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In this letter, we propose an approach based on the use of Sentinel-2 spectral indices and self-organizing map (SOM) to automatically map burned areas and burned severity. These analyses were performed on a test area in Chania, located in Crete, affected by a fire (around 200 ha) that occurred from July 13, 2018 to July 28, 2018. The investigated area is characterized by heterogeneous land cover types made up of natural and agricultural lands. To identify different levels of fire severity without using fixed thresholds, we applied SOM to the three spectral indices normalized difference vegetation index (NDVI), normalized burn ratio (NBR), and burned area index for sentinel (BAIS) used to enhance burned areas. This is a particular critical issue because fixed threshold values are generally not suitable for fragmented landscapes, vegetation types, and geographic regions different from those for which they were devised. To cope with this issue, the methodological approach herein proposed is based on three steps: 1) indices computation; 2) maps of the difference of the three indices computed using the data acquired from prefire and postfire occurrences; and 3) unsupervised classification obtained processing all the difference maps using the SOM. The obtained results were validated using an independent data set, which showed high correlation with satellite-based fire severity.

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo), then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.

Fragmentation is a disruption in the connectivity of landscapes. The aims of this paper are (i) to quantitatively assess the fragmentation rates in three landscape units located in a hydrocarbon basin, and (ii) to model their behavior between 2001 and 2013 using landscape metrics at different scales of resolution. The following metrics were selected using principal component analysis (PCA): The Clumpiness Index (CLUMPY), patch density (PD), perimeter-area fractal dimension (PAFRAC) and effective mesh size (MESH). Results from our investigations pointed out that hydrocarbon activity increased the fragmentation at the sites. In particular, the CLUMPY index increased in all three landscape units, the average of PD decreased from 60 to 14 patches per 100 hectares, whereas the mean of MESH was quite constant, however, due to oil production, it decreased mainly in the coastal valleys. Finally, the PAFRAC also decreased at sites with oil production, being more evident in the plateau and coastal canyons. As a whole, outputs from our analyses clearly pointed out that the monitoring of landscape fragmentation trends in arid and semi-arid zones can be successfully achieved using metrics derived from satellite spectral information. Fil: Buzzi, Mariana Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales y Ciencias de la Salud - Sede Puerto Madryn. Departamento de Biología y Ambiente; Argentina Fil: Rueter, Barbara Lisa. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales y Ciencias de la Salud - Sede Puerto Madryn. Departamento de Biología y Ambiente; Argentina Fil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina Fil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; Italia

Soil sealing is currently one of the most critical barriers to sustainable development, particularly in developing countries such as Egypt. Agriculture is a major component of the Egyptian economy and the country&rsquo, s main source of food security. Urbanization is devouring vast areas of agricultural land, and therefore, in the present study, urbanization was used to determine the degree of soil sealing in a region of Kafr El Sheikh Governorate, Egypt. In this work, remote sensing data were used to monitor changes in land use and land cover (LULC) between 1984 and 2016. A field survey and population data were also used in the analysis. Support vector machine (SVM) classification was used to produce LULC maps of the study area. An accuracy assessment was performed by calculating overall accuracy and individual kappa coefficients. Additionally, soil sealing was assessed using data from 1984 to 2016, and the potential expansion of soil sealing until 2048 was simulated using the cellular automata (CA)&ndash, Markov model. Our analysis showed that in the study area (i) about 90% of the soils had soil capability degrees between class II and class III, (ii) soil sealing was not uniformly distributed in the study area, (iii) between 1984 and 2016, the area of soil sealing in fertile soils due to urbanization increased by 19,500 hectares, and (iv) between 1984 and 2000, the urban area increased by around 29%, whereas between 2000 and 2010 it increased by around 43.6%. The results suggest that the magnitude of soil sealing is a good indicator of the soil loss rate and the potential for agricultural development in the Nile Delta. The model predicted that by 2048 an area of 32,290 hectares of agricultural soil will be lost to urbanization. This study indicates that the change of LULC has a negative impact on soil sealing. Between 2000 and 2010, the area of agricultural land decreased by 4%, despite an increase in land reclamation in the north of the study area. The amount of soil sealing was found to increase towards the southeast and northeast of the study area, except for the northern parts, where the amount of soil sealing increased towards the east. Our analyses and forecasts are useful for decision-makers responsible for soil-sealing mitigation strategies and soil-sealing protection plans in the Kafr El Sheikh Governorate, Egypt.

