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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 Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar / Sören Holm in Remote sensing of environment, vol 197 (August 2017)
[article]
Titre : Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar Type de document : Article/Communication Auteurs : Sören Holm, Auteur ; Ross Nelson, Auteur ; Göran Stahl, Auteur Année de publication : 2017 Article en page(s) : pp 85 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] Etats-Unis
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] placette d'échantillonnage
[Termes IGN] variance
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Previous studies have utilized ground plots, airborne lidar scanning or profiling data, and space lidar profiling data to estimate biomass across large regions, but these studies have failed to take into account the variance components associated with multiple models because the proper variance equations were not available. Previous large-domain studies estimated the variances of their biomass density estimates as the sum of the GLAS sampling variability plus the model variability associated with the models that predict airborne lidar estimates of biomass density (Y) as a function of satellite lidar measurements (X). This approach ignores the additional variability associated with the predictive models used to estimate ground biomass density as a function of airborne lidar measurements. This paper addresses that shortcoming. Analytic variance expressions are provided that include sampling variability and model variability in situations where multiple models are employed to generate estimates of biomass. As an example, the forest biomass of the continental US is estimated, by forest stratum within state, using a space lidar system (ICESat/GLAS). An airborne laser system (ALS) is used as an intermediary to tie the GLAS measurements of forest height to a small subset of US Forest Service (USFS) ground plots by flying the ALS over the ground plots and, independently, over individual GLAS footprints. Two sets of models are employed to relate satellite measurements to the ground plots. The first set of equations relates USFS ground plot estimates of total aboveground dry biomass density (Y1) to spatially coincident ALS forest canopy measurements (X1). The second set of models predicts those ALS canopy height measurements (X1) used in the first set of models to GLAS waveform measurements (X2). The following important conclusions are noted. (1) The variability associated with estimation of the plot-ALS model coefficients is significant and should be included in the overall estimate of biomass density variance. In the continental US, the total variance of mean forest biomass density (98.06 t/ha) increases by a factor of 3.6 ×, i.e., from 1.91 to 6.94 t2/ha2, when plot-ALS model variance is included in the calculation of total variance. (2) State-level results are more variable, but on average, the percent model variance at the state level, i.e., (model variance / total variance) ∗ 100, increases from 16% to 59% when plot-ALS model variance is included. (3) The overall model variance is driven in large part by the number of plots overflown by the ALS and the number of GLAS pulses overflown by the ALS. Given a choice of improving precision by either increasing the number of plot-ALS observations or increasing ALS-GLAS observations, there is no obvious benefit to selecting one over the other. However, typically the number of ground plots overflown is the limiting factor. (4) If heteroskedasticity is evident in either the ground-air or air-satellite models, it can modeled using weighted regression techniques and incorporated into these model variance formulas in straightforward fashion. The results are unambiguous; in a hybrid three-phase sampling framework, both the ground-air and air-satellite model variance components are significant and should be taken into account. Numéro de notice : A2017-655 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.04.004 En ligne : https://doi.org/10.1016/j.rse.2017.04.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87050
in Remote sensing of environment > vol 197 (August 2017) . - pp 85 - 97[article]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]Estimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data / Joachim Maack in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)
[article]
Titre : Estimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data Type de document : Article/Communication Auteurs : Joachim Maack, Auteur ; Marcus Lingenfelder, Auteur ; Christina Eilers, Auteur ; Thomas Smaltschinski, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 118 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre hors forêt
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] biomasse
[Termes IGN] classification
[Termes IGN] détection d'objet
[Termes IGN] distribution spatiale
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données localisées des bénévoles
[Termes IGN] inventaire de la végétation
[Termes IGN] lasergrammétrie
[Termes IGN] OpenStreetMap
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Trees Outside Forests (TOF) represent a source of lignocellulosic biomass that has received increasing attention in the recent years. While some studies have already investigated the potential of TOF in Germany, a spatial explicit analysis, specifically for Baden-Wuerttemberg, is still lacking. We used a unique wall-to-wall airborne Light Detection and Ranging (LiDAR) dataset combined with OpenStreetMap (OSM) data to map and classify TOF of the federal state of Baden-Wuerttemberg (∼35.000 km2) in south-western Germany. Furthermore, from annual biomass potentials of TOF areas collected from available literature, we calculated the mean annual biomass supply for all TOF areas in Baden-Wuerttemberg. This combination of remote sensing-based classification and available literature resulted in a mean annual biomass supply between ∼490,000–730,000 t from TOF in Baden-Wuerttemberg. The classification congruence on three reference sites was very high (∼99%) using a simple filter technique applied to the LiDAR data and masking man-made objects using OSM data. In contrast, the available literature revealed a high variability of biomass potentials, supporting the demand for an inventory system. Still, the results demonstrate the applicability of LiDAR based vegetation mapping and the value of OSM data in Baden-Wuerttemberg to detect man-made objects. Numéro de notice : A2017-367 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.02.002 En ligne : https://doi.org/10.1016/j.jag.2017.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85795
in International journal of applied Earth observation and geoinformation > vol 58 (June 2017) . - pp 118 - 125[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
Réserver ce documentExemplaires(3)
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)PermalinkForest 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)PermalinkTransferability of multi- and hyperspectral optical biocrust indices / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkReconstructing 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)Permalink