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.
Detailed spatial data on grassland use intensity is needed in several European policy areas for various applications, e.g., agricultural management, supporting nature conservation programs, improving ...biodiversity strategies, etc. Multisensory remote sensing is an efficient tool to collect information on grassland parameters. However, there is still a lack of studies on how to process, combine, and implement large radar and optical image datasets in a joint observation framework to map grassland types on large heterogeneous study areas. In our study, we assessed the usefulness of 2521 Sentinel-1 and 586 Sentinel-2 satellite images and topographic data for mapping grassland use intensity. We focused on the distinction between intensively and extensively managed permanent grassland in a large heterogeneous study area in Slovenia. We provided dense Satellite Image Time Series (SITS) for 2017, 2018 and 2019 to identify important differences, e.g., management practices, between the two grassland types analysed. We also investigated the effectiveness of combining two different remote-sensing products, the optical Normalised Difference Vegetation Index (NDVI) and radar coherence. Grassland types were distinguished using an object-based approach and the Random Forest classification. With the use of SITS only, the models achieved poor performance in the case of cloudy years (2018). However, the performance improved with additional features (environmental variables). The feature selection method based on Mean Decrease Accuracy (MDA) provided a deeper insight into the high-dimensional multisensory SITS. It helped select the most relevant features (acquisition dates, environmental variables) that distinguish between intensive and extensive grassland types. The addition of environmental variables improved the overall classification accuracy by 7–15%, while the feature selection additionally improved the final overall classification accuracy (using all available features) by 2–3%. Although the reference dataset was limited (1259 training samples), the final overall classification accuracy was above 88% in all years analysed. The results show that the proposed Random Forest classification using combined multisensor data and environmental variables can provide better and more stable information on grasslands than single optical or radar data SITS on large heterogeneous areas. Therefore, a combined approach is recommended to distinguish different grassland types.
The Sentinel-2 is a high resolution optical satellite mission, developed by the European Space Agency (ESA) for the European Commission. Currently the mission has two satellites in orbit: Sentinel-2A ...from 23rd June 2015, and Sentinel-2B from 7th March 2017. Revisit time for this twin constellation is five days. The twin satellites represent a very stable data source, but for the first six operational months (i.e. the second half of the 2015). From the very start of the mission, the Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU) is collecting its own archive of Sentinel-2 data for the extent of the Republic of Slovenia. The data is saved in its original processing state (Level-1C) and in four levels of processing. The archive has now been operating for six full years, which is a good milestone for a statistical overview. We analyse the archive of Sentinel-2 data of Slovenia from several aspects at the country level. A special focus is placed on cloud cover, as only cloud-free data can give an impression of the actual spatial and temporal usability of optical data.
The paper deals with the production of large scale vegetation products derived from PROBA-V 100 m satellite images. Image compositing and mosaicking are needed to create seamless products on a global ...or continental scale. In the study, we have analysed and compared two compositing methods. Evaluation of the methods was focused on the visual inspection of the product seamlessness, and efficiency in eliminating (reducing) the influence of inadequate pixels that were misclassified by a cloud detection algorithm. During the compositing procedure, we also created a raster quality map and calculated statistical properties of the mosaics in order to assess the quality of the product.
This article presents a review and comparison of the detection capability of water facilities or water surfaces with different systems of remote sensing: optical and radar satellite sensors, as well ...as optical sensors on aircraft. The capabilities of water detection are estimatedfrom several aspects: differences in the spatial and spectral resolution of imagery, the complexity of imagery pre-processing requirements, and the method of analysis and interpretation feasibilities for the type and purpose of mapping. Particular attention is paid to evaluating the applicability of remote sensing data in light of the detection efficiency of water areas in heterogeneously structured environments.
Remote sensing has developed various methods and technologies for contactless and cost-effective mapping of large area land cover/land use maps and other thematic maps. The key factor for the ...availability and reliability of these maps for use in Earth sciences is the development of effective procedures for satellite data analysis and classification. The most appropriate approach for classifying low and medium resolution satellite images (pixel size is coarser than, or at best similar to, the size of geographical objects) is pixel-based classification in which an individual pixel is classified into the closest class based on its spectral similarity. With increasing spatial resolution, pixel-based classification methods became less effective, since the relationship between the pixel size and the dimension of the observed objects on the Earth's surface has changed significantly. Therefore object-oriented classification has become increasingly popular over the past decade. This combines segmentation (which is a fundamental phase of the approach) and contextual classification. Segmentation divides the image into homogeneous pixel groups (segments), which are -during the semantic classification process - arranged into classes based on their spectral, geometric, textural and other features during. The intent of this paper is to present the theoretical argumentation and methodology of object-based image analysis of remote sensing data, provide an overview of the field and point out certain restrictions as regards the current operational solutions.
