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Auteur Mbulisi Sibanda |
Documents disponibles écrits par cet auteur (3)
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Stand-volume estimation from multi-source data for coppiced and high forest Eucalyptus spp. silvicultural systems in KwaZulu-Natal, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
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Titre : Stand-volume estimation from multi-source data for coppiced and high forest Eucalyptus spp. silvicultural systems in KwaZulu-Natal, South Africa Type de document : Article/Communication Auteurs : Timothy Dube, Auteur ; Mbulisi Sibanda, Auteur ; Cletah Shoko, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2017 Article en page(s) : pp 162 - 169 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] cubage de peuplement
[Termes IGN] données auxiliaires
[Termes IGN] écosystème forestier
[Termes IGN] Eucalyptus camaldulensis
[Termes IGN] image SPOT 5
[Termes IGN] KwaZulu-Natal (Afrique du Sud)
[Termes IGN] peuplement forestier
[Termes IGN] régression
[Termes IGN] taillisRésumé : (Auteur) Forest stand volume is one of the crucial stand parameters, which influences the ability of these forests to provide ecosystem goods and services. This study thus aimed at examining the potential of integrating multispectral SPOT 5 image, with ancillary data (forest age and rainfall metrics) in estimating stand volume between coppiced and planted Eucalyptus spp. in KwaZulu-Natal, South Africa. To achieve this objective, Partial Least Squares Regression (PLSR) algorithm was used. The PLSR algorithm was implemented by applying three tier analysis stages: stage I: using ancillary data as an independent dataset, stage II: SPOT 5 spectral bands as an independent dataset and stage III: combined SPOT 5 spectral bands and ancillary data. The results of the study showed that the use of an independent ancillary dataset better explained the volume of Eucalyptus spp. growing from coppices (adjusted R2 (R2Adj) = 0.54, RMSEP = 44.08 m3/ha), when compared with those that were planted (R2Adj = 0.43, RMSEP = 53.29 m3/ha). Similar results were also observed when SPOT 5 spectral bands were applied as an independent dataset, whereas improved volume estimates were produced when using combined dataset. For instance, planted Eucalyptus spp. were better predicted adjusted R2 (R2Adj) = 0.77, adjusted R2Adj = 0.59, RMSEP = 36.02 m3/ha) when compared with those that grow from coppices (R2 = 0.76, R2Adj = 0.46, RMSEP = 40.63 m3/ha). Overall, the findings of this study demonstrated the relevance of multi-source data in ecosystems modelling. Numéro de notice : A2017-643 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87002
in ISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 162 - 169[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017103 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing / Mbulisi Sibanda in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)
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Titre : Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing Type de document : Article/Communication Auteurs : Mbulisi Sibanda, Auteur ; Timothy Dube, Auteur ; Victor M. Bangamwabo, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1006 - 1018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aire protégée
[Termes IGN] chasse
[Termes IGN] couvert végétal
[Termes IGN] distribution spatiale
[Termes IGN] habitat animal
[Termes IGN] Mammalia
[Termes IGN] régression logistique
[Termes IGN] surveillance écologique
[Termes IGN] ZimbabweMots-clés libres : braconnage Résumé : (auteur) The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa. Numéro de notice : A2016-670 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1094529 Date de publication en ligne : 27/10/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1094529 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81902
in Geocarto international > Vol 31 n° 9 - 10 (October - November 2016) . - pp 1006 - 1018[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments / Mbulisi Sibanda in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)
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Titre : Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments Type de document : Article/Communication Auteurs : Mbulisi Sibanda, Auteur ; Onisimo Mutanga, Auteur ; Mathieu Rouget, Auteur Année de publication : 2015 Article en page(s) : pp 55 – 65 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] biomasse forestière
[Termes IGN] capteur multibande
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] image Sentinel-MSI
[Termes IGN] Sentinel-2
[Termes IGN] télédétection aérospatialeRésumé : (auteur)The major constraint in understanding grass above ground biomass variations using remotely sensed data are the expenses associated with the data, as well as the limited number of techniques that can be applied to different management practices with minimal errors. New generation multispectral sensors such as Sentinel 2 Multispectral Imager (MSI) are promising for effective rangeland management due to their unique spectral bands and higher signal to noise ratio. This study resampled hyperspectral data to spectral resolutions of the newly launched Sentinel 2 MSI and the recently launched Landsat 8 OLI for comparison purposes. Using Sparse partial least squares regression, the resampled data was applied in estimating above ground biomass of grasses treated with different fertilizer combinations of ammonium sulfate, ammonium nitrate, phosphorus and lime as well as unfertilized experimental plots. Sentinel 2 MSI derived models satisfactorily performed (R2 = 0.81, RMSEP = 1.07 kg/m2, RMSEP_rel = 14.97) in estimating grass above ground biomass across different fertilizer treatments relative to Landsat 8 OLI (Landsat 8 OLI: R2 = 0.76, RMSEP = 1.15 kg/m2, RMSEP_rel = 16.04). In comparison, hyperspectral data derived models exhibited better grass above ground biomass estimation across complex fertilizer combinations (R2 = 0.92, RMSEP = 0.69 kg/m2, RMSEP_rel = 9.61). Although Sentinel 2 MSI bands and indices better predicted above ground biomass compared with Landsat 8 OLI bands and indices, there were no significant differences (α = 0.05) in the errors of prediction between the two new generational sensors across all fertilizer treatments. The findings of this study portrays Sentinel 2 MSI and Landsat 8 OLI as promising remotely sensed datasets for regional scale biomass estimation, particularly in resource scarce areas. Numéro de notice : A2015-892 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.005 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.10.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79442
in ISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 55 – 65[article]