LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is an evenly spaced in situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth observation (EO) was introduced in 2018: the LUCAS Copernicus module. The module surveys the land cover extent up to 51 m in four cardinal directions around a point of observation, offering in situ data compatible with the spatial resolution of high-resolution sensors. However, the use of the Copernicus module being marginal, the goal of the paper is to facilitate its uptake by the EO community. First, the paper summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth observation land cover and land use applications with high-resolution satellite imagery. As a result, a total of 63 364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal directions, a polygon was delineated for each of these points. Through geospatial analysis and by semantically linking the LUCAS core and Copernicus module land cover observations, 58 426 polygons are provided with level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this paper (https://doi.org/10.6084/m9.figshare.12382667.v4 d'Andrimont, 2020) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and Sentinel-2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150 000 LUCAS points for which in situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in situ surveying and the need to collect in situ data relevant for Earth observation in the European Union.

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LUCAS Copernicus 2018: Earth Observation relevant in-situ data on land cover throughout the European Union

10.5194/essd-2020-178 ◽

2020 ◽

Cited By ~ 1

Author(s):

Raphaël d'Andrimont ◽

Astrid Verhegghen ◽

Michele Meroni ◽

Guido Lemoine ◽

Peter Strobl ◽

...

Keyword(s):

Land Use ◽

European Union ◽

Land Cover ◽

Earth Observation ◽

The European Union ◽

In Situ Data ◽

Ground Survey ◽

Level 3 ◽

High Resolution Satellite Imagery

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is a regular in-situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth Observation was introduced in 2018: the LUCAS Copernicus module, aiming at surveying land cover extent up to 51 meters in four cardinal directions around a point of observation. This paper first summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to a 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth Observation land cover and land use applications with high resolution satellite imagery. As a result, a total of 63,364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal direction, a polygon was delineated for each of such point. Through geo-spatial analysis and by semantically linking the LUCAS core and Copernicus land cover observations, 58,428 polygons are provided with a level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this manuscript (https://doi.org/10.6084/m9.figshare.12382667.v3) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and -2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150,000 LUCAS points for which in-situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in-situ surveying and the need to collect in-situ data relevant for Earth Observation in the European Union.

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A Sentinel-1 based European crop parcel map using 2018 in-situ LUCAS Copernicus observations

10.5194/egusphere-egu2020-18703 ◽

2020 ◽

Author(s):

Verhegghen Astrid ◽

d'Andrimont Raphaël ◽

Lemoine Guido ◽

Strobl Peter ◽

van der Velde Marijn

Keyword(s):

Remote Sensing ◽

European Union ◽

Land Cover ◽

Growing Season ◽

Earth Observation ◽

Soft Wheat ◽

High Quality ◽

In Situ Data ◽

Crop Type

Efficient near-real time and wall-to-wall land monitoring is now possible with unprecedented detail because of the fleet of Copernicus Sentinel satellites. This remote sensing paradigm is the consequence of the freely accessible, global, Copernicus data, combined with affordable cloud computing. However, to translate this capacity in accurate products, and to truly benefit from the high spatial detail (~10m) and temporal resolution (~5 days in constellation) of the Sentinels 1 and 2, high quality and timely in-situ data remains crucial. Robust operational monitoring systems are in need of both training and validation data.&#160;Here, we demonstrate the potential of Sentinel 1 observations and complementary high-quality in-situ data to generate a crop type map at continental scale. In 2018, the Land Cover and Land Use Area frame Survey (LUCAS) carried out in the European Union contained a specific Copernicus module corresponding to 93.091 polygons surveyed in-situ. In contrast to the usual LUCAS point observation, the Copernicus protocol provides data on the extent of homogeneous land cover for a maximum size of 100 x 100 m, making it meaningful for remote sensing applications. After filtering the polygons to retrieve only high quality sample, a sample was selected to explore the accuracy of crop type maps at different moments of the 2018 growing season over Europe. The time series of 10 days VV and VH were classified using Random Forest models. The crops that were mapped correspond to the 13 major crops in Europe and are those that are monitored and forecast by the JRC MARS activities (soft wheat, maize, rapeseed, barley, potatoes, ...). Overall, reasonable accuracies were obtained (~80%). Although no a priori parcel delineation was used, it was encouraging to observe the relative homogeneity of pixel classification results within the same parcel. In the context of forecasting, we specifically assessed at what time in the growing season accuracies moved beyond a set threshold for the different crops. This ranged from May for winter crops such as soft wheat, and September for summer crops such as maize.&#160;Our results contribute to the discussion regarding the usefulness, benefits, as well as weaknesses, of the newly acquired LUCAS Copernicus data. Doing so, this study demonstrates the potential of in-situ surveys such as LUCAS Copernicus module&#160; specifically targeted for Earth Observation applications. Future improvements to the LUCAS Copernicus survey methodology are suggested. Importantly, now that LUCAS has been postponed to 2022, and aligned with the Copernicus space program, we advocate for a European Union wide systematic and representative in-situ sample campaign relevant for Earth Observation applications, beyond the traditional LUCAS survey.&#160;

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RapidAI4EO: Advancing the State-of-the-Art in Continuous Land Monitoring

