Browsing by Author "Xulu, Sifiso"
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- ItemDrought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data(MDPI, 2018-08-30) Xulu, Sifiso; Peerbha, Kabir; Gebreslasie, Michael; Ismail, RiyadSouth Africa has a long history of recurrent droughts that have adversely affected its economic performance. The recent 2015 drought has been declared the most serious in 26 years and impaired key agricultural sectors including the forestry sector. Research on the forests’ responses to drought is therefore essential for management planning and monitoring. The effects of the latest drought on the forests in South Africa have not been studied and are uncertain. The study reported here addresses this gap by using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) and precipitation data retrieved and processed using the JavaScript code editor in the Google Earth Engine (GEE) and the corresponding normalized difference infrared index (NDII), Palmer drought severity index (PDSI), and El Niño time series data for KwaMbonambi, northern Zululand, between 2002 and 2016. The NDVI and NDII time series were decomposed using the Breaks for Additive Seasonal and Trend (BFAST) method to establish the trend and seasonal variation. Multiple linear regression and Mann–Kendall tests were applied to determine the association of the NDVI and NDII with the climate variables. Plantation trees displayed high NDVI values (0.74–0.78) from 2002 to 2013; then, they decreased sharply to 0.64 in 2015. The Mann–Kendall trend test confirmed a negative significant (p = 0.000353) trend between 2014 and 2015. This pattern was associated with a precipitation deficit and low NDII values during a strong El Niño phase. The PDSI (−2.6) values indicated severe drought conditions. The greening decreased in 2015, with some forest remnants showing resistance, implying that the tree species had varying sensitivity to drought. We found that the plantation trees suffered drought stress during 2015, although it seems that the trees began to recover, as the NDVI signals rose in 2016. Overall, these results demonstrated the effective use of the NDVI- and NDII-derived MODIS data coupled with climatic variables to provide insights into the influence of drought on plantation trees in the study area.
- ItemMonitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data(MDPI, 2019) Dlamini, Lubanzi Z. D.; Xulu, SifisoConsidering the negative impact of mining on ecosystems in mining areas, the South African government legislated the Mineral and Petroleum Resources Development Act (No. 28 of 2002), to compel mining companies to restore the land affected by mining. Several studies have used remotely sensed data to observe the status and dynamics of surface mines. Advances in remote sensing along the cloud-based Google Earth Engine (GEE) now promise an enhanced observation strategy for improved monitoring of mine environments. Despite these advances, land rehabilitation at Richards Bay Minerals (RBM) is mainly restricted to field-based approaches which are unable to reveal seamless patterns of disturbance and restoration. Here, we illustrate the value of the trajectory-based LandTrendr algorithm in conjunction with GEE for mine rehabilitation studies. Our automated method produced disturbance and recovery patterns (1984–2018) over the RBM site. The study revealed that RBM has progressively been mining different portions of the mineral-rich coastal area after which restoration was undertaken. The duration of mining over each site ranged from 2 to 6 years. The LandTrendr outputs correspond with independent reference datasets that were classified with an overall accuracy of 99%; it captures mine-induced disturbance efficiently and offers a practical tool for mine restoration management.
