Sources of spatial data in LPIS

From Wikicap - European Commission

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Photographs and other images taken from airborne or satellite platforms are key means for documenting the surface of the Earth and the state of the environment. Since these images, once acquired, have geometrical distortions caused by the optics and the camera/sensor tilt, as well as the differences of the elevations of the Earth‘s surface, they undergo a process of removing these distortions, called “ortho-rectification”. The resulting specific product is called “orthoimagery”. There are two major categories of sensors that derive image data depending on the technology used for data capturing – passive and active. Passive sensors rely on the natural energy (radiation) that is emitted or reflected by the object or scene being observed. Reflected sunlight is the source of radiation measured by passive optical sensors. Active sensors, such as SAR and LIDAR, rely their own energy source for illumination. The sensor emits radiation which is directed towards the target to be investigated. The radiation reflected from that target is detected and measured by the sensor.

Aerial images

The most common source of reliable and up-to-date reference information for the LPIS elaboration and maintenance (upkeep), is the orthorectified image data of the Earth‘s surface from passive optical airborne sensors. The current state of technological development of digital airborne sensors (cameras) allows the production of orthoimagery over vast areas in relatively short time, with image content and quality easily compliant with cartographic scale of 1:5.000 or better. Although the digital technology provides better quality in terms of radiometry and detail comparing to the classical analogue technology, there are some specific aspects, regarding the height of the flight and the processing chain. Acquiring aerial photography has also some constraints such as restrictions over military zones and air traffic lanes, as well as cumbersome administrative procedures to obtain flight permission in some countries. Cloud cover is not as restricting for aerial photography as for satellite imagery, due to the flexibility of flight planning and the alternative to fly at lower altitudes, but meteorological conditions are in any case affecting the radiometric quality of the photos.

Satellite images

Orthoimagery, suitable for LPIS purposes can be easily obtained also from very-high resolution (VHR) space-borne sensors, as they can provide the same information content and being of equal quality as the airborne sensors. However, much more attention should be paid during the ortho-rectification process. Contrary to the production of aerial orthoimagery, where the process can be assumed straightforward and the producer is in control of the relevant internal and external conditions, the quality of the satellite orthoimagery is very much dependent of ancillary data (GCPs, DEM) over which the producer often is not having direct control. The image content can be seriously downgraded, if an inappropriate orthoimage production process is or irrelevant ancillary data are used. It has been observed during the CwRS campaigns, that often the orthoimage producers pays usually little attention to radiometric quality, colour balance and the preservation of the image detail, at the expense of thorough check of geometric quality.

Radar images

Synthetic Aperture Radar (SAR) imagery is acquired by the so-called active sensors that capture the fraction of energy emitted by the sensor that is reflected by the Earth surface. SAR imagery is much more difficult to generate and interpret, as the information provided is not natural for the human cognitive perception. However, SAR is sensitive to the structure and alignment of vegetation, as well as to the soil moisture content, thus it can provide different type of information regarding the land cover and land use comparing to optical. One of the strongest points of SAR is that it can acquire and collect data in cloudy conditions; in such case it can be an efficient complementary data source to optical data, especially in areas with cloudy weather. SAR sensor records also some specific characteristic of the signal, such as phase and polarimetry that opens further possibilities with respect to feature detection, especially when full polarimetric, multi-temporal SAR data is available. Combining the information from optical and SAR sensors increases the information available for distinguishing each target class and its respective signature, and thus there is a better chance of performing a more accurate classification.


Lidar is a relatively novel active remote sensing technology that measures distance by illuminating a target with a laser and analysing the reflected light. The popularity of Lidar recently increased due to the ability of the technology to produce high-resolution datasets in various application domains. Lidar can be particularly relevant for the LPIS due to its capacity to extract easily small landscape features and produce highly accurate digital surface models. As radar, Lidar also requires specific skills and expensive software and equipment to manipulate, these factors still limit the broad implementation of Lidar for land monitoring. However, there are already successful examples in some EU Member States, such as Finland, in using Lidar data in the process of LPIS update.

