Centre dedocumentation
scientifique

Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 153 - 168 Titre : Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa Type de document : Article/Communication Auteurs : Cecilia Masemola, Auteur ; Moses Azong Cho, Auteur ; Abel Ramoelo, Auteur Année de publication : 2020 Article en page(s) : pp 153 - 168 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Acacia (genre) [Termes IGN] Afrique du sud (état) [Termes IGN] canopée [Termes IGN] cartographie automatique [Termes IGN] chlorophylle [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] échantillonnage [Termes IGN] espèce exotique envahissante [Termes IGN] essence indigène [Termes IGN] image Sentinel-MSI [Termes IGN] Leaf Area Index [Termes IGN] modèle de transfert radiatif [Termes IGN] réflectance [Termes IGN] surveillance de la végétation Résumé : (auteur) Invasive alien plants (IAPs) threaten biodiversity and critical ecosystem services worldwide. There is, therefore, an urgent need to develop intervention measures to control the spread of IAPs. Efforts to control and monitor the spread of IAPs would require their current and detailed distribution over a large geographic area. Recently launched multispectral instrument on-board Sentinel-2 provides free data with good spatiotemporal and spectral resolution, compared to Landsat datasets. The Sentinel-2 dataset, therefore, can be a useful source of the IAPs spatial information required for detection and monitoring purposes. We combined Sentinel-2 data with a radiative transfer model to discriminate IAPs (Acacia mearnsii and Acacia dealbata) from surrounding native tree species in Van Reenen, KwaZulu-Natal, South Africa. The forward mode of combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL was used to simulate reflectance corresponding to bands of Sentinel-MSI, while the PROSAIL model inversion retrieved leaf area index (LAI) and canopy chlorophyll contents (CCC) of the IAPs and native species. Both reflectance and retrieved properties were used to map the distribution of the species within the study area. Our results showed that A. mearnsii and A. dealbata could be accurately discriminated from the surrounding native trees using integrated PROSAIL Sentinel-2 based model. We found that CCC– and LAI-based (% accuracy = 92.8%, 91.4% for CCC and LAI, respectively) modelling produced a higher classification accuracy than field sampling-based modelling (Accuracy = 90.2% (IAP), 82.2% (NAT) and kappa coefficient = 0.84 (IAP), 0.78 (NAT)). Simulated bands corresponding to Sentinel-2 data, on the other hand, produced species maps comparable to field sampling-based maps. Overall, the integrated PROSAIL Sentinel-2 inversion approach proved suitable for detecting and mapping IAPs over a large area. Due to the high spatiotemporal coverage of Sentinel-2, satellite images, the model developed showed the potential to contribute to the IAPs monitoring systems. Numéro de notice : A2020-352 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.009 Date de publication en ligne : 13/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95235 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020083	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020082	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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 IGN] Afrique du sud (état) [Termes IGN] analyse en composantes principales [Termes IGN] bande infrarouge [Termes IGN] biodiversité [Termes IGN] espèce végétale [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-OLI [Termes IGN] indice de diversité [Termes IGN] indice de végétation [Termes IGN] matrice de co-occurrence [Termes IGN] régression linéaire [Termes 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]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017113	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 27 - 40 Titre : Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data Type de document : Article/Communication Auteurs : Abel Ramoelo, Auteur ; Andrew K. Skidmore, Auteur ; Moses Azong Cho, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 27 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Afrique du sud (état) [Termes IGN] azote [Termes IGN] données environnementales [Termes IGN] herbe [Termes IGN] image hyperspectrale [Termes IGN] indice de végétation [Termes IGN] parc naturel national [Termes IGN] parcours [Termes IGN] phosphore [Termes IGN] régression non linéaire [Termes IGN] savane Résumé : (Auteur) Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems. Numéro de notice : A2013-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32547 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2012031	SL	Revue	Centre de documentation	Revues en salle	Disponible