Impact of communal land use and conservation on woody vegetation structure in the Lowveld savannas of South Africa

Wessels, Konrad J; Mathieu, Renaud; Erasmus, Barend; Asner, Gregory P.; Smit, Izak P.J.; Aardt, Jan A. N. van; Main, Russell; Fisher, Jolene T.; Marais, Werner; Kennedy-Bowdoin, Ty; Knapp, David; Emerson, Ruth; Jacobson, James

doi:10.1016/j.foreco.2010.09.012

Cited by 61 publications

(49 citation statements)

References 43 publications

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“…9. Terrain characteristics and woody cover on both geologies are well expressed in the maps, with the granite landscapes being characterized by more densely wooded hilly terrain with a well-developed drainage network (Levick et al 2010), and basalt landscape as sparse woodlands with flatter terrain and a weakly developed stream network (Bucini et al 2010;Wessels et al 2011). Fine-leaved woodlands with dense thickets (Acacia welwitschii) (mark B) also are observed on the ecca shale geology between the basalts and granites.…”

Section: Woody Cover Statistics and Spatial Patternsmentioning

confidence: 78%

“…This was selected following analysis of semi-variogram on tree height, where Wessels et al (2011) using the same dataset found that samples taken 50 m apart, or further, could be treated as independent (Beale et al 2010). Then, randomly 60% of the LiDAR data were selected for model calibration and the remaining 40% were used for validation.…”

Section: Estimates Of Woody Cover From Sar Datamentioning

confidence: 99%

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Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images

Urbazaev

Thiel

Mathieu

et al. 2015

Remote Sensing of Environment

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Woody vegetation cover affects several ecosystem processes including carbon and water cycling, energy fluxes, and fire regimes. In order to understand the dynamics of savanna ecosystems, information on the spatial distribution of woody vegetation over large areas is needed. In this study we sought to assess multi-temporal ALOS PALSAR L-band backscatter to map woody cover in southern African savannas. The SAR data were acquired from the JAXA archive, covering various modes and seasons between 2007 and 2010. We used high resolution airborne LiDAR data as reference data to interpret SAR parameters (including backscatter intensities and polarimetric decomposition components), to develop SAR-based models as well as to validate SAR-based woody cover maps. The LiDAR survey was carried out in April 2008 with the Carnegie Airborne Observatory (CAO, http://cao.ciw.edu). The highest correlations to the reference data were obtained from SAR backscatters of the dry season, followed by the wet season, and the end of the wet season. The volume components from polarimetric decompositions (Freeman-Durden, Van Zyl) were calculated for the end of wet season, and showed similar correlations to the LiDAR data, when compared to cross-polarized backscatters (HV). We observed increased correlation between the SAR and LiDAR datasets with an increase in the spatial scale at which datasets were integrated, with an optimum value at 50 m. We modeled woody cover using three scenarios: (1) a single date scenario (i.e., woody cover map based on a single SAR image), (2) a multi-seasonal scenario (i.e., woody cover map based on SAR images from the same year and different seasons, based on key phonological difference), and (3) a multi-annual scenario (i.e., woody cover map based on SAR data from different years). Predicted SAR-based woody cover map based on Fine Beam Dual Polarization dry season SAR backscatters of all years yielded the best performance with an R² of 0.71 and RMSE of 7.88%. However, single dry season SAR backscatter achieved only a slightly lower accuracy (R²=0.66, RMSE=8.45%) as multi-annual SAR data, suggesting that a single SAR scene from the dry season can also be used for woody cover mapping. Moreover, we investigated the impact of the number of samples on the model prediction performance and showed the benefits of a larger spatially explicit LiDAR dataset compared to much 3 smaller number of samples as they can be collected in the field. Collectively, our results demonstrate that L-band backscatter shows promising sensitivity for the purposes of mapping woody cover in southern African savannas, particularly during the dry season leaf-off conditions.

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Section: Woody Cover Statistics and Spatial Patternsmentioning

confidence: 78%

Section: Estimates Of Woody Cover From Sar Datamentioning

confidence: 99%

Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images

Urbazaev

Thiel

Mathieu

et al. 2015

Remote Sensing of Environment

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“…The Sabi Sands Wildetuin consists of a group of private owners with a strong eco-tourism based approach to conservation with the Kruger National Park being more geared towards largescale public conservation via the inclusion of large tracts of land for protection. The communal rangelands of BBR are primarily utilised for livestock ranching, fuelwood harvesting and various non-commercial farming practices (Wessels et al, 2011, Wessels et al, 2013. This study region was selected to represent the differences in the woody structure (e.g.…”

Section: Study Areamentioning

confidence: 99%

“…Ground sampling sites were located to represent the diversity in woody structure of the different vegetation types, management regimes, and geological substrates mentioned above. Each site covered a 100m X 100m area and vegetation measurements were taken from four clustered 25m X 25m sampling plots (with minimum distance > 50m, identified from geostatistic range assessments, Wessels et al 2011), located at each of the four corners of the site (Figure 2). The 100 x 100m sites were positioned using high resolution imagery from Google Earth as well as earlier LiDAR datasets acquired in 2008 -2010 to ensure that they are representative of the surrounding landscape.…”

Section: Field Datamentioning

confidence: 99%

Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data

Naidoo

Mathieu

Main

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The demarcation of the sites was done using 1∶250;000 geology maps and 2008 SPOT 5 images. 26 The site selection process captured the nutrient contrast from low to high between granitic-derived soils to gabbro-derived soils, respectively, and along the rainfall gradient. A line transect sampling design was used to collect field data 27 in each site.…”

Section: Field Data Collectionmentioning

confidence: 99%

Potential of Sentinel-2 spectral configuration to assess rangeland quality

Ramoelo

Cho

Mathieu

et al. 2015

J. Appl. Remote Sens

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Abstract. Sentinel-2 is intended to improve vegetation assessment at local to global scales. Today, estimation of leaf nitrogen (N) as an indicator of rangeland quality is possible using hyperspectral systems. However, few studies based on commercial imageries have shown a potential of the red-edge band to accurately predict leaf N at the broad landscape scale. We intend to investigate the utility of Sentinel-2 for estimating leaf N concentration in the African savanna. Grass canopy reflectance was measured using the analytical spectral device (ASD) in concert with leaf sample collections for leaf N chemical analysis. ASD reflectance data were resampled to the spectral bands of Sentinel-2 using published spectral response functions. Random forest (RF), partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) were used to predict leaf N using all 13 bands. Using leave-one-out cross validation, the RF model explained 90% of leaf N variation, with a root mean square error of 0.04 (6% of the mean), which is higher than that of PLSR and SMLR. Using RF, spectral bands centered at 705 nm (red edge) and two shortwave infrared bands centered at 2190 and 1610 nm were found to be the most important bands in predicting leaf N.

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Impact of communal land use and conservation on woody vegetation structure in the Lowveld savannas of South Africa

Cited by 61 publications

References 43 publications

Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images

Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images

Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data

Potential of Sentinel-2 spectral configuration to assess rangeland quality

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