10.5194/egusphere-egu21-13713 ◽

2021 ◽

Author(s):

Giovanni Marchisio ◽

Patrick Helber ◽

Benjamin Bischke ◽

Tim Davis ◽

Annett Wania

Keyword(s):

Land Use ◽

European Union ◽

Time Series ◽

High Resolution ◽

Land Cover ◽

Training Data ◽

Third Party ◽

The European Union ◽

Horizon 2020 ◽

High Cadence

New catalogues of nearly daily or even intraday temporal data will soon dominate the global archives. However, there has been little exploration of artificial intelligence (AI) techniques to leverage the high cadence that is already possible to achieve through the fusion of multiscale, multimodal sensors. Under the sponsorship of the European Union&#8217;s Horizon 2020 programme, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. Focus is on the CORINE Land Cover programme, which is the flagship of CLMS.&#160;Specific objectives of the project are to: 1) explore and stimulate the development of new spatiotemporal monitoring applications based on the latest advances in AI and Deep Learning (DL); 2) demonstrate the fusion of Copernicus high resolution satellite imagery and third party very high resolution imagery; 3) provide intensified monitoring of Land Use and Land Cover, and Land Use change at a much higher level of detail and temporal cadence than it is possible today.&#160;Our strategy is two-fold. The first aspect involves developing vastly improved DL architectures to model the phenomenology inherent in high cadence observations with focus on disentangling phenology from structural change. The second involves providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the lifetime of this project. To this end we will create the most complete and dense spatiotemporal training sets ever, combining Sentinel-2 with daily, harmonized, cloud-free, gap filled, multispectral 3m time series resulting from fusion of open satellite data with Planet imagery at as many as 500,000 patch locations over Europe.&#160;The daily time series will span the entire year 2018, to coincide with the latest release of CORINE. We plan to open source these datasets for the benefit of the entire remote sensing community.This talk focuses on the description of the datasets whose inspirations comes from the recently released EuroSAT (Helbert et al, 2019) and BigEarthNet corpora (Sumbul et al, 2019). The new corpora will look at the intersection of CORINE 2018 with all the countries in the EU, balancing relative country surface with relative LULC distribution and most notably adding the daily high resolution time series at all locations for the year 2018. Annotations will be based on the CORINE ontology. The higher spatial resolution will support modeling of more LC classes, while the added&#160; temporal dimension should enable disambiguation of land covers across diverse climate zones, as well as an improved understanding of land use.This project has received funding from the European Union&#8217;s Horizon 2020 research and innovation programme under grant agreement No 101004356.

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Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union

Scientific Data ◽

10.1038/s41597-020-00675-z ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Raphaël d’Andrimont ◽

Momchil Yordanov ◽

Laura Martinez-Sanchez ◽

Beatrice Eiselt ◽

Alessandra Palmieri ◽

...

Keyword(s):

Land Use ◽

European Union ◽

Land Cover ◽

Land Surface ◽

Environmental Parameters ◽

Ground Truth ◽

The European Union ◽

Full Resolution ◽

Multi Temporal

Abstract Accurately characterizing land surface changes with Earth Observation requires geo-located ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.

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Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

10.20944/preprints202009.0574.v1 ◽

2020 ◽

Author(s):

Juan Carlos Laso Bayas ◽

Linda See ◽

Hedwig Bartl ◽

Tobias Sturn ◽

Mathias Karner ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Survey Data ◽

Target Location ◽

Low Cost ◽

Mobile App ◽

Sample Survey ◽

In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

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Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Land ◽

10.3390/land9110446 ◽

2020 ◽

Vol 9 (11) ◽

pp. 446

Author(s):

Juan Carlos Laso Bayas ◽

Linda See ◽

Hedwig Bartl ◽

Tobias Sturn ◽

Mathias Karner ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Survey Data ◽

Target Location ◽

Mobile App ◽

Sample Survey ◽

High Quality ◽

In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of large amounts of high quality, spatially explicit field data at a low cost.

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Spatio-Temporal Changes of Land Use/Land Cover of Pindrangi Village Using High Resolution Satellite Imagery

Journal of Remote Sensing & GIS ◽

10.4172/2469-4134.1000201 ◽

2017 ◽

Vol 06 (02) ◽

Author(s):

Gangaraju M ◽

Anitha P ◽

Krishna TV ◽

Sravan KA

Keyword(s):

Land Use ◽

High Resolution ◽

Land Cover ◽

Satellite Imagery ◽

Temporal Changes ◽

Land Use Land Cover ◽

Spatio Temporal ◽

High Resolution Satellite Imagery

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Can We Use Satellite-Based Soil-Moisture Products at High Resolution to Investigate Land-Use Differences and Land–Atmosphere Interactions? A Case Study in the Savanna

Remote Sensing ◽

10.3390/rs12111701 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1701

Author(s):

Carlos Román-Cascón ◽

Marie Lothon ◽

Fabienne Lohou ◽

Nitu Ojha ◽

Olivier Merlin ◽

...

Keyword(s):

Land Use ◽

Soil Moisture ◽

High Resolution ◽

Satellite Data ◽

Tree Cover ◽

Optical Data ◽

Pass Filter ◽

Low Pass Filter ◽

In Situ Data

The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.

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