The study aims to clarify the relationship between soil organic carbon (SOC) and human activity under arid conditions, in the east area of the Nile Delta, Egypt. SOC is one of the critical factors in food production and plays an important role in the climate change because it affects the physio-chemical soil characteristics, plant growth, and contributes to sustainable development on global levels. For the purpose of our investigations, 120 soil samples (0&ndash, 30 cm) were collected throughout different land uses and soil types of the study area. Multiple linear regressions (MLR) were used to investigate the spatiotemporal relationship of SOC, soil characteristics, and environmental factors. Remote sensing data acquired from Landsat 5 TM in July 1995 and operational land imager (OLI) in July 2018 were used to model SOC pool. The results revealed significant variations of soil organic carbon pool (SOCP) among different soil textures and land-uses. Soil with high clay content revealed an increase in the percentage of soil organic carbon, and had mean SOCP of 6.08 &plusmn, 1.91 Mg C ha&minus, 1, followed by clay loams and loamy soils. The higher values of SOCP were observed in the northern regions of the study area. The phenomenon is associated with the expansion of the human activity of initiating fish ponds that reflected higher values of SOC that were related to the organic additions used as nutrients for fish. Nevertheless, the SOC values decreased in southeast of the study area with the decrease of soil moisture contents and the increase in the heavy texture profiles. As a whole, our findings pointed out that the human factor has had a significant impact on the variation of soil organic carbon values in the Eastern Nile Delta from 1995 to 2018. As land use changes from agricultural activity to fish ponds, the SOCP significantly increased. The agriculture land-use revealed higher SOCP with 60.77 Mg C ha&minus, 1 in clay soils followed by fish ponds with 53.43 Mg C ha&minus, 1. The results also showed a decrease in SOCP values due to an increasing in land surface temperature (LST) thus highlighting that influence of temperature and ambient soil conditions linked to land-use changes have a marked impact on surface SOCP and C sequestration.

In this letter, we performed investigations on the potentiality of the Sentinel-1, C-band synthetic-aperture radar (SAR), for the characterization and mapping of burned areas and fire severity. To this aim, we focused on fire occurred on July 13, 2017, in Metaponto (South of Italy). Both VH and VV polarizations were considered. Radar Burn Difference (RBD) and radar burn ratio (RBR) were computed between Sentinel-1 data acquired before and after the fire using both single- and time-averaged scenes (to reduce speckle noise effects). The most marked differences between burned and unburned areas were observed in the VH polarization of both RBD and RBR. The novelty of our approach is based on the use of three steps data processing devised to identify different levels of fire severity without using fixed thresholds. The burned areas are first: 1) highlighted using the ratio between multitemporal data set acquired before and after the fire occurrence; 2) further enhanced by Getis–Ord spatial statistic; and 3) finally, categorized using ISODATA unsupervised classification. The approach herein proposed pointed out that: 1) the time-averaged ratio of VH polarization of Sentinel-1 well perform in mapping burned area and 2) the use of Getis–Ord spatial statistic coupled with ISODATA unsupervised classification suitably captures the diverse levels of burned severity as confirmed by in situ assessment.

