J
John Odindi
Researcher at University of KwaZulu-Natal
Publications - 123
Citations - 2093
John Odindi is an academic researcher from University of KwaZulu-Natal. The author has contributed to research in topics: Environmental science & Land cover. The author has an hindex of 21, co-authored 95 publications receiving 1320 citations.
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Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
TL;DR: An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types, indicating pursuit of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount.
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A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data
TL;DR: In this article, the authors investigated the use of hyperspectral data in predicting yield of Swiss chard grown under different irrigation water sources, and showed that 95% and 97% of fresh and dry yields variation could be explained.
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Prediction of future urban surface temperatures using medium resolution satellite data in Harare metropolitan city, Zimbabwe
Terence Darlington Mushore,Terence Darlington Mushore,John Odindi,Timothy Dube,Onisimo Mutanga +4 more
TL;DR: In this paper, the impact of urban growth on future micro-climate of Harare by predicting future distribution of land use and land cover, as well as land surface temperature using Cellular Automata Markov Chain analysis was determined.
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Determining extreme heat vulnerability of Harare Metropolitan City using multispectral remote sensing and socio-economic data
TL;DR: Using Landsat-8-derived bio-physical surface properties and socio-demographic factors, this article found that vulnerability to heat-related distress was high in over 40 percent of the city, mainly in densely built-up areas with low-income groups.
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Advancements in satellite remote sensing for mapping and monitoring of alien invasive plant species (AIPs)
TL;DR: It is necessary to explore the use of robust and advanced non-parametric image classifiers with data provided by the new generation of multispectral sensors with improved spatial and spectral resolutions to promote long-term and large scale mapping of AIPs, especially in financially constrained countries.