Prevention and management of wildfires: vulnerability mapping and machine learning-based algorithm development for fuel mapping using hyperspectral imagery

Fire is a major ecological disturbance and threatening factor of ecosystem sustainability around the world and specifically in Mediterranean regions. Natural vegetation ecosystems are important environmental resources that provide various benefits to the human society whereas it also acts as fuel for wildfires. Hyperspectral imagery (HSI) is a passive technology which has the ability to classify the wildfire fuel types in a scene by means of several (hundreds) narrow band spectral acquisitions. This PhD thesis focused on developing a wildfire vulnerability map using GIS data for Sardinia and a procedure for wildfire fuel mapping using PRISMA HSI. Firstly, wildfire vulnerability map was generated using the vulnerability index comprising of the three main components: exposure, sensitivity and coping capacity. Exposure, representing the presence of assets (people, property and ecosystems) in areas where wildfires occur. Sensitivity, representing the degree to which these assets can be affected by a wildfire, linked to their predisposition to suffer certain type and magnitude of losses. Coping capacity, related to the measures applied to anticipate potential effects or to respond in case of fire occurs, based on institutional practices within several countries. Composite indices for each of the components were created using GIS data of population density, fuel types, location of protected areas, roads infrastructure and surveillance activities, taking into account the effect of the third dimension wherever is necessary. The additive type model was selected for the aggregation of components by allocating weights in the order of importance, mainly to differentiate the effects of individual elements and to streamline the interpretation of the outputs. Specifically, non-coping capacity was improved by including road density along with other institutional variables such as firefighters and surveillance areas. The vulnerability map of Sardinia developed by combining exposure map, sensitivity map and non-coping capacity map was shown. In this map, the value ranges from 0 to 1 representing from lower to higher vulnerable pixels correspondingly. Secondly, a semi-supervised machine learning approach for discriminating the wildfire fuel types was developed for the hyperspectral imagery (HSI) of PRISMA, a recently launched satellite of Italian Space Agency. Though machine learning classifiers provide better accuracy comparatively, many remote sensing specialists hesitate to use them because of the unavailability of required datasets. So, here, a procedure was developed to generate samples using single spectral signature as input data point for each class to apply support vector machine classifier and followed by, unmixing of mixed pixels by fully constrained linear mixing model. The procedure developed for classifying the fuel types available in the image of south-west Sardinia covering a part of Monte-Arcosu Forest and 18 different fuel types were classified in this region of interest. In order to correlate the classified fuel types to fuel models of Anderson or Scott/Burgan, further classification was carried out. Fuel types were classified according to the sparse/dense type, plain/mountainous type, open/closed type, and climatic conditions and for which available maps such as biomass, DEM, Tree Cover Density Map and iso-bioclimatic condition map were used respectively. Relative Greenness map was generated using time-series Sentinel-2 data. Then, the procedure of conversion from classified map to fuel map according to the JRC Anderson Codes and Scott/Burgan standard fuel models has been presented. The procedure was implemented on the HSI images obtained for south of Sardinian Island and for north-west of Latium in Italy as demonstration purpose. The classified map has been validated in different ways i.e. by using reference data, ground data and field data and obtained an overall accuracy of greater than 80% for all the cases. The stability of this approach was also tested by repeating the procedure on another HSI obtained on Latium in Italy and obtained degree of confidence greater than 95%. The proposed approach in this work can be used to generate wildfire fuel map using hyperspectral (PRISMA) data with higher accuracy over any part of Europe using LUCAS points as input. SWOT analysis has been conducted to understand the Strengths, Weaknesses, Opportunities and Threats of PRISMA hyperspectral imagery for wildfire fuel mapping. Though it is not possible to overcome all the weaknesses and threats, strategies to overcome some of them were discussed. Thus, the most vulnerable spots of wildfires can be referred using the developed vulnerability map whereas the wildfire fuels can be mapped-in for the areas of interest with hyperspectral image of PRISMA as per the proposed approach. Fuel map is useful to fire managers, researchers, policy makers and systems in applications such as study of fire behaviours, fire potential, fire emissions, carbon budget, fuel management, fire effects and ecosystem modelling. With this, it can be considered that this work has a major role in the prevention and management of wildfires

