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Leibniz Institute of Ecological Urban and Regional Development
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Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesArchival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues / Arnaud Le Bris (2019)
Titre : Archival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Sébastien Giordano
Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] automatisation
[Termes IGN] chaîne de traitement
[Termes IGN] détection de changement
[Termes IGN] égalisation radiométrique
[Termes IGN] géoréférencement indirect
[Termes IGN] image numérisée
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du sol
[Termes IGN] orientation absolue
[Termes IGN] orthoimage
[Termes IGN] orthophotoplan argentique
[Termes IGN] vingtième siècleRésumé : (auteur) Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid20th century. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Thus, they offer time series data which can answer to a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. For instance, IGN (the French national mapping agency) has digitized its archival aerial photogrammetric surveys: images can be downloaded from a web service (http://remonterletemps.ign.fr/) Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. A first bottleneck for accurate comparison between epochs is their fine georeferencing. Such information has generally been lost and must be retrieved. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the-art shows that one major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is often manually performed, and extremely time-consuming. This contribution proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a the use of the coarse absolute image orientation from existing metadata to derive coarse Digital Surface Model (DSM) and orthoimage that are then used to improve absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. A new iteration of the georeferencing process can then be done using these ground references. At the end, images orientation is retrieved and orthoimages as well as digital surface models can be computed for each epoch. Another issue is the radiometric equalization of images (to remove atmospheric as well as vignetting effects), in order to obtain a homogeneous mosaic suitable with automatic classification processes. Last but not least, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, color, infrared...). Planimetric and altimetric accuracies of derived products depend on these characteristics. Thus, automatic change detection and analysis processes have to cope with these uncertainties. Numéro de notice : C2019-069 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97024
Titre : Joint analysis of SAR and optical satellite images time series for grassland event detection Type de document : Article/Communication Auteurs : Anatol Garioud Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] cohérence des données
[Termes IGN] détection d'événement
[Termes IGN] détection de changement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] prairie
[Termes IGN] puits de carboneRésumé : (auteur) Throughout Europe, grasslands are a major component of the landscape comprising 40% of agricultural land. Permanent Grassland (PM) means land used to grow herbaceous forage crops naturally (self-seeded) or through cultivation (sown) and that has not been included in the crop rotation of the holding for five years or more. PM are major ecosystems associated with high biodiversity which provide a wide range of ecosystem services (e.g. carbon sequestration, water quality, flood and erosion control). Grasslands have an important carbon storage capacity which is valuable for climate protection. Different studies have demonstrated that grassland managements such as grazing or mowing can cause significant effects on carbon storage in soils. Identifying and mapping grassland management practices over time can thus have important impact on climate studies. Remote sensing allows a synoptic and regular monitoring through systematic acquisitions of Earth Observation imagery. The emergence of free and easily Sentinel's satellite data provided by the European Copernicus program, offers new possibilities for grassland monitoring. Sentinel-1 (51) and Sentinel-2 (52) missions acquire radar and optical satellite image time series at high temporal resolution and fine spatial resolution. They fully match the requirements both for yearly and real-time monitoring. In this work, we target to jointly exploit both data sources to dynamically detect mowing events (MowEve) on permanent grasslands. Thematic related analysis of the datasets will highlight strengths and weaknesses of both optical and radar imagery. (i) 52 appears efficient for MowEve detection, with significant variations in the vegetation status that can be easily detected in the spectral signal extracted from the time series of images. But the temporal revisit of 52 although nominally 5 days is often reduced even by half due to the frequent cloud cover (ii) SAR images acquisitions being independent of illumination conditions or cloud cover allows for systematic acquisitions and revisit rate of 6 days. Data consistency makes S1 data essential during fast phenomena such as MowEve. Yet, radar data appears very sensitive to soil moisture, precipitations and geometrical properties making interpretation of their time series more challenging. MowEve detection being weakly supervised, the proposed methodology relies on applying traditional change detection strategies on a low-level fused 51 and S2 data representation. Recurrent Neural Networks will be trained to derive yearly or real-time synthetic 52 vegetation indices from both 52 and S1 observations. Furthermore, through attention mechanisms, our proposed RNN architecture will be able to take into account external data (climate, clouds, topography, etc.) so as to dynamically weight at parcel-level the contribution of optical and radar images. Such method will contribute to obtain dense temporal optical profiles without missing data and compatible with MowEve detection. An experimental evaluation will be carried out on a test site covering an area of 110x110 Km in France (Macon region). Object-oriented analysis will be presented based on permanent grasslands derived from the Land Parcel Identification System. The proposed approach will be compared with traditional MowEve methods essentially based on thresholding independently the different modalities. Numéro de notice : C2019-067 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97022