In this paper, we present and discuss the preliminary tools we devised for the automatic recognition of burnt areas and burn severity developed in the framework of the EU-funded SERV_FORFIRE project. The project is focused on the set up of operational services for fire monitoring and mitigation specifically devised for decision-makers and planning authorities. The main objectives of SERV_FORFIRE are: (i) to create a bridge between observations, model development, operational products, information translation and user uptake, and (ii) to contribute to creating an international collaborative community made up of researchers and decision-makers and planning authorities. For the purpose of this study, investigations into a fire burnt area were conducted in the south of Italy from a fire that occurred on 10 August 2017, affecting both the protected natural site of Pignola (Potenza, South of Italy) and agricultural lands. Sentinel 2 data were processed to identify and map different burnt areas and burn severity levels. Local Index for Statistical Analyses LISA were used to overcome the limits of fixed threshold values and to devise an automatic approach that is easier to re-apply to diverse ecosystems and geographic regions. The validation was assessed using 15 random plots selected from in situ analyses performed extensively in the investigated burnt area. The field survey showed a success rate of around 95%, whereas the commission and omission errors were around 3% of and 2%, respectively. Overall, our findings indicate that the use of Sentinel 2 data allows the development of standardized burn severity maps to evaluate fire effects and address post-fire management activities that support planning, decision-making, and mitigation strategies.

Since the mid-1980 s satellite remote sensing data have been used in forest fire monitoring for applications related to the diverse phases of fire management as, fire prevention, danger estimation, detection of active fires, estimation of fire effects (burned area mapping, fire severity estimation, smoke plumes, biomass losses, etc), post fire recovery, fire regime characterization, etc. Today satellite technologies can fruitfully support both research and operational activities for fire monitoring and management at different temporal and spatial scales and with cost effective tools. This paper provides a short overview of satellite remote sensing for forest fire danger estimation at different scale of observations.

This article is focused on the FIRESAT methodology developed for and funded by the Protezione Civile of the Basilicata Region. The fire monitoring system is based on satellite free of charge data and developed for the dynamic estimation of vegetation fire susceptibility and fire impact in forest and non-forestry ecosystems. The purpose of this fire risk system is the timely (daily) and detailed (from 1 km down to 30 m) monitoring of the vegetation and meteorological conditions which can affect the proneness of vegetation to fire. FIRESAT provides operational monitoring tools for a systematic forest fire management from risk estimation to fire severity mapping, including the estimation of fire damage on hydro-geological risk and vegetation fire resilience.

This chapters presents and discusses the results of investigations performed by ITACA Mission of CNR in the Nasca drainage basin, including the ceremonial center of Cahuachi, from 2007 to 2014. The employed approach was based on the multiscale and multisensor integration of remote-sensing methods, including geophysics . Most of the applications have been intended for preventive archaeology, in particular, for providing information on the presence of buried sites and structures by identifying different surface characteristics such as arid bare ground in the Pampas and vegetated areas in the river oases. The operational use of earth-observation technologies has been the occasion to develop ad hoc approaches to data acquisition, processing, and interpretation for the detection of earthen buried structures that is a crucial and challenging issue due to the subtle physical contrast between earthen remains and the surrounding subsoil. This made necessary amassing experience in, and the testing of, various techniques to investigate the subsoil by geophysical and remote methods, working side by side with archaeologists, involved in the interpretation of the results. The obtained results have been the discovery of previously unknown sites in the Nasca drainage basin, the identification of buried ritual offering, the characterization of the stepped structure of Templo Sur, and the mapping of areas of archaeological interest.

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 investigation of the temporal fluctuations in forest fires is an important topic in the context of environmental sciences. Time-scaling scale-invariant methodologies have been used to characterize the temporal properties of forest-fire sequences detected in the Italian territory. Our findings reveal that the fire sequences can be considered as fractal processes with a high degree of time-clusterization of the events. The time-clustering phenomenon is clearly visible from timescales of order of days. Furthermore the fire sequences observed with decreasing latitude tend to be more time-clusterized.

The fire-induced variability in vegetation dynamics was analyzed by means of a fluctuation analysis applied to a time series of satellite SPOT-VEGETATION normalized difference of vegetation index (NDVI) data from 1998 to 2003. We used the detrended fluctuation analysis (DFA), which allows capturing persistent behavior in nonstationary signals. We analyzed four vegetation sites, two affected and the other two not affected by fires. Our results point out that the persistence of vegetation dynamics is significantly increased by the occurrence of fires.

