Gabriele Nolè

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

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.

Visual Impact Assessment in Urban Planning

Nearly half a century has passed since Lynch described visual quality of American cities. Although the issue of visual impact assessment in urban planning is not new, only few experiences exist considering visual aspect when realizing new development zones. Visual aspects are fundamental in urban planning, since each plan choice can generate manipulation or obstruction of urban elements, producing negative effects on the image of the city. Viewshed analysis can help to achieve a more objective and consequently more effective analysis of visual impacts. Traditional viewshed analyses (single, multiple and cumulative) do not show which target is visible from a certain cell. On this purpose, a new viewshed analysis has been developed, the Identifying Viewshed, which shows how many and which objects are visible in several areas. The implemented extension has been tested in three different contexts, Laurenzana and Venosa, in Basilicata Region, and Pisa, in Tuscany.

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.

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.

A Quantitative Prediction of Soil Consumption in Southern Italy

Landuse/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 negative impacts of soil consumption are well known, to our knowledge there are no case studies presenting a precise quantitative measurement of the intensity of such phenomenon for the region of interest. This study aims at forecasting the development of urban settlements through the application of the cellular automata model SLEUTH; the first region to be investigated has been the Municipality of Altamura (Apulia region, Italy). This area has been used as a pilot case study to explore many difficulties and advantages in applying such a methodology to the whole southern Italian region. The final goal was to frame and populate an atlas of soil consumption in southern Italy, which intends to offer useful support to sustainable planning and policies.

Establishing a Remote Sensing Science Center in Cyprus:First Year of Activities of ATHENA Project

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.

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.

A quantitative measure of habitat quality to support the implementation of sustainable urban planning measures

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.

Spatial Analysis for the Study of Environmental Settlement Patterns: The Archaeological Sites of the Santa Cruz Province

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.