Ground survey


Although not considered primary source of information for the LPIS, data collected on the field using GNSS application, provides valuable contribution for the LPIS upkeep. Most of the field information using GNSS used for the LPIS, is gathered during the classical on-the-spot checks of the farmer declarations. Moreover field inspectors are required to report any findings on the correctness of the reference parcels and the EFA layer. Some EU Member States are also conducting occasionally more systematic field surveying using GNSS. GNSS can be regarded as autonomous geo-spatial positioning system with global coverage provided by satellites. It allows small electronic receivers (portable GNSS equipment) to determine their location (longitude, latitude, and altitude) to high precision using time signals transmitted along a line of sight by radio from satellite. The signals also allow the electronic receivers to calculate the current local time to high precision, which allows time synchronisation. There are several operational GNSS systems, the most common being GPS, GLONASS and DORIS, available at global level. The European system GALILEO and the Chinese BeiDou are still under deployment. To achieve higher precision, GNSS data is often augmented with correction data for network of ground-based GNSS stations for better real-time positioning and for enhancing the measurements for positioning with post-processing. Most EU member states already built such national networks, which are extensively used in GNSS area measurements required for on-the-spot control. The European Geostationary Navigation Overlay Service (EGNOS), currently provides also corrections to the geolocation data provided by GPS.

Classical geodetic surveying

In condition (poor satellite coverage, obstructions of the GNSS signal, mountainous terrain), classical surveying might be a feasible option. Geodetic surveys require the use of sophisticated instruments, accurate methods of observations and their computation with accurate adjustment.

Third-party data

Cadastral data

Geospatial data from the cadastre is used as a source of information for the LPIS, especially in those EU Member State having their LPIS reference parcel based on cadastre. The purpose of cadastre is primary to the outline the property rights and the usage boundaries of the cadastral parcels. If cadastral parcels represent a good match of the land cover and land use limits of the ground, cadastral registry can be considered as the optimal land management system for LPIS. However, this appears to be not the case in many areas. Use of cadastral systems remain popular due to the following advantages:

  • they are available and familiar to the farmers;
  • they are very detailed (scale 1:1.000 – 1:5.000) and can be very accurate;
  • they provide reference parcels with a unique reference number;
  • they provide readily available gross area and sometimes official land use, almost always in digital format, allowing efficient administrative cross checks;
  • they allow possible cross-checks with ownership information, if needed.

In any case for the purpose of the LPIS, cadastral systems need to be used in great care, due to the following drawbacks:

  • they may have variable geometric accuracy, use local and/or various CRS;
  • ownership boundaries often do not correspond to land cover boundaries;
  • they may suffer from heterogeneous quality and date of updating;
  • they may originate from “irregular” map-sheets coverage (format, irregular shape, scales, north orientation);
  • they may not be fully available as digital maps in rural areas;
  • they are managed by external body with specific update cycle, business rules and quality procedure, over which the MS Administration responsible for IACS doesn’t have direct control.

Other third-party data

Other third party data, available in digital format that interacts with LPIS and can be a potential source for LPIS update is given in the table below. Third party data sets serve as a support to LPIS with various possibilities to identify crop classes or to locate protected, sensitive areas, and other relevant information within the parcels. This information comes from the spatial data sets under custody of other institutions, like Mapping Agencies, Environmental Agencies etc. and have different specifications from LPIS. Note that such use does not require modifying these data to the LPIS accuracy specification.

Dataset Cartographic scale Geographic coverage Usability for the IACS/LPIS
Administrative Boundaries Varies, depending on the administrative level (from 1:1.000.000 for NUTS levels, to 1:10.000 or even better for LAU levels) National Administrative boundaries at local administrative level – LAU (municipalities, village lands), can be relevant for some of the administrative cross checks related to the 2nd pillar schemes under EU CAP
NATURA 2000 1: 100.000 National Outline the geographic area where certain agriculture practices or activities can be restricted.
Other protected areas (wetlands, national reverses,...) Usually also 1: 100.000 National Outline the geographic area where certain agriculture practices or activities can be restricted.
Soil data From 1: 250.000 to 1:25.000 in some countries Local/regional/ national Used in case when specific agriculture land cover types (mainly permanent grassland) subject to pro-rata that are present only on specific soils. Soil information can be important for the definition of PG-ELP and the correspondent minimum level of maintenance
Less Favoured Areas 1: 100.000? National LFAs benefit from area compensatory allowances, and from a number of payments for structural adjustment.
Land cover/land use thematic data (GIO) Variable (1:100.000 – 1:10.000) Local/regional/ national For cross-checking of the thematic information stored in IACS/LPIS on land cover/land use
Spatial data on servitude buffers along pipelines or high-voltage power lines; servitude zones along roads, water bodies, tailing ponds, dumpsites, or industrial facilities Variable Local/regional/ national These are spatial datasets that define zones in rural areas with restrictions for agriculture activity

Table 1: Third party data in LPIS

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