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Forest and interface fires change the dynamics of land cover because of the combination of various environmental and socio-economic factors that make Mediterranean regions particularly vulnerable to fire. They represent one of the main causes of environmental degradation and have an important negative impact on the landscape, strongly influence ecological processes and compromise ecosystems. Fires are a problem that, while varying in severity and frequency, affects all European countries. However, the Mediterranean countries tend to be the most affected, in Italy for example, the issue of fires is an important issue and addresses the problem through laws, policies and fire prevention measures. The Italian legislation reiterates the obligation for each municipal administration to adopt a simple and fast tool that allows the safety of the population in the event of an interface or forest fires that threaten settlements, sensitive places or local infrastructures. The estimation of fire danger is very important for quantifying the impact on the territory and plays an important role in mitigation framework programmes, representing a strategic support for policy makers for planning fire prevention strategies and policies and for monitoring fire areas. The National Manual for the Processing of Civil Protection Plans establishes the guidelines for a methodological approach with the aim of suggesting the minimum requirements to be met for the drafting of risk maps on the Italian territory. The Puglia Region is among the Italian regions particularly affected by the phenomenon of fires, the civil protection in 2019 identified around 600 fires. Fire danger estimation is very important for quantifying the impact on land and plays an important role in mitigation framework programs, representing a strategic support for policy makers in planning fire prevention strategies and policies and monitoring fire-affected areas. For this reason, the Apulia Region and the other regions of central and southern Italy have expressed the need to create a permanent Technical Table of Forest Fire Prevention Actions (AIB) in order to design strategies shared with the different stakeholders to improve the AIB system. The AIB Plan is produced using spatial and satellite analysis models and techniques appropriate for the interpretation of forecasting, monitoring, and supervising systems that can be used to improve forecasting, prevention, and active management of wildfires. Through the Plan for Forecasting, Preventing and Actively Fighting Forest Fires, the Apulia Region supports municipal planning activities in the field of forest fire risk by providing municipal-scale risk zoning expressed in terms of an overall risk index calculated on a municipal basis. This paper presents an experimental methodology carried out within the framework of the research agreement with the Apulia Civil Protection Department to update the fire risk classification at the municipal level calculated by formulating, in the form of weighted and normalized indices, risk factors such as forest cover, potential risk, actual risk, road infrastructure density, land cover, historical fire occurrence and climate variability. All indices were processed in a GIS environment and validated with the tools currently in use by the Apulia Region Civil Protection Department.

Land abandonment is among the most complex la nd use change processes driven by a multiplicity of anthropogenic and natural factors, such as agricultural over-exploitation, implementation of agricultural policies, socio-economic and climatic aspects. Therefore, it is necessary to deepen the effects of land abandonment based on methodologies that are as multidisciplinary as possible. Environmental and social problems related to abandonment include soil erosion and environmental degradation. Approaches combining GIS (Geographic Information System), remote sensing, and image analysis techniques allow for assessments and predictions based on integrating theoretical models with advanced geospatial and geostatistical models. One of the most widely used models for soil erosion estimation is the Revised Universal Soil Loss Equation (RUSLE). The present work developed a model using remote sensing and GIS tools to investigate some factors of the RUSLE equation to evaluate the adverse effects of soil erosion in areas covered by arable crops and subsequently abandoned. To identify potentially degraded areas, two factors of the RUSLE were related: the C Factor describing the vegetation cover of the soil and the A Factor representing the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, based on the deviations from the average erosion values and mapping of the areas of vegetation degradation relating to arable land, the areas identified and mapped are susceptible to soil degradation.

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment.

Sustainable land management is one of the key actions for the achievement of objectives set by the 2030 Agenda for Sustainable Development. In particular, land represents a fundamental resource to address issues of climate change, biodiversity preservation, maintaining ecosystem services, and at the same time ensuring shared prosperity and well-being. Therefore, it is necessary to activate strategies to monitor changes in land use and land cover in order to evaluate strategies for proper management. To do this, the new open source geospatial analysis tools and the increasing availability of remote sensed open data can allow the activation of methodologies for monitoring changes in land use and land cover in order to provide data usable in other research areas or, for example, to implement a decision support system for environmental sustainability. In this study, a GIS approach based on open remote sensing data has been used to perform a spatial analysis of land cover changes within the Basilicata region (Southern Italy) that is spatially expeditious yet accurate. The results showed a very evident land transformation with important repercussions on the environmental components. The ease of use of techniques makes this methodology replicable in other territory and can be used as a preliminary approach to sustainable development model.

