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

Abstract

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.

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

“…First, we computed reference AGB from the field inventory, and then used it to predict AGB based on three TLS-derived canopy height metrics-canopy height, canopy cover, and their product as used by [14]; secondly, we derived AGB from L-band SAR backscatter intensities with TLS-derived AGB in the first stage as reference data. The final stage involved the use of the multi-temporal and dual polarization L-band SAR data, which was acquired during the dry season and processed after methodologies adopted from [31,39,41] SAR data, which was acquired during the dry season and processed after methodologies adopted from [31,39,41] to estimate the change in biomass over a four-year period between 2007 to 2010. …”

“…This study used four scenes with dual (HH-HV) polarizations for dry season since TLS data was captured during the dry season. Additionally, backscatter works well in the dry season due to low canopy and soil moisture [39,61]. Table 2 shows the specifications of L-band SAR datasets used in this study.…”

“…An increase in the ENL significantly reduces speckle noise in the multi-looked SAR data [39]. Accordingly, Argenti et al [64] suggested that no filtering should be attempted on heterogeneous (point targets) areas.…”

“…By using different polarizations of SAR with Landsat Enhanced Thematic Mapper (ETM) data, Wijaya et al [36] developed empirical models to estimate how forest parameters responded to σ 0 in Sumatra, while [37] assessed the relationship between dualpol σ 0 and AGB using models after [38]. Relevant studies by [39] utilized multi-temporal and dual-pol SAR in assessing structural components of the vegetation within KNP. The capability of L-band SAR backscatter intensity in quantifying small-scale forest degradation in the Miombo woodlands of Mozambique over a three-year period was demonstrated in [32].…”

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

“…The ), rainfall (mean annual precipitation of 1200mm in the west to 550mm in the east), management regimes (communal and protected) and disturbance regimes (fire, elephant damage, grazing and browsing patterns of herbivores, fuelwood harvesting). The general topography is flat to gently undulating with an average elevation of 400m 42 . The area undergoes short, dry winters and possesses a wet season (convective rainfall) with an annual rainfall between 235mm to 1000mm.…”