The harmonised data model for assessing Land Parcel Identification Systems compliance with requirements of direct aid and agri-environmental schemes of the CAP

Sagris, Valentina; Wojda, Piotr; Pavel, Milenov; Devos, Wim

doi:10.1016/j.jenvman.2012.12.019

Cited by 26 publications

(21 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main functions of those spatial databases are localization, identification and quantification of agricultural land via detailed geospatial data, in order to facilitate the distribution of CAP subsidies [5].…”

Section: Use Of Remote Sensing For Cap Subsidy Controlmentioning

confidence: 99%

“…The operational applicability of parcel-based approaches is conditioned by the geographic information available in the national LPIS. As described in [5], the LPIS Conceptual Model (LCM) requires the geometry of the reference parcel to be stored. However, and depending on the type of reference parcel used by each Member State, it might not represent a single crop.…”

Section: Parcel-based Crop Identificationmentioning

confidence: 99%

“…However, and depending on the type of reference parcel used by each Member State, it might not represent a single crop. As discussed in [5], the LCM class Reference Parcel is associated with a land cover type, which includes, among others, arable land, irrigated rice or grassland, but information about specific crops, like wheat or barley, are stored in a separate class (Agricultural Parcel) with no geometry. To complicate matters, the reference parcel might include very small non-eligible features.…”

Section: Parcel-based Crop Identificationmentioning

confidence: 99%

See 2 more Smart Citations

Reliable Crop Identification with Satellite Imagery in the Context of Common Agriculture Policy Subsidy Control

Schmedtmann

Campagnolo

2015

Remote Sensing

View full text Add to dashboard Cite

Agricultural subsidies in the context of the Common Agricultural Policy (CAP) represent over 40% of the EU's yearly budget. To ensure that funds are properly spent, farmers are controlled by National Control and Paying Agencies (NCPA) using tools, such as computer-assisted photo interpretation (CAPI), which aims at identifying crops via remotely-sensed imagery. CAPI is time consuming and requires a large team of skilled photo interpreters. The objective of this study was to develop a reliable control system to partially replace CAPI for crop identification, with the overreaching goal of reducing control costs and completion time. Validated control data provided by the Portuguese Control and Paying Agency and an atmospherically-corrected Landsat ETM+ time series were used to perform parcel-based crop classification, leading to an accuracy of only 68% due to high similarity between crops' spectral signatures. To address this problem, we propose an automatic control system (ACS) that couples crop classification to a reliability requirement. This allows the decision-maker to set a reliability level, which restricts automatic crop identification to parcels that are classified with high certainty. While higher reliability levels reduce the risk of misclassifications, lower levels increase the proportion of automatic control decisions (ACP). With a reliability level of 80%, more than half of the parcels in our study area are automatically identified with an overall accuracy of 84%. In particular, this allows automatically controlling over 85% of all parcels classified as maize, rice, wheat or vineyard.Remote Sens. 2015, 7 9326

show abstract

Section: Use Of Remote Sensing For Cap Subsidy Controlmentioning

confidence: 99%

Section: Parcel-based Crop Identificationmentioning

confidence: 99%

Section: Parcel-based Crop Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Reliable Crop Identification with Satellite Imagery in the Context of Common Agriculture Policy Subsidy Control

Schmedtmann

Campagnolo

2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The workflow tool that is used to guide the operator is called the ETS Manager. ETS refers to Executable Test Suite which is often used in conformance testing (Sagris et al, 2013).…”

Section: Workflow Toolmentioning

confidence: 99%

“…The main advantage of the ETS Manager is that it follows the workflow of the LPIS Quality Assurance Framework (Sagris et al, 2013). All operators follow the same set of rules which makes standardisation and reporting the results of the LPIS QAF to the European Commission a lot easier.…”

Section: Workflow Toolmentioning

confidence: 99%

Spatial Data Quality and a Workflow Tool

Meijer

Vullings

Bulens

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission II, WG II/4KEY WORDS: Spatial Data Quality, Communication, Producer, Consumer, Fitness for Use, Open Data, LPIS, ETS ABSTRACT:Although by many perceived as important, spatial data quality has hardly ever been taken centre stage unless something went wrong due to bad quality. However, we think this is going to change soon. We are more and more relying on data driven processes and due to the increased availability of data, there is a choice in what data to use. How to make that choice? We think spatial data quality has potential as a selection criterion. In this paper we focus on how a workflow tool can help the consumer as well as the producer to get a better understanding about which product characteristics are important. For this purpose, we have developed a framework in which we define different roles (consumer, producer and intermediary) and differentiate between product specifications and quality specifications. A number of requirements is stated that can be translated into quality elements. We used case studies to validate our framework. This framework is designed following the fitness for use principle. Also part of this framework is software that in some cases can help ascertain the quality of datasets.

show abstract

A machine learning modeling framework for Triticum turgidum subsp. durum Desf. yield forecasting in Italy

et al. 2023

View full text Add to dashboard Cite

The forecasting of crop yield is one of the most critical research areas in crop science, which allows for the development of decision support systems, optimization of nitrogen fertilization, and food safety. Many tested modeling approaches can be differentiated according to the models and data used. The models used are traditional crop models that require data that are often difficult to measure. New modeling approaches based on artificial intelligence algorithms have proven to be of high performance, flexible, and can be tested based on available data. In this study, four independent field experiments conducted on Triticum turgidum subsp. durum Desf. in central–southern Italy were used to train a set of machine learning (ML) algorithms to predict the yield using 16 variables: fertilization, nitrogen management, pedoclimatic, and remote sensing data. Four ML algorithms were calibrated and validated over two independent sites, and a linear regression model was used as a control. The calibrated models can predict the grain yield in the two regions by using ancillary data, topsoil physical and chemical properties, multispectral drone imagery, climatic data, and nitrogen fertilizer applied at the site. Among the four ML algorithms, stochastic gradient boosting (root‐mean‐square error = 0.58 t ha−1) outperformed others during calibration and transferability. Nitrogen application rate, seasonal precipitation, and temperature are the most important features for predicting wheat yield.

show abstract

The harmonised data model for assessing Land Parcel Identification Systems compliance with requirements of direct aid and agri-environmental schemes of the CAP

Cited by 26 publications

References 10 publications

Reliable Crop Identification with Satellite Imagery in the Context of Common Agriculture Policy Subsidy Control

Reliable Crop Identification with Satellite Imagery in the Context of Common Agriculture Policy Subsidy Control

Spatial Data Quality and a Workflow Tool

A machine learning modeling framework for Triticum turgidum subsp. durum Desf. yield forecasting in Italy

Contact Info

Product

Resources

About