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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)
[article]
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 IGN] Afrique (géographie physique)
[Termes IGN] biomasse forestière
[Termes IGN] dégradation de la flore
[Termes IGN] Enhanced vegetation index
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] Namibie
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prédiction
[Termes IGN] savane
[Termes IGN] variationRésumé : (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
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 77 - 91[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017093 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017092 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions / João M.B. Carreiras in Remote sensing of environment, vol 196 (July 2017)
[article]
Titre : Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; Shaun Quegan, Auteur ; Thuy Le Toan, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 154 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bande P
[Termes IGN] Biomass
[Termes IGN] biomasse aérienne
[Termes IGN] couvert forestier
[Termes IGN] image radar moirée
[Termes IGN] modèle numérique
[Termes IGN] puits de carbone
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The magnitude of the global terrestrial carbon pool and related fluxes to and from the atmosphere are still poorly known. The European Space Agency P-band radar BIOMASS mission will help to reduce this uncertainty by providing unprecedented information on the distribution of forest above-ground biomass (AGB), particularly in the tropics where the gaps are greatest and knowledge is most needed. Mission selection was made in full knowledge of coverage restrictions over Europe, North and Central America imposed by the US Department of Defense Space Objects Tracking Radar (SOTR) stations. Under these restrictions, only 3% of AGB carbon stock coverage is lost in the tropical forest biome, with this biome representing 66% of global AGB carbon stocks in 2005. The loss is more significant in the temperate (72%), boreal (37%) and subtropical (29%) biomes, with these accounting for approximately 12%, 15% and 7%, respectively, of the global forest AGB carbon stocks. In terms of global carbon cycle modelling, there is minimal impact in areas of high AGB density, since mainly lower biomass forests in cooler climates are affected. In addition, most areas affected by the SOTR stations are located in industrialized countries with well-developed national forest inventories, so that extensive information on AGB is already available. Hence the main scientific objectives of the BIOMASS mission are not seriously compromised. Furthermore, several space sensors that can estimate AGB in lower biomass forests are in orbit or planned for launch between now and the launch of BIOMASS in 2021, which will help to fill the gaps in mission coverage. Numéro de notice : A2017-808 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.05.003 En ligne : https://doi.org/10.1016/j.rse.2017.05.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89247
in Remote sensing of environment > vol 196 (July 2017) . - pp 154 - 162[article]Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
[article]
Titre : Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms Type de document : Article/Communication Auteurs : Lien T.H. Pham, Auteur Année de publication : 2017 Article en page(s) : pp 86 - 97 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse forestière
[Termes IGN] carte thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] image SPOT 4
[Termes IGN] image SPOT 5
[Termes IGN] mangrove
[Termes IGN] surveillance de la végétation
[Termes IGN] teneur en carbone
[Termes IGN] texture d'image
[Termes IGN] Viet NamRésumé : (Auteur) Mangrove forests are well-known for their provision of ecosystem services and capacity to reduce carbon dioxide concentrations in the atmosphere. Mapping and quantifying mangrove biomass is useful for the effective management of these forests and maximizing their ecosystem service performance. The objectives of this research were to model, map, and analyse the biomass change between 2000 and 2011 of mangrove forests in the Cangio region in Vietnam. SPOT 4 and 5 images were used in conjunction with object-based image analysis and machine learning algorithms. The study area included natural and planted mangroves of diverse species. After image preparation, three different mangrove associations were identified using two levels of image segmentation followed by a Support Vector Machine classifier and a range of spectral, texture and GIS information for classification. The overall classification accuracy for the 2000 and 2011 images were 77.1% and 82.9%, respectively. Random Forest regression algorithms were then used for modelling and mapping biomass. The model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy (R2adj = 0.73). Among the different variables, vegetation association type was the most important variable identified by the Random Forest model. Based on the biomass maps generated from the Random Forest, total biomass in the Cangio mangrove forest increased by 820,136 tons over this period, although this change varied between the three different mangrove associations. Numéro de notice : A2017-332 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85485
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 86 - 97[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017063 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017062 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Ecological functions of vegetation as potentials of ecosystem services (floodplain alder forest in the Tríbeč microregion) / Pavol Eliáš in Journal of forest science, vol 63 n° 3 (October 2015)
[article]
Titre : Ecological functions of vegetation as potentials of ecosystem services (floodplain alder forest in the Tríbeč microregion) Type de document : Article/Communication Auteurs : Pavol Eliáš, Auteur ; Patricia Mariničová, Auteur Année de publication : 2017 Article en page(s) : pp 126 - 135 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Ecologie
