Search

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.

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.

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.

Italy is a geologically complex and relatively young territory that is particularly vulnerable to natural disasters (i.e., earthquakes, flooding, landslides). In particular, landslide activity is considered one of the most relevant processes shaping the Italian peninsula, often closely affecting urban and industrial areas and infrastructures. Landslide activity in southern Italy strongly influenced the development of urban areas by controlling the shape and the areal extend of the urban settlements. In this paper, we propose a multidisciplinary approach capable of assessing the correlation between landslides and the spatial distribution of urban fabric. To this aim, we consider as case studies, the two municipalities of Pisticci and Corleto Perticara, in the Basilicata region in southern Italy, that strongly differ for the geological contexts and landslide types. The evolution in time of the landslide activity and the shape variations of the two considered settlement systems is described in terms of shape index from 1950 to 2006. Starting from the description of the two study areas, from both the geological and the urban point of view, the shape index will be calculated. Eventually, the results obtained will be compared with the perimeters of the urban centers defined by the respective urban planning regulations.

Urban sprawl phenomenon is one of the higher threats to the preservation of soil resources. Soil consumption is strongly related to social and economic factors that characterize a territory. Several models have been developed to recreate future scenarios based on past expansion development dynamics. Among cellular automata models for urban growth simulation, SLEUTH is considered as an effective tool to support urban management. However, the model does not explicitly include demographic dynamics and socio-economic ones. This paper compares the results of two different simulations performed on the same study area through the SLEUTH model. While the first simulation is performed using the classical method of calibration of the model, the second one proposes the inclusion of some socio-economic variables within the simulation process. The results show a better match with the actual development trends of settlement by the simulation that takes into account the social and economic aspects of the analysed territory.

The increasing accessibility of Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) data, grants the opportunity to experiment new methods to support disaster risk management. However, while SAR analyses are becoming extremely popular, thanks, in particular, to the availability of open source satellite images such as those from the Copernicus project, LiDAR analyses are still less common because of the scarce availability of this type of data over significant time frequencies. In this paper we propose an innovative procedure based on the use of SAR and LiDAR data to rapidly assess seismic damage in the early post-emergency phases. The methodology was applied to the case study of the town of Amatrice (Central Italy), which was hit by a strong earthquake swarm that started in August 2016. Specifically, SAR data is used for a large-scale analysis of terrain displacements following the seismic event, while LiDAR reliefs are used to carry out a change detection and to identify the level of damage at a building-scale in the urban settlement of Amatrice. Results will show how the proposed approach can be extremely effective both in the non-emergency phases to monitor seismic-affected areas and support emergency planning, as well as during the immediate post-earthquake phases to assess the damage it has caused and to support first aid dispositions.

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.

Recent Italian earthquakes have been highlighted the role of the historical centers and their high vulnerability. To define effective mitigation strategies, the historical environments must be accurately investigated. The first and fundamental step is an assessment based on the knowledge of dimensional characteristics, construction techniques and materials. The seismic vulnerability evaluation depends on the quality and completeness of information available for the buildings under examination. In this paper, the characterization of an historical center and its buildings has been treated considering a new approach and existing common techniques for assessment of buildings' dimensional characteristics. Using GIS, data collected on the investigated historical center has been analyzed to obtain the typological characterization of the surveyed buildings in order to evaluate the seismic vulnerability, urban resilience, and recovery strategies for historical center. Based on comparison with an existing classical procedure, the approach seems also very promising in order to carry out a first evaluation of post seismic damage.

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.

A limited sector of the left side Sinni River, including Episcopia village, has been investigated matching geological and geomorphological data to In-SAR Interferometry analysis. A large Deep-Seated Gravitational Slopes (DSGSD), affecting the Episcopia slope, has been documented for the first time. The DSGSD largely develops within phyllites belonging to the Liguride Units and, in the upper portion of the slope, within the sandy and conglomerate deposits of the Sant’Arcangelo Basin. Field survey has shown typical DSGSD features as well as trenches at the top and an evident bulge at the base corresponding to the Sinni River. These data allow us to hypothesize a listric geometry of the DSGSD plane reaching a depth of about 700 m.

Landscape preservation in Italy is a major issue in national cultural heritage conservation policies. Urban settlements growth is among the most threatening factors for the correct landscape preservation. Such phenomenon may result in corrupting the correct landscape-system functioning, particularly when the development occurs without precise planning prescriptions. Land-use/cover evolution dynamic is a subject widely and thoroughly investigated, especially concerning consumption of natural and other lands due to anthropogenic activities. This paper focuses on a region in southern Italy, where soil consumption is known to represent a urging matter of concern. However, although the negative impacts of soil consumption are well known, to our knowledge there are no case studies presenting a precise quantitative assessment of the intensity of such phenomenon for the region of interest. Furthermore, this study aims at forecasting the development of urban settlements through the application of the cellular automata model SLEUTH; the case study concerns the Municipality of Altamura (Apulia region, Italy). Results highlight how current landscape preservation instruments alone cannot ensure a reduction in soil consumption phenomenon and how urban areas expansion is incompatible with a correct landscape conservation in the study area.

The spread of new satellite and LiDAR data is recently leading to the development of effective methodologies to support the monitoring and management of disaster risks, assessing the level of damages in the very early post-event phase. The increasing availability of SAR images and the diffusion of LiDAR data due to technologies such as solutions such as drones offers the opportunity to experiment new techniques for monitoring the territory. The paper will examine the case study of Amatrice (Central Italy), the Municipality most affected by the seismic swarm started in August 2016, and discuss the results obtained with the technique of interferometric differentiation and detection of change.

