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Catalogue GéoSAS
type: Catalog
label: Catalogue GéoSAS (UMR SAS - INRAE - Institut Agro)
open search description: eng/portal.opensearch
uri lookup endpoint: eng/rdf.search?any=
publisher: UMR SAS - INRAE - Institut Agro
themes: Thésaurus INRAE
themes: Registre de thème INSPIRE
themes: GEMET
themes: UMR SAS Themes
themes: INSPIRE Service taxonomy
themes: GéoBretagne v 2.0
themes: Continents, countries, sea regions of the world.
themes: Themes Parchemins
language: fre
dataset: An Unexpectedly Large Count of Trees in the West African Sahara and Sahel - Bassin Arachidier au Sénégal
dataset: UMR SAS - INRAE - Institut Agro
dataset: Thésaurus INRAE
dataset: Registre de thème INSPIRE
dataset: GEMET
dataset: UMR SAS Themes
dataset: INSPIRE Service taxonomy
dataset: GéoBretagne v 2.0
dataset: Continents, countries, sea regions of the world.
dataset: Themes Parchemins
dataset: Urban sprawl
dataset: remote sensing
dataset: vegetation map
dataset: deep learning
dataset: Very High spatial resolution optical imagery
dataset: DOI Indice de fragmentation des espaces non artificialisés
dataset: Indice_fragmentation2019
dataset: HySPex image
dataset: Vector Layer WARNING: 15Gb!!
dataset: Geopackage
dataset: NASA African trees tilemap
dataset: utm_28_tiles
dataset: utm_29_tiles
dataset: AgroParisTech - UMR TETIS
dataset: INRAE - UMR TETIS
dataset: CEVA - Centre d'Étude et de Valorisation des Algues
dataset: UMR DECOD - Agrocampus Ouest
dataset: ONERA
dataset: UMR TETIS - CNRS
dataset: CNRS UMR TETIS
dataset: NASA ORNL DAAC
dataset: INRAE - UMR LISAH
dataset: Laboratoire Écologie Fonctionnelle et Environnement (CNRS, INTP)
dataset: DRAAF OCCITANIE
dataset: Direction del’Aménagement, du Foncier et de l’Urbanisme Occitanie
dataset: BRL
dataset: CACG
dataset: LETG - Univ Rennes 2
dataset: IGN
dataset: Aix-Marseille Université
catalog record:
  references: http://localhost:8080/geonetwork/srv/resources/records/999888e6-99dc-428c-b7bc-63e645e2dcbd/formatters/xml
catalog record: issued
catalog record: modified
description: The Geoffrey's Tube Z3950 Server (Sample Record - Please Delete!)
  abstract: This catalog is for registering all metadata records held by the Geofffrey's Tube Palace Hotel Ballroom.
  updated: 2010-03-24
catalog record:
  issued: 2022-05-17
  modified: 2022-05-17
  references: http://localhost:8080/geonetwork/srv/resources/records/570ff399-9710-48d0-981e-9ee591ac396f/formatters/xml
dataset: Projet Artisols - Densité du bâti - Occitanie - 2015 à 2019
  abstract: La densité de bâti est calculée par maille de 150 mètres de côté et sur la base d'une extraction du bâti à partir d'imagerie très haute résolution spatiale (1.5m) SPOT 6/7, pour les années 2015 à 2019.
  theme: Urban sprawl
  theme: imageryBaseMapsEarthCover
  spatial: Polygon
    as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-0.423392844 42.29544137, -0.423392844 45.03704172, 4.87182105 45.03704172, 4.87182105 42.29544137, -0.423392844 42.29544137))
  temporal: 2015-01-01T00:00:00Z / 2019-12-31T00:00:00Z
  updated: 2022-05-17T19:10:29
  publisher: INRAE - UMR TETIS
  accrual periodicity: notPlanned
  language: fre
  license: license
  distribution: DY5FWD
  distribution: Indice_fragmentation2019
  data quality: Cette donnée a été générée selon un maillage régulier (mailles de 150 x 150 m) dans lequel a été calculée la densité de bâti à partir des données sur le bâti résidentiel et d’activité uniquement (résolution spatiale de 1.5m). Ce calcul a donc été obtenu en faisant le rapport entre le nombre de pixels bâtis et celui de pixels total dans chacune des mailles.
