Sentinel-1 and Sentinel-2 Time Series Breakpoint Detection as Part of the South African Land Degradation Monitor (SALDi)

2021 ◽  
Author(s):  
M. Urban ◽  
A. Hirner ◽  
J. Ziemer ◽  
M. M. Mueller ◽  
U. Gessner ◽  
...  
Keyword(s):  
Time Series ◽  
South African ◽  
Land Degradation ◽  
The South ◽  
Breakpoint Detection ◽  
Sentinel 2

scholarly journals Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

2020 ◽  
Vol 12 (1) ◽  
pp. 198 ◽  
Author(s):  
Frederick D.L. Hunter ◽  
Edward T.A. Mitchard ◽  
Peter Tyrrell ◽  
Samantha Russell
Keyword(s):  
Time Series ◽  
Land Degradation ◽  
Classification Accuracy ◽  
Grass Species ◽  
Support Vector ◽  
Invasive Shrub ◽  
Seasonal Time Series ◽  
Large Populations ◽  
Dry Seasons ◽  
Sentinel 2

In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.

scholarly journals Die definiëring van persoonlike besparing: Die Suid-Afrikaanse situasie 1965.1 tot 1984.4

1987 ◽  
Vol 18 (1) ◽  
pp. 29-34
Author(s):  
P. Styger ◽  
J. H.P. Van Heerden
Keyword(s):  
South Africa ◽  
Time Series ◽  
South African ◽  
The South ◽  
Good Definition ◽  
Reserve Bank ◽  
Term Insurance ◽  
The Sixties ◽  
Personal Savings ◽  
Definition Of

The definition of personal savings: The South African situation 1965.1 to 1984.4 The personal savings definition and through that also the time series of personal savings in South Africa in common use, can be described as personal savings as published, amongst others, by the South African Reserve Bank. In the computation of the above time series there are certain deficiencies and the time series has been queried since the sixties. The objective of this study was to undertake an empirical study of the definition of personal savings in South Africa and through that also the time series of personal savings, and possibly to improve on these. It was indicated that the published personal savings cannot be regarded as a good definition of personal savings in South Africa. Various alternative personal savings definitions were studied critically and it was indicated that it would seem that long-term insurance premiums plus pension fund contributions (i.e. contractual personal savings) should constitute the personal savings definition for South Africa.

scholarly journals The relationship between βeta and stock returns in the JSE securities exchange in South Africa

2011 ◽  
Vol 9 (1) ◽  
pp. 558-566
Author(s):  
Raphael Tabani Mpofu
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Analysis ◽  
Financial Markets ◽  
Stock Returns ◽  
South African ◽  
Time Series Data ◽  
Series Data ◽  
The South ◽  
The Relationship

The purpose of this study was is to examine the relationship between stock βeta and returns in the JSE Securities Exchange. If the model is applicable in its entirety or can explain the beta-stock returns relationship, it raises an important academic question, mainly, how should the South African financial market be viewed by investors and portfolio managers, given the political-social-economical classifications that South Africa finds itself in, sometimes referred to as developing, emerging or underdeveloped? The time-series data used was from Sharenet as well as from the South African Reserve Bank macro-economic time series data. The sample period consisted of 10 years of monthly time series data between January 2001 and December 2010. Regression analysis was applied using the conditional approach. When using the conditional capital asset pricing model (CAPM) and cross-sectional regression analysis, the findings strongly supported the significant relationship between stock excess returns and βeta. However, the results do not provide strong evidence of a CAPM relation between risks and realized return trade-off in the South African financial markets. These results demonstrate that the South African financial markets are complex and financial tools, such as the CAPM can be used to explain complex financial phenomenon as in other developed markets, although complete reliance on the CAPM should be relied upon.

