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  1. Article ; Online: Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

    Odile Close / Sophie Petit / Benjamin Beaumont / Eric Hallot

    Land, Vol 10, Iss 1, p

    2021  Volume 55

    Abstract: Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the ... ...

    Abstract Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.
    Keywords change detection ; Sentinel-2 ; LULUCF ; Agriculture ; S
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article: Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium

    Close, Odile / Benjamin, Beaumont / Fripiat, Xavier / Hallot, Eric / Petit, Sophie

    Land. 2018 Dec. 08, v. 7, no. 4

    2018  

    Abstract: Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and ... ...

    Abstract Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and Forestry (LULUCF) sector are still mostly relying on annual census and periodic surveys for such inventories. This study proposes a new approach based on per-pixel supervised classification using Sentinel-2 imagery from 2016 for mapping greenhouse gas emissions and removals associated with the LULUCF sector in Wallonia, Belgium. The Land Use/Cover Area frame statistical Survey (LUCAS) of 2015 was used as training data and reference data to validate the map produced. Then, we investigated the performance of four widely used classifiers (maximum likelihood, random forest, k-nearest neighbor, and minimum distance) on different training sample sizes. We also studied the use of the rich spectral information of Sentinel-2 data as well as single-date and multitemporal classification. Our study illustrates how open source data can be effectively used for land use and land cover classification. This classification, based on Sentinel-2 and LUCAS, offers new opportunities for LULUCF inventory of greenhouse gas on a European scale.
    Keywords cost effectiveness ; databases ; forestry ; greenhouse gas emissions ; greenhouse gases ; inventories ; land use and land cover maps ; land use change ; remote sensing ; satellites ; statistical analysis ; surveys ; Belgium
    Language English
    Dates of publication 2018-1208
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2682955-1
    ISSN 2073-445X
    ISSN 2073-445X
    DOI 10.3390/land7040154
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension

    Céline Bassine / Julien Radoux / Benjamin Beaumont / Taïs Grippa / Moritz Lennert / Céline Champagne / Mathilde De Vroey / Augustin Martinet / Olivier Bouchez / Nicolas Deffense / Eric Hallot / Eléonore Wolff / Pierre Defourny

    Data, Vol 5, Iss 117, p

    The 2018 Map for Wallonia

    2020  Volume 117

    Abstract: Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as ...

    Abstract Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.
    Keywords land cover ; map ; landscape ; remote sensing ; Bibliography. Library science. Information resources ; Z
    Subject code 710
    Language English
    Publishing date 2020-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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