Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data

Weigand, Matthias; Staab, Jeroen; Wurm, Michael; Taubenböck, Hannes

doi:10.1016/j.jag.2020.102065

Cited by 82 publications

(71 citation statements)

References 40 publications

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“…and 10 ). Recently, several researchers have started to use LUCAS data in large scale land cover mapping processes, especially as a source of training and/or validation data for supervised classification approaches at regional/national scale [14][15][16][17][18][19][20] .…”

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confidence: 99%

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

et al. 2020

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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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confidence: 99%

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

et al. 2020

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“…As we were only interested in agricultural areas, we masked all non-agricultural landscapes using land cover maps. We considered two products: the CORINE land cover data of 2018 and the Land Cover DE product of the German Aerospace Center (DLR) [28,29]. Previous investigation demonstrated superior quality of the DLR product in our study area, but we still occasionally observed some urban and other non-agricultural areas as being misclassified.…”

Section: Land Cover Maskmentioning

confidence: 99%

Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Wagner

Oppelt

2020

Remote Sensing

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Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage.

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“…PHC has the capacity, which is not available in other UMX-based methods such as the STDFA, to easily group together pixels that have similar temporal reflectance changes. The land cover map is a simple and effective division of surface information [53][54][55] and is unchanged in a short time period. Therefore, land cover maps can be used to coarsely classify time-series images [56][57][58].…”

Section: Fcmstrfm Improvements To Existing Modelsmentioning

confidence: 99%

A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

et al. 2020

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The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

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Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data

Cited by 82 publications

References 40 publications

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

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

Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

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