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 Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data

2013 ◽  
Vol 130 ◽  
pp. 39-50 ◽  
Author(s):  
J. Christopher Brown ◽  
Jude H. Kastens ◽  
Alexandre Camargo Coutinho ◽  
Daniel de Castro Victoria ◽  
Christopher R. Bishop
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Mato Grosso ◽  
Index Data

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850 ◽  
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series

2021 ◽  
Vol 13 (2) ◽  
pp. 289
Author(s):  
Misganu Debella-Gilo ◽  
Arnt Kristian Gjertsen
Keyword(s):  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Land ◽  
Satellite Image ◽  
Agricultural Land Use ◽  
Monitoring Method ◽  
Multiple Observations ◽  
Land Use Types ◽  
Sentinel 2

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals Aplicação do Índice da Vegetação por Diferença Normalizada (NDVI) à Análise Multitemporal da Dinâmica de Áreas Agrícolas no Alto Curso da Bacia do Rio Uberabinha, Minas Gerais Application of the (...)

2015 ◽  
Vol 25 (44) ◽  
pp. 149-164
Author(s):  
Vanderlei De Oliveira Ferreira ◽  
Mirella Velluma Portilho Magalhães
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Visual Interpretation ◽  
Land Occupation ◽  
Reconstruction Process ◽  
Seasonal Scale ◽  
Agricultural Areas

O mapeamento do uso do solo é essencial para acompanhamento do processo de reconstrução continuada da paisagem, sendo útil para definição de estratégias de utilização dos recursos naturais. O presente artigo relata pesquisa dedicada a inventariar e compreender a dinâmica do uso agrícola do solo sob uma perspectiva multitemporal (escala sazonal) no alto curso da bacia do rio Uberabinha, no Triângulo Mineiro, a montante da sede municipal de Uberlândia. Utilizou-se a técnica do NDVI (Normalized Difference Vegetation Index) devido à sua aptidão para levantamento de áreas agrícolas. O mapeamento foi elaborado por meio da interpretação visual, recorrendo-se às imagens do sensor LANDSAT 5 e ResourceSat-1, com a composição colorida 4R5G3B. Foi possível diferenciar os diversos estádios fenológicos da cobertura vegetal, percebendo situações de manejo e forma de ocupação do solo em diferentes épocas do ano. Observa-se, por exemplo, que não há recorrência ao pousio da terra entre uma cultura e outra. Os produtores adotam o método de plantio direto, intercalando culturas, além de forrageiras e leguminosas para melhorar a qualidade nutricional do solo.Palavras chave: Mapeamento; Sensoriamento Remoto; Uso agrícola do solo; Escala sazonal.AbstractThe mapping of the land use is essential for accompaniment of the reconstruction process continued of landscape, being useful for define strategies of utilization of the natural resources. This article reports the research dedicated to inventory and understand the dynamics of agricultural land use under a multitemporal perspective (seasonal scale) in the high course of the basin of the Uberabinha river, in the Triângulo Mineiro, the upstream of the municipal headquarters of Uberlândia. We used the technique of NDVI (Normalized Difference Vegetation Index) due to its aptitude for survey of agricultural areas. The mapping was prepared by visual interpretation, resorting to images of the sensor LANDSAT 5 and ResourceSat-1, with colorful makeup 4R5G3B. It was possible to differentiate the several phenological stages of the vegetation cover, realizing management situations and forms of land occupation in differents epochs of the year. It is observed that there is no recurrence to fallow of the land between one culture and another. The producers adopt the method of tillage, interspersing cultures, besides forages and legumes for improve the nutritional quality of the soil. Keywords: Mapping; Remote Sensing; Agricultural land use; Seasonal scale. 

scholarly journals Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

2020 ◽  
Vol 12 (18) ◽  
pp. 2919
Author(s):  
Ann-Kathrin Holtgrave ◽  
Norbert Röder ◽  
Andrea Ackermann ◽  
Stefan Erasmi ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Optical Data ◽  
Agricultural Land Use ◽  
Land Use Type ◽  
Auxiliary Data ◽  
Land Use Types ◽  
Sentinel 2

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

scholarly journals On diversity effects of alternative agricultural policy reforms in Finland: an agricultural sector modelling approach

2008 ◽  
Vol 13 (3) ◽  
pp. 229 ◽  
Author(s):  
A. MIETTINEN ◽  
H. LEHTONEN ◽  
R. HIETALA-KOIVU
Keyword(s):  
Land Use ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Agricultural Land ◽  
Policy Support ◽  
Agricultural Land Use ◽  
Policy Reforms ◽  
Sector Model ◽  
Positive Effects ◽  
Diversity Effects

The European Union has decided to reform its agricultural policy and decouple Common Agricultural Policy support partially from production. The aim of this study is to predict the diversity effects of agricultural policy reforms in which direct aid payments are disconnected from production, and compare the outcomes with the effects of a policy in which Common Agricultural Policy support is coupled to production. The study employs a dynamic regional sector model of Finnish agriculture. The sector model predicts regional agricultural land use, numbers of livestock, stocking densities, pesticide application areas, and nutrient balances. Diversity of agricultural land use is measured by Shannon’s diversity index. The results indicate that if agricultural support is independent from production, the amount of fallow land will increase considerably in the future. This will decrease the diversity of agricultural land use at landscape level, but may not be harmful at species level since green fallow has some positive effects, especially on the densities and abundance of farmland birds. Instead, the decrease in bovine animals is likely to run down biological diversity, since it simplifies crop rotation and diminishes grazing.;

scholarly journals Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

2020 ◽  
Vol 12 (14) ◽  
pp. 2195 ◽  
Author(s):  
Blanka Vajsová ◽  
Dominique Fasbender ◽  
Csaba Wirnhardt ◽  
Slavko Lemajic ◽  
Wim Devos
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Policy Framework ◽  
Pixel Size ◽  
Control Approach ◽  
Continuous State ◽  
Ndvi Time Series ◽  
High Level ◽  
Sentinel 2

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.

scholarly journals Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3524
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
W. Dean Hively
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Cover Crops ◽  
Cover Crop ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Agricultural Landscapes ◽  
Absolute Difference ◽  
Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

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