Fire effects consist not only in direct damage to the vegetation but also in the modification of both chemical and physical soil properties. Fire can affect the alteration of soil properties in different ways depending on fire severity and soil type. The most important consequences concern changes in soil responsiveness to the water action and the subsequent increase in sediment transport and erosion. Post fire soil loss can increase in the first year by several orders of magnitude compared to pre-fire erosion. In this study a distributed model based on the Revised Universal Soil Loss Equation (RUSLE) is used to estimate potential post-fire soil loss for four different fire events occurred in Basilicata region in 2017. Geographic Information System techniques and remote sensing data have been adopted to build a prediction model of post-fire soil erosion risk. Results show that this model is not only able to quantify post-fire soil loss but also to identify the complexity of the relationships between fire severity and all the factors that influence soil susceptibility to erosion.

Fires represents one of the main causes of environmental degradation and have an important negative impact on the landscape. Fires, in fact, strongly influenced ecological processes and compromise the ecosystems. Measurements of the post-fire damage levels over burned areas are important to quantify fire's impact on landscapes. Remote sensing and geo-statistical approaches are useful tools for the monitoring and analysis of burned areas on a regional scale, because provides reliable and rapid diagnosis of burned areas. Spatial autocorrelation statistics, such as Moran's I and Getis-Ord Local Gi index, were also used to measure and analyze dependency degree among spectral features of burned areas. This approach improves characterization of a burnt area and improves the estimate of the severity of the fire. This paper provides an application of fire severity studies describing post-fire spectral responses of fire affected vegetation to obtain a burned area map. The aim of this work is to implement a procedure, using ESA Sentinel 2 data and spatial autocorrelation statistics in a GIS open-source environment, a graphical model that analyzes the change detection of the potential burned area, as case of study Northern part of Apulia Region (Italy) was used. The burned area was delineated using the spectral indices calculated using Sentinel two images in the period July-August 2020 and using also the land use map of the area.

Soil degradation is a phenomenon that describes the degradation of soil quality due to which agricultural land in particular is unproductive as a consequence of the loss of ability to produce crops and biomass. The causes are many but, especially in the inland areas of the Mediterranean regions, some dynamics related to agriculture have particularly influenced the grading process. Specifically, agricultural over exploitation with unsustainable practices and land abandonment are causing ecological alterations that require contextual analysis to assess the medium and long-term effects. The aim of this work is to investigate the role of some factors that make up the RUSLE index have in the detection and monitoring of potentially degraded areas. In particular, the areas cultivated with arable crops were chosen as the area to be analyzed, because the average annual rate of soil erosion (A factor in RUSLE equation) is high despite the presence of vegetation cover and shown evident problems due to the phenomenon of degradation. In order to identify the potential degraded areas, two factor of RUSLE index have been correlated: C factor that describes the vegetation cover of the soil and A factor which represent the amount of potential soil erosion. All methodologies have been applied in a rural area in the northern part of Basilicata Region (Italy) using GIS and remote sensing approaches, as allows the possibility to perform a series of a complex studies and can be efficiently implemented in environmental monitoring plans.

The processes of land transformation related to soil erosion and land degradation are complex phenomena that require an approach as detailed and multidisciplinary as possible. In some Mediterranean inland areas, these issues seem to be very connected to the dynamics of transformation and abandonment of agricultural areas. In order to carry out this preliminary investigation for the assessment of dynamics and relationships between processes and land cover, an approach based on GIS and remote sensing has been applied. The study started with implementation of the Revised Universal Soil Loss Equation (RUSLE) model to calculate soil erosion on a monthly and annual basis. The resulting data were then processed through a Getis-Ord local autocorrelation index in order to produce a persistent erosion map. All datasets created were correlated with the cover classes that need more attention, i.e., arable land and post-cultivation vegetation area. All the techniques and methodologies, have been applied in a rural area of the Basilicata Region (South Italy) using exclusively a Free and Open Source Software (FoSS) GIS approach as it guarantees the possibility to perform a series of complex analyses in a simple and effective way so that they can be implemented in environmental monitoring actions and plans.

