Using an airborne hyperspectral and LiDAR integrated sensor approach to spectrally discriminate and map savanna bush encroaching species in the Greater Kruger National Park region

Includes abstract.Includes bibliographical references (leaves 105-113).Bush encroachment is an environmental phenomenon which affects arid and semi-arid savanna rangelands across the world. Bush encroachment has numerous negative and positive impacts on these savanna ecosystems depending on the land use practices and associated rangeland management regimes

Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa

Please read abstract in article.The Council for Scientific and Industrial Research (CSIR), The Southern Africa Science Service Centre for Climate and Adaptive Land Management (SASSCAL), The National Research Foundation of South Africa (NRF), University of Pretoria.https://www.springer.com/journal/12518Geography, Geoinformatics and Meteorolog

L-band synthetic aperture radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

Woody canopy cover (CC) is the simplesttwo dimensional metric for assessing the presence ofthe 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 regionshttp://www.elsevier.com/locate/jag2017-10-31hb2016Geography, Geoinformatics and Meteorolog

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).Greg Asner, through the CAO campaign and acknowledged partners, provided funding for the LiDAR acquisition and LiDAR processing, as well as interpretation and review of the results.http://www.mdpi.com/journal/remotesensingam2016Electrical, Electronic and Computer EngineeringGeography, Geoinformatics and Meteorolog

Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) synthetic aperture radar data

Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterised by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2 of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics (R2 = 0.83 for CC and AGB and R2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment.Council for Scientific and Industrial Research (CSIR) – South Africa, the Department of Science and Technology, South Africa (Grant Agreement DST/CON 0119/2010, Earth Observation Application Development in Support of SAEOS) and the European Union’s Seventh Framework Programme (FP7/2007-2013, Grant Agreement No. 282621, AGRICAB) for funding this study. The Xband StripMap TerraSAR-X scenes were acquired under a proposal submitted to the TerraSAR-X Science Service of the German Aerospace Center (DLR). The C-band Quad-Pol RADARSAT-2 scenes were provided by MacDonald Dettwiler and Associates Ltd. – Geospatial Services Inc. (MDA GSI), the Canadian Space Agency (CSA), and the Natural Resources Canada’s Centre for Remote Sensing (CCRS) through the Science and Operational Applications Research (SOAR) programme. The L-band ALOS PALSAR FBD scenes were acquired under a K&C Phase 3 agreement with the Japanese Aerospace Exploration Agency (JAXA). The Carnegie Airborne Observatory is supported by the Avatar Alliance Foundation, John D. and Catherine T. MacArthur Foundation, Gordon and Betty Moore Foundation, W.M. Keck Foundation, the Margaret A. Cargill Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., and William R. Hearst III. The application of the CAO data in South Africa is made possible by the Andrew Mellon Foundation, Grantham Foundation for the Protection of the Environment, and the endowment of the Carnegie Institution for Science.http://www.elsevier.com/locate/isprsjprs2016-07-31hb201

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Quantifying the structure of the woody element in Savannahs using integrated optical and Synthetic Aperture Radar (SAR) approach : a stepping stone towards country wide monitoring in South Africa

Savannahs, which are defined as a heterogeneous mixture of herbaceous and woody plant components, occupy one fifth of the global land surface and is the largest biome in South Africa. The woody vegetation structure of savannahs is particularly important as it influences the fire regime, nutrient cycling and the water cycle of these environments and provides fuelwood to sustain the local human populace. Remote Sensing has been proven in numerous studies to be the preferred tool for quantifying and mapping this woody vegetation structure (in this study, defined as woody biomass, woody canopy volume and woody canopy cover metrics) over large areas, mainly due to its superior information gathering capabilities, wide spatial coverage and temporal repeatability. Active remote sensing sensors such as Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are particularly useful in studying woody biomass and other canopy structural metrics, because of their capacity to image within-canopy properties. Passive optical imagery acquired over multiple seasons can also provide tree phenological information which can be used to ascertain the best period for monitoring tree structure, i.e. when tree canopies has sufficient leaves while the grasses are dry. The combined strength of these active (SAR and LiDAR) and passive (optical) sensor technologies, are yet to be applied to their full potential in the dynamic and heterogeneous savannah environment, with a special relevance in Southern African landscapes. This PhD study aimed to evaluate various methods for estimating and upscaling woody structural metrics of South African savannahs using integrated SAR and optical remote sensing datasets and LiDAR datasets as training and validation. Before this aim could be tackled, two current global-scale remote sensing woody structural products (25m JAXA ALOS PALSAR Forest/Non-Forest or FNF and 30m Landsat-based Vegetation Continuous Field or VCF) were evaluated, within the South African context, with the help of high resolution airborne LiDAR datasets. These datasets were resampled to match the products’ criteria and definition used to depict forests. It was found that the FNF product grossly under-represented the distribution of forests in savannah environments (20-80% CC ranges), due to the inadequate HV backscatter threshold chosen in its creation. The FNF product also showed a limited ability in detecting closed forest cover class (90-100%) and Natural Forest and Scrub Forest tree structural classes. The Landsat VCF product displayed strong CC underestimation with increasing variability and mean error from CC values of greater than 30%. The moderate accuracies at the 10-20% CC range (and in the Open Woodland tree structural class) suggests that the VCF product could be potentially applicable in low CC environments such as grasslands and sparse savannahs but can also marginally detect closed canopy environments (90-100% CC range). These results provide the justification for developing new, locally calibrated woody structural products for South Africa. Next, the aim of this study was addressed, firstly, by developing methodologies for the estimation of key woody structural metrics (above ground biomass, woody canopy cover and woody canopy volume) for the Greater Southern Kruger National Park Region using multi-frequency SAR parameters (X-, C- and L-band backscatter and polarisations). Secondly, the most suitable SAR frequency was then tested against and in combination with various Landsat-5 TM optical features (textures, vegetation indices and multi-seasonal band reflectance) for improved regional modelling of woody canopy cover. In both cases, In-situ field measurements of woody vegetation structure were “scaled-up” to landscape and regional scales by using LiDAR, SAR and/or optical sensor products to produce reliable maps of woody structural metrics. A Random Forest modelling approach was predominantly used to meet the modelling challenges in this study and the LiDAR datasets were used for model calibration and validation.Thesis (PhD)--University of Pretoria, 2017.Geography, Geoinformatics and MeteorologyPhDUnrestricte