Russell Main

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

Abstract Woody canopy cover (CC) is the simplest two dimensional metric for assessing the presence of the woody component in savannahs, but detailed validated maps are not currently available in southern African savannahs. A number of international EO programs (including in savannah landscapes) advocate and use optical LandSAT imagery for regional to country-wide mapping of woody canopy cover. However, previous research has shown that L-band Synthetic Aperture Radar (SAR) provides good performance at retrieving woody canopy cover in southern African savannahs. This study’s objective was to evaluate, compare and use in combination L-band ALOS PALSAR and LandSAT-5 TM, in a Random Forest environment, to assess the benefits of using LandSAT compared to ALOS PALSAR. Additional objectives saw the testing of LandSAT-5 image seasonality, spectral vegetation indices and image textures for improved CC modelling. Results showed that LandSAT-5 imagery acquired in the summer and autumn seasons yielded the highest single season modelling accuracies (R2 between 0.47 and 0.65), depending on the year but the combination of multi-seasonal images yielded higher accuracies (R2 between 0.57 and 0.72). The derivation of spectral vegetation indices and image textures and their combinations with optical reflectance bands provided minimal improvement with no optical-only result exceeding the winter SAR L-band backscatter alone results (R2 of ∼0.8). The integration of seasonally appropriate LandSAT-5 image reflectance and L-band HH and HV backscatter data does provide a significant improvement for CC modelling at the higher end of the model performance (R2 between 0.83 and 0.88), but we conclude that L-band only based CC modelling be recommended for South African regions.

The assessment of data mining algorithms for modelling Savannah Woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets

The woody component in African Savannahs provides essential ecosystem services such as fuel wood and construction timber to large populations of rural communities. Woody canopy cover (i.e. the percentage area occupied by woody canopy or CC) is a key parameter of the woody component. Synthetic Aperture Radar (SAR) is effective at assessing the woody component, because of its capacity to image within-canopy properties of the vegetation while offering an all-weather capacity to map relatively large extents of the woody component. This study compared the modelling accuracies of woody canopy cover (CC), in South African Savannahs, through the assessment of a set of modelling approaches (Linear Regression, Support Vector Machines, REPTree decision tree, Artificial Neural Network and Random Forest) with the use of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) datasets. This study illustrated that the ANN, REPTree and RF non-parametric modelling algorithms were the most ideal with high CC prediction accuracies throughout the different scenarios. Results also illustrated that the acquisition of L-band data be prioritized due to the high accuracies achieved by the L-band dataset alone in comparison to the individual shorter wavelengths. The study provides promising results for developing regional savannah woody cover maps using limited LiDAR training data and SAR images.

Spectral variability within species and its effects on Savanna tree species discrimination

Differences in within-species phenology and structure driven by factors including topography, edaphic properties, and climatic variables present important challenges for species differentiation with remote sensing in the Kruger National Park, South Africa. The objective of this study was to examine probable factors including intraspecies spectral variability and the spectral sample size that could affect remote sensing of Savanna tree species across a land-use gradient in the Kruger National park. Eighteen species were examined: Acacia gerradii, Acacia nigrescens, Combretum apiculatum, Combretum collinum, Combretum hereroense, Combretum imberbe, Combretum zeyheri, Dichrostachys cinerea, Euclea sp (E. divinurum and E. natalensis, Gymnosporia sp (G. buxifolia and G. senegalensis), Lonchocarpus capassa, Peltoforum africanum, Piliostigma thonningii, Pterocarpus rotundifolia, Sclerocarya birrea, Strychnos sp (S. madagascariensis, S. usambarensis), Terminalia sericea and Ziziphus mucronata. Discriminating species using the K-nearest neighbour (K = 1) classifier with spectral angle mapper (SAM) yielded a higher classification accuracy (48% overall accuracy) compared to 16% for the classification involving the mean spectra for each species as the training spectral set. Within-species spectral variability and the training sample size were identified as important factors affecting classification accuracy of the tree species. We recommend a non-parametric classifier such as K-nearest neighbour classifier for classifying and mapping tree species in a highly complex environment such as the savanna system of the Kruger National Park.

Hyper-Temporal C-Band SAR for Baseline Woody Structural Assessments in Deciduous Savannas

Savanna ecosystems and their woody vegetation provide valuable resources and ecosystem services. Locally calibrated and cost effective estimates of these resources are required in order to satisfy commitments to monitor and manage change within them. Baseline maps of woody resources are important for analyzing change over time. Freely available, and highly repetitive, C-band data has the potential to be a viable alternative to high-resolution commercial SAR imagery (e.g., RADARSAT-2, ALOS2) in generating large-scale woody resources maps. Using airborne LiDAR as calibration, we investigated the relationships between hyper-temporal C-band ASAR data and woody structural parameters, namely total canopy cover (TCC) and total canopy volume (TCV), in a deciduous savanna environment. Results showed that: the temporal filter reduced image variance; the random forest model out-performed the linear model; while the TCV metric consistently showed marginally higher accuracies than the TCC metric. Combinations of between 6 and 10 images could produce results comparable to high resolution commercial (C- & L-band) SAR imagery. The approach showed promise for producing a regional scale, locally calibrated, baseline maps for the management of deciduous savanna resources, and lay a foundation for monitoring using time series of data from newer C-band SAR sensors (e.g., Sentinel1).