Daily fire hazard index for the prevention and management of wildfires in the region of Sardinia

The purpose of this paper is to show how the process for the calculation of the Daily Fire Hazard Index described in (Laneve, Cadau, 2007) [1] was improved upon by taking into account the effects of wind speed and direction, examining the wildfire insurgence data in the Italian region of Sardinia. The Daily Fire Hazard Index was developed in the context of the S2IGI project with the objective to provide a daily estimate of the likelihood of wildfire insurgence, in order to help coordinate the firefighting activities. Using land cover maps, fuel maps and MODIS satellite imagery, an algorithm was developed to estimate the relative amount of live and dead vegetation. Meteorological data is used to determine the temperature, the relative humidity and the wind speed. After using the FAO Penman-Monteith method (1998) [2] for the determination of the reference evapotranspiration of the vegetation, a simple algorithm was used to correct the surface temperature accounting for the effect of the magnitude of the wind speed. After determining the wind direction using the meteorological forecast data, the correction factor takes into account the fact that in Sardinia, the majority of the wildfires occur in days of strong Mistral winds

Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique

Multi-Response Optimization of Coagulation and Flocculation of Olive Mill Wastewater: Statistical Approach

Olive oil production is one of the important industrial sectors within the agro-food framework of the Mediterranean region, economically important to the people working in this sector, although there is also a threat to the environment due to residues. The main wastes of the olive oil extraction process are olive mill wastewater (OMW) and olive husks which also require proper treatment before dismissal. In this research work, the main goal is to introduce grey relational analysis, a technique for multi-response optimization, to the coagulation and flocculation process of OMW to select the optimum coagulant dosage. The coagulation and flocculation process was carried out by adding aluminum sulfate (Alum) to the waste stream in different dosages, starting from 100 to 2000 mg/L. In previous research work, optimization of this process on OMW was briefly discussed, but there is no literature available that reports the optimal coagulant dosage verified through the grey relational analysis method; therefore, this method was applied for selecting the best operating conditions for lowering a combination of multi-responses such as chemical oxygen demand (COD), total organic carbon (TOC), total phenols and turbidity. From the analysis, the 600 mg/L coagulant dosage appears to be top ranked, which obtained a higher grey relational grade. The implementation of statistical techniques in OMW treatment can enhance the efficiency of this process, which in turn supports the preparation of waste streams for further purification processes in a sustainable way

Multi-Response Optimization of Coagulation and Flocculation of Olive Mill Wastewater: Statistical Approach

Olive oil production is one of the important industrial sectors within the agro-food framework of the Mediterranean region, economically important to the people working in this sector, although there is also a threat to the environment due to residues. The main wastes of the olive oil extraction process are olive mill wastewater (OMW) and olive husks which also require proper treatment before dismissal. In this research work, the main goal is to introduce grey relational analysis, a technique for multi-response optimization, to the coagulation and flocculation process of OMW to select the optimum coagulant dosage. The coagulation and flocculation process was carried out by adding aluminum sulfate (Alum) to the waste stream in different dosages, starting from 100 to 2000 mg/L. In previous research work, optimization of this process on OMW was briefly discussed, but there is no literature available that reports the optimal coagulant dosage verified through the grey relational analysis method; therefore, this method was applied for selecting the best operating conditions for lowering a combination of multi-responses such as chemical oxygen demand (COD), total organic carbon (TOC), total phenols and turbidity. From the analysis, the 600 mg/L coagulant dosage appears to be top ranked, which obtained a higher grey relational grade. The implementation of statistical techniques in OMW treatment can enhance the efficiency of this process, which in turn supports the preparation of waste streams for further purification processes in a sustainable way

