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

PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UID/EEA/00066/2019 UIDB/00239/2020The 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.publishersversionpublishe

Uncovering Vegetation Changes in the Urban–Rural Interface through Semi-Automatic Methods

Barbosa, B., Rocha, J., Costa, H., & Caetano, M. (2022). Uncovering Vegetation Changes in the Urban–Rural Interface through Semi-Automatic Methods. Applied Sciences, 12(5), 1-14. [2294]. https://doi.org/10.3390/app12052294 -------------- Funding: This research was funded by Portuguese Foundation for Science and Technology, I.P. (FCT), under the framework of the Project “FORESTER—Data fusion of sensor networks and fire spread modelling for decision support in forest fire suppression” [name of funder] grant number PCIF/SSI/0102/2017. The APC was funded by the Research Unit UIDB/00295/2020 and UIDP/00295/2020.Forest fires are considered by Portuguese civil protection as one of the most serious natural disasters due to their frequency and extent. To address the problem, the Fire Forest Defense System establishes the implementation of fuel management bands to aid firefighting. The aim of this study was to develop a model capable of identifying vegetation removal in the urban–rural interface defined by law for fuel management actions. The model uses normalised difference vegetation index (NDVI) of Sentinel-2 images time series and is based on the Welch t-test to find statistically significant differences between (i) the value of the NDVI in the pixel; (ii) the mean of the NDVI in the pixels of the same land cover type in a radius of 500 m; and (iii) their difference. The model identifies a change when the t-test points for a significant difference of the NDVI value in the ‘pixel’ as comparted to the ‘difference’ but not the ‘mean’. We use a moving window limited to 60 days before and after the analysed date to reduce the phenological variations of vegetation. The model was applied in five municipalities of Portugal and the results are promising to identify the places where the management of fuel bands was not carried out. This indicates which model could be used to assist in the verification of the annual management of the fuel bands defined in the law.publishersversionpublishe

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Detecção Automática de Alterações de Coberto Vegetal em Áreas de Interface Urbano-Rural

Para fazer face aos incêndios florestais o SDFCI estabelece faixas de gestão de combustíveis como forma de auxiliar o combate e mitigação deste problema. O objetivo do trabalho foi elaborar um modelo capaz de identificar a remoção da vegetação nestas faixas através da análise do NDVI em séries temporais de imagens Sentinel 2. O modelo busca diferenças estatisticamente significantes, através do Welch t-test, nas informações contidas nas imagens. O modelo foi aplicado no concelho de Figueiró dos Vinhos e os resultados mostraram-se promissores na identificação de áreas onde não foi feita a gestão, ou seja, áreas de infração a legislação.info:eu-repo/semantics/publishedVersio

Detecção automática de alterações de coberto vegetal em áreas de interface urbano-rural

Para fazer face aos incêndios florestais o Sistema de Defesa da Floresta contra Incêndios estabelece faixas de gestão de combustíveis como forma de auxiliar o combate e mitigação deste problema. O objetivo do trabalho foi elaborar um modelo capaz de identificar o controlo da biomassa nestas faixas através da análise do NDVI em séries temporais de imagens Sentinel 2. O modelo busca diferenças estatisticamente significativas, através do Welch t-test, nas informações contidas nas imagens. O modelo foi aplicado no concelho de Figueiró dos Vinhos e os resultados mostraram-se promissores na identificação de áreas onde não foi feita a gestão, ou seja, áreas de infração à legislação.To deal with forest fires, the Forest Defense System Against Fire establishes fuel management buffers as a way to help combat and mitigate this problem. The objective of the study was to develop a model capable of identifying the fuel management in these areas by analysing the NDVI in time series of Sentinel 2 images. The model seeks statistically significant differences, through the Welch t-test, in the information contained in the images. The model was applied in the municipality of Figueiró dos Vinhos and the results were promising in identifying areas where management was not carried out, i.e., areas of infringement of legislation.info:eu-repo/semantics/publishedVersio

Improving the estimation of fire danger, fire propagation and fire monitoring : new insights using remote sensing data and statistical methods

This thesis covers three major topics related to wildfires, remote sensing and meteorology: (i) quantifying and forecasting fire danger combining numerical weather forecasts and satellite observations of fire intensity; (ii) mapping burned areas from satellite observations with multiple spatial and spectral resolution; and (iii) modelling fire progression taking into account weather conditions and fuel (vegetation) availability. Regarding the first topic, an enhanced Fire Weather Index (FWI) is proposed by using statistical methods to combine the classical FWI with an atmospheric instability index with the aim of better forecasting the fire danger conditions favourable to the development of convective fires. Furthermore, the daily definition of the classical FWI was extended to an hourly timescale, allowing for assessment of the variability of the fire danger conditions throughout the day. For the second topic, a method is proposed to map and date burned areas using sequences of daily satellite data. This method, tested over several regions around the globe, provide burned area maps that outperform other existing methods for the task, particularly regarding the consistency and accuracy of the date of burning. Furthermore, a method is proposed for fast assessment of burned areas using 10-meter resolution satellite data and making use of Google Earth Engine (GEE) as a tool for preprocessing and downloading of data that is then used as input to a deep learning model that combines a coarse burned area map with the medium resolution data to provide a refined burned area map with 10-meter resolution at event level and with low computational requirements. Finally, for the third topic, a method is proposed to estimate the fire progression over a 12-hour period with resource to an ensemble of models trained based on the reconstruction of past events. Overall, I am confident that the results obtained and presented in this thesis provide a significant contribution to the remote sensing and wildfires scientific community while opening interesting paths for future research on the topics described

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens