The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.
COBISS.SI-ID: 43706157
Overgrowth of agricultural land and consequently its abandonment is becoming a serious problem in Slovenia. Identification of overgrowth is very important for establishment of permanent monitoring of the overgrowth in the first place and for evaluation of agricultural policy with the aim of reducing the rate of abandonment land. Traditionally monitoring of overgrowth is based on agricultural and forest land use data, which has two main disadvantages. Firstly, data based on different versions of methodology are inappropriate for permanent monitoring and secondly overgrowth photointerpretation of orthophotos time series is very complex and time demanding. Therefore, we propose a time independent methodology based on automatic object-based image analysis of orthophotos and height data.
COBISS.SI-ID: 43527213
In this paper, we are dealing with the ability to interpret mass satellite data for identification of un-justified uses of permanent meadows. We want to identify ploughing or other permanent changes or anomalies in the grassland land cover. For the analysis, we focused on the three-year time series of Sentinel-2 satellite images. These were analysed on a sample of selected permanent meadows using three different approaches: BFAST Monitor time series method, the standard deviation of time series and the analysis of time profiles. With the latter two methods in addition to test their ability to detect irregularities, we also validated the first method since it can be limited while dealing with short time series. The anomalies while using BFAST Monitor method are seen as a deviation from the historical trend, in time profiles as a drop of the NDVI values and as an increase of standard deviation. The results indicate usefulness of Sentinel-2 data and suitability of the time series analysis methodology to detect unjustified use of permanent meadows.
COBISS.SI-ID: 43644973
The Slovenian national orthophoto in the scale 1 : 5000 is made from the Cyclical Aerophotogrammetric Survey (CAS) images and the digital terrain model (DTM). It is the most widely used spatial source for a variety of GIS analyses and visual photointerpretation in Slovenia. It is used also as a main source for acquisition of changes in the actual use of agricultural land. Different vector or raster spatial layers can be shown on it, which may be more precise than the orthophoto. The users of the orthophoto are often not aware, that orthophoto may include positional errors. Using the geometrical connection between the original image and the orthophoto we derive the maximal radial displacement expected on orthophoto for objects of different height above or below the DTM. We show that radial displacement of higher objects, like higher rocky cliffs (height 50 m), tall buildings (like church towers) and forest edge, may exceed the permitted positional error of the orthophoto, at least in the extreme cases when these objects are located near the seam lines of the orthophoto. We also mention orthophotos made from images of unmanned aerial vehicles (drones), where the problem of its positional accuracy is even broader, as for their production the DTM with nonuniform vertical accuracy is used.
COBISS.SI-ID: 63506018
This paper presents land cover and land use models in Austria, Germany, The Netherlands, and Great Britain and the activities under the European projects EAGLE, HELM and LUCAS. These models have been set up for, and depending on, national requirements and also depending on international spatial data harmonisation. The data on land use of agricultural and forest land, set up for the purpose of implementing the Common (European) Agricultural Policy, are also applicable for setting up the national data base on land cover and land use. In Slovenia, the land cover data set up by the Statistical Office of the Republic of Slovenia are no longer upda ted, while the data by the ministry responible for agriculture do not cover all land uses with the same level of detail. The comparative analysis of the models can serve as a reflection and starting point for the establishment of a land cover and land use model in Slovenia as well.
COBISS.SI-ID: 8709217