Antonio Lanorte

Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis

Abstract 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 Morans 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.

Multiscale fuel type mapping in fragmented ecosystems: preliminary results from hyperspectral MIVIS and multispectral Landsat TM data

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.

Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

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.

Characterization and Mapping of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in Southern Italy by Using Hyperspectral MIVIS Data

Abstract 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 prefire planning. This research aims to investigate the usefulness of hyperspectral data to recognize and map fuel types in order to ascertain how well remote sensing data can provide an exhaustive classification of fuel properties. For this purpose airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired in November 1998 have been analyzed for a test area of 60 km2 selected inside Pollino National Park in the south of Italy. Fieldwork fuel-type recognitions, performed at the same time as remote sensing data acquisition, were used as a ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: 1) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and ...

Dynamic Fire Danger Mapping from Satellite Imagery and Meteorological Forecast Data

Abstract 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 eco...

Agricultural plastic waste spatial estimation by Landsat 8 satellite images

Abstract 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.

Using spatial autocorrelation techniques and multi-temporal satellite data for analyzing urban sprawl

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.

Satellite time-series analysis

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, spectral and spatial capabilities of the systems. These improvements have led to the possibility of using satellite data for studying the temporal evolution of environmental and man-made systems on short, medium or long timescales. Therefore, it seemed appropriate to collect together in a special issue a set of articles related to the analysis of long-term archives of satellite data. In the past two decades, there has been a large increase in the use of Earth observation (EO) technologies in various applications for several reasons: (i) technological improvements of satellite sensors; (ii) the availability of user-friendly software and routines for data processing and analysis; (iii) the increasing interest in studying the dynamics of environmental changes; (iv) the large use of multitemporal satellite data in an ever-increasing number of strategic and challenging applications; and (v) the availability, free of charge, of long-term satellite time series. Following the 50th anniversary of the launch of Sputnik in 1957 (Cracknell and Varotsos 2007a,b) and NASA’s 50th anniversary, several US space programmes have been celebrating their own anniversaries. The US geostationary meteorological satellite/geostationary operational environmental satellite (GOES) series has celebrated its 35th anniversary and the TIROS-N mission (1978) with the Advanced Very High Resolution Radiometer (AVHRR) on board each successive NOAA meteorological polar satellite has celebrated its 30th anniversary. In addition to TIROS-N, 1978 was a key year for extending EO applications to the marine environment to assess the ocean colour (Nimbus-7 Coastal Zone Color Scanner (CZCS)), sea surface elevation and sea state (Seasat altimeter, scatterometer and synthetic aperture radar (SAR)) and the atmospheric ozone (Nimbus-7 Total Ozone Mapping Spectrometer (TOMS)). The Landsat programme will shortly celebrate its 40th anniversary, while the programme’s workhorse, Landsat-5, recently achieved a remarkable 25th year in space. The first Landsat satellite was launched in 1972, under its original name, the ‘Earth Resources Technology Satellite’ (ERTS); it carried the multispectral scanner (MSS), which generated 80 m resolution data and marked a new era for civilian remotesensing applications. Later, in the 1980s, the Thematic Mapper (TM) was launched, offering the highest (30 m) spatial resolution sensor available at that time for civilian applications. TM data has been successfully used for a number of environmental studies, focusing mainly on land-use, land-cover and environmental changes. The subsequent availability of the 10 m resolution imagery from the French SPOT satellites

Investigating dynamical trends in burned and unburned vegetation covers using SPOT-VGT NDVI data

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.

On the Use of the Principal Component Analysis PCA for Evaluating Vegetation Anomalies from LANDSAT-TM NDVI Temporal Series in the Basilicata Region Italy

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.