Remote sensing has developed various methods and technologies for contactless and cost-effective mapping of large area land cover/land use maps and other thematic maps. The key factor for the ...availability and reliability of these maps for use in Earth sciences is the development of effective procedures for satellite data analysis and classification. The most appropriate approach for classifying low and medium resolution satellite images (pixel size is coarser than, or at best similar to, the size of geographical objects) is pixel-based classification in which an individual pixel is classified into the closest class based on its spectral similarity. With increasing spatial resolution, pixel-based classification methods became less effective, since the relationship between the pixel size and the dimension of the observed objects on the Earth's surface has changed significantly. Therefore object-oriented classification has become increasingly popular over the past decade. This combines segmentation (which is a fundamental phase of the approach) and contextual classification. Segmentation divides the image into homogeneous pixel groups (segments), which are - during the semantic classification process - arranged into classes based on their spectral, geometric, textural and other features during. The intent of this paper is to present the theoretical argumentation and methodology of object-based image analysis of remote sensing data, provide an overview of the field and point out certain restrictions as regards the current operational solutions.
Do podatkov Sentinel je, odvisno od misije in vrste podatkov, mogoče dostopati prek običajnih dostopnih točk ESE in EUMETSAT-a2 ali prek platform DIAS3 (Data and Information Access Services'), ki ...zagotavljajo (delno) zrcaljenje podatkov, tematskih platform TEP4 (Thematic Exploitation Platforms) ali regionalnih podatkovnih storitev5 (Copernicus Collaborative Ground Segment), kot tudi drugih uveljavl2 2 podatkovnih centrov6 ali spletišč za hitro prikazovanje in iskanje satelitskih podatkov po obširnih ? bazah podatkov teh centrov (med temi želimo posebej izpostaviti platformo Sentinel Hub, ki jo zelo uspešno upravlja slovensko podjetje Sinergise) (ESA Sentinel Data Access, 2021; ESA Earth Online, TS 2021). Medagencijski Odbor za satelite za opazovanje Zemlje (Committee on Earth Observation Satellites, CEOS) spodbuja pripravo in distribucijo tako imenovanih ,podatkov, pripravljenih za analizo' (Analysis Ready g Data, ARD), s čimer želi podpreti hitro, zanesljivo in avtomatizirano uporabo podatkov Sentinel ter ^ omogočiti interoperabilnost tako med različnimi obdobji zajema satelitskih podatkov kot z drugimi zbirkami satelitskih podatkov, na primer s podatki Landsat (CEOS, 2020). Doseganje ustrezne primerljivosti in boljše konsistentnosti satelitskih podatkov ter s tem boljše povezljivosti in združljivosti podatkov je zahtevna naloga, zato pri njej sodelujejo vsi pomembnejši proizvajalci podatkov, tj. vesoljske agencije (NASA, ESA, JAXA itd.) ter glavni centri za dostavo in dostop do podatkov (ponudniki storitev v oblaku, regionalna podatkovna vozlišča). ESIN portal Copernicus Open Access Hub (https://scihub.copernicus.eu/, prej znan kot Sentinels Scientific Data Hub) zagotavlja brezplačen in odprt dostop do uporabniških izdelkov Sentinel-1, Sentinel-2, Sentinel-3 in Sentinel-5P Prenos posnetkov s tega spletišča v naš arhiv smo realizirali prek vmesnika API Hub, ki
This article aims to present the change detection methodology as experienced in the use of optical remote sensing imagery (Landsat) and its pitfalls when multitemporal analyses are performed with ...pixel-based (raster algebra) techniques. The existing methodologyrecommends fundamental data preparation (geometric, radiometric, topographic corrections) and offers numerous change detection techniques. Regardless of the carefully performed preparationscertain noise remains, which can drastically weight the imagery comparisons. This noise behaves as a detected change and could have such a false effect on the identified change pattern (i.e. false, non-intrinsic changes) that the quantitative evaluation might fail.Since this noise originates from the pre-processing algorithms as well as the natural and technological conditions during data acquisition it can not be completely removed by data corrections. A multiresolution change detection approach is therefore proposed. Taking into account the neighbourhood and change information from joining different spatial scales, the multi-resolution approach effectively reduces the amount of false changes. In the discussion the remote sensing imagery for surface change detection is evaluated.
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