catalog record:
  issued: 2022-05-23
  modified: 2022-05-23
  references: http://localhost:8080/geonetwork/srv/resources/records/b1d9869a-934d-4b60-a48f-8ed8947e97a7/formatters/xml
dataset: Cartographie de l'occupation du sol de la zone des Niayes (Sénégal) en 2018 (1.5 m de résolution)
  abstract: La zone d'étude se situe au nord ouest du Sénégal et s'étale sur 5 100km². Elle inclue la ville de Thiès et une partie de la ville de Dakar, et remonte jusqu'à Kébémer, aux portes du désert de Loumpoul. La carte d'occupation 2018 a été réalisée à partir d'une image à Très Haute Résolution Spatiale (THRS) à 1.5m Spot6 acquise le 15/10/2010 et une série temporelle d'images Sentinel2 (résolution 10m) acquises entre le 01/01/2018 et le 31/12/2018. Le traitement a été réalisé via la chaine de traitement Moringa développée dans l'UMR TETIS. La méthodologie s'appuie sur une Segmentation Orientée Objet de l'image THRS puis d'une classification de chaque polygone (algorithme Random Forest) s'appuyant sur l'image SPOT6, la série temporelle Sentinel2, le SRTM à 30m et de nombreux indices calculés à partir de ces images (NDVI, indices de texture, pente,...). L’algorithme a été entrainé via un jeu de données acquises sur le terrain complété de données obtenues par photo-interprétation. Des corrections manuelles par photo-interprétation ont été réalisées afin d'améliorer le résultat. La précision globale est de 93%. La classification contient 13 classes d'occupation du sol : culture irriguée hors bas-fond, culture irriguée de bas-fond, culture pluviale, plantation, dune sable - arbustes, eau, savane arbustive, savane herbacée, sol nu, sol inondable, sol faiblement végétalisé, végétation naturelle dense et zone urbaine.
  theme: Cartographie de l'occupation du sol
  theme: Classification des sols
  theme: Télédétection
  theme: http://localhost:8080/geonetwork/srv/resources/registries/vocabularies/General/concepts/Spot6/7
  theme: Sentinel-2
  theme: Environnement
  theme: environment
  theme: imageryBaseMapsEarthCover
  spatial: Polygon
    as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-17.4065 14.4563, -17.4065 15.4181, -16.2306 15.4181, -16.2306 14.4563, -17.4065 14.4563))
  temporal: 2021-04-01T00:00:00Z / 2021-04-01T00:00:00Z
  updated: 2022-05-17T19:09:59
  publisher: CIRAD - UMR TETIS
  accrual periodicity: notPlanned
  language: fre
  license: license
  distribution: KJAS6S
  distribution: wms
  data quality: Cette carte a été produite avec la chaîne de traitement Moringa
description: title
description: issued
description: modified
concept: Urban sprawl
  in scheme: General
concept: Cartographie de l'occupation du sol
  in scheme: General
concept: Classification des sols
  in scheme: General
concept: Télédétection
  in scheme: General
concept: Spot6/7
  in scheme: General
concept: Sentinel-2
  in scheme: General
concept: Environnement
  in scheme: General
distribution: DOI Densité urbaine
  access URL: https://doi.org/10.15454/DY5FWD
  format: file for download
distribution: DensBati_TU2019
  access URL: Indice_fragmentation2019
  format: OGC:WMS
distribution: DOI
  access URL: http://dx.doi.org/10.18167/DVN1/KJAS6S
  format: file for download
distribution: Classif_2018
  access URL: http://idg-tetis-gs.teledetection.fr:8080/geoserver/idg-tetis/wms/
  format: OGC:WMS
organization: Geoffrey's Tube Australia
organization: Geoffrey's Tube Craftsmen
  member: http://localhost:8080/geonetwork/srv/resources/persons/d79890e274
organization: INRAE - UMR TETIS

