scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

scholarly journals INTEGRATION OF SENTINEL-2 AND LANDSAT-8 DATA FOR SURFACE REFLECTANCE TIME-SERIES ANALYSIS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 205-210 ◽  
Author(s):  
G. Berdou ◽  
S. Shrestha ◽  
M. Hahn
Keyword(s):  
Atmospheric Correction ◽  
Surface Characteristics ◽  
Global Scale ◽  
Landsat 8 ◽  
Observation Data ◽  
Common Reference ◽  
Key Factor ◽  
Land Covers ◽  
Earth Observation Data ◽  
Sentinel 2

Abstract. Integration of Sentinel-2 and Landsat-8 imagery is a key factor to provide earth observation data at a global scale with higher temporal resolution. Integration of data from two sensors is possible with the consistent harmonized data framed in common reference and processing, which can be used for comparing geophysical surface characteristics. This study focuses on the analysis of the atmospheric correction methods available for both Landsat-8 and Sentinel-2 products to convert the top of the atmosphere to the bottom of atmosphere reflectance. Other investigations (De Keukelaere, 2018) carried out similar analyses focusing on data acquired over water, while this study emphasises the analyses over land covers. Two processing algorithms iCOR and Sen2COR are utilized to perform atmospheric corrections, and results are statistically and visually compared. Comparisons based on same images processed with different algorithms show very strong correlation for some classes (urban: 0.99), while correlation values around 0.85 were achieved between images from different sensors.

scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

scholarly journals Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

2018 ◽  
Vol 7 (10) ◽  
pp. 405 ◽  
Author(s):  
Urška Kanjir ◽  
Nataša Đurić ◽  
Tatjana Veljanovski
Keyword(s):  
Land Use ◽  
Time Series ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Control ◽  
Sentinel 2

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.

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals Information-optimal Abstaining for Reliable Classification of Building Functions

2021 ◽  
Vol 2 ◽  
pp. 1-10
Author(s):  
Gabriel Dax ◽  
Martin Werner
Keyword(s):  
Regional Scale ◽  
Sensor Technology ◽  
Observation Data ◽  
Classification Schemes ◽  
The Past ◽  
Social Media Text ◽  
Novel Method ◽  
Earth Observation Data ◽  
Reliable Classification

Abstract. In the past decade, major breakthroughs in sensor technology and algorithms have enabled the functional analysis of urban regions based on Earth observation data. It has, for example, become possible to assign functions to areas in cities on a regional scale. With this paper, we develop a novel method for extracting building functions from social media text alone. Therefore, a technique of abstaining is applied in order to overcome the fact that most tweets will not contain information related to a building function albeit they have been sent from a specific building as well as the problem that classification schemes for building functions are overlapping.

scholarly journals Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

2019 ◽  
Vol 11 (8) ◽  
pp. 907 ◽  
Author(s):  
Vasileios Syrris ◽  
Paul Hasenohr ◽  
Blagoj Delipetrev ◽  
Alexander Kotsev ◽  
Pieter Kempeneers ◽  
...  
Keyword(s):  
Random Forests ◽  
Computational Models ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Computational Aspects ◽  
Learning Set ◽  
Earth Observation Data ◽  
Class Segmentation ◽  
Sentinel 2

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability.

Supporting the Common Agricultural Policy with Sentinel-2 data and deep recurrent networks

2021 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco Javier García-Haro ◽  
Beatriz Martínez ◽  
Sergio Sánchez-Ruiz ◽  
María Amparo Gilabert
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Recurrent Networks ◽  
The Common ◽  
Sentinel 2
Sign in / Sign up

Export Citation Format

Share Document