High resolution fire hazard index based on satellite images

In December 2015, after 3 year of activity, the FP7 project PREFER (Space-based Information Support for Prevention and REcovery of Forest Fires Emergency in the MediteRranean Area) came to an end. The project was designed to respond to the need to improve the use of satellite images in applications related to the emergency services, in particular, to forest fires. The project aimed at developing, validating and demonstrating information products based on optical and SAR (Synthetic Aperture Radar) imagery for supporting the prevention of forest fires and the recovery/damage assessment of burnt area. The present paper presents an improved version of one of the products developed under the PREFER project, which is the Daily Fire Hazard Index (DFHI)

A Common Approach to Foster Prevention and Recovery of Forest Fires in Mediterranean Europe

Most countries of Mediterranean Europe are strongly affected by forest fires, with major socio-economic and environmental impacts that can spread over several regions and countries. A transnational approach allows creating synergies regarding resource sharing and problem-solving strategies. The access to high quality and up-to-date information is critical to improve fire hazard mitigation measures and promote comparable appraisals between different regions. Several collaborative initiatives have been implemented in Europe to foster research and service development, focusing on common issues amongst countries. The PREFER project was one of these initiatives, with the purpose of contributing to protect human communities and forests from fire hazard, by providing cartographic products through the implementation of a new systematic framework. The participation of end users, such as civil protection organizations and forest services, covering the Euro-Mediterranean region, was crucial to ensure the operational application of the mapping products. Fuel classification, daily fire hazard indices, vulnerability assessment and damage severity levels were some of the mapping applications developed for several test areas in Mediterranean Europe. This chapter illustrates the potential enhancements for forest fire management offered by this framework, bearing in mind the benefits of applying shared and harmonized approaches for common issues

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

Achievements of the prefer project in the prevention phase of the forest fire management

The three years FP7 project PREFER (Space-based information support for the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area) devoted to develop a satellite based service infrastructure capable to provide up-to-date information to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region, has been successfully completed at the end of 2015. However, the project consortium will make available its products for the 2016 summer season, too. The present paper aims at presenting the project achievements emphasizing the most innovative information products developed in the framework of the project. For such products the methodology, validation and demonstration results will be presented and discussed

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

Development of a vegetation damage severity index based on hyperspectral sensor data

The SAP4PRISMA (Development of algorithms and products for supporting the PRISMA mission) project is one of the five research projects funded by ASI (Italian Space Agency) with the objective to develop applications capable of suitably exploiting the data acquired by the satellite hyperspectral sensor PRISMA. PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth observation system combining a hyperspectral sensor with a panchromatic medium-resolution camera. The mission, fully supported by the Italian Space Agency (ASI), is de-voted to Earth Observation and Remote Sensing Research to answer to the users increasing de-mand of accurate quantitative information about the Earth system. SAP4PRISMA project is focus-ing its research activities only on those geophysical parameters/applications/products that are suit-able for the characteristics of the mission and in perspective for further international hyperspectral missions (EnMAP, HyspIRI, etc.). The project is structured in interconnected research activities aimed at consolidating the methodological issues for retrieving geophysical and agro-environmental parameters to be used as inputs for the development of innovative complex prod-ucts (e.g., nitrate leaching, land degradation and fuel maps, etc.). The products proposed in the frame work of the SAP4PRISMA project regard: (a) land degradation and vegetation status, (b) products development for agricultural areas, (c) management and monitoring of natural and in-duced hazards. Regarding the application of PRISMA for the management and monitoring of natu-ral and anthropogenic hazards, we focus on the assessment of the damage severity and mainly on the effects of fire in vegetated areas interested by a fire. Moreover, project goal is to develop an index that, in the presence of an area where the vegetation shows a sharp decline, is able to under-stand the causes, that may not necessarily be linked to the occurrence of a fire (e.g., oil spills, floods, etc.). This paper aims at showing the results reached up-to-now in the process of develop-ing such an index called DSI (Damage Severity Index)

Urban growth assessment around Winam Gulf of Kenya based on satellite imagery

Urban growth and population dynamics are among the most critical information needed for future economic development planning, natural resources allocation and environmental management. In the present work, two methods, the first based on night-time images produced by NOAA and population maps provided by Oak Ridge National Laboratory's (ORNL) LandScan, and the second one on SAR imagery, were used in order to assess the expansion of urban areas surrounding the Winam Gulf (Lake Victoria, Kenya) at different scales. In the time covered by night-time lights imagery, the study highlighted a period of constant growth rate between 2002 and 2006 and a negative trend after 2006 and 2008. This decrease may be related to two main events occurring in the study area between 2006 and 2007: the decline of the Lake Victoria level and the abnormal proliferation of the floating weeds within the Winam Gulf. Meanwhile, the urban feature extraction obtained at a different scale within a particular district from 1997 up to 2008 results in a constant growth rate. Population movements around this zone explain different dynamics that should be studied in detail in order to understand their particular roots

Sugarcane biomass estimate based on sar imagery: A radar systems comparison

SBAM (Satellite Based Agricultural Monitoring) is a project funded by Italian Space Agency in the framework of Italian-Kenya cooperation. The project has four main objectives: a) to produce an updated map of the agricultural areas for Kenya based on Landsat 8 and Sentinel 2 imagery; b) to develop an automatic monitoring system able to classify agricultural areas and detect land use changes; c) to develop and deliver to the Kenyan partner of the project a system capable to download and process automatically Landsat8, Sentinel2, MODIS and MSG/SEVIRI images by providing standard products (vegetation indices, statistics, temporal analysis, etc.); d) to provide a tool for assessing changes in the agricultural area stability and crop yield. and study the feasibility of a tool capable to forecast crop yields. The paper is devoted to describe the activity carried out in the field of forecasting crop yield by using biomass estimate based on SAR images. The results obtained by using images acquired by X-band (Cosmo-Skymed), C-Band (Sentinel-1) and L-band (PALSAR) systems on a study area devoted to sugarcane will be described

Satellite-based products for supporting forest fires prevention and recovery in Europe

The main purpose of the FP7 PREFER project is to set up a space-based service infrastructure and up-to-date cartographic products, based on remote sensing data, to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region. This region is particularly affected by uncontrolled forest fires, with negative consequences on ecosystems, such as desertification and soil erosion, as well as on the local economy and, in extreme situations, causing also the loss of human lives. The present paper aims at illustrating the potential improvement in the forest fires fighting that may result from the use of information obtained by exploiting satellite imagery. The potentiality of satellite based information products will be demonstrated by reporting the results of the demonstration activity carried out during the 2015 summer season

Are the PREFER project products devoted to support fire prevention and recovery suitable to South-America ?

PREFER (Space-based information support for the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area) is a three years project devoted to develop a satellite based service infrastructure capable to provide up-to-date information to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region. The project has been successfully completed at the end of 2015. This paper aims at illustrating some of the project achievements and discuss their applicability to manage forest fires in South-America