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A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 7 (July 2019) . - pp 509 - 520 Titre : A novel method for separating woody and herbaceous time series Type de document : Article/Communication Auteurs : Qiang Zhou, Auteur ; Shuguang Liu, Auteur ; Michael J Hill, Auteur Année de publication : 2019 Article en page(s) : pp 509 - 520 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Afrique australe [Termes descripteurs IGN] bois [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image Landsat-SWIR [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] plante herbacée [Termes descripteurs IGN] savane [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) Mapping the spatial distribution of woody and herbaceous vegetation in high temporal resolution in savannas would be beneficial for modeling interrelationships between trees and grasses, and monitoring fuel loads and biomass for livestock. In this study, we developed a frequency decomposition method to separate woody and herbaceous vegetation components using Normalized Difference Vegetation Index (NDVI) time series. The results were validated using fractional cover data derived from high-resolution images. The validation revealed a close relationship between our decomposed NDVI and corresponding fractional cover (R2 = 0.55 and 0.64 for woody and herbaceous components, respectively). We examined the spatial and temporal patterns of the decomposed NDVI, where woody and herbaceous NDVI showed different responses to precipitation. The methods proposed in this study can be used to separate the woody and herbaceous NDVI time series as an alternative approach for monitoring woody and herbaceous vegetation interrelationships related to climatic drivers. Numéro de notice : A2019-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.7.509 date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.14358/PERS.85.7.509 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93062 [article]

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Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor / Timothy Dube in Geocarto international, vol 33 n° 2 (February 2018)  

Public [article]  inGeocarto international > vol 33 n° 2 (February 2018) . - pp 178 - 188 Titre : Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor Type de document : Article/Communication Auteurs : Timothy Dube, Auteur ; Tawanda W. Gara, Auteur ; Onisimo Mutanga, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 178 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] données dendrométriques [Termes descripteurs IGN] forêt sèche [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] savane [Termes IFN] productivité forestière Résumé : (Auteur) Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R2 of 0.67 and a RMSE-CV of 2.2 t ha−1) when compared to when the red-edge band was included as a co-variate (R2 of 0.73 and a RMSE-CV of 2.04 t ha−1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R2 of about 0.67 and a RMSE-CV of 2.20 t ha−1), when compared to those obtained using all indices and RE-computed indices(R2 of 0.76 and a RMSE-CV of 1.88 t ha−1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint. Numéro de notice : A2018-033 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1240717 En ligne : https://doi.org/10.1080/10106049.2016.1240717 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89206 [article]

Forest biomass and carbon / Gopal Shukla (2018)  

Public Titre : Forest biomass and carbon Type de document : Monographie Auteurs : Gopal Shukla, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 112 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-78984-362-0 Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] densité de la végétation [Termes descripteurs IGN] énergie renouvelable [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] matière organique [Termes descripteurs IGN] Pinus (genre) [Termes descripteurs IGN] puits de carbone [Termes descripteurs IGN] savane [Termes descripteurs IGN] structure d'un peuplement forestier [Termes descripteurs IGN] Theobroma cacao [Termes descripteurs IGN] zone intertropicale [Vedettes matières IGN] Ecologie forestière Résumé : (éditeur) Forests grow and their biomass increases; they absorb carbon from the atmosphere and store it in plant tissue. Understanding the biomass of forest vegetation is essential for determining the storage of carbon in the dominant tree component and computing carbon cycling at a regional as well as global level. This book consisting of five chapters will give a comprehensive understanding of biomass production vis-à-vis carbon storage in relation to litter and nutrient dynamics of the forest by analyzing the mode and magnitude of biomass production and carbon storage as a function of various silvicultural factors. Note de contenu : 1- Effects of forest stand structure in biomass and carbon 2- Tree stock, structure and use of common woody species of a town neighboring forest reserve in Tanzania: Implication for managing carbon accumulation 3- Plant diversity, ecological services, and carbon stock assessment in cocoa agroforestry plantations of forest and savannah transitions in Cameroon 4- Effects of eucalyptus and pinus forest management on soil organic carbon in Brazilian wooded-savanna 5- Determinants and tools to evaluate the ecological sustainability of using forest biomass as an alternative energy source Numéro de notice : 25955 Affiliation des auteurs : non IGN Thématique : FORET Nature : Monographie DOI : 10.5772/intechopen.69011 En ligne : https://doi.org/10.5772/intechopen.69011 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96421

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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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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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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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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Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

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Employing ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)  

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Land-use and land tenure explain spatial and temporal patterns in terrestrial net primary productivity (NPP) in Southern Africa / Godfrey Pachavo in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

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Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data / Abel Ramoelo in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)

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The spatial prediction of tree species diversity in savanna woodlands of Southern Africa / G. Mutowo in Geocarto international, vol 27 n° 8 (December 2012)  

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A robust signal preprocessing chain for small-footprint waveform LiDAR / J. Wu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

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