Applicability limits of Sentinel-2 data compared to higher resolution imagery for CAP checks by monitoring

The Common Agricultural Policy (CAP) ‘checks by monitoring’, replacing the on-the-spot-checks presently used to verify that the area-based direct aid is granted correctly to EU farmers, can be introduced already as of crop campaign 2019. In fact, according to the recently adopted Article 40a of the implementing regulation (EU) 746/2018 of 18 May 2018 amending the Implementing Regulation (EU) No. 809/2014, several MS Regions are, opting to introduce an agricultural aid check system based on monitoring. Such checks rely on automatic methods to observe, track and assess the CAP eligibility criteria, commitments and obligations. Regular and systematic observations are carried out using the Copernicus Sentinel imagery or equivalent, making use of automatic machine learning techniques coupled with an efficient handling of farmer aid applications. In the case where the spatial resolution of above mentioned imagery is not sufficient to conclude on the support (eligibility, holding compliance), the competent authority must undertake appropriate ‘follow up activity’. This can be in form of efficient interaction with the beneficiaries, or for example by making use of ‘time stacks’ of information derived from a higher resolution image source (i.e. High High Resolution- HHR- satellite imagery with a ground sampling distance approximately two or more times better than the Sentinel-2). Before introducing such HHR approach, it is supposed that the MS has run through the so-called ‘sifting” preparatory operation. At the end of such iterative process, the set of “small” parcels for which alternative check methods should be made will be known. The question is to understand when the HHR use is effective (i.e. adequate to accomplish its purpose), and therefore really gives an enhanced information, superior to that extracted from the coarser resolution imagery.JRC.D.5 - Food Securit

Second discussion document on the introduction of monitoring to substitute OTSC: rules for processing application in 2018-2019

This document describes the main concepts and components of the 'checks by monitoring’. It elaborates and details the discussion document on monitoring as a substitute of the current sample approach (on the spot checks) of aid applications or payment claims. This 2nd discussion document elaborates on the rationale, concepts and procedures that form the heart of the monitoring approach, a move from a sequential compliance control followed by penalties towards a continuous monitoring that informs proactively when lodging claims and sends out warning alerts to prevent unintended non-compliances. This agricultural parcel (AP) monitoring takes advantage of the substantial modification of the control framework, most notably the timing of changes to the application and content of the control file.JRC.D.5 - Food Securit

Geodata and technologies for a greener agriculture in Europe

In recent years GTCAP’s research and development work principally focused on 1) Promote the checks by monitoring approach as a key control system for paying agencies and 2) Make better use of new technologies for monitoring environmental and climate requirements. This report compiles the findings and other outcomes of the GTCAP activities on the CAP Green Infrastructure aspects. The GTCAP’s green Infrastructure work focused on activities exploring the nexus of land and environment and employed cutting-edge technology more effectively for monitoring environmental and climate requirements. The primary focus lay on farming practices that contribute to reaching climate and environmental goal. The work carried out has focused on the identification of the elements of these practices that should be extracted and documented to allow monitoring them. Standardization of the land cover/land use semantics and classification systems and elaborating the link with the visible biophysical phenomena are another essential part of the work done in the last two years. The conceptual framework and approaches elaborated are applied and discussed in four case studies implemented in the last two years and described in this report.JRC.D.5 - Food Securit

An improved workflow for image- and laser-based virtual geological outcrop modelling

Photorealistic 3D models, representing an object’s surface geometry textured with conventional photography, are used for visualization, interpretation and spatial measurement in many disparate fields, such as cultural heritage, archaeology and throughout the earth sciences, including geology. Virtual models of geological outcrops allow for large quantities of geometric data, such as sizes of features, thicknesses of strata, or surface orientations to be extracted in relatively short time and in areas with difficult accessibility. However, standard analysis is limited to interpretation of the three standard spectral bands (red, green, blue; RGB) acquired in the visible spectrum by the conventional digital camera. Complementing the photorealistic 3D outcrop models with auxiliary spectral data, for example in the form of hyperspectral imagery, can provide domain experts with additional geochemical information, adding great potential to studies of mineralogy and lithology. The existing workflows for creation of photorealistic outcrop models and integration with terrestrial panoramic hyperspectral data are complex and require specific knowledge from the field of geomatics. One such processing step is selection of images taking part in the texture mapping process. Although automated texture mapping measures are available, in highly redundant image sets they do not necessarily provide the best results when using all available photos. Therefore selection of the most suitable texture candidates is required to increase the realism of the textured models and the processing efficiency. Especially for large models of rugged terrain, represented by millions of triangles, manual selection of the best texture candidates can be challenging, because the user must account for occlusions and ensure that image overlap is sufficient to cover relevant model triangles. The existing workflow for integration of hyperspectral and 3D data also requires specific skills in geomatics as homologous points between the two datasets need to be manually selected for registration. Finding such correspondences involves interpretation of data acquired with different sensors, in different parts of the electromagnetic spectrum, projections and resolutions. The need to complete such challenging data processing steps by users from outside the geomatics domain poses a serious obstacle to these methods becoming standardised across geological research and industry. The research presented in this thesis addressed the two aforementioned limitations in the data processing workflows with an aim to make the method more accessible for users from outside of the geomatics domain. Firstly, a new interactive framework was developed, that provides analytical and graphical assistance in selection of an image subset for geometrically optimised texturing in photorealistic 3D models. Visualisation of spatial relationships between different components of the datasets was used to support the user’s decision in tasks requiring specific technical background. Novel texture quality measures were proposed and new automatic image sorting procedures, originating in computer vision and information theory, were implemented and tested. The image subsets provided by the automatic procedures were compared to manually selected sets and their suitability for 3D model texturing was assessed. Results indicated that the automatic sorting algorithms can be a valid alternative to manual methods. The resulting textured models were of comparable quality and completeness, and the time spent in time-consuming reprocessing was reduced. Anecdotal evidence indicated an increased user confidence in the final textured model quality and completeness. Secondly, a method for semi-automatic registration of terrestrial hyperspectral imagery with laser and image data was developed. The proposed data integration procedure employed the Scale Invariant Feature Transform (SIFT) to automatically find homologous points between digital RGB images registered in the scanner coordinate system and short wave infrared cylindrical hyperspectral data. The need for large numbers of homologous points to be matched required optimisation of the SIFT operator, as well as a routine for eliminating false matches. The proposed method automatically provides the control points that are used for registering the hyperspectral imagery. The results obtained on two datasets with different characteristics indicated that the proposed method can be used as an alternative to manual data integration, saving time and minimizing user input during processing. The increased automation of the workflows for creation of photorealistic outcrop models and integration with auxiliary image data, complemented with computer assistance to support users’ decision in the processing steps requiring background in geomatics, facilitate adoption of such techniques in wider community