European Commission in 2009 assessed that in the period 2015–2030 about 11% of agricultural land in the EU are under high potential risk of abandonment due to factors, which has strong and known environmental and socio-economic consequences. The diverse impacts of abandonment need to be addressed via a broader set of policy instruments to alleviate the negative effects or even - reverse the trends in the early stages of the process. The clear identification of abandoned agricultural land is fundamental for a correct mapping for the future management and monitoring of the territories. In this context, this study proposes an innovative method for the detection and mapping of abandoned arable land through the use of remote sensing techniques and geo-statistical analysis. The combined use of Sentinel 2 images and the Landsat constellation, the use of NDVI index and change detection analysis made it possible to identify the change in agricultural use and/or abandonment of land in the eastern part of the Basilicata region in the period 1990–2020. (Italy). All process has been developed integrating Remote Sensing and Geographic Information System (GIS), using open-source software.

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM–OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (SVM) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time.

Fires are one of the main causes of environmental degradation as they have an impact on flora and fauna, can also strongly influence ecological and geomorphological processes and permanently compromise the functionality of the ecosystems and soils on which they impact. The severity of the fire event influences the superficial hydrological response and the consequent loss of soil. Precipitation on the basins recently affected by fires produces an increase in the outflow which commonly transports and deposits large volumes of sediment, both inside and downstream of the burned area. In the years following the fire, the loss of soil is very high and the degradation processes of the soils are much greater than in the pre-event. The aim of this study is to evaluate the potential annual loss due to post-fire erosion using remote sensing techniques, RUSLE (Revised Universal Soil Loss Equation) methodology and GIS tecniques in nine different event occurred in 2019 in the northern part of the Apulia Region (Southern Italy). Geographic Information System techniques and remote sensing data have been adopted to study the post-fire soil erosion risk. Satellite images are the most appropriate for environmental monitoring as they provide high resolution multispectral optical images, infact are able to monitor the development of vegetation by assessing the water content an changes in chlorophyll levels. This study can be useful to spatial planning authorities as a tool for assessing and monitoring eroded soil in areas affected by fires, representing a useful tool for land management.

Soil erosion in fired areas is one of the main environmental problem involves degrading the quality of the soil and reducing the productivity of the affected lands. The aim of this work is to implement a procedure that analyzes the change detection of the potential soil eroded in a burned area, and discriminate the amount of potential soil loss. As part of the MESARIP project (in agreement with the Regional Civil Protection) in order to implement the analyses of soil erosion pre and post fire event, using Sentinel 2 data and with the RUSLE (Revised Universal Soil Loss Equation) method in a GIS open source environment, a graphical model has been developed. The application of the RUSLE requires a series of consequential spatial analysis elaborations and, according to this scheme, the model has been developed with the Graphical Modeler. QGIS contains in a single environment a multiplicity of tools and algorithms native to other open source GIS software, such as, for example, SAGA GIS and GRASS GIS. The user interface is very simple and requires basic and thematic input data such as DEM, MASK areas or vegetation indices etc. The advantages in the construction of the model can be identified in the standardization of map algebra operations and also in the speed of execution of the steps. Currently the model has been tested in some burned areas in 2019 located in the northern part of the Apulia Region and will be tested in operational mode during the 2020 summer season.

The use of plastic materials in agriculture involves several benefits but it results in huge quantities of agricultural plastic waste to be disposed of. Input and output data on the use of plastics in agriculture are often difficult to obtain and poor waste management schemes have been developed. The present research aims to estimate and map agricultural plastic waste by using satellite images. Waste was evaluated by means of the indexes relating waste production to crop type and plastic application as defined by the land use map realized by classifying the Landsat 8 image. The image classification was carried out using Support Vector Machines (SVMs), and the accuracy assessment showed that the overall accuracy was 94.54% and the kappa coefficient equal to 0.934. Data on the plastic waste obtained by the satellite land use map were compared with the data obtained by using the institutional land use map; a difference of 1.74% was identified on the overall quantity of waste.

