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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.

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.