Centre de documentation
scientifique

Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning / J.F. Roberts in Computers & geosciences, vol 167 (October 2022)  

Public [article]  inComputers & geosciences > vol 167 (October 2022) . - n° 105192 Titre : Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning Type de document : Article/Communication Auteurs : J.F. Roberts, Auteur ; R. Mwangi, Auteur ; F. Mukabi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image Sentinel-MSI [Termes IGN] informatique en nuage [Termes IGN] Kenya [Termes IGN] langage de programmation [Termes IGN] observation de la Terre [Termes IGN] Python (langage de programmation) [Termes IGN] surveillance forestière Résumé : (auteur) Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - “Python for Earth Observation”. Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences. Numéro de notice : A2022-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105192 Date de publication en ligne : 09/07/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101575 [article]

Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques / J.O. Ondieki in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102700 Titre : Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques Type de document : Article/Communication Auteurs : J.O. Ondieki, Auteur ; C.O. Mito, Auteur ; M.I. Kaniu, Auteur Année de publication : 2022 Article en page(s) : n° 102700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de groupement [Termes IGN] carte thématique [Termes IGN] classification par maximum de vraisemblance [Termes IGN] données géologiques [Termes IGN] image Landsat-OLI [Termes IGN] Kappa de Cohen [Termes IGN] Kenya [Termes IGN] minerai [Termes IGN] pollution radioactive [Termes IGN] précision de la classification [Termes IGN] radioactivité [Termes IGN] signature spectrale Résumé : (auteur) This study investigates the utility of using remote sensing and geographic information system techniques to accurately infer the presence of radioactive minerals in a typical high background radiation area (HBRA) by analyzing spectral signatures of associated soil, rocks and vegetation. To accomplish this, both unsupervised (K-Means Clustering) and supervised classification techniques based on a maximum likelihood classifier (MLC) were applied to Landsat-8 Imager data from Mrima Hill on Kenya's south coast. The hill is surrounded by dense tropical forest and deeply weathered soils which are rich in Nb, Th, and rare earth elements. Due to high activity concentrations of 232Th (>8 times higher than the world average value for soil), the hill has been designated as a geogenic HBRA. Based on the underlying geological formations, four classifications of vegetation and two classifications of soil/rocks were established and used to indicate the presence of radioactive minerals in the area. Measurements of air-absorbed gamma dose-rates in the area were successfully used to validate these findings. The application of the MLC method on Landsat satellite data shows that this method can be used as a powerful tool to explore and improve radioactive minerals mapping in HBRAs, the overall classification accuracy of Landsat8 OLI data using botanical technique is 80% and the Kappa Coefficient is 0.6. The overall classification accuracy using soil/rocks spectral signatures is 91% and the Kappa Coefficient is 0.7. Finally, the study demonstrated the general utility of remote sensing techniques in radioactive mineral surveys as well as environmental radiological assessments, particularly in resource-constrained settings. Numéro de notice : A2022-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102700 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102700 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99956 [article]

Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 42 - 58 Titre : Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery Type de document : Article/Communication Auteurs : Christian Geiss, Auteur ; Patrick Aravena Pelizari, Auteur ; Lukas Blickensdörfer, Auteur ; Hannes Taubenböck, Auteur Année de publication : 2019 Article en page(s) : pp 42 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage automatique [Termes IGN] classification [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Cologne [Termes IGN] échantillon [Termes IGN] échantillonnage [Termes IGN] image à très haute résolution [Termes IGN] image multibande [Termes IGN] invariant [Termes IGN] Kenya [Termes IGN] séparateur à vaste marge Résumé : (Auteur) We follow the idea of learning invariant decision functions for remote sensing image classification with Support Vector Machines (SVM). To do so, we generate artificially transformed samples (i.e., virtual samples) from available prior knowledge. Labeled samples closest to the separating hyperplane with maximum margin (i.e., the Support Vectors) are identified by learning an initial SVM model. The Support Vectors are used for generating virtual samples by perturbing the features to which the model should be invariant. Subsequently, the model is relearned using the Support Vectors and the virtual samples to eventually alter the hyperplane with maximum margin and enhance generalization capabilities of decision functions. In contrast to existing approaches, we establish a self-learning procedure to ultimately prune non-informative virtual samples from a possibly arbitrary invariance generation process to allow for robust and sparse model solutions. The self-learning strategy jointly considers a similarity and margin sampling constraint. In addition, we innovatively explore the invariance generation process in the context of an object-based image analysis framework. Image elements (i.e., pixels) are aggregated to image objects (as represented by segments/superpixels) with a segmentation algorithm. From an initial singular segmentation level, invariances are encoded by varying hyperparameters of the segmentation algorithm in terms of scale and shape. Experimental results are obtained from two very high spatial resolution multispectral data sets acquired over the city of Cologne, Germany, and the Hagadera Refugee Camp, Kenya. Comparative model accuracy evaluations underline the favorable performance properties of the proposed methods especially in settings with very few labeled samples. Numéro de notice : A2019-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.001 Date de publication en ligne : 12/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92666 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019053	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data / Elisa Schäfer in Ecological indicators, vol 64 (May 2016)  

