scholarly journals Crowdsourced Street-Level Imagery as a Potential Source of In-Situ Data for Crop Monitoring

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 127 ◽  
Author(s):  
Raphaël d'Andrimont ◽  
Momchil Yordanov ◽  
Guido Lemoine ◽  
Janine Yoong ◽  
Kamil Nikel ◽  
...  
Keyword(s):  
Land Cover ◽  
Rural Areas ◽  
Arable Land ◽  
Potential Contribution ◽  
High Quality ◽  
Crop Monitoring ◽  
Street Level ◽  
In Situ Data ◽  
The Eu

New approaches to collect in-situ data are needed to complement the high spatial (10 m) and temporal (5 d) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowdsourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: (1) what is the spatial availability of these images across the European Union (EU), and (2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary—the largest open-access platform for such images—against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337,031 points. For 37.78% of the LUCAS points a crowdsourced image is available within a 2 km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowdsourced image within 300 m from a LUCAS point, illustrating the huge potential of crowdsourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785,710 Dutch parcels and the pictures’ meta-data such as camera orientation and focal length. Availability of crowdsourced imagery looking at parcels was assessed for eight different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowdsourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

scholarly journals Crowd-sourced street-level imagery as a potential source of in-situ data for crop monitoring

2018 ◽  
Author(s):  
Raphaël d'Andrimont ◽  
Momchil Iordanov ◽  
Guido Lemoine ◽  
Janine Yoong ◽  
Kamil Nikel ◽  
...  
Keyword(s):  
Land Cover ◽  
Rural Areas ◽  
Arable Land ◽  
Potential Contribution ◽  
High Quality ◽  
Crop Monitoring ◽  
Street Level ◽  
In Situ Data ◽  
The Eu

New approaches to collect in-situ data are needed to complement the high spatial (10~m) and temporal (5-day) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowd-sourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: 1) what is the spatial availability of these images across the European Union (EU)? and 2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary - the largest open-access platform for such images - against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337031 points. For 37.78% of the LUCAS points a crowd-sourced image is available within a 2-km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowd-sourced image within 300-m from a LUCAS point, illustrating the huge potential of crowd-sourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowd-sourced imagery looking at parcels was assessed for 8 different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowd-sourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

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

Land ◽  
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.

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

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

<p>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. </p><p>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. </p><p>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  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. </p>

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

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.

SMART – Space monitoring of Arctic Tundra landscapes

2021 ◽  
Author(s):  
Jennifer Sobiech-Wolf ◽  
Tobias Ullmann ◽  
Wolfgang Dierking
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Surface ◽  
In Situ Measurements ◽  
German Research Foundation ◽  
Marine Research ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Infrared Measurements

<p>Satellite remote sensing as well as in-situ measurements are common tools to monitor the state of Arctic environments. However, remote sensing products often lack sufficient temporal and/or spatial resolution, and in-situ measurements can only describe the environmental conditions on a very limited spatial scale. Therefore, we conducted an air-borne campaign to connect the detailed in-situ data with poor spatial coverage to coarse satellite images. The SMART campaign is part of the ongoing project „Characterization of Polar Permafrost Landscapes by Means of Multi-Temporal and Multi-Scale Remote Sensing, and In-Situ Measurements“, funded by the German Research Foundation (DFG).  The focus of the project is to close the gap between in-situ measurements and space-borne images in polar permafrost landscapes. The airborne campaign SMART was conducted in late summer 2018 in north-west Canada, focussing on the Mackenzie-Delta region, which is underlain by permafrost and rarely inhabited. The land cover is either dominated by open Tundra landscapes or by boreal forests. The Polar-5 research-aircraft from the Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Germany, was equipped with a ground penetrating radar, a hyperspectral camara, a laserscanner, and an infrared temperature sensor amongst others. In parallel to the airborne acquisition, a team collected in-situ data on ground, including manual active layer depth measurements, geophysical surveying using 2D Electric Resistivity Tomography (ERT), GPR, and mapping of additional land cover properties. The database was completed by a variety of satellite data from different platforms, e.g. MODIS, Landsat, TerraSAR-X and Sentinel-1.  As part of the project, we analysed the performance of MODIS Land surfaces temperature products compared to our air-borne infrared measurements and evaluated, how long the land surface temperatures of this Arctic environment can be considered as stable. It turned out that the MODIS data differ up to 2°C from the air-borne measurements. If this is due to the spatial difference of the measurements or a result of data processing of the MODIS LST products is part of ongoing analysis.</p>