New Approach of Sample Generation and Classification for Wildfire Fuel Mapping on Hyperspectral (Prisma) Image

Hyperspectral images have its applications in various fields. Here, hyperspectral image from PRISMA which is a fundamental satellite of Italian Space Agency is being used for discriminating the wildfire fuel types on Sardinian Island of Italy. PRISMA is an on-demand mission and the available data in the archive are limited. There is no literature available on land use/vegetation classification using PRISMA data. In this paper, a new approach for generating samples to form a dataset for classifying the wildfire fuels and for classifying mixed pixels using iso-bioclimatic conditions are proposed. The classified map created using the dataset and using the iso-bioclimatic conditions is been validated. From the accuracy assessment, SVM classifier showed an overall accuracy of 86% and kappa coefficient of 0.79. Then, the classified map is converted into fuel map. This study suggests that the proposed approach can be used to generate samples for land use/vegetation classification and to assign vegetation types to mixed pixels depending upon the iso-bioclimatic conditions

Unveiling Temperature Patterns in Tree Canopies across Diverse Heights and Types

Forests are some of the major ecosystems that help in mitigating the effects of climate change. Understanding the relation between the surface temperatures of different vegetation and trees and their heights is very crucial in understanding events such as wildfires. In this work, relationships between tree canopy temperature and canopy height with respect to vegetation types were extracted. The southern part of Sardinia Island, which has dense forests and is often affected by wildfires, was selected as the region of interest. PRISMA hyperspectral imagery has been used to map all the available vegetation types in the region of interest using the support vector machine classifier with an accuracy of >80% for all classes. The Global Ecosystem Dynamics Investigation’s (GEDI) L2A Raster Canopy Top Height product provides canopy height measurements in spatially discrete footprints, and to overcome this issue of discontinuous sampling, Random Forest Regression was used on Sentinel-1 SAR data, Sentinel-2 multispectral data, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to estimate the canopy heights of various vegetation classes, with a root mean squared error (RMSE) value of 2.9176 m and a coefficient of determination (R2) value of 0.791. Finally, the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and emissivity product provides ground surface temperature regardless of land use and land cover (LULC) types. LST measurements over tree canopies are considered as the tree canopy temperature. We estimated the relationship between the canopy temperature of five vegetation types (evergreen oak, olive, juniper, silicicole, riparian trees) and the corresponding canopy heights and vegetation types. The resulting scatter plots showed that lower tree canopy temperatures correspond with higher tree canopies with a correlation coefficient in the range of −0.4 to −0.5 for distinct types of vegetation

Mathematical Modelling of a Propellent Gauging System: A Case Study on PRISMA

Propellant gauging is crucial for a spacecraft approaching the end of its lifespan. Current gauging systems for satellites typically have an accuracy rate of a few months to a year at the end of their operational life. Therefore, it is essential to determine the appropriate gauging system for mission operations. This research focuses on modeling the propellant gauging system for PRISMA, an Earth Observation (EO) satellite of the Italian Space Agency. The analysis centers on implementing algorithms that calibrate the remaining propellant mass in the satellite tank using traditional methods such as bookkeeping (BKP) and pressure-volume-temperature (PVT). To enhance accuracy in quantification, an unconventional approach called thermal propellant gauging (TPG) has been considered. Preliminary computations were conducted using data obtained from the PRISMA thermal model to understand the calibration accuracy of the three methods. At the end of its operational life, the BKP and PVT methods exhibited error rates of 4.6% and 4.8%, respectively, in calculating the mass. In contrast, the TPG method demonstrated a significantly higher precision with an error rate of 1.86%. However, at the beginning of the satellite’s operational life, the PVT and TPG methods showed error rates of 1.0% and 1.3%, respectively, while the BKP technique reported an error rate of 0.1%. Based on these findings, it has been concluded that combining the BKP and TPG approaches yields superior results throughout the satellite’s lifespan. Furthermore, the researchers have determined the specific time duration for which each of these distinct approaches can be effectively utilized

An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input