Public [article]  inEcological indicators > vol 64 (May 2016) . - pp 49 - 58 Titre : Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data Type de document : Article/Communication Auteurs : Elisa Schäfer, Auteur ; Janne Heiskanen, Auteur ; Vuokko Heikinheimo, Auteur ; Petri Pellikka, Auteur Année de publication : 2016 Article en page(s) : pp 49 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] arbre (flore) [Termes IGN] biodiversité [Termes IGN] canopée [Termes IGN] forêt tropicale [Termes IGN] image aérienne [Termes IGN] Kenya [Termes IGN] placette d'échantillonnage [Termes IGN] segmentation d'image Résumé : (auteur) With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data. Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed. According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE = 3 species; r2 = 0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest. In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be applied also for estimating beta diversity. Numéro de notice : A2016-346 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecolind.2015.12.026 En ligne : http://dx.doi.org/10.1016/j.ecolind.2015.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81035 [article]

Mapping a future for Kenya’s Amboseli elephants / Mark Sowers in ArcNews, vol 37 n° 2 (Summer 2015) 

Public [article]  inArcNews > vol 37 n° 2 (Summer 2015) . - pp 20 - 21 Titre : Mapping a future for Kenya’s Amboseli elephants Type de document : Article/Communication Auteurs : Mark Sowers, Auteur Année de publication : 2015 Article en page(s) : pp 20 - 21 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] distribution spatiale [Termes IGN] Kenya [Termes IGN] Mammalia [Termes IGN] parc naturel national [Termes IGN] positionnement par GPS Résumé : (documentaliste) Le suivi des éléphants dans le parc national Amboseli du Kenya existe depuis plusieurs années et permet de connaître leurs parcours et leur répartition géographique. Le suivi par GPS permet de mieux comprendre leurs habitudes. Numéro de notice : A2015-531 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77718 [article]

Documents numériques

en open access Mapping a future for Kenya’s Amboseli elephants Adobe Acrobat PDF

An inventory of the above ground biomass in the Mau Forest Ecosystem, Kenya / Mwangi James Kinyanjui in Open journal of forestry, vol 4 n° 10 (July 2014)  

Permalink

Conflation of provisional cadastral and topographical datasets / David Nyangau Siriba (2011) 

Permalink

Cartography of human wildlife conflicts in Kenya / Elodie Buard (2007)    

Permalink

Comparison and integration of radar and optical data for land use / cover mapping / Nathaniel D. Herold in Geocarto international, vol 21 n° 4 (December 2006 - February 2007)  

Permalink

Assessing the accuracy of satellite derived global and national urban maps in Kenya / A.J. Tatem in Remote sensing of environment, vol 96 n° 1 (15/05/2005)  

Permalink

Calculating NDVI for NOAA/AVHRR data after atmospheric correction for extensive images using 6S code: a case study in the Marsabit district Kenya / K. Tachiiri in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 3 (May 2005)  

Permalink

Predicting mammal species richness and abundance using multi-temporal NDVI / B.O. Oindo in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 6 (June 2002)  

Permalink

Environnement et cartographie des camps de refugies au Kenya : une application de la vidéographie aérienne / L. Cambresy in Bulletin du comité français de cartographie, n° 166 (décembre 2000 - février 2001)

Permalink

Radar and optical data integration for land-use-land-cover mapping / Barry N. Haack in Photogrammetric Engineering & Remote Sensing, PERS, vol 66 n° 6 (June 2000)  

Permalink

The effects of vegetative barrier strips on surface runoff and runoff and soil erosion in Machakos Kenya / Mirjam Van Roode (2000)

Permalink