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Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data / Kalifa Goïta in Geocarto international, vol 34 n° 3 ([01/03/2019])  

Public [article]  inGeocarto international > vol 34 n° 3 [01/03/2019] . - pp 243 - 259 Titre : Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data Type de document : Article/Communication Auteurs : Kalifa Goïta, Auteur ; Jacques Mouloungou, Auteur ; Goze Bertin Bénié, Auteur Année de publication : 2019 Article en page(s) : pp 243 - 259 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] forêt tropicale [Termes IGN] Gabon [Termes IGN] hauteur des arbres [Termes IGN] image Landsat-ETM+ [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier local [Termes IGN] Libreville (Gabon) [Termes IGN] mangrove [Termes IGN] MNS SRTM [Termes IGN] puits de carbone Résumé : (Auteur) The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R2 > 0.85). Numéro de notice : A2019-450 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1386720 Date de publication en ligne : 06/02/2018 En ligne : https://doi.org/10.1080/10106049.2017.1386720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92838 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2019031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Rule-based classification of a very high resolution image in an urban environment using multispectral segmentation by cartographic data / M. Bouziani in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 8 (August 2010) . - pp 3198 - 3211 Titre : Rule-based classification of a very high resolution image in an urban environment using multispectral segmentation by cartographic data Type de document : Article/Communication Auteurs : M. Bouziani, Auteur ; Kalifa Goïta, Auteur ; D. He, Auteur Année de publication : 2010 Article en page(s) : pp 3198 - 3211 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] base de données localisées [Termes IGN] classification à base de connaissances [Termes IGN] détection de changement [Termes IGN] données cartographiques [Termes IGN] image à très haute résolution [Termes IGN] milieu urbain [Termes IGN] précision de la classification [Termes IGN] segmentation d'image Résumé : (Auteur) Classification algorithms based on single-pixel analysis often do not give the desired result when applied to high-spatial-resolution remote-sensing data. In such cases, classification algorithms based on object-oriented image segmentation are needed. There are many segmentation algorithms in the literature, but few have been applied in urban studies to classify a high-spatial-resolution remote-sensing image. Furthermore, the user must specify the spectral and spatial parameters that are data dependent. In this paper, we propose an automatic multispectral segmentation algorithm inspired by the specific idea of guiding a classification process for a high-spatial-resolution remote-sensing image of an urban area using an existing digital map of the same area. The classification results could be used, for example, for high-scale database updating or change-detection studies. The algorithm developed uses digital maps and spectral data as inputs. It generates the segmentation parameters automatically. The algorithm is able to provide a segmented image with accuracy greater than 90%. The segmentation results are then used in a rule-based classification using spectral, geometric, textural, and contextual information. The classification accuracy of the proposed rule-based classification is at least 17% greater than the maximum-likelihood classification results. Results and future improvements will be discussed. Numéro de notice : A2010-308 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2044508 En ligne : https://doi.org/10.1109/TGRS.2010.2044508 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30502 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge / M. Bouziani in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 1 (January - February 2010)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 65 n° 1 (January - February 2010) . - pp 143 - 153 Titre : Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge Type de document : Article/Communication Auteurs : M. Bouziani, Auteur ; Kalifa Goïta, Auteur ; D. He, Auteur Année de publication : 2010 Article en page(s) : pp 143 - 153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] base de données cartographiques [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à très haute résolution [Termes IGN] image Ikonos [Termes IGN] image Quickbird [Termes IGN] milieu urbain [Termes IGN] Québec (Canada) [Termes IGN] Rabat (Maroc) Résumé : (Auteur) The updating of geodatabases (GDB) in urban environments is a difficult and expensive task. It may be facilitated by an automatic change detection method. Several methods have been developed for medium and low spatial resolution images. This study proposes a new method for change detection of buildings in urban environments from very high spatial resolution images (VHSR) and using existing digital cartographic data. The proposed methodology is composed of several stages. The existing knowledge on the buildings and the other urban objects are first modelled and saved in a knowledge base. Some change detection rules are defined at this stage. Then, the image is segmented. The parameters of segmentation are computed thanks to the integration between the image and the geodatabase. Thereafter, the segmented image is analyzed using the knowledge base to localize the segments where the change of building is likely to occur. The change detection rules are then applied on these segments to identify the segments that represent the changes of buildings. These changes represent the updates of buildings to be added to the geodatabase. The data used in this research concern the city of Sherbrooke (Quebec, Canada) and the city of Rabat (Morocco). For Sherbrooke, we used an Ikonos image acquired in October 2006 and a GDB at the scale of 1:20,000. For Rabat, a QuickBird image acquired in August 2006 has been used with a GDB at the scale of 1:10,000. The rate of good detection is 90%. The proposed method presents some limitations on the detection of the exact contours of the buildings. It could be improved by including a shape post-analysis of detected buildings. The proposed method could be integrated into a cartographic update process or as a method for the quality assessment of a geodatabase. It could be also be used to identify illegal building work or to monitor urban growth. Numéro de notice : A2010-236 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2009.10.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2009.10.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30430 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2010011	SL	Revue	Centre de documentation	Revues en salle	Disponible