Monitoring land degradation in Southern Africa by assessing changes in primary productivity.

Land degradation is one of the most serious environmental problems of our time. Land degradation describes circumstances of reduced biological productivity. The fundamental goal of this thesis was to develop land degradation monitoring approaches based on remotely sensed estimates of vegetation production, which are capable of distinguishing human impacts from the effects of natural climatic and spatial variability. Communal homelands in South Africa (SA) are widely regarded to be severely degraded and the existence adjacent, non-degraded areas with the same soils and climate, provides a unique opportunity to test regional land degradation monitoring methods. The relationship between 1km AVHRR, growth season sumNDVI and herbaceous biomass measurements (1989-2003) was firstly tested in Kruger National Park, SA. The relationship was moderately strong, but weaker than expected. This was attributed to the fact that the small areas sampled at field sites were not representative of the spatial variability within 1x1km. The sumNDVI adequately estimated inter-annual changes in vegetation production and should therefore be useful for monitoring land degradation. Degraded areas mapped by the National-Land-Cover in north-eastern SA were compared to non-degraded areas in the same land capability units. The sumNDVI of the degraded areas was consistently lower, regardless of large variations in rainfall. However, the ecological stability and resilience of the degraded areas, as measured by the annual deviations from each pixel's mean sumNDVI, were no different to those of non-degraded areas. This suggests that the degraded areas may be in an alternative, but stable ecological state. To monitor human-induced land degradation it is essential to control for the effects of rainfall on vegetation production. Two methods were tested (i) Rain-Use Efficiency (RUE=NPP/Rainfall) and (ii) negative trends in the differences between the observed sumNDVI and the sumNDVI predicted by the rainfall using regressions calculated for each pixel (RESTREND). RUE had a strong negative correlation with rainfall and did not provide a reliable index of degradation. The RESTREND method identified areas in and around the degraded communal lands that exhibit negative trends in production per unit rainfall. This research made a significant contribution to the development of remote sensing based land degradation monitoring methods

Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS

SBG Applications: Terrestrial Ecosystems - Carbon and Conservation

No abstract availabl

Abrasive waterjet machining of three-dimensional structures from bulk metallic glasses and comparison with other techniques

Bulk metallic glasses (BMGs) are a promising class of engineering materials, but they can be difficult to machine due to high hardness and a metastable structure. Crystallization due to machining can have negative effects, such as a decreased load-bearing capacity of fabricated parts, and thus should be avoided. Here, a Zr-based BMG was machined using abrasive waterjet (AWJ), electrical discharge, ns-pulsed laser engraving, and conventional dry-milling techniques. Characterization of the processed material indicated that AWJ preserves the amorphous phase and provides the combination of speed and flexibility required to rapidly fabricate small three-dimensional parts, while the other techniques did not achieve these goals. As proof-of-principle, a screw, similar to an orthopedic implant, was rapidly machined from the BMG using AW

Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method

Recent development has identified the benefits of using hyper-temporal satellite time series data for land cover change detection and classification in South Africa. In particular, the monitoring of human settlement expansion in the Limpopo province is of relevance as it is the one of the most pervasive forms of land-cover change in this province which covers an area of roughly 125 000km2. In this paper, a spatiotemporal autocorrelation change detection (STACD) method is developed to improve the performance of a pixel based temporal Autocorrelation change detection (TACD) method previously proposed. The objective is to apply the algorithm to large areas to detect the conversion of natural vegetation to settlement which is then validated by an operator using additional data (such as high resolution imagery). Importantly, as the objective of the method is to indicate areas of potential change to operators for further analysis, a low false alarm rate is required while achieving an acceptable probability of detection. Results indicate that detection accuracies of 70% of new settlement instances are achievable at a false alarm rate of less than 1% with the STACD method, an improvement of up to 17% compared to the original TACD formulation.http://www.elsevier.com/locate/jag2016-08-30hb201

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

The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images

This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land cover change examples. The simulated land cover change comprises of concatenated time series that are produced by blending actual time series of pixels from human settlements to those from adjacent areas covered by natural vegetation. The method employs an iteratively retrained MLP to capture all local patterns and to compensate for the time-varying climate in the geographical area. The iteratively retrained MLP was compared to a classical batch mode trained MLP. Depending on the length of the temporal sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months and longer provide minor improvements to classification accuracy and change detection performance at the cost of longer time delays.The CSIR Strategic Research Panelhttp://www.elsevier.com/locate/jagai201