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Mapping grassland management intensity using Sentinel-2 satellite data / Marijke Elisabeth Bekkema in GI Forum, vol 2018 n° 1 ([01/01/2018])  

Public [article]  inGI Forum > vol 2018 n° 1 [01/01/2018] . - pp 194 - 213 Titre : Mapping grassland management intensity using Sentinel-2 satellite data Type de document : Article/Communication Auteurs : Marijke Elisabeth Bekkema, Auteur ; Marieke Eleveld, Auteur Année de publication : 2018 Article en page(s) : pp 194 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] habitat animal [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] oiseau [Termes descripteurs IGN] prairie Résumé : (auteur) For the conservation of biodiversity in general and the monitoring of meadow birds in particular, actual grassland - use intensity maps are highly desirable. A method to map and assess grassland management intensity was developed using C5.0 decision tree classification on Sentinel-2 satellite data. Monoculture and extensively managed grasslands on both peat and clay soils could be accurately detected at parcel level in Friesland, the Netherlands. Field - survey - based validation returned an overall classification accuracy of 84.3% (KHAT 0.65). The Sentinel-2 Red-Edge Position vegetation index was found to be a good indicator of fertilization. Availability of springtime imagery, preferably acquired in April before the first mowing date, is essential for accurate classification. The spectral responses of grassland types on peat and clay soils differ significantly. Hence, successful classification requires training data for both soil types. The resulting grassland management map was used to assess the distribution of meadow bird nests. Redshank (79%) and godwit (77%) in particular choose to breed on extensive parcels. With the increasing availability of satellite imagery, remote sensing techniques can be used to monitor agri-environmental measures (at parcel and landscape scale) that impact the conservation of grassland biodiversity. Numéro de notice : A2018-301 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1553/giscience2018_01_s194 En ligne : http://dx.doi.org/10.1553/giscience2018_01_s194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90399 [article]

Suivi écologique des prairies semi-naturelles : analyse statistique de séries temporelles denses d’images satellite à haute résolution spatiale / Maylis Lopes (2018) 

Public contenu dans 27èmes Journées de la Recherche de l'IGN / Journées Recherche de l'IGN 2018, 27es Journées (22 - 23 mars 2018; Cité Descartes, Champs-sur-Marne, France) (2018)   Titre : Suivi écologique des prairies semi-naturelles : analyse statistique de séries temporelles denses d’images satellite à haute résolution spatiale Type de document : Article/Communication Auteurs : Maylis Lopes, Auteur ; Mathieu Fauvel, Auteur ; Stéphane Girard, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2018 Conférence : Journées Recherche de l'IGN 2018, 27es Journées 22/03/2018 23/03/2018 Champs-sur-Marne France programme sans actes Importance : 49 p. Format : 30 x 21 cm Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] couleur (variable spectrale) [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] signature spectrale [Termes descripteurs IGN] surveillance écologique Résumé : (Auteur) Les prairies semi-naturelles (PSN) représentent une source importante de biodiversité dans les paysages agricoles qu’il est important de surveiller. La télédétection constitue un puissant outil pour assurer ce suivi grâce à la couverture spatiale des satellites et leur fréquence de revisite. Cependant jusqu’à récemment, le fonctionnement écologique des PSN a été peu étudié dans nos paysages fragmentés du fait des résolutions spatiale ou temporelle limitées des capteurs. Les satellites de nouvelle génération, tels que Sentinel-2, offrent de nouvelles opportunités pour le suivi des prairies car ils fournissent gratuitement des images des surfaces terrestres à haute résolution spatiale et temporelle. Cependant, le nouveau type de donnée issue de ces satellites implique des problèmes liés au traitement de données massives et de grande dimension en raison du nombre croissant de pixels à traiter et du nombre élevé de variables spectro-temporelles. Dans ce travail, nous analysons tout d’abord la réponse spectro-temporelle des PSN. Puis nous proposons des outils et méthodes robustes adaptés au suivi écologique des PSN à partir de séries temporelles denses d’images satellites à haute résolution spatiale. Numéro de notice : C2018-039 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91142

