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.
During a project meeting in Sofia on September 21, 2016, Cerved teamed up with TRAGSA to brainstorm ideas of re-using the TRAGSA methods for processing satellite imagery to analyse green areas in urbanized cities.
Fundamentals of Tragsa Processing
A common feature in Vegetation Spectra is the high contrast observed between the red band and the Near Infrared (NIR) region. The optical instrument carried by Sentinel 2 satellites samples 13 spectral bands, including high resolution bands in the red (bands 4, 5 & 6) as well as bands in the NIR (8 & 8A). Refer to this blog post for more details about processing Sentinel 2 data.
Using the TRAGSA methodology it is possible to isolate and enhance the vegetation, to locate green areas in urban areas. Green areas are important input to the Cerved’s innovative real estate evaluation model (which is being developed within one of the Cerved’s business cases in the project, as introduced in this blog post). Cerved uses open data, to generate indicators of green areas defined for the model: green area coverage and distance to the wood. Operations that Cerved performs to compute these indicators are similar to those that TRAGSA does on satellite data, such as clustering of green areas into big areas and isolating trees and group of trees. This motivated us to experiment with satellite data and TRAGSA’s methodology, to see whether we could potentially use more complete, structured and up-to-date source of green areas information as input to our real estate evaluation model.
We identified a highly urbanized Italian city but with particular attention to green areas, which is the city of Turin.
The steps that we followed:
- extraction of city boundaries of Turin in GeoJSON format by SPAZIODATI
- selections of good quality imagery for Turin from the Sentinel data repository by TRAGSA
- processing S2 imagery in order to get a vector layer which indicates the presence or absence of a green area in each pixel (1/0) by TRAGSA
- display of the green areas of the tiles (see the screenshot below) prototype Amerigo visualisation service, under development by SPAZIODATI
- data processing and aggregation of the tiles into census cells areas, in order to develop green areas indicators for each census cell, by CERVED
- integration and testing of the score dedicated to green areas within the business model CCRS (Cerved Cadastral Report Service) by CERVED
The result of this experiment was extremely surprising; the detail and accuracy of this new score in identifying the green areas (not only public green areas) is far greater than accuracy of the other scores, developed on public and open green areas of datasets.