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).

Could the dynamics of vegetation covers be significantly influenced by fires? Is it possible to identify and quantify such effect? In order to detect fire-induced variability in vegetational dynamics, we performed a fluctuation analysis of time series of satellite SPOT-VEGETATION normalized difference of vegetation index (NDVI) data from 1998 to 2003 of two shrub-land test sites. We used the detrended fluctuation analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. We analyzed two shrub-land sites, one in “healthy conditions” (fire-unaffected) and the other in “ill conditions” (fire-affected). Our results point out to the active role played by fires in increasing the persistence of the vegetation dynamics.

Is it possible to quantitatively characterize the dynamic variability induced by fires on vegetation? To answer this question, we performed a fluctuation analysis of time series of satellite SPOT‐VEGETATION Normalized Difference Vegetation Index (NDVI) data from 1998 to 2003 in order to discriminate fire‐induced variability in the vegetation dynamics of forests in southern Italy. We used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in non‐stationary signal fluctuations. We analysed two forest sites, one fire‐unaffected and the other fire‐affected. Our findings suggest that fires play an important role in the temporal evolution of forests, increasing the persistence of the vegetation dynamics.

In this work, we discuss the use of principal component analysis (PCA) for evaluating the vegetation interannual anomalies. The analysis was preformed on a temporal series (1999–2002) of the yearly Maximum Value Composit of SPOT/VEGETATION NDVI acquired for Sicily Island. The PCA was used as a data transform to enhance regions of localized change in multi-temporal data sets. This is a direct result of the high correlation that exists among images for regions that do not change significantly and the relatively low correlation associated with regions that change substantially. Both naturally vegetated areas (forest, shrub-land, herbaceous cover) and agricultural lands have been investigated in order to extract the most prominent natural and/or man-induced alterations affecting vegetation behavior. Our findings suggest that PCA can provide valuable information for environmental management policies involving biodiversity preservation and rational exploitation of natural and agricultural resources.

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.

The potential of NOAA-advanced very high resolution radiometer (AVHRR) time series for environmental studies was investigated at the pixel scale. We analyzed a multitemporal set of annual maximum value composite (MVC) of normalized difference vegetation index. Local area coverage data of middle and southern Italy were processed from measurements taken between 1985 and 1999 of the afternoon viewing of NOAA 9, 11, and 14. Significant artificial anomalies were found due to satellite switch, short-wave calibration instability and illumination effects. We removed such systematic errors and achieved a strong reduction of standard deviation values (around 50%). Interesting results were obtained from a change detection analysis performed at the pixel level. Outcomes from satellite-based analysis were compared with independent time series data, such as ancillary meteorological data, forest fire archives, and results from field surveys. Results showed that the geographical areas where MVC data indicated a decrease in vegetation activity match well with areas affected by forest fires, intense human activity, or rapid decline of coniferous forests. In contrast, an increase in MVC was found in regions recently involved in a growth of intensive farming or invaded by alien plants which are now recognized as a threat to native species. Our exploratory results indicate that high-quality AVHRR data can profitably support studies on interannual dynamics of surface parameters. In particular, our parameterization of illumination and calibration adjustments can be directly applied to Mediterranean-like ecosystems.

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.

Multi-temporal series of satellite SPOT-VEGETATION normalized difference of vegetation index (NDVI) and normalized difference infrared index (NDII) data from 1998 to 2003 were exploited for studying persistence in mediterranean ecosystems of the Sardinia region (southern Italy). Three different vegetation covers (shrub-land, transitional and forest) were analysed. The NDVI and NDII provide information on the vegetation status and water content, respectively. The detrended fluctuation analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations, was applied to the NDVI and NDII time series for the three vegetation covers. Our findings point out that the investigated ecosystems are characterized by persistent behaviour of NDVI and NDII, which means that they are governed by positive feedback mechanisms, which tend to destabilize the systems under external forces. The NDII exhibits a more persistent behaviour than NDVI, and the shrub-land shows a more persistent feature than the other two cover types.