UMR SAS - INRAE - Institut Agro
type: Organization

Thésaurus INRAE
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

Registre de thème INSPIRE
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

GEMET
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

UMR SAS Themes
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

INSPIRE Service taxonomy
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

GéoBretagne v 2.0
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

Continents, countries, sea regions of the world.
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

Themes Parchemins
type: ConceptScheme
uri: https://geosas.fr:/geonetwork/srv/eng/thesaurus.download

http://localhost:8080/geonetwork/srv/resources/records/3dbfb4b3-3177-445e-8f1e-c26fbac9a29a
type: CatalogRecord
issued: 2022-05-17
modified: 2022-05-17
references: http://localhost:8080/geonetwork/srv/resources/records/3dbfb4b3-3177-445e-8f1e-c26fbac9a29a/formatters/xml
format: text/html
  type: IMT
  label: HTML

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format: application/xml
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  label: XML

Projet Artisols - Indice de fragmentation des espaces non artificialisés - Occitanie - 2019
type: Dataset
abstract: Le territoire régional est composé de taches urbaines de différentes tailles et formes, distribuées de manière variée. Les espaces urbains sont reliés les uns aux autres par le réseau routier, en particulier le réseau composé des routes primaires et secondaires. Cet espace urbain et routier ainsi composé est fortement artificialisé avec une forte composante d’imperméabilisation qui perdure au fil du temps. La dynamique spatiale et temporelle de cet espace peut être évaluée par les surfaces qui deviennent artificialisées, par la consommation des surfaces interstitielles, généralement agricoles ou naturelles. Ces surfaces à faible imperméabilisation, moins artificialisées, sont ainsi des îlots séparés les uns des autres par la trame urbaine et routière. Ces ilots sont de tailles variées et composent une structure fragmentée.Cet indice prend la forme d'une taille effective de maille, à savoir une grandeur qui exprime « la probabilité que deux points choisis au hasard dans un territoire [...] ne soient pas séparés par des obstacles tels que les voies de communication ou des zones bâties » (J. Jaeger, 2007).
theme: Urban sprawl
theme: imageryBaseMapsEarthCover
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-0.423392844 42.29544137, -0.423392844 45.03704172, 4.87182105 45.03704172, 4.87182105 42.29544137, -0.423392844 42.29544137))
temporal: 2019-01-01T00:00:00Z / 2019-12-31T00:00:00Z
updated: 2022-05-17T19:10:33
publisher: INRAE - UMR TETIS
accrual periodicity: notPlanned
language: fre
license: license
distribution: DOI Indice de fragmentation des espaces non artificialisés
distribution: Indice_fragmentation2019
data quality: L'indice de fragmentation des milieux non artificialisés est construit à partir des zones qui ne sont ni des taches artificialisées à 100 mètres ni du réseau routier. Cette grandeur est ici exprimée en km². Le calcul de la taille effective de maille nécessite de construire une couche géographique des ilots non urbains et non routiers. En mode vectoriel, cela est établie en faisant l’union des polygones de taches urbaines, des routes (qui ont été transformées en polygones par zones tampons) et de l’emprise de la Région Occitanie. L’union produit l’ensemble des polygones présents dans la région, dont ceux taches urbaines, ceux en zones tampons routiers, et ceux en reliquat dans la région. Ce sont ces derniers qui sont les ilots non urbains et non routiers. Une formule de taille effective de maille est ensuite calculée.Cet indice est calculé par commune et pour l'année 2019. Plus il est faible, plus les espaces non artificialisés sont fragmentés.

http://localhost:8080/geonetwork/srv/resources/records/ba5aec36-a8ba-43ab-93ed-db95585f6cf2
type: CatalogRecord
issued: 2022-05-17
modified: 2022-05-17
references: http://localhost:8080/geonetwork/srv/resources/records/ba5aec36-a8ba-43ab-93ed-db95585f6cf2/formatters/xml
format: text/html
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format: application/xml
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  label: XML

