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 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.

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.

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

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.

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

2020 ◽  
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.

scholarly journals Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

2020 ◽  
Vol 12 (17) ◽  
pp. 2735 ◽  
Author(s):  
Carlos M. Souza ◽  
Julia Z. Shimbo ◽  
Marcos R. Rosa ◽  
Leandro L. Parente ◽  
Ane A. Alencar ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Data Series ◽  
Forest Conversion ◽  
Pixel Resolution ◽  
Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

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