The dynamics of vegetation covers in burned and unburned areas can be monitored by using satellite data, which provide a wide spatial coverage and internal consistency of data sets. Several indices based on satellite data can be used for this aim. In particular, NDVI (Normalized Difference Vegetation Index) is the most widely used index for vegetation monitoring based on remote sensing. This paper aims to perform a dynamical characterization of burned and unburned vegetation covers, using time series of remotely sensed data of some fire-affected and fire-unaffected sites. For this purpose, we used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in nonstationary signals. Our results point out that the persistence of vegetation dynamics is significantly increased by the occurrence of fires.

A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up especially for large areas. The advent of satellite 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. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher than 78%.

Fuel types is 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. In the present study, forest fuel mapping is considered from a remote sensing perspective. The purpose is to delineate forest types by exploring the use of coarse resolution satellite remote sensing MODIS imagery. In order to ascertain how well MODIS data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers and complex topography was analysed. The study area is located in the South of Italy. Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing MODIS data, were used as ground-truth dataset to assess the obtained results. 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 two different approach, maximum likelihood (ML) classification algorithm and spectral Mixture Analysis (MTMF); (III) accuracy assessment for the performance evaluation based on the comparison of MODIS-based results with ground-truth. Results from our analyses showed that the use of remotely sensed MODIS data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 73% for ML classifier and higher than 83% for MTMF.

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- Temathic Mapper (TM) acquired in 1998 were analysed for a test area of Southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, were used to assess the results obtained for the considered test areas.. 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; (III) accuracy assessment for the performance evaluation based on the comparison of satellite-based results with ground-truth. Two different approaches have been adopted for fuel type mapping: the well-established classification techniques and spectral mixture analysis. Results from preliminary analysis have showed that the use of unmixing techniques allows an increase in accuracy at around 7% compared to the accuracy level obtained by applying a widely used classification algorithm.

In the Mediterranean regions, fires are considered a major cause of land degradation. Every year, around 45,000 forest fires break out in the Mediterranean basin causing the destruction of about 2.6 million hectares ( FAO, 2001 ). In Italy, as in other countries of the Mediterranean Basin, a small number of fires generally destroy a large percentage of the total burned areas every year. In these cases, the use of coarse resolution satellite sensors appears to be very useful for the discrimination of burned areas. In this study, SPOT-Vegetation (SPOT-VGT) data at full spatial resolution were analysed in order to investigate the spectral features of burned areas observed in the Mediterranean ecosystems in the Calabria Region during the 1998 fire season. Among the total fire events occurred in the considered period wildland fires larger than 1000 ha were selected for this study. SPOT-VGT imagery acquired before and after fire events were considered. Single channels or spectral indices suitable/or specifically designed for burned areas mapping were analysed. In particular near-infrared (NIR), short-wave infrared reflectance (SWIR), albedo, normalized difference of vegetation index (NDVI), normalized difference of infrared index (NDII), burned area index (BAI), global environmental monitoring index (GEMI) and soil adjusted vegetation index (SAVI), were considered in this study. The changes observed before and after fire occurrence in the considered parameters were presented and discussed. Results showed that among the spectral indices considered in this work, the highest discrimination capability was generally observed for NDII, SAVI, GEMI, BAI and NIR, nevertheless, strongly differences were observed from one fire event to another, and this fact suggests that the discrimination capability must be analysed coupled with the specific land covers affected by fire.

[1] Pre- and post-fire vegetational dynamical behaviours in a forest site of Northern Italy were investigated, using the 1998 to 2003 time series of Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION sensor. The detrended fluctuation analysis (DFA) was adopted to detect persistent behaviour, in order to characterize the stability/instability properties of vegetation dynamics. Our analysis points to the following results: i) both pre- and post-fire vegetation dynamics are persistent; ii) post-fire dynamical behaviour is characterized by a larger degree of persistence; iii) a link between persistence and resilience can be identified.

