding FB treatments. João E. Pereira-Pires thanks the Fundação para a Ciência e Tecnologia (FC&T), Portugal for the Ph.D. Grant 2020.05015.BD.
References

1. R. Barbero, J. T. Abatzoglou, N. K. Larkin, C. A. Kolden, and B. Stocks, "Climate change presents increased potential for very large fires in the contiguous United States," *Int. J. Wildl. Fire*, vol. 24, no. 7, p. 892, 2015, doi: 10.1071/WF15083.

2. C. Tymstra, B. J. Stocks, X. Cai, and M. D. Flannigan, "Wildfire management in Canada: Review, challenges and opportunities," *Prog. Disaster Sci.*, vol. 5, p. 100045, 2020, doi: 10.1016/j.pdisas.2019.100045.

3. Jesús San-Miguel-Ayanz et al., *Forest Fires in Europe, Middle East and North Africa 2018*. Rome: Publications Office of the European Union, 2019.

4. E. Chuvieco et al., "Historical background and current developments for mapping burned area from satellite Earth observation," *Remote Sens. Environ.*, vol. 225, no. March, pp. 45-64, 2019, doi: 10.1016/j.rse.2019.02.013.

5. D. M. J. S. Bowman, G. J. Williamson, J. T. Abatzoglou, C. A. Kolden, M. A. Cochrane, and A. M. S. Smith, "Human exposure and sensitivity to globally extreme wildfire events," *Nat. Ecol. Evol.*, vol. 1, no. 3, pp. 1-6, 2017, doi: 10.1038/s41559-016-0058.

6. D. Ascoli, L. Russo, F. Giannino, C. Siettos, and F. Moreira, "Firebreak and Fuelbreak," in *Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires*, Cham: Springer International Publishing, 2018, pp. 1-9.

7. DPFVAP - ICNF, "Primary Fuelbreak Network Manual," Portugal, 2014.

8. P. Potapov et al., "Mapping global forest canopy height through integration of GEDI and Landsat data," *Remote Sens. Environ.*, vol. 253, no. October 2020, p. 112165, Feb. 2021, doi: 10.1016/j.rse.2020.112165.

9. S. P. Healey, Z. Yang, N. Gorelick, and S. Ilyushchenko, "Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation," *Remote Sens.*, vol. 12, no. 17, p. 2840, Sep. 2020, doi: 10.3390/rs12172840.

10. Á. Barsi, Z. Kugler, I. László, G. Szabó, and H. M. Abdulmutalib, "ACCURACY DIMENSIONS IN REMOTE SENSING," *ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.*, vol. XLII-3, no. 3, pp. 61-67, Apr. 2018, doi: 10.5194/isprs-archives-XLII-3-61-2018.

11. E. Hamunyela, J. Reiche, J. Verbesselt, and M. Herold, "Using space-time features to improve detection of forest disturbances from Landsat time series," *Remote Sens.*, vol. 9, no. 6, pp. 1-17, 2017, doi: 10.3390/rs9060515.

12. T. Hermosilla, M. A. Wulder, J. C. White, N. C. Coops, and G. W. Hobart, "An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites," *Remote Sens. Environ.*, vol. 158, pp. 220-234, 2015, doi: 10.1016/j.rse.2014.11.005.

13. W. Wang, Z. Chen, X. Li, H. Tang, Q. Huang, and L. Qu, "Detecting spatio-temporal and typological changes in land use from Landsat image time series," *J. Appl. Remote Sens.*, vol. 11, no. 3, p. 035006, Jul. 2017, doi: 10.1117/1.JRS.11.035006.

14. Z. Zhu and C. E. Woodcock, "Continuous change detection and classification of land cover using all available Landsat data," *Remote Sens. Environ.*, vol. 144, pp. 152-171, 2014, doi: 10.1016/j.rse.2014.01.011.

15. Y. Hao, Z. Chen, Q. Huang, F. Li, B. Wang, and L. Ma, "Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series," *Remote Sens.*, 2020, doi: 10.3390/rs12030478.
16. N. W. Ku and S. C. Popescu, "A comparison of multiple methods for mapping local-scale mesquite tree aboveground biomass with remotely sensed data," *Biomass and Bioenergy*, vol. 122, no. January, pp. 270-279, 2019, doi: 10.1016/j.biombioe.2019.01.045.

17. J. Lee, J. Im, K. Kim, and L. J. Quackenbush, "Machine learning approaches for estimating forest stand height using plot-based observations and Airborne LiDAR data," *Forests*, vol. 9, no. 5, 2018, doi: 10.3390/f9050268.

18. N. Lang, K. Schindler, and J. D. Wegner, "Country-wide high-resolution vegetation height mapping with Sentinel-2," *Remote Sens. Environ.*, vol. 233, no. July, p. 111347, Nov. 2019, doi: 10.1016/j.rse.2019.111347.

19. S. Puliti et al., "Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data," *Remote Sens. Environ.*, vol. 236, no. November 2019, p. 111501, 2020, doi: 10.1016/j.rse.2019.111501.

20. S. Wittke, X. Yu, M. Karjalainen, J. Hyyppä, and E. Puttonen, "Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest," *Int. J. Appl. Earth Obs. Geoinf.*, vol. 76, no. November 2018, pp. 167-178, 2019, doi: 10.1016/j.jag.2018.11.009.

21. M. F. Cougo et al., "Radarsat-2 backscattering for the modeling of biophysical parameters of regenerating mangrove forests," *Remote Sens.*, vol. 7, no. 12, pp. 17097-17112, 2015, doi: 10.3390/rs71215873.

22. J. E. Pereira-Pires, V. Aubard, R. A. Ribeiro, J. M. Fonseca, J. M. N. Silva, and A. Mora, "Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network," *Remote Sens.*, vol. 12, no. 6, p. 909, Mar. 2020, doi: 10.3390/rs12060909.

23. J. Dubayah, R., Tang, H., Armston, J., Luthcke, S., Hofton, M., Blair, "GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V001 [Data set]." NASA EOSDIS Land Processes DAAC, 2020, doi: 10.5067/GEDI/GEDI02_B.001.

24. S. Clerc and MPC Team, "S2 MPC - L1C Data Quality Report - ESA," 2020.

25. M. Guizar-Sicairos, S. T. Thurman, and J. R. Fienup, "Efficient subpixel image registration algorithms," *Opt. Lett.*, vol. 33, no. 2, p. 156, 2008, doi: 10.1364/ol.33.000156.

26. J. E. Pereira-Pires et al., "Pixel-based and object-based change detection methods for assessing fuel break maintenance," in *2020 International Young Engineers Forum (YEF-ECE)*, Jul. 2020, pp. 49-54, doi: 10.1109/YEF-ECE49388.2020.9171818.

27. N. Lang, K. Schindler, and J. D. Wegner, "Country-wide high-resolution vegetation height mapping with Sentinel-2," *Remote Sens. Environ.*, vol. 233, no. April, p. 111347, 2019, doi: 10.1016/j.rse.2019.111347.

28. V. Aubard, J. E. Pereira-Pires, M. L. Campagnolo, J. M. C. Pereira, A. Mora, and J. M. N. Silva, "Fully Automated Countrywide Monitoring of Fuel Break Maintenance Operations," *Remote Sens.*, vol. 12, no. 18, p. 2879, Sep. 2020, doi: 10.3390/rs12182879.

29. O. Mutanga and A. K. Skidmore, "Narrow band vegetation indices overcome the saturation problem in biomass estimation," *Int. J. Remote Sens.*, vol. 25, no. 19, pp. 3999-4014, Oct. 2004, doi: 10.1080/01431160310001654923.