The 2030 Agenda by United Nations highlights the necessity of undertake concrete actions to “protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss”. However, human activities on land use are strongly threatening habitat quality, causing their fragmentation and a dramatic loss of biodiversity all over the world. This paper proposes an application of the InVEST Habitat Quality model as a tool to support the definition of sustainable development policies able to favour the preservation of habitat structures while promoting their exploitation as cultural and landscape assets. The model is applied to the Basilicata Region (Southern Italy). Results show how modelling the impacts of human activities on biodiversity and ecosystem services can strongly help planning activities in distinguish those areas that should undergo to a conservation regime to preserve habitat integrity from those which are most prone to transformations, taking advantage by the social and economic benefit deriving from the human activities connected to their use.

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.

Among the objectives of the Sustainable Development Goals by United Nations, "Affordable and Clean Energy" aims at ensuring access to affordable, reliable, sustainable and modern energy for all. However, in Europe there is not a precise understanding of the unleashed potential that could be achieved through the exploitation of solar and wind resources. This study presents an application to retrieve spatial explicit estimates of Direct Normal Irradiance (DNI) through the use of data from geo-stationary satellites. The energetic demand of large metropolitan areas in Europe is then retrieved and compared with the potential production of energy for domestic use through solar panels. Results of this comparison are presented based on the assumption that only the 1 % of the built up area could be covered with solar panels, and hence devoted to energy production. Outcomes suggest that even such a little coverage, if spread systematically over urban areas can in most of the cases satisfy urban population domestic needs.

The investigations conducted previously on the Nasca puquios, with particular reference to those systematically performed by Schreiber and Lancho Rojas (1995), Schreiber and Lancho Rojas (Los puquios de Nasca: un sistema de galerias filtrantes. Editorial Los Pinos, Lima, 1998), Schreiber and Lancho Rojas (Aguas en el Desierto. Los puquios de Nasca. Fondo Editorial, Lima, Peru, 2006), have had the merit of placing the theme of Nasca water management in the center of debate on the ancient Nasca world. Water has become the crossroads of all the other research lines focused on the influence of environment and climate on the Nasca culture, as well as on the geoglyphs, ceremonial architecture, and settlements. In this cultural framework, some studies based on multitemporal, multiscale, and multisensor analyses have been performed in the Rio Grande de Nasca drainage in order to investigate the interaction between the puquios and environmental conditions, as well as to improve knowledge of the local environmental settings. Herein we present and discuss the investigations, based on the integration of remote sensing, GIS -based analyses, and geophysical prospecting, in the framework of the ITACA mission of the Italian CNR. Outputs from satellite-based investigations, along with all the information already available from previous studies, were the input data used in the statistical spatial analyses and geophysical prospecting. The main findings provided information useful to: (i) improve the current knowledge on the puquios at both intra-site and inter-site levels and their relationship with environmental and human settings, (ii) identify unknown puquios, (iii) and support planning for subsequent archaeological excavations. As a whole, the investigations based on the adopted integrated approach provided new insights into this hyper-arid area which has been the homeland for numerous civilizations, despite the extreme physical environment that presents serious obstacles to human occupation.

ATHENA H2020 Twinning project is a three-year duration project and its main objective is to strengthen the Cyprus University of Technology (CUT) Remote Sensing Science and Geo-Environment Research Laboratory in the field of “Remote Sensing Archaeology” by creating a unique link between two internationally-leading research institutions: National Research Council of Italy (CNR) and the German Aerospace Centre (DLR). Through the ATHENA project, CUT’s staff research profile and expertise will be raised while S&T capacity of the linked institutions will be enhanced. In this paper the abovementioned objectives are presented through the various activities accomplished in the first year of the project. These activities include both virtual training by experts in topics such as active remote sensing sensors and sophisticated algorithms, as well as scientific workshops dedicated to specific earth observation and cultural heritage aspects. During this first year, outreached activities have been also performed aiming to promote remote sensing and other non-destructive techniques, including geophysics, for monitoring and safeguarding archaeological heritage of Cyprus. The ATHENA center aims to serve the local community of Cyprus, but at the same time to be established in the wider area of eastern Mediterranean.

The aim of this work is to use spatial analysis to study relationships between environmental parameters and archaeological sites in the Santa Cruz province (Patagonia, Argentina) and to develop an analysis protocol that could reveal including and excluding factors, useful for the research and the discovery of new archaeological sites on the territory. Consequently, a model that considers interactions between sites and some parameters such as the elevation, the slope, the aspect, the landforms, the land use and the distances from water was constructed. Moreover a final sensibility map was produced, that wants to help the archaeologist to know, in every point in the space, how many and which are parameters that could increase the probability to find new archaeological sites and that are survey priorities on the study region. Fil: Danese, M.. Consiglio Nazionale delle Ricerche; Italia Fil: Cassiodoro, Gisela Eva. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina Fil: Guichon, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina Fil: Goñi, Rafael. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina Fil: Masini, Nicola. Consiglio Nazionale delle Ricerche; Italia Fil: Nolé, Gabriele. Consiglio Nazionale delle Ricerche; Italia Fil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; Italia

The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985–2010, based on the multi-temporal Landsat data Thematic Mapper (TM), has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy). Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

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).

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.

Location of a wind farm needs a visual impact assessment procedure considering different points of view, such as natural-environmental sides and landscape aspects. About the first point, used evaluation approaches are nearly consolidated in literature. Instead, debate is still open on what concerns the second point, especially on establishing an as objective and effective as possible method. Some instruments already exist, such as viewshed analysis (single, multiple, cumulative, identifying), allowing to follow step by step visual evaluation by supporting wind farm location process from site choice to single turbine location. Consequently, this paper aims to implement an open source model to identify a wind farm in Laurenzana municipality (Basilicata, southern Italy).