Tree cover, tree height and bare soil cover differences along a land use degradation gradient in semi-arid savannas, South Africa

High resolution airborne hyperspectral and discrete return LiDAR data were used to assess bare soil and tree cover differences along a land use transect consisting of state-owned, privately-owned conservation areas, and communal areas in South African savannas. The results show that tree cover is higher in conservation areas as compared to communal areas where local people use fuel wood for personal consumption. Low impact communal sites (limited use) tend to have higher tree cover than higher impacted communal sites. Generally communal areas have altered tree height distribution but in diverse way depending on the geology or the level of human utilization. Bare soil cover was generally found to be quite low (< 10%) in all different land uses, suggesting that the degradation level in communal areas might not be as high as generally perceived.

Woody cover assessments in a Southern African savanna, using hyper-temporal C-band ASAR-WS data

Southern African savanna ecosystems and their woody resources are under pressure. Governments in the region need locally calibrated, cost effective, and regularly updated information on these resources in order to satisfy both national and international commitments to manage them. Using LiDAR data as a calibration dataset, this paper sets out to investigate the potential of hypertemporal C-band ASAR SAR data in mapping woody structural related parameters in a savanna environment. Images spanning three years where grouped by years (2007-2009), season (Wet or Dry) and polarization (HH or VV), and relationships were sought for the woody parameter total canopy cover (TCC). Results show that: Dry season combinations of images outperformed wet season images; HH co-polarised images outperformed VV images; temporally filtered images showed marked improvement on unfiltered images. While non-parametric random forest models achieved better validation accuracies than other models did. The single best result was achieved by combining all the temporally filtered images, from all of the various scenarios (R2=0.74; RMSE=8.52; SEP=35.27). The results show promise in delivering regional scale, locally calibrated, baseline products for the management of Southern Africas woody resources.

Evaluating the Seasonality of Remote Sensing Indicators of System State for Eucalyptus Grandis Growing on Different Site Qualities

The stability of remote sensing indicators of leaf water, chlorophyll and nutrients to discriminate between Eucalyptus grandis growing on different site qualities in KwaZulu-Natal, South Africa was evaluated for two growing seasons (winter and early summer). Site quality was defined by the total available soil water (TAW). Canopy reflectance spectra for 68 trees (winter) and 45 trees (summer) from good, medium, and poor sites were collected on clear sky days using an ASD spectroradiometer. Two-way analysis of variance showed that the discriminatory capabilities of leaf water, chlorophyll, and nutrient concentrations for E. grandis growing on different site qualities, are seasonal by nature. Leaf water and chlorophyll indices were better indicators of site quality in winter than foliar nutrient concentrations of N, P, and K, which in turn performed better in summer. These results will help to define the sensing timeframe for monitoring E. grandis growth in KwaZulu-Natal.

Integrating remote sensing and ancillary data for regional ecosystem assessment: Eucalyptus grandis agro-system in KwaZulu-Natal, South Africa

The ability of various ecosystems to perform vital functions such as biodiversity production, and water, energy and nutrient cycling depends on the ecosystem state, i.e. health. Ecosystem state assessment has been a topic of intense research, but has reached a point at which accurate large scale (e.g. regional to global scale) modelling and monitoring are hindered by limitations in conventional assessment methods such as direct field sampling, modelling from environmental drivers such as temperature, precipitation and available nutrients, and modelling from remote sensing data. The Ecosystem-Earth Observation (Eco-EO) research group at the Council for Scientific and Industrial Research (CSIR), South Africa has highlighted the need in remote sensing research for an integrated sensing approach at the systems level. This perspective is based on the assumption that a modelling approach that exploits the strength of the various techniques (in situ environmental variables, direct field observation and remote sensing data) could potentially improve the assessment of ecosystem state at various geographic scales. In this light, the Eco-EO research group has embarked on an agro-system state assessment project since 2007 as a first step towards the implementation of the integrated modelling approach for various ecosystems. The agro-system consists of a monoculture forest plantation of Eucalyptus grandis situated in KwaZulu-Natal, South Africa. This paper presents preliminary results from the KwaZulu-Natal E. grandis experimental study.