Soil consumption represent an important indicator of soil management, in last few years the European States have been promoted the use and installation of renewable energy sources, with a consequent soil consumption increase. The aim of this work is to implement a procedure that analyzes the change detection of the soil consumption and discriminate those related to soil consumption due to installation of renewable energy sources from that due to built-up areas. The select test site is the Municipality of Melfi (Southern Italy) because is highly significant because is characterized by fragmented and various environments. The increase of urbanization is due to the growth of built-up areas and the exponential development of renewable sources installation. The work herein presented concerns an application study on these processes with the images of Sentinel-2 satellite. In order to produce a synthetic map of soil consumption, the Sentinel-2 images were classified using a supervised classification. A first map of soil consumption was obtained divided the area characterized by urbanization from the area with the presence of the renewable energy sources. Eolic class have been subdivided and reclassified, divided the relevant street from the turbine pad. Eolic class have been reclassified discriminate the relevant street from the turbine pad and subdivided into other subclasses referred to the power wind turbines, in order to quantify the soil consumption related to each one. All processes have been processes developed integrating Remote Sensing and Geographic Information System (GIS), using open source software.

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

This paper focuses on the potential of the latest generation of Airborne laser scanning (ALS) for the detection and the spatial characterization of microtopographic relief linked to ancient landscapes and palaeoenvironmental features. ALS is an optical measurement technique for obtaining high-precision information about the Earth's surface including basic terrain mapping (Digital terrain model, bathymetry, corridor mapping), vegetation cover (forest assessment and inventory), coastal, and urban areas. Recent studies examined the possibility of using ALS in archaeological investigations to identify earthworks, although the ability of ALS measurements in this context has not yet been studied in detail. In this study, the investigations based on ALS survey and aerial photos were carried out for the natural protected area, Bosco dell'Incoronata in the Apulia Region (Southern Italy). The investigated area is an important site from the naturalistic, historic and archaeological point of view. It is an ancient lowland forest, still present in the medieval time, which has been characterized by a long and intensive human activity from Neolithic to Middle Ages. The LiDAR based analysis allowed us to identify features not visible from ground or from optical data set because hidden by forest canopy and dense understory. The DTM enabled us to identify some microtopographic relief linked to traces of past landscapes, as in the case of the Cervaro paleaoriverbed. It is quite interesting to note that the river changed many times from North to South side compared to the present stream, and traces of past human activities can be still evident close to the diverse paleaoriverbeds. Nevertheless, intensive and systematic study of the ancient landscapes of the Bosco dell'Incoronata is just beginning and so far questions tend to be raised rather than answered. The current study emphasizes the potential of aerial LiDAR (Light Detection And Ranging) survey for detecting surface discontinuities and microtopographic relief linked to palaeoenvironmental features, even hidden by under dense canopy and understory.

Remote sensing data can usefully support the fire management operational applications in different spatial and temporal scales with a synoptic point of view and low cost technologies. The satellite monitoring systems together with other geographic information, historical data and field measurements, can provide the fire management operators useful tools of fire danger assessment, fire prevention, fire-fighting and post-fire planning. The FIRE-SAT monitoring system was applied to a fire event which developed in a wildland-urban interface area of the Potenza town (Basilicata, Italy) on July 2015, in order to assess the fire occurrence danger, to evaluate the fire effects and to simulate the fire propagation.