Multi-temporal series of satellite SPOT-VEGETATION Normalized Difference of Vegetation Index (NDVI) data from 1998 to 2003 were exploited for studying persistence in Mediterranean ecosystems of southern Italy. We used Multiple Segmenting Method (MSM), which is well suited to analyze scaling behaviour in short time series, and the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. Our findings point out to the characterization of Mediterranean ecosystems as governed by persistent mechanisms.

[1] To detect fire-induced variability in vegetational dynamics, a time series 1998 to 2003 of Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION sensor was analyzed for burned and unburned test sites located in the Italian Peninsula. The statistical analysis was performed by using the detrended fluctuation analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. A comparison of results obtained for sites in “healthy condition ” (fire un-unaffected) and in “ill condition” (fire affected) showed that fires contribute in increasing the persistence of time dynamics of vegetation. This result indicates that fires drive unstable patterns in vegetational dynamics of burned areas. The adopted methodology could be fruitfully applied to investigate other types of vegetational stresses.

SPOT-Vegetation (SPOT-VGT) data at full spatial resolution were used in order to assess the potentiality and feasibility of using low resolution optical satellite data for the estimation of time/space dynamics of surface moisture content. The seasonal trend of diverse parameters (single channels or spectral indices) suitable/or specifically designed for moisture estimation have been analyzed in some test sites of southern Italy. The investigations were performed by using imagery selected from a long time series of ten-day compositions of VGT data acquired from 1998 to 2002. The preliminary results indicate that the use of the VGT data for obtaining information on the time/space dynamics of surface moisture content can be useful.

Over the years, several satellite-based indicators have been developed for Fire Susceptibility Estimation (FSE) most of which are based on AVHRR data. Unfortunately, these indicators yield different results when applied to different ecosystems or geographic regions and this creates confusion concerning their effectiveness. This work aims at evaluating the performance of the existing AVHRR-based FSE methods. Such evaluation was performed in the Basilicata and Calabria Region by using NOAA-12,-14 summery imagery selected from a long time series acquired from 1995 to 1999. Fire susceptibility maps obtained from the considered methods were compared to fire archives provided by the Italian National Forestry Service. The most satisfactory results were obtained from methods based on the cross analyses of NDVI (Normalized Difference Vegetation Index) with thermal channels.

[1] The effects of solar zenith angle (SZA) changes, short-wave calibration instability, and satellite switches on the interannual vegetation variability inferred from 1-km advanced very high resolution radiometer–normalized difference vegetation index (AVHRR-NDVI) were evaluated. The annual maximum value composite (MVC) of NDVI over middle and southern Italy was processed from afternoon viewings of NOAA 9, 11, and 14 for the period 1985–1999. MVCs were computed from imagery relative to the period of maximum photosynthetic activity. Raw data were compared with data corrected according to a procedure we recently developed and validated. Our results show that the SZA changes cause an error that increases as the NDVI value decreases. The maximum magnitude is 6.2% for forests and up to 41.8% for lakes. Calibration and intercalibration anomalies modify NOAA 11 NDVI values of 7–8% for all classes except for lakes and artificial surfaces, which show a variation of almost 11–12%; the NOAA 14 NDVI anomalies are set on about 11% with the exception of artificial surfaces (∼17%) and sparsely vegetated areas (∼9%). We also evaluated the improvement obtained from the use of the last update of calibration coefficients of NOAA 14. The positive impact of these coefficients is apparent for all the considered land covers, but a persistent overestimation of NOAA 14 calibration coefficients was still found. Altogether, the artificial anomalies can induce a relative error on NDVI variations up to 130%. This result strongly points out the relevance of performing an accurate correction of systematic errors and a careful validation before drawing conclusions from multitemporal AVHRR-NDVI analyses focused on the detection of long-term vegetation changes.