New proDataMarket paper: Combining Sentinel-2 and LiDAR data for objective and automated identification of agricultural parcel features

Combining Sentinel-2 and LiDAR data for objective and automated identification of agricultural parcel features by Jesús Estrada, Héctor Sanchez, Lorena Hernanz, María José Checa and Dumitru Roman

This new proDataMarket paper explains how a comprehensive strategy combining remote sensing and field data can be helpful for more effective agriculture management. Satellite data are suitable for monitoring large areas over time, while LiDAR provides specific and accurate data on height and relief. Both types of data can be used for calibration and validation purposes, avoiding field visits and saving useful resources. In this paper we propose a process for objective and automated identification of agricultural parcel features based on processing and combining Sentinel2 data (to sense different types of irrigation patterns) and LiDAR data (to detect landscape elements). The proposed process was validated in several use cases in Spain, yielding high accuracy rates in the identification of parcel features. An important application example of the work reported in this paper is the European Union (EU) Common Agriculture Policy (CAP) funds assignment service, which would significantly benefit from a more objective and automated process for identification of agricultural parcel features, thereby enabling the possibility for the EU to save significant amounts of money yearly.

Although some issues regarding the generation and improvement of agricultural property datasets were already explained in our previous blog entry (Data workflow in CAPAS), this paper highlights the current results of generation and usage of this new information.

Irrigation patterns map, obtained using Sentinel-2 Process 

The main result of this analysis is how the use of the external, and usually underused, data sources offers a powerful and accurate tool for generating new contrast and validation data for the information used by Spanish CAP Payment Agency, in order to provide a better service to landowners and farmers. As a conclusion, the use of Sentinel-2 series and LiDAR can help to detect areas that are not eligible for grant assignment, support cross-check, and these datasets can be used as a tool for choosing field samples.

The document is available Here.

New Paper: Understanding territorial distribution of Properties of Managers and Shareholders: a Data-driven Approach

Understanding territorial distribution of Properties of Managers and Shareholders: a Data-driven Approach by S. Pozzati, D. Sanvito, C. Castelli, D. Roman, Territorio Italia 2 (2016), DOI: 10.14609/Ti_2_16_2e.

  • Abstract: The analysis and better understanding of the distribution of wealth of individuals in cities can be a precious tool, especially in support of the estimation of real estate values. These analyses can also be used to facilitate decision making in various sectors, such as public administration or the real estate market. In this paper, by making use of publicly available data and of data owned by Cerved, (a credit scoring company in Italy), we can observe the territorial distribution of the properties of managers and shareholders – categories of people usually linked to high economic well-being – and, based on that, we identify the areas of the cities where the value of real estate properties is presumably higher. More specifically, we introduce the Manager and Shareholder Concentration (MSHC) score and validate its accuracy and effectiveness in three Italian cities (Turin, Rome and Milan).
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