- ItemRemote sensing of forest health and vitality: a South African perspective(NISC (Pty) Ltd, 2019-05) Xulu, Sifiso; Gebreslasie, Michael T; Peerbhay, Kabir Y.Commercial forestry plantations are an important and valuable segment of the South African economy and forest managers are required to maximise and sustain forest productivity. However, various factors such as the outbreak of damaging agents are constantly hampering forest health and thus decrease productivity. It is therefore important to detect the presence and spread of these agents within plantation forests, a task efficiently achieved using remote sensing technology. A wide assortment of sensors with varying resolutions are available and have been extensively used for this purpose. This paper reviews the current status of remote sensing of forest health in South Africa by providing insight on the latest developments on the use of the technology in forest plantations. A systematic search was executed on Google Scholar, ScienceDirect® and EBSCOhost® databases that identified 627 articles of which 29 made reference to remote sensing of forest health in South Africa. Four key results were found: (1) the latest technology is capable of detecting and monitoring forest health with great accuracy, especially with the adoption of machine learning methods; (2) studies employing remote sensing to characterise forest health have burgeoned since 2006 with even more applying hyperspectral data; (3) most studies were spatially concentrated in the KwaZulu-Natal Midlands region around Pietermaritzburg with only a few over the Western Cape; and (4) the remote detection of pest outbreaks and pathogens have received much attention followed by alien invasive plants and a few studies directed to fragmentation. Present and future partnerships may open up opportunities for exploiting remote sensing further; this should address growing expectations from government and industry for more detailed and accurate information concerning the health and condition of South Africa’s plantation forests.
- ItemTime Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought(MDPI, 2018-11-30) Mbatha, Nkanyiso; Xulu, SifisoThe variability of temperature and precipitation influenced by El Niño-Southern Oscillation (ENSO) is potentially one of key factors contributing to vegetation product in southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the ENSO and Indian Ocean Dipole/Dipole Mode Index (DMI) is important. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform was used to analyze the vegetation response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP) to climatic variability. The MODIS enhanced vegetation index (EVI), burned area index (BAI), and normalized difference infrared index (NDII) were also analyzed. The study used the Modern Retrospective Analysis for the Research Application (MERRA) model monthly mean soil temperature and precipitations. The Global Land Data Assimilation System (GLDAS) evapotranspiration (ET) data were used to investigate the HiP vegetation water stress. The region in the southern part of the HiP which has land cover dominated by savanna experienced the most impact of the strong El Niño. Both the HiP NDVI inter-annual Mann–Kendal trend test and sequential Mann–Kendall (SQ-MK) test indicated a significant downward trend during the El Niño years of 2003 and 2014–2015. The SQ-MK significant trend turning point which was thought to be associated with the 2014–2015 El Niño periods begun in November 2012. The wavelet coherence and coherence phase indicated a positive teleconnection/correlation between soil temperatures, precipitation, soil moisture (NDII), and ET. This was explained by a dominant in-phase relationship between the NDVI and climatic parameters especially at a period band of 8–16 months.
- ItemUnsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa(MDPI, 2019-06) Xulu, Sifiso; Peerbhay, Kabir; Gebreslasi, Michael; Riyad, IsmailDrought limits the production of plantation forests, notably in the drought-prone Zululand region of South Africa. During the last 40 years, the country has faced a series of severe droughts, however that of 2015 stands out as the most extreme and prolonged. The 2015 drought impaired forest productivity and led to widespread tree mortality in this region, but the identification of tree response to drought stress remains uncertain because of its spatial variability. To address this problem, a method that can capture drought patterns and identify trees with similar reactions to drought stress is desired. This could improve the accuracy of detecting trees su ering from drought stress which is key for forest management planning. In this study, we aimed to evaluate the utility of unsupervised mapping approaches in compartments of Eucalyptus trees with similar drought characteristics based on the Normalized Di erence Water Index (NDWI) and to demonstrate the value of cloud-based Google Earth Engine (GEE) resources for rapid landscape drought monitoring. Our results showed that calculating distances between pixels using three di erent matrices (Random Forest (RF) proximity, Euclidean and Manhattan) can accurately detect similarities within a dataset. The RF proximity matrix produced the best measures, which were clustered using Wards hierarchical clustering to detect drought with the highest overall accuracy of 87.7%, followed by Manhattan (85.9%) and Euclidean similarity measures (79.9%), with user and producer results between 84.2% to 91.2%, 42.8% to 98.2% and 37.2% to 94.7%, respectively. These results confirm the value of the RF proximity matrix and underscore the capability of automatic unsupervised mapping approaches for monitoring drought stress in tree plantations, as well as the value of using GEE for providing cost effective datasets to resource stricken countries.