Experiences from the South African land degradation neutrality target setting process

2019 ◽  
Vol 101 ◽  
pp. 54-62 ◽  
Author(s):  
Graham Paul von Maltitz ◽  
James Gambiza ◽  
Klaus Kellner ◽  
Thizwilondi Rambau ◽  
Lehman Lindeque ◽  
...  
Keyword(s):  
South African ◽  
Land Degradation ◽  
The South ◽  
Target Setting ◽  
Setting Process

Earth Observation Strategies for degradation monitoring in South Africa with Sentinels – Results from the SPACES 2 SALDi-project 

2021 ◽  
Author(s):  
Christiane Schmullius ◽  
Marcel Urban ◽  
Kai Heckel ◽  
Hilma Sevelia Nghiyalwa ◽  
Andreas Hirner ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Soil Moisture ◽  
Ecosystem Services ◽  
South African ◽  
Land Degradation ◽  
Ecosystem Service ◽  
Data Cube ◽  
Assessment Tools ◽  
Individual Development

<p>The project ‘South African Land Degradation Monitor (SALDi)’ contributes to the German-South African Science Program SPACES by addressing the dynamics and functioning of multi-use landscapes with respect to land use, land cover change, water fluxes, and implications for habitats and ecosystem services. Particularly, SALDi aims: i) to develop an automated system for high temporal (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics, ii) to develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate, iii) to advance current soil degradation process assessment tools as a limiting factor for ecosystem services. Protected areas (SANParks and other) within our six study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem service dynamics of multi-used landscape (cropland, rangeland, forests) are evaluated. Our study regions follow a climatic SW-NE transect: 1-Overberg, 2-Kai !Garib/Augrabies Falls, 3-Sol Plaatje/Kimberley, 4-Mantsopa/Ladybrand, 5-Bojanala Platinum/Pilanesberg, 6-Ehlanzeni /Mpumalanga.</p><p>We are utilizing Sentinel-1A/B C-Band VV/VH-SAR time series with a 10 m resolution. The revisit time is 12 days on average for South Africa. Pre-processing is done using pyroSAR, a Python framework for large-scale SAR-processing providing processing utilities in ESA’s Sentinel Application Platform (SNAP) as well as GAMMA Remote Sensing software. The first two analytical approaches for the evaluation of the Sentinel-1 time series to detect surface changes, are based on the recognition of irregularities in the radar backscatter or coherence dynamics. Sentinel-2A/B data were pre-processed to L2A and used to calculate a wide range of vegetation indices (e.g. NDVI, EVI, SAVI, REIP) using DLR’s Sen2Cor-processor. The time frame starts with the first Sentinel-1 and -2 acquisitions and continues. The analysis-ready data, that is, harmonized, standardized, interoperable, radiometrically and geometrically consistent data, is being ingested in the SALDi Data Cube. Algorithms and models for developing products such as land degradation indicators are being developed using Jypiter notebooks. SANSA in collaboration with SARAO (South African Radio Astronomy Observatory), is developing the open data cube Digital Earth South Africa (DESA) based on SPOT data. Other datasets from different sensors will be ingested at a later stage. SALDi’s Data Cube will be open access to make it available to the wider scientific community, and also for teaching and training purposes. The application/use of the individual development stages should be possible on the fly for the partners in South Africa. The SASSCAL platform shall be used for distribution of the finalised SALDi Data Cube.</p><p>This presentation demonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping and breakpoint analyses of the complex savanna systems, invasive slangbos (Seriphium plumosum) bush encroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons with VHR airborne DMC surface products, field trips and permanently installed soil moisture networks and interaction with local South African stakeholders.</p><p> </p>

scholarly journals Forecasting The South African Business Cycle Using Fourier Analysis

2016 ◽  
Vol 15 (4) ◽  
pp. 175-192
Author(s):  
Daniel Thomson ◽  
Gary Van Vuuren
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Business Cycle ◽  
Fourier Analysis ◽  
South African ◽  
Historical Data ◽  
Current Position ◽  
The South ◽  
Mode Decomposition ◽  
The Business Cycle

A Fourier transform analysis is proposed to determine the duration of the South African business cycle, measured using log changes in nominal gross domestic product (GDP). The most prominent cycle (two smaller, but significant, cycles are also present in the time series) is found to be 7.1 years, confirmed using Empirical Mode Decomposition. The three dominant cycles are used to estimate a 3.5 year forecast of log monthly nominal GDP and these forecasts compared to observed (historical) data. Promising forecast potential is found with this significantly-reduced number of cycle components than embedded in the original series. Fourier analysis is effective in estimating the length of the business cycle, as well as in determining the current position (phase) of the economy in the business cycle.

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