Toulouse 2015 Hyperspectral 1.6 spatial resolution VNIR - SWIR image
type: Dataset
abstract: Full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.
theme: geoscientificInformation
theme: environment
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((1.2842469 43.4444622, 1.2842469 43.7644622, 1.6042469 43.7644622, 1.6042469 43.4444622, 1.2842469 43.4444622))
temporal: 2015-07-01T00:00:00Z / 2015-07-31T00:00:00Z
updated: 2022-05-17T19:09:10
publisher: UMR TETIS - CNRS
accrual periodicity: notPlanned
language: eng
license: license
distribution: HySPex image
data quality: Full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.

http://localhost:8080/geonetwork/srv/resources/records/51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c
type: CatalogRecord
primary topic: http://localhost:8080/geonetwork/srv/resources/resources/https://doi.org/10.3334/ORNLDAAC/1832
issued: 2022-05-17
modified: 2022-05-17
references: http://localhost:8080/geonetwork/srv/resources/records/51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c/formatters/xml
format: text/html
  type: IMT
  label: HTML

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format: application/xml
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  label: XML

An Unexpectedly Large Count of Trees in the West African Sahara and Sahel - Bassin Arachidier au Sénégal
type: Dataset
abstract: This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50 cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NVDI) images at 0.5 m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time.
theme: remote sensing
theme: vegetation map
theme: deep learning
theme: Very High spatial resolution optical imagery
theme: environment
theme: imageryBaseMapsEarthCover
theme: biota
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-17.14375 13.49101, -17.14375 15.35907, -13.91848 15.35907, -13.91848 13.49101, -17.14375 13.49101))
temporal: 2005-11-01T00:00:00Z / 2018-03-31T00:00:00Z
updated: 2022-05-17T19:08:39
publisher: NASA ORNL DAAC
accrual periodicity: asNeeded
language: eng
license: unrestricted
distribution: Vector Layer WARNING: 15Gb!!
distribution: Geopackage
distribution: NASA African trees tilemap
distribution: utm_28_tiles
distribution: utm_29_tiles
data quality: The mapping of woody plants at the level of single trees was achieved by the use of satellite data at very high spatial resolution (0.5 m) from DigitalGlobe satellites, combined with modern machine-learning techniques. More than 50,000 DigitalGlobe multispectral images from the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites, were collected from 2005–2018 (in November to March) from 12° to 24° N latitude within Universal Transverse Mercator zones 28 and 29 (provided under the NextView license from the National Geospatial Intelligence). Normalized difference vegetation index (NDVI) images were used to distinguish tree crowns from the non-vegetated background because the images were taken from a period during which only woody plants are photosynthetically active in the study area. A set of decision rules was applied to select images for the mosaic, consisting of 25 × 25 km tiles. This resulted in 11,128 images that were used for the study. The neural network model (UNet; publicly available at https://doi.org/10.5281/zenodo.3978185) was used to automatically segment the tree crowns—that is, to detect tree crowns in the input images. The segmented areas were then converted to polygons for counting the trees and measuring their crown size. Using machine learning coupled to training data of 89,899 manually delineated and annotated trees, the location of individual trees over 1,300,000 km2 and their crown area were determined from the input images. Every tree with a crown area >3 m2 was enumerated resulting in 1,837,565,501 trees.

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type: CatalogRecord
issued: 2022-05-17
modified: 2022-05-17
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ArtiSols Project
type: Dataset
abstract: Le projet ArtiSols s’inscrit dans une démarche d’évaluation et de qualification du processus d’artificialisation des sols en région Occitanie. L’accentuation de ce phénomène face à l’accroissement des enjeux liés au logement, à l’alimentation des populations, à la consommation énergétique et des ressources naturelles pose la question des impacts sur les sols, essentiels à la régulation des écosystèmes mais également à la production agricole. C’est pourquoi la caractérisation de l’artificialisation constitue un point de départ dans la maîtrise de politiques d’aménagement adaptées aux enjeux des territoires. Ce projet régional s’articule de façon complémentaire avec le projet national piloté par le Ministère de la Transition Écologique et Solidaire (DGALN) dans le cadre du Plan Biodiversité (Action 7, visant la mise en place d’un observatoire national sur l’artificialisation des sols).
keyword: Artificialisation des sols
keyword: croissance urbaine
keyword: télédétection
keyword: occupation des sols
keyword: qualité
publisher: INRAE - UMR TETIS
language: eng