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Remote sensing of species diversity using Landsat 8 spectral variables / Sabelo Madonsela in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 116 - 127 Titre : Remote sensing of species diversity using Landsat 8 spectral variables Type de document : Article/Communication Auteurs : Sabelo Madonsela, Auteur ; Moses Azong Cho, Auteur ; Abel Ramoleo, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2017 Article en page(s) : pp 116 - 127 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Afrique du sud (état) [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] matrice de co-occurrence [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] savane Résumé : (Auteur) The application of remote sensing in biodiversity estimation has largely relied on the Normalized Difference Vegetation Index (NDVI). The NDVI exploits spectral information from red and near infrared bands of Landsat images and it does not consider canopy background conditions hence it is affected by soil brightness which lowers its sensitivity to vegetation. As such NDVI may be insufficient in explaining tree species diversity. Meanwhile, the Landsat program also collects essential spectral information in the shortwave infrared (SWIR) region which is related to plant properties. The study was intended to: (i) explore the utility of spectral information across Landsat-8 spectrum using the Principal Component Analysis (PCA) and estimate alpha diversity (α-diversity) in the savannah woodland in southern Africa, and (ii) define the species diversity index (Shannon (H′), Simpson (D2) and species richness (S) – defined as number of species in a community) that best relates to spectral variability on the Landsat-8 Operational Land Imager dataset. We designed 90 m × 90 m field plots (n = 71) and identified all trees with a diameter at breast height (DbH) above 10 cm. H′, D2 and S were used to quantify tree species diversity within each plot and the corresponding spectral information on all Landsat-8 bands were extracted from each field plot. A stepwise linear regression was applied to determine the relationship between species diversity indices (H′, D2 and S) and Principal Components (PCs), vegetation indices and Gray Level Co-occurrence Matrix (GLCM) texture layers with calibration (n = 46) and test (n = 23) datasets. The results of regression analysis showed that the Simple Ratio Index derivative had a higher relationship with H′, D2 and S (r2 = 0.36; r2 = 0.41; r2 = 0.24 respectively) compared to NDVI, EVI, SAVI or their derivatives. Moreover the Landsat-8 derived PCs also had a higher relationship with H′ and D2 (r2 of 0.36 and 0.35 respectively) than the frequently used NDVI, and this was attributed to the utilization of the entire spectral content of Landsat-8 data. Our results indicate that: (i) the measurement scales of vegetation indices impact their sensitivity to vegetation characteristics and their ability to explain tree species diversity; (ii) principal components enhance the utility of Landsat-8 spectral data for estimating tree species diversity and (iii) species diversity indices that consider both species richness and abundance (H′ and D2) relates better with Landsat-8 spectral variables. Numéro de notice : A2017-723 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88408 [article]

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Improving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 77 - 91 Titre : Improving the prediction of African savanna vegetation variables using time series of MODIS products Type de document : Article/Communication Auteurs : Miriam Tsalyuk, Auteur ; Maggi Kelly, Auteur ; Wayne M. Getz, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes descripteurs IGN] Afrique (géographie physique) [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] dégradation de la flore [Termes descripteurs IGN] Enhanced vegetation index [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Namibie [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] savane [Termes descripteurs IGN] variation Résumé : (Auteur) African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems. Numéro de notice : A2017-537 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86575 [article]

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Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 150 - 161 Titre : Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data Type de document : Article/Communication Auteurs : Timo P Pitkänen, Auteur ; Niina Käyhkö, Auteur Année de publication : 2017 Article en page(s) : pp 150 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] boisement naturel [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] erreur de classification [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] prairie [Termes descripteurs IGN] structure de données localisées Résumé : (Auteur) Mapping structural changes in vegetation dynamics has, for a long time, been carried out using satellite images, orthophotos and, more recently, airborne lidar acquisitions. Lidar has established its position as providing accurate material for structure-based analyses but its limited availability, relatively short history, and lack of spectral information, however, are generally impeding the use of lidar data for change detection purposes. A potential solution in respect of detecting both contemporary vegetation structures and their previous trajectories is to combine lidar acquisitions with optical remote sensing data, which can substantially extend the coverage, span and spectral range needed for vegetation mapping. In this study, we tested the simultaneous use of a single low-density lidar data set, a series of Landsat satellite frames and two high-resolution orthophotos to detect vegetation succession related to grassland overgrowth, i.e. encroachment of woody plants into semi-natural grasslands. We built several alternative Random Forest models with different sets of variables and tested the applicability of respective data sources for change detection purposes, aiming at distinguishing unchanged grassland and woodland areas from overgrown grasslands. Our results show that while lidar alone provides a solid basis for indicating structural differences between grassland and woodland vegetation, and orthophoto-generated variables alone are better in detecting successional changes, their combination works considerably better than its respective parts. More specifically, a model combining all the used data sets reduces the total error from 17.0% to 11.0% and omission error of detecting overgrown grasslands from 56.9% to 31.2%, when compared to model constructed solely based on lidar data. This pinpoints the efficiency of the approach where lidar-generated structural metrics are combined with optical and multitemporal observations, providing a workable framework to identify structurally oriented and dynamically organized landscape phenomena, such as grassland overgrowth. Numéro de notice : A2017-513 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86459 [article]

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Retrieving grassland canopy water content by considering the information from neighboring pixels / Binbin He in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)  

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Change detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)  

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Apport des données Sentinel-1 pour la cartographie des milieux humides / Cécile Cazals (2017)  

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Les prairies de l’estuaire de la Loire : étude de la dynamique de la végétation de 1982 à 2014 / Mathieu Le Dez in Mappemonde [en ligne], n° 119 (janvier 2017)  

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Exposure-related forest-steppe: A diverse landscape type determined by topography and climate / Martin Hais in Journal of Arid Environments, vol 135 (December 2016)  

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Plant community mycorrhization in temperate forests and grasslands: relations with edaphic properties and plant diversity / Maret Gerz in Journal of vegetation science, vol 27 n° 1 (January 2016)  

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UAS Experiences in Africa / Marius Schrôder in GIM international [en ligne], vol 29 n° 12 (December 2015)

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Land cover changes assessment using object-based image analysis in the Binah River watershed (Togo and Benin) / Hèou Maléki Badjana in Earth and space science, vol 2 n° 10 (October 2015)  

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Regional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine / Dmytro Movchan in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)  

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Understanding the effects of ALS pulse density for metric retrieval across diverse forest types / Phil Wilkes in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)

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