Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

By Jordi Inglada, Arthur Vincent, Marcela Arias, Benjamin Tardy, David Morin, Isabel Rodes. Remote Sensing, 2017

424 citations recorded by the source corpus.

A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/f98941fa2d1c7d22e663feeb7246d7b65b12b1a0

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Citations in the corpus

Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition (2020, methodology): “[82] used linear interpolation to fill the invalid pixels by using the previous and/or following cloud-free-date pixels to map land cover over France.”
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery (2020, background): “…and refinement of training data, the production of a large and geographically distributed training dataset, and the possibility of using the same land-cover classes as existing land-cover products (Inglada et al., 2017; Jokar Arsanjani et al., 2016b; Liu et al., 2017; Radoux et Earth Syst.”
Land cover 2.0 (2018, background): “…et al. 2003; Wulder et al. 2006; Pengra et al. 2015; Midekisa et al. 2017), pre-existing, yet scale appropriate, non-remote sensing sources (Inglada et al. 2017), or from other land cover classi ﬁ cation products that are subjected to ﬁ ltering (Hansen et al. 2008; Gray and Song 2013;…”
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, neutral): “This makes it possible for maps to be reproducible and to be automatically produced at a global scale [4].”
Self-Supervised Pretraining of Transformers for Satellite Image Time Series Classification (2020, methodology): “For this reason, SITS have been widely used in variousapplicationdomains,suchasecology[4],agriculture[5], forest[6],landmanagement[7],disastermonitoring[8]riskas-sessment[9],etc.Inthemeantime Inthemeantime,newchallengesarealsobeing introducedbythequestionofhowtoextractvaluableknowledge…”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, background): “In-situ collection of representative training data is di ﬃ cult over large, remote or distant areas (Inglada et al. 2017) and the use of timely high-resolution satellite or aerial imagery for this purpose is not always feasible and often cost-prohibitive.”
Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas (2017, background): “This map is a thematic raster file with values between 11 and 222, where each value corresponds to a type of land cover [64].”
Remote Sensing and Cropping Practices: A Review (2018, background): “However, these data raise new issues, and techniques have to be developed to handle irregularly-spaced time series and to identify and remove cloud contaminated observations, as found in the Harmonic Analysis of Time-Series (HANTS) [213], or the temporal gap-filling and resampling method in the iota”
Analyzing concept drift and shift from sample data (2018, neutral): “9 (Inglada et al. 2017).”