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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 234 - 250 Titre : Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data Type de document : Article/Communication Auteurs : Laven Naidoo, Auteur ; Renaud Mathieu, Auteur ; Russell Main, Auteur ; Waldo Kleynhans, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 234 - 250 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Afrique du sud (état) [Termes IGN] bande C [Termes IGN] bande L [Termes IGN] bande X [Termes IGN] biomasse [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] image TerraSAR-X [Termes IGN] savane Résumé : (auteur) Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterised by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2 of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics (R2 = 0.83 for CC and AGB and R2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment. Numéro de notice : A2015-713 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.04.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.04.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78353 [article]

Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 167 - 179 Titre : Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment Type de document : Article/Communication Auteurs : Laven Naidoo, Auteur ; Moses Azong Cho, Auteur ; Renaud Mathieu, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 167 - 179 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Afrique du sud (état) [Termes IGN] arbre (flore) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] hauteur des arbres [Termes IGN] image hyperspectrale [Termes IGN] lasergrammétrie [Termes IGN] parc naturel national [Termes IGN] savane Résumé : (Auteur) The accurate classification and mapping of individual trees at species level in the savanna ecosystem can provide numerous benefits for the managerial authorities. Such benefits include the mapping of economically useful tree species, which are a key source of food production and fuel wood for the local communities, and of problematic alien invasive and bush encroaching species, which can threaten the integrity of the environment and livelihoods of the local communities. Species level mapping is particularly challenging in African savannas which are complex, heterogeneous, and open environments with high intra-species spectral variability due to differences in geology, topography, rainfall, herbivory and human impacts within relatively short distances. Savanna vegetation are also highly irregular in canopy and crown shape, height and other structural dimensions with a combination of open grassland patches and dense woody thicket – a stark contrast to the more homogeneous forest vegetation. This study classified eight common savanna tree species in the Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. The most important predictors, which were found to play an important role in the different classification models and contributed to the success of the hybrid dataset model when combined, were species tree height; NDVI; the chlorophyll b wavelength (466 nm) and a selection of raw, continuum removed and Spectral Angle Mapper (SAM) bands. It was also concluded that the hybrid predictor dataset Random Forest model yielded the highest classification accuracy and prediction success for the eight savanna tree species with an overall classification accuracy of 87.68% and KHAT value of 0.843. Numéro de notice : A2012-199 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31646 [article]

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