[Termes IGN] biomasse forestière
[Termes IGN] ilex aquifolium
[Termes IGN] lit majeur
[Termes IGN] service écosystémique
[Termes IGN] Slovaquie
[Termes IGN] sous-boisRésumé : (auteur) Ecological functions of a black alder riparian forest (Aegopodio-Alnetum glutinosae) as capacities for the provision of ecosystem services were analysed and indicated by an inductive (bottom-up) approach. In July 2015, species richness and diversity of the forest stand were analysed in Hlboká valley, near Husárka water reservoir, Tríbeč Mountains, western Slovakia, by phytocoenological relevé, aboveground biomass of the herb layer and litter mass were estimated by a harvest method. The quantitative data obtained for the forest vegetation were used for estimation of capacities (potentials) of the floodplain forest to provide non-market ecosystem services. The forage, melliferous and therapeutic potential were estimated and calculated by Jurko’s methods of ecological and socio-economic evaluations of vegetation. Aboveground biomass (production potential) of the forest understorey community was relatively high (seasonal maximum standing crop 59.03 g·m–2 in dry mass). The result of evaluation of the forage potential of the alder forest was higher than 50%, it means medium forage quality community. The melliferous potential was evaluated close to 190% (= high potential). The share of medicinal plants in the studied plant community was higher than 30%, referred to as extremely rich therapeutic potential. The analyses confirmed that the forest community represents high potentials for providing production ecosystem services. Numéro de notice : A2017-182 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.17221/58/2016-JFS En ligne : http://doi.org/10.17221/58/2016-JFS Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84743
in Journal of forest science > vol 63 n° 3 (October 2015) . - pp 126 - 135[article]Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation / Michael Schlund in International journal of applied Earth observation and geoinformation, vol 56 (April 2017)
[article]
Titre : Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation Type de document : Article/Communication Auteurs : Michael Schlund, Auteur ; Klaus Scipal, Auteur ; Malcolm W.J. Davidson, Auteur Année de publication : 2017 Article en page(s) : pp 65 - 76 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] base de données d'occupation du sol
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] erreur systématique
[Termes IGN] estimation statistique
[Termes IGN] image radar moiréeRésumé : (auteur) The European Space Agency (ESA) is currently implementing the BIOMASS mission as 7th Earth Explorer satellite. BIOMASS will provide for the first time global forest aboveground biomass estimates based on P-band synthetic aperture radar (SAR) imagery. This paper addresses an often overlooked element of the data processing chain required to ensure reliable and accurate forest biomass estimates: accurate identification of forest areas ahead of the inversion of radar data into forest biomass estimates. The use of the P-band data from BIOMASS itself for the classification into forest and non-forest land cover types is assessed in this paper. For airborne data in tropical, hemi-boreal and boreal forests we demonstrate that classification accuracies from 90 up to 97% can be achieved using radar backscatter and phase information. However, spaceborne data will have a lower resolution and higher noise level compared to airborne data and a higher probability of mixed pixels containing multiple land cover types. Therefore, airborne data was reduced to 50 m, 100 m and 200 m resolution. The analysis revealed that about 50–60% of the area within the resolution level must be covered by forest to classify a pixel with higher probability as forest compared to non-forest. This results in forest omission and commission leading to similar forest area estimation over all resolutions. However, the forest omission resulted in a biased underestimated biomass, which was not equaled by the forest commission. The results underline the necessity of a highly accurate pre-classification of SAR data for an accurate unbiased aboveground biomass estimation. Numéro de notice : A2017-370 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2016.12.001 En ligne : https://doi.org/10.1016/j.jag.2016.12.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85789
in International journal of applied Earth observation and geoinformation > vol 56 (April 2017) . - pp 65 - 76[article]Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes / Jari Vauhkonen in Annals of Forest Science, vol 74 n° 1 (March 2017)PermalinkTerrestrial laser scanning as a tool for assessing tree growth / Jonathan Sheppard in iForest, biogeosciences and forestry, vol 10 n° 1 (February 2017)PermalinkFeasibility of Terrestrial laser scanning for collecting stem volume information from single trees / Ninni Saarinen in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkForest inventory-based projection systems for wood and biomass availability, ch. France [National woody biomass projection systems based on forest inventory - Projecting wood resources and potential wood supply of French forests: an approach to cope with forest system diversity] / Antoine Colin (2017)PermalinkLand Surface Remote Sensing in Continental Hydrology, ch. 3. Using satellite scatterometers to monitor continental surfaces / Pierre-Louis Frison (2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 3. Utilisation des diffusiomètres satellitaires pour le suivi des surfaces continentales / Pierre-Louis Frison (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkFrom inventory to consumer biomass availability - the ITOC model / Udo Mantau in Annals of Forest Science, vol 73 n° 4 (December 2016)PermalinkOverview of methods and tools for evaluating future woody biomass availability in European countries / Susana Barreiro in Annals of Forest Science, vol 73 n° 4 (December 2016)PermalinkAboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data / Ibrahim Fayad in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)Permalink