Titre : Retrieving relevant land cover and land use data to study urban climate change Type de document : Article/Communication Auteurs : Bénédicte Bucher Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Projets : URCLIM / Masson, Valéry Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] changement climatique
[Termes IGN] climat urbain
[Termes IGN] image Landsat
[Termes IGN] métadonnées géographiques
[Termes IGN] occupation du sol
[Termes IGN] pertinence
[Termes IGN] plateforme collaborative
[Termes IGN] recherche d'information géographique
[Termes IGN] spécification
[Termes IGN] spécification de processus
[Termes IGN] utilisation du solRésumé : (auteur) The study of urban local phenomena related to climate, like heat islands, road icing, streets overflow during high precipitation events or air pollution, is necessary to develop efficient adaptation strategies to climate change. The URCLIM project studies more specifically urban climate knowledge production and services design. It is funded by the "European Research Area for Climate Services" that targets "the user-driven development, translation and transfer of climate knowledge to researchers and decision—makers in policy and business [..] as well as guidance in the use of climate knowledge." Climate scientists model interactions between meteorological phenomena (wind, moisture, temperature) described at a given scale and the surface of earth described at a finer scale in order to calculate finer meteorological phenomena, e.g. temperature variations depending on trees in cities. The climate community designs such generic canopy models adapted for a set of similar places. To obtain land data required to feed these canopy models, instead of each team producing ad hoc land data on his experimental site, this community has developed a joint approach: 1) agree on common formal specifications of such land models, also known as Local climate Zones, 2) design a production procedure of such Local climate zones data affordable by the community itself. The World Urban Database and Access Portal Tools, WUDAPT support collaborative production of Local climate Zones level 0 (resolution from 500m to 1km) based on Landsat satellite imagery. Producing Local climate Zones level 1 (50 to 100 meters), requires other sources related to buildings and vegetation (Masson et al. 2019). This requires discovering and reusing heterogeneous spatial data whereas there is neither one search engine nor a set of well identified catalogues that can be searched with user-oriented query words. This presentation will concentrate firstly on analyzing what are the relevance criteria from the urban climate scientist perspective to retrieve an existing urban land model or to produce it. We consider for example an accessibility criterion as well as an extrapolation criterion. Second we review the contribution of available metadata and ontologies to make proper recommendations to this scientist who wishes to design an urban land model for his specific study. Important metadata are: features catalogues, spatial and temporal coverage, temporal, geometric and semantic resolutions and accuracies. Last we demonstrate a metadata curation process based on the URCLIM infolab, a collaborative metadata platform (Bucher and Van Damme 2018). Numéro de notice : C2019-066 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97021
Titre : The difficult way towards Land cover and land use data harmonization across scales, space and time in Europe Type de document : Article/Communication Auteurs : Dominique Laurent Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Projets : TimeMachine / Gouet-Brunet, Valérie Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] harmonisation des données
[Termes IGN] infrastructure européenne de données localisées
[Termes IGN] INSPIRE
[Termes IGN] métamodèle
[Termes IGN] occupation du sol
[Termes IGN] utilisation du solMots-clés libres : Knowledge Exchange Network Résumé : (auteur) Describing land, through its physical properties and functional characteristics, to support decision from local to global level, in particular to observe land evolution through time, at a sustainable cost for society is a domain with many challenges and opportunities. This is the domain of land cover and land use data design which receives attention from a vast community, from raw data providers (satellite imagery, in situ data) to data curators and integrators as well as users. One of the first attempts to harmonize Land Cover and Land Use data has been the INSPIRE Directive (voted in 2007) that aims to provide the legal framework for achieving a European Spatial Data Infrastructure where the Commission could reuse national data used by members for their national policies. Among its Implementing Rules, interoperability is addressed through the definition of common data models for land cover and for land use information. INSPIRE being based on existing data, these models have been defined to be quite flexible. On the one hand, this European legal context and the new technical opportunities may push data producers to design new land cover and land use products, with for example more concern for European reusability of national products. On the other hand, land cover and land use data are often used to compute evolution indicators, which requires stable enough or at least comparable specifications; which rather push data providers to stick to former data specification. More recently, UN-GGIM: Europe (United Nations initiative on Global Geographic Information Management) has set up a Working Group on spatial data the most useful to analyze, achieve or monitor the Sustainable Development Goals, called core data. This group defines priorities for the production of new data or the enhancement of existing one. Land Cover and Land Use are identified as core data themes. The EuroGeographics INSPIRE KEN (Knowledge Exchange Network) and EuroSDR organised a workshop in November 2017 on this topic: how to make the most of available technologies (in terms of precision, accuracy and cost) as well as how to achieve products comparability and reusability across scales, space and time. Main conclusion was that quite diverse national practices must be accounted, though the concept of separating land cover and land use was widely adopted. Besides, attendees express the need to connect to new communities: deep learning to cope with big data, and communities studying the surveyed phenomena to integrate more domain knowledge in land cover and land use surveying process. Last, meta-models like EAGLE supporting the comparison of classifications were recognized as a key SDI component.
The presentation will remind why data harmonization is useful, it will provide an overview of what has been achieved and explain the remaining difficulties.Numéro de notice : C2019-068 Affiliation des auteurs : IGN (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97023