scholarly journals Land Cover Change and Landscape Transformations (2000–2018) in the Rural Municipalities of the Upper Silesia-Zagłębie Metropolis

2020 ◽  
Vol 12 (23) ◽  
pp. 9911
Author(s):  
Katarzyna Pukowiec-Kurda ◽  
Hana Vavrouchová
Keyword(s):  
Land Cover ◽  
Rural Areas ◽  
Arable Land ◽  
Mining Industry ◽  
Upper Silesia ◽  
Metropolitan Regions ◽  
Mining Areas ◽  
Rural Municipalities ◽  
High Degree ◽  
Landscape Transformations

Dynamic changes in the landscape have been observed in recent years. They are particularly visible in areas with a high degree of anthropopressure. An example of such areas is metropolitan regions and their immediate rural surroundings. The purpose of this article is to identify changes in land cover in the rural municipalities within metropolises and detect the processes of landscape transformation in rural areas, which are extremely sensitive to anthropopressure. The dynamics of land cover changes in the years 2000–2018 were determined using a change index (ChI), and their directions were determined using the indicator of changes in types of land cover. Corine Land Cover for level 2 groups (1.1–4.2) was used as research material, and the Upper Silesia-Zagłębie Metropolis was selected as the model area. The greatest changes in the landscape were observed in built-up areas, industrial areas, meadows and mining areas. This is due to the disappearance of the mining industry that was traditional for this region and the ongoing suburbanization process, as well as the re-industrialization of modern industry and the abandonment of arable land in rural areas.

scholarly journals Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology

2016 ◽  
Vol 8 (11) ◽  
pp. 905 ◽  
Author(s):  
Juan Laso Bayas ◽  
Linda See ◽  
Steffen Fritz ◽  
Tobias Sturn ◽  
Christoph Perger ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Mobile Technology ◽  
In Situ Data

A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data

2016 ◽  
Vol 8 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Benjamin Mack ◽  
Patrick Leinenkugel ◽  
Claudia Kuenzer ◽  
Stefan Dech
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
In Situ Data

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

2021 ◽  
Vol 13 (3) ◽  
pp. 1119-1133
Author(s):  
Raphaël d'Andrimont ◽  
Astrid Verhegghen ◽  
Michele Meroni ◽  
Guido Lemoine ◽  
Peter Strobl ◽  
...  
Keyword(s):  
Land Use ◽  
European Union ◽  
High Resolution ◽  
Land Cover ◽  
Earth Observation ◽  
The European Union ◽  
In Situ Data ◽  
Cardinal Directions ◽  
High Resolution Satellite Imagery

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.

scholarly journals SEMANTIC INDEXING OF TERRASAR-X AND IN SITU DATA FOR URBAN ANALYTICS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 185-188
Author(s):  
D. Espinoza Molina ◽  
K. Alonso ◽  
M. Datcu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Semantic Indexing ◽  
Land Use Land Cover ◽  
Test Dataset ◽  
In Situ Data ◽  
Types Of Information ◽  
Feedback Techniques ◽  
Geo Database

This paper presents the semantic indexing of TerraSAR-X images and in situ data. Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land cover catalogue. All the generated information is stored into a geo-database supporting the link between different types of information and the computation of queries and analytics. We used 11 TerraSAR-X scenes over Germany and LUCAS as in situ data. The semantic index is composed of about 73 land use land cover categories found in TerraSAR-X test dataset and 84 categories found in LUCAS dataset.

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