The sedimentary budget assessment is an important topic for the scientific and social community, because it is crucial to understand the dynamics of orogenic belts and cope with a number of practical problems, such as soil conservation and sediment accumulation in reservoir. Sediment yield or denudation rate estimates in southern-central Italy are generally obtained either by applying simple empirical relationships based on the statistical regression between the geomorphic parameters of the drainage network and the suspended sediment yield measured at the drainage basins outlets, or by using qualitative–quantitative sediment delivery ratio or erosion models. In this work, we carry out a study of catchment dynamics and a sedimentary yield computation of several mountain catchments of the central-western sector of the Basilicata region (southern Italy), which are located upstream of an artificial reservoir. The sediment yield and erosion rate have been computed through both an indirect assessment of the suspended sediment yield, based on the Tu index (mean annual suspension sediment yield), and the application of the RUSLE and USPED empirical methods. The results obtained have been compared with the historical data of sediment accumulation, measured in the artificial reservoir of the Camastra dam where a detailed evaluation of the volumes of historical (i.e. about 40 years) sediment storage was collected. The collected dataset represents a basic tool both for the investigation of the morpho-dynamics of a typical mountain catchment of the Mediterranean area and the evaluation of sediment budget related to fluvial and hillslope processes. The 38-year-long record of the sediment storage in the Camastra artificial reservoir located at the outlet of the studied mountain catchments permits the validation of empirical relationships based on the geomorphic and climatic parameters of the drainage basin. Among the three different methods of sediment yield evaluation (Tu index, RUSLE and USPED), the Tu index showed the best prediction ability, although USPED erosion model also furnished a good estimation. As also confirmed by other works, the indirect estimation of sediment yield based on Ciccacci's empirical relationships can represent a good proxy of short-term denudation rates in the Mediterranean areas with geological and geomorphological features similar to the study area but the use of empirical models with increasing complexity such as the USPED erosion model can help to explore the spatial distribution of the sediment yield sectors of the drainage basin, the erosion hot-spots, and the role of landslides in sediment mobilization and hillslope-channel connectivity processes.

Up to nowadays, satellite data have become increasingly available, thus offering a low cost or even free of charge unique tool, with a great potential for quantitative assessment of urban expansion and urban sprawl, as well as for monitoring of land use changes and soil consumption. This growing observational capacity has also highlighted the need for research efforts aimed at exploring the potential offered by data processing methods and algorithms, in order to exploit as much as possible this invaluable space-based data source. The work herein presented concerns an application study on the process of urban sprawl conducted with the use of satellite ASTER data. The selected test site is highly significant, being it a coastal zone (with the presence of sand and rocks) characterized by a fragmented ecosystem and small towns, with an increasing rate of urbanization and soil consumption. In order to produce synthetic maps of urban areas, ASTER images were classified using two automatic classifiers, Maximum Likelihood (MLC) and Support Vector Machines (SVMs) applied by changing setting parameters, with the aim to compare their respective performances in terms of robustness, speed and accuracy. All process steps have been developed integrating Geographical Information System and Remote Sensing, and adopting free and open source software. Results pointed out that the SVM classifier with RBF kernel was generally the best choice (with accuracy higher than 90%) among all the configurations compared, and the use of multiple bands globally improves classification. One of the critical elements found in this case study is given by the presence of sand and sand mixed with rocks. The use of different configurations for the SVMs, i.e. different kernels and values of the setting parameters, allowed us to calibrate the classifier also to cope with a specific need, as in our case, to achieve a reliable discrimination of sand from urban area. © 2014 Elsevier B.V. All rights reserved.

MODIS-NDVI data from 2002 to 2014 were analysed to evaluate the effect of fire on vegetation in a test site located in Daxing'anling region (Inner Mongolia and Heilongjiang Province). Fire-affected and fire-unaffected areas were processed using two statistical approaches: detrended fluctuation analysis (DFA) and Fisher–Shannon (FS) method. The DFA allows the detection of scaling behaviour in nonstationary signals, whereas the FS method permits to identify the organization/order structure in complex signals. Our findings show that the results obtained by jointly using the two methods are consistent, enabling the characterization and discrimination between the fire-affected and fire-unaffected areas. In particular, among the investigated indices, the mean value of Fisher information measure (FIM) represents the most significant in discriminating between burned and unburned sites; its mean value for burned sites is about 2.5 that is significantly larger than that obtained for unburned sites (∼1.3). FIM is also characterized by the larger effectiveness in discriminating the two classes of sites on the base of its receiver operating characteristic based performance. The scaling exponents estimated by means of the DFA of the fire-affected pixels are averagely higher than those of the fire-unaffected ones, which, furthermore, are characterized by lower organization and higher disorder degree. Both of the two methods would contribute to identify the impact of fires on vegetation.

The time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher–Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher–Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers.

