Toward structural assessment of semi-arid African savannahs and woodlands: The potential of multitemporal polarimetric RADARSAT-2 fine beam images

Mathieu, Renaud; Naidoo, Laven; Cho, Moses Azong; Leblon, Brigitte; Main, Russell; Wessels, Konrad J; Asner, Gregory P.; Buckley, Joseph R.; Aardt, Jan van; Erasmus, Barend; Smit, Izak P.J.

doi:10.1016/j.rse.2013.07.011

Cited by 55 publications

(48 citation statements)

References 63 publications

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“…X-band was considered the least effective for predicting CC in savannahs 15 while C-band did yield positive results 15,17 though not as effective as L-band data.…”

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confidence: 89%

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L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

Naidoo

Mathieu

Main

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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“…X-band was considered the least effective for predicting CC in savannahs 15 while C-band did yield positive results 15,17 though not as effective as L-band data.…”

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confidence: 89%

“…Techniques, generally ranging from parametric methods (e.g. multi-linear regression 17,21 ) to the more specialized such as non-parametric machine learning algorithms 15,21,47 (e.g. decision trees) and linear and non-linear spectral unmixing 19,49 , of coarser optical pixels, have been implemented for extracting fractional canopy cover at the regional scale.…”

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confidence: 99%

L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

Naidoo

Mathieu

Main

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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“…Remotely sensed data have been widely used to provide spatially explicit information about the heterogeneity of woody plant distribution over extensive dry regions [e.g., Mathieu et al, 2013]. Both satellite and airborne remote sensing imagery have been widely applied to retrieve vegetation canopy cover in dryland environments [e.g., Yang et al, 2012].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies [e.g., Cots-Folch et al, 2007;Mathieu et al, 2013] showed the dependence of accuracy of canopy cover maps as a function of block size. We also tested various block sizes that ranged from 100 to 5000 m 2 in an attempt to determine the best size at which PCC should be measured, although the choice of the optimal block size is rarely discussed, most likely because of the effort required to build up comprehensive field datasets.…”

Section: Discussionmentioning

confidence: 99%

“…The spatial resolution of commercially available satellite and aerial images produced by passive and active technology ranges from several meters (MODIS, IKONOS, WorldView, and RapidEye) to tens of centimetres (LiDAR and aerial images) [Schowengerdt, 2007]. Previous studies have constructed canopy cover maps of large areas from mid or low spatial resolution datasets, such as Landsat Thematic Mapper (TM), NASA Multi-angle Imaging SpectroRadiometer (MISR), and Linear Imaging Self Scanning Sensor (LISS III) [Carreiras et al, 2006;Chopping et al, 2008;Yang et al, 2012] or high spatial resolution datasets, such as Compact Airborne Spectrographic Imager (CASI), Color-Infrared (CIR) orthophotos, and LiDAR [Bunting and Lucas, 2006;Kral, 2009;Mathieu et al, 2013]. The spatial resolution of most satellite remote sensing images is too low to identify many objects from their shape or spatial detail [Schowengerdt, 2007;Frohn and Chaudhary, 2008].…”

Section: Introductionmentioning

confidence: 99%

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A robust approach to generate canopy cover maps using UltraCam-D derived orthoimagery classified by support vector machines in Zagros woodlands, West Iran

Erfanifard

Khodaei

Shamsi

2014

European Journal of Remote Sensing

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An approach was developed to construct a percent canopy cover (PCC) map of Zagros semi-arid woodlands, West Iran, using UltraCam-D airborne imagery. We detected crowns of Persian oak coppice trees on the imagery by use of the support vector machine (SVM) classifier optimized via Taguchi method. Then, PCC was calculated in raster grids with various block sizes and their accuracy metrics revealed the appropriate sizes. Results showed the optimized SVM success in separating Persian oak crowns as revealed in receiver operating characteristic (ROC) curve analysis (area under curve: AUC ~ 0.82). After filtering the raster maps and reassessing their accuracies, validation outputs of the final PCC map with 3000 m 2 resolution yielded an overall accuracy of 90% (KHAT=0.71) and was introduced as the optimal map in this study.

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Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Lopes

Frison

Durant

et al. 2020

Remote Sens Ecol Conserv

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Up-to-date land cover maps are important for biodiversity monitoring as they are central to habitat and ecosystem distribution assessments. Satellite remote sensing is a key technology for generating these maps. Until recently, land cover mapping has been limited to static approaches, which have primarily led to the production of either global maps at coarse spatial resolutions or geographically restricted maps at high spatial resolutions. The recent availability of optical (Sentinel-2) and radar (Sentinel-1) satellite image time series (SITS) which provide access to high spatial and very high temporal resolutions, is a game changer, offering opportunities to map land cover using both temporal and spatial information. These data moreover open interesting perspectives for land cover mapping based on data combination approach. However, the usefulness of combining dense time series (more than 30 images per year) and data combination approaches to map natural vegetation has so far not been assessed. To address this gap, this contribution tests the idea that the combined consideration of optical and radar data combination and time series analyses can significantly improve natural vegetation mapping in the Pendjari National Park, a Sahelian savanna protected area in Benin. Results highlight that the combination of Sentinel-1 and Sentinel-2 SITS performs as well as Sentinel-2 SITS alone in terms of classification accuracy. Land cover maps are however qualitatively better when considering the data combination approach. Our results also clearly show that the use of dense/hypertemporal optical time series significantly improves classification outcomes compared to using multitemporal only a few images per year) or monotemporal data. Altogether, this work thus demonstrates the ability of dense SITS to improve discrimination of natural vegetation types using information on their phenology, leading to more detailed and more reliable maps for environmental management.

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Toward structural assessment of semi-arid African savannahs and woodlands: The potential of multitemporal polarimetric RADARSAT-2 fine beam images

Cited by 55 publications

References 63 publications

L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

A robust approach to generate canopy cover maps using UltraCam-D derived orthoimagery classified by support vector machines in Zagros woodlands, West Iran

Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

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