http://localhost:8080/geonetwork/srv/resources/records/83867504-286c-4436-b3fc-436ffdc1d912
type: CatalogRecord
issued: 2022-05-17
modified: 2022-05-17
references: http://localhost:8080/geonetwork/srv/resources/records/83867504-286c-4436-b3fc-436ffdc1d912/formatters/xml
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http://localhost:8080/geonetwork/srv/resources/records/83867504-286c-4436-b3fc-436ffdc1d912/formatters/xml
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HYEP Project
type: Dataset
abstract: ObjectivesThe hyperspectral images (HI) is at the moment still too poorly considered; nevertheless its specificities make a weighty auxiliary for the monitoring of the elements of the urban area. The HYEP project has for objective to propose a panel of methods and processes designed for hyperspectral imaging. We take into account other existing sensors in order to compare the performances. If the IH is complementary to the sensors HRS and VHRS due to its better radiometric richness, it allows to identify and to characterize the natural or anthropogenic elements in a complementary perspective. To this end methods for the extraction of information had to be adapted, created even. The methodological part of the project articulated at the same time in the solidification of the current approaches and the design of new methods. Results have been presented along the project duration to scientific community and local authorities. One of the milestones of the works was the comparison of the results to various spatial resolutions to specify the contribution of such a hyperspectral sensor with regard to those existing or to come. Data and data processing Methods: existing or adapted The methods were chosen among all the existing methods by adapting them to the signal, among spectral ranges and to the characteristics of urban areas. Since data arose from airborne platforms, the first developments were realized to counter the effects of the atmosphere on the IH (atmospheric correction - 3 tested methods) and a database of spectral signature for diverse elements of land use in town (roofs, roads, the vegetation etc.) was established. It allowed encircling better the spectral values of materials. It was set up based simultaneously on the literature, in situ and laboratory measurements. Its contributions in various classification processing were tested. Methods for information, extraction, pansharpening or classification purposes were used for various spatial and spectral resolutio…
keyword: remote sensing
keyword: VHRS imagery
keyword: HYPERSPECTRAL imagery
keyword: urban studies
publisher: UMR TETIS - CNRS
language: eng

Urban sprawl
type: Concept
in scheme: General

remote sensing
type: Concept
in scheme: General

vegetation map
type: Concept
in scheme: General

deep learning
type: Concept
in scheme: General

Very High spatial resolution optical imagery
type: Concept
in scheme: General

DOI Indice de fragmentation des espaces non artificialisés
type: Distribution
format: file for download

Indice_fragmentation2019
type: Distribution
format: OGC:WMS

HySPex image
type: Distribution
format: file for download

Vector Layer WARNING: 15Gb!!
type: Distribution
format: file for download

Geopackage
type: Distribution
format: file for download

NASA African trees tilemap
type: Distribution
format: file for download

utm_28_tiles
type: Distribution
format: OGC:WMS

utm_29_tiles
type: Distribution
format: OGC:WMS

AgroParisTech - UMR TETIS
type: Organization

INRAE - UMR TETIS
type: Organization

CEVA - Centre d'Étude et de Valorisation des Algues
type: Organization

UMR DECOD - Agrocampus Ouest
type: Organization

ONERA
type: Organization

UMR TETIS - CNRS
type: Organization

CNRS UMR TETIS
type: Organization

NASA ORNL DAAC
type: Organization

INRAE - UMR LISAH
type: Organization
member: INRAE - UMR LISAH

Laboratoire Écologie Fonctionnelle et Environnement (CNRS, INTP)
type: Organization
member: http://localhost:8080/geonetwork/srv/resources/persons/d79889e9341

DRAAF OCCITANIE
type: Organization
member: http://localhost:8080/geonetwork/srv/resources/persons/d79889e9382