Remote sensing fire danger prediction model is applied to Northern China. This study was carried out in the Daxing'anling region, which is located in Heilongjiang Province and Inner Mongolia (50.5 degrees-52.25 degrees N, 122 degrees-125.5 degrees E), the northern China. The method integrated by dead fuel moisture content and relative greenness index, which is based on the fire potential index (FPI), was used to predict the fire danger level of the study area. The case that fire happened on the late June 2010 was used to validate the modified method. The results pointed out that the fire affected areas were located in high fire danger level on 26th, 27th, 28th June, 2010 respectively. The ROC analyses of the predicted accuracy on these days were 90.98 %, 73.79 % and 69.07 % respectively. Results from our investigation pointed out the reliability of the adopted method.

Up to nowadays, satellite data have become increasingly available, thus offering a low cost or even free of charge unique tool, with a great potential for operational monitoring of vegetation cover, quantitative assessment of urban expansion and urban sprawl, as well as for monitoring of land use changes and soil consumption. This growing observational capacity has also highlighted the need for research efforts aimed at exploring the potential offered by data processing methods and algorithms, in order to exploit as much as possible this invaluable space-based data source. The work herein presented concerns an application study on the monitoring of vegetation cover with the use of multi-temporal (2010-2014) satellite Modis data. The selected test site is the Iguazu park highly significant, being it one of the most threatened global conservation priorities (http://whc.unesco.org/en/list/303/). In order to produce synthetic maps of the investigated areas to monitor the status of vegetation and ongoing subtle changes, satellite data were processed using Principal Component Analysis (PCA). Results from our investigations pointed out an ongoing degradation trend.

Over the years, there have been a large number of satellite remote-sensing missions that have been providing enormous amounts of remotely sensed data with continuous improvements in the radiometric...

The characterization of fuel types is very important for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of ASTER-based results with ground-truth. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%.

This study aims at ascertaining if and how remote sensing data can improve fire danger estimation based on meteorological variables. With this goal in mind, a dynamic estimation of fire danger was performed using an approach based on the integration of satellite information within a comprehensive fire danger rating system. The performances obtained with and without using satellite data were carried out for fires that occurred during the fire season in the year 2003 in the Calabria region (southern Italy). This study area was selected, first, because it is highly representative of Mediterranean ecosystems and, second, because it is an interesting test case for wildfire occurrences within the Mediterranean basin. The results obtained have shown that the use of satellite data reduced efficiently the overestimated danger areas, thus improving at least by 10% the fire forecasting rate obtained without using satellite-based maps. Such findings can be directly extended to other similar Mediterranean ecosystems.

Fuel types are one of the most important factors that should be taken into consideration for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is extremely difficult because the description of fuel properties is very complex. In the present study, forest fuel mapping is considered from a remote sensing perspective by the assessment and mapping of general vegetation complexes. The purpose is to delineate forest types by exploring the use of remote sensing QuickBird data. In order to ascertain how well QuickBird data can provide an exhaustive classification of fuel properties, a sample area characterized by mixed vegetation covers and complex topography data was analysed. The selected sample area has an extension at around 60 km 2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing QuickBird data, were used as ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of QuickBird-based results with ground-truth data. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 75%.

Fires induce dynamical trends in vegetation covers. In order to investigate the effects of fires in dynamical patterns of vegetation cover, normalized difference vegetation index data from the SPOT-VEGETATION sensor over the times series 1998–2003 were analysed for burned and unburned test sites located in the Italian Peninsula. The statistical analysis was carried out by means of three different methods: (i) power spectral density (PSD), which reveals scaling as well as periodic trends; (ii) the multiple segmenting method (MSM), which is well suited to analysing scaling behaviour for short time series; and (iii) detrended fluctuation analysis (DFA), a method that allows persistence in non-stationary signal fluctuations to be captured. Results from the statistical analyses showed that the scaling exponents α of the pixel time series for fire-affected sites range around mean values of ~1.38 (PSD), ~1.19 (MSM) and ~1.22 (DFA), while those for fire-unaffected sites vary around mean values of ~0.86 (PSD), ~0.63 (MSM) and ~0.65 (DFA). The two classes of vegetation (fire affected and fire unaffected) are significantly discriminated from each other (with the t-Student test, p < 0.0001) for all three methods adopted. The scaling exponents of both fire-affected and fire-unaffected sites show the persistent character of the vegetation dynamics though the fire-affected sites show larger exponents. Such a result shows that fires contribute by increasing the persistence of the time dynamics of vegetation and, therefore, drive unstable behavioural trends in vegetation dynamics of burned areas.