Optimizing the Use of Digital Airborne Images for 2.5D Visualization

Monoscopic virtual representations of 3D geometries are rapidly becoming important products of many databases and software applications. Many GIS tools � even freeware, such as Google Earth � permit the visualization of city planning models as well as landscapes derived from 3D geometries (digital surface models draped with imagery, called 2.5D visualization). These applica-tions also are steadily becoming less qualitative, and more metric, as they are integrated into GIS environments. Up until now, such image rendering has usually been made with non-photogrammetric sensors, and has not been based upon state-of-the-art air survey systems. In the photogrammetry domain, the orthogonally projected image remains the paradigm. This approach however neglects imagery that may better represent the surfaces of objects such as building fa-cades. We propose that off-nadir parts of vertical imagery � typically ignored after the orthorectifi-cation process � provide us systematically with much data that can be used to optimize the 2.5D rendering processJRC.G.3 - Agricultur

Validation and certification of the area measurments systems in the light of the common agricultural policy

Since the beginning of 2008, Members States are obliged by Article 30(1) of Commission Regulation (EC) No 796/2004 to provide proof of quality of the tools/methods used in the annual control process of the area based subsidies. Using reliable equipment giving reproducible results within defined statistically predictable limits is of benefit for all the stakeholders: farmers, national administration and the European Commission. In order to evaluate reliability and precision of GNSS receivers in area measurements, tests should be made under different conditions, using the same settings and the method of measurements as used in the control process. In order to guaranty a standardised approach in these tests, measurement validation scheme, based on the ISO 5725 norm, has been designed. Member States can assure the quality of the area measurements required by the Regulation by using one of the two ways: validation tests or by buying certified instruments. The principles of the validation and certification processes of the area measurements systems in the light of the Common Agricultural Policy are presented in this paper.JRC.DG.G.3-Monitoring agricultural resource

Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images

The scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in visible light imagery, using the default input parameters does not yield satisfactory results when matching imagery acquired at non-overlapping wavelengths. In this paper, optimization of the SIFT parameters for matching multi-wavelength image sets is documented. In order to integrate hyperspectral panoramic images with reference imagery and 3D data, corresponding points were required between visible light and short wave infrared images, each acquired from a slightly different position and with different resolutions and geometric projections. The default SIFT parameters resulted in too few points being found, requiring the influence of five key parameters on the number of matched points to be explored using statistical techniques. Results are discussed for two geological datasets. Using the SIFT operator with optimized parameters and an additional outlier elimination method, allowed between four and 22 times more homologous points to be found with improved image point distributions, than using the default parameter values recommended in the literature

Enabling spatial data interoperability though the use of a semantic meta model - the peatland example from JRC SEPLA project

Numerous geographic data on peatland exist but definitions vary, and the correspondent classes are often neither harmonized nor interoperable. This hinders the efforts to employ the available national datasets on peatlands and wetlands for policy monitoring and reporting. The existing meta-languages, such as ISO-Land Cover Meta Language (LCML) and EAGLE, offer the possibility to “deconstruct” the relevant nomenclatures in an object-oriented manner, allowing the comparability and interoperable use of related information. The complex nature of peatlands calls for a dedicated and structured vocabulary of keywords and terms, comprising the biotic substrate and the soil. In the SEPLA project, a semantic meta-model has been developed, combining the hierarchical ontology of the LCML with the matrix structure of the EAGLE model. The necessary elements were provided to describe peatland bio-physical characteristics, while representing the definitions in a concise and user-friendly manner (semantic passports). The proposed semantic meta-model is innovative as it enables the documentation of the spatial distribution of peatland characteristics, considering also their temporal dimension, their intrinsic relation with land use, and the soil. It has been successfully implemented for the translation of the national peatland nomenclature into common land categories relevant for reporting under LULUCF regulation, as part of the EU Climate Law.JRC.D.5 - Food Securit