Direction del’Aménagement, du Foncier et de l’Urbanisme Occitanie
type: Organization
member: http://localhost:8080/geonetwork/srv/resources/persons/d79889e9423

BRL
type: Organization
member: http://localhost:8080/geonetwork/srv/resources/persons/d79889e9465

CACG
type: Organization
member: http://localhost:8080/geonetwork/srv/resources/persons/d79889e9506

LETG - Univ Rennes 2
type: Organization

IGN
type: Organization

Aix-Marseille Université
type: Organization

Jean-Pierre Chéry
type: Agent

Eric Barbe
type: Agent

Alexia Cornic
type: Agent

Xavier Briottet
type: Agent

Christiane Weber
type: Agent

Claudia Lavalley
type: Agent

NASA
type: Agent

Kenji OSE
type: Agent

Remi CRESSON
type: Agent

INRAE - UMR LISAH
type: Agent

Thomas HOUET
type: Agent

Clément MALLET
type: Agent

Sébastien GADAL
type: Agent

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http://localhost:8080/geonetwork/srv/resources/records/570ff399-9710-48d0-981e-9ee591ac396f/formatters/xml
format: application/xml
  type: IMT
  label: XML

http://localhost:8080/geonetwork/srv/resources/records/570ff399-9710-48d0-981e-9ee591ac396f
format: text/html
  type: IMT
  label: HTML

http://localhost:8080/geonetwork/srv/resources/records/b1d9869a-934d-4b60-a48f-8ed8947e97a7/formatters/xml
format: application/xml
  type: IMT
  label: XML

http://localhost:8080/geonetwork/srv/resources/records/b1d9869a-934d-4b60-a48f-8ed8947e97a7
format: text/html
  type: IMT
  label: HTML

CIRAD - UMR TETIS

UMR 1069 SAS INRA - Agrocampus Ouest

Kenji Ose

Eric Barbe

Alexia Cornic

Audrey Jolivot

Urban sprawl
in scheme: General

Cartographie de l'occupation du sol
in scheme: General

Classification des sols
in scheme: General

Télédétection
in scheme: General

Spot6/7
in scheme: General

Sentinel-2
in scheme: General

Environnement
in scheme: General

DOI Densité urbaine
access URL: https://doi.org/10.15454/DY5FWD
format: file for download

DensBati_TU2019
access URL: Indice_fragmentation2019
format: OGC:WMS

DOI
access URL: http://dx.doi.org/10.18167/DVN1/KJAS6S
format: file for download

Classif_2018
access URL: http://idg-tetis-gs.teledetection.fr:8080/geoserver/idg-tetis/wms/
format: OGC:WMS

Geoffrey's Tube Australia

Geoffrey's Tube Craftsmen
member: http://localhost:8080/geonetwork/srv/resources/persons/d79890e274

INRAE - UMR TETIS

CIRAD - UMR TETIS

UMR 1069 SAS INRA - Agrocampus Ouest

Kenji Ose

Eric Barbe

Alexia Cornic

Audrey Jolivot

Urban sprawl
in scheme: General

Cartographie de l'occupation du sol
in scheme: General

Classification des sols
in scheme: General

Télédétection
in scheme: General

Spot6/7
in scheme: General

Sentinel-2
in scheme: General

Environnement
in scheme: General

DOI Densité urbaine
access URL: https://doi.org/10.15454/DY5FWD
format: file for download

DensBati_TU2019
access URL: Indice_fragmentation2019
format: OGC:WMS

DOI
access URL: http://dx.doi.org/10.18167/DVN1/KJAS6S
format: file for download

Classif_2018
access URL: http://idg-tetis-gs.teledetection.fr:8080/geoserver/idg-tetis/wms/
format: OGC:WMS

Geoffrey's Tube Australia

Geoffrey's Tube Craftsmen
member: http://localhost:8080/geonetwork/srv/resources/persons/d79890e274

INRAE - UMR TETIS

CIRAD - UMR TETIS

UMR 1069 SAS INRA - Agrocampus Ouest

Kenji Ose

Eric Barbe

Alexia Cornic

Audrey Jolivot