In this paper, we present and discuss the investigations we conducted in the context of the MITRA project focused on the use of low cost technologies (data and software) for pre-operational monitoring of land degradation in the Basilicata Region. The characterization of land surface conditions and land surface variations can be efficiently approached by using satellite remotely sensed data mainly because they provide a wide spatial coverage and internal consistency of data sets. In particular, Normalized Difference Vegetation Index (NDVI) is regarded as a reliable indicator for land cover conditions and variations and over the years it has been widely used for vegetation monitoring. For the aim of our project, in order to detect and map vegetation anomalies ongoing in study test areas (selected in the Basilicata Region) we used the Principal Component Analysis applied to Landsat Thematic Mapper (TM) time series spanning a period of 25 years (1985-2011).

The characterization of 1 / f α scaling behaviour in normalized difference vegetation index (NDVI) satellite data of forest ecosystems in southern Italy has been performed using the multiple segmenting method (MSM) and detrended fluctuation analysis (DFA). Our findings point out to the characterization of Mediterranean forest ecosystems as governed by persistent mechanisms, which indicate positive feedbacks tending to destabilize the system under external forces or perturbations.

This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) and Landsat Thematic Mapper (TM) data acquired in 1998 were analysed for a test area in southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, was used to assess the results obtained for the considered test areas. Results from preliminary analysis showed that the use of unmixing techniques allows an increase in accuracy of around 7% compared with the accuracy level obtained by applying a widely used classification algorithm.

Fluctuation dynamics of time series of satellite SPOT-VEGETATION Normalized Difference of Vegetation Index (NDVI) data from 1998 to 2003 were analyzed to discriminate fire-induced variability in the vegetational dynamics of shrub-land in southern Italy. We used detrended fluctuation analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. We analyzed two shrub-land covers, one in "healthy conditions" (fire-unaffected) and the other in "ill conditions" (fire-affected). Our findings suggest that fires play an important role in the temporal evolution of the shrub-land, increasing the persistence of the vegetation dynamics.

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Il problema della gestione del fenomeno degli incendi boschivi è molto complesso, perché comprende una serie diaspetti connessi alle caratteristiche della vegetazione, alla morfologia del territorio, ai fattori meteorologici, ai fattoriantropici, etc. Risulta pertanto fondamentale e molto utile un approccio modellistico. I modelli matematici fornisconoun supporto essenziale nella valutazione dell’efficacia di possibili strategie di previsione e controllo del fuoco.Abstract FIRE_SAT project has been funded by the Civil Protectionof the Basilicata Region in order to set up alow cost methodology for fire danger monitoringand fire effect estimation based on satellite EarthObservation techniques.To this aim, NASA Moderate Resolution ImagingSpectroradiometer (MODIS), ASTER, Landsat TMdata were used. Novel data processing techniqueshave been developed by researchers of the ARGONLaboratory of the CNR-IMAA for the operativemonitoring of fire. In this paper we only focuson the danger estimation model which has beenfruitfully used since 2008 to 2012 as an reliable operativetool to support and optimize fire fightingstrategies from the alert to the management ofresources including fire attacks.The daily updating of fire danger is carried outusing satellite MODIS images selected for theirspectral capability and availability free of chargefrom NASA web site. This makes these data setsvery suitable for an effective systematic (daily) and sustainable low-cost monitoring of large areas.

Traditional methods of recording fire burned areas and fire severity involve expensive and time-consuming field surveys. Available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper focuses on multiscale characterization of fire severity using multisensor satellite data. To this aim, both MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been processed using geo-statistic analyses to capture pattern features of burned areas. Even if in last decades different authors tried to integrate geo-statistics and remote sensing image processing, methods used since now are only variograms, semivariograms and kriging. In this paper, we propose an approach based on the use of spatial indicators of global and local autocorrelation. Spatial autocorrelation statistics, such as Moran's I and Getis–Ord Local Gi index, were used to measure and analyze dependency degree among spectral features of burned areas. This approach enables the characterization of pattern features of a burned area and improves the estimation of fire severity.

This paper presents the results we obtained in the context of the FIRE-SAT project focused on the use of satellite data for pre-operational monitoring of fire danger and fire effects in the Basilicata Region. The use of satellite data was manyfold, to obtain: (i) fuel property (type and loading) maps, mainly obtained from satellite Landsat TM data, (ii) fuel moisture estimation (mainly from MODIS), (iii) fire danger/susceptibility indices as well as (iv) post fire effects including fire severity and vegetation recovery assessment. Results obtained during the first year of project (2008) suggested that the integrated model identified the main fire danger zones by means of the integration of fuel types with daily fuel moisture and Greenness maps. MODIS multitemporal data analyses enable us to dynamically estimate fire severity as well as to map fire affected areas and evaluate the vegetation recovery capability over time. The pre-operative use of the integrated model, carried out within the framework of the FIRE-SAT project funded by the Basilicata Region, pointed out that the system enables us to timely monitor spatial and temporal variations of fire susceptibility and promptly provide useful information on both fire severity and post fire regeneration capability.

Satellite time series offer great potential for a quantitative assessment of urban expansion, urban sprawl and for monitoring of land use changes and soil consumption. This study deals with the spatial characterization of expansion of urban areas by using spatial autocorrelation techniques applied to multi-date Thematic Mapper (TM) satellite images. The investigation focused on several very small towns close to Bari. Urban areas were extracted from NASA Landsat images acquired in 1976, 1999 and 2009, respectively. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and spatial autocorrelation techniques to reveal spatial patterns. Urban areas were analyzed using both global and local autocorrelation indexes. This approach enables the characterization of pattern features of urban area expansion and it improves land use change estimation. The obtained results showed a significant urban expansion coupled with an increase of irregularity degree of border modifications from 1976 to 2009.

In this study, we analyzed the dynamics of semi-natural vegetation observed before and after fire occurrence. Investigations were carried out for two test areas located in Southern Italy using Normalized Difference Vegetation Index (NDVI) from Terra MODIS data. The MOD09A1 (Level 3) was taken into consideration. It is an 8-day composite product available at a spatial resolution of 500 m. NDVI time series ranging from 2001 to 2009 have been analyzed using a physical-statistical method called Detrended fluctuation analysis (DFA). The same method has been previously adopted by the same authors group for analyzing VEGETATION/ NDVI time series recorder before and after fire for natural (i.e. unmanaged) vegetation covers. Results from this paper clearly pointed out the diverse vegetation behavior observed for natural and managed areas before and after fire occurrence.

Satellite remote sensing can successfully cope with different aspects of fire management problems, such as danger estimation, fire detection, burned area mapping and post-fire vegetation recovery. In particular, remote sensing can provide valuable data on type (namely distribution and amount of fuels) and status of vegetation in a consistent way at different spatial and temporal scales. The characterization and mapping of fuel types is one of the most important factors that should be taken into consideration for wildland fire prevention and pre-fire planning. In this paper, we provide a brief overview on the use of satellite data for the characterization and mapping of fuel type. Such research activities are part of the FUELMAP project, funded by JRC and focused on the development of fuel models for European ecosystems.

What are the ecological effects of fires? The evaluation of fire-affected areas and fire severity is of primary importance to answer this question, because fire strongly affects the ecological processes, such as, productivity level, creation of altered patches, modification in vegetation structure and shifts in vegetation cover composition, as well as land surface processes (such as surface energy, water balance, carbon cycle). Traditional methods of recording fire burned areas and fire severity involve expensive and time -consuming field survey. The available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper is focused on preliminary results we obtained from ongoing research focused on the evaluation of spatial variability of fire effects on vegetation. For the purposes of this study satellite ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been used. Both single (post-fire) and multi-date (pre and post fire) ASTER images were processed for some test areas in Southern Italy. Spatial autocorrelation statistics, such as Moran's I, Geary's C, and Getis-Ord Local Gi index (see Anselin 1995; Getis and Ord 1992), were used to measure and analyze the degree of dependency among spectral features of burned areas.