Research Articles (Earth Sciences)

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https://hdl.handle.net/10566/273

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Browsing by Author "Abdel-Rahman, Elfatih Mohamed"

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    Potential of resampled multispectral data for detecting desmodium-brachiaria intercropped with maize in a 'push-pull' system
    (ProQuest, 2020) Dube, Timothy Y.; Mudereri, B.T.; Abdel-Rahman, Elfatih Mohamed
    Poor crop yields remain one of the main causes of chronic food insecurity in Africa. This is largely caused by insect pests, weeds, unfavourable climatic conditions and degraded soils. Weed and pest control, based on the climate-adapted ‘push-pull’ system, has become an important target for sustainable intensification of food production adopted by many small-holder farmers. However, essential baseline information using remotely sensed data is missing, specifically for the ‘push-pull’ companion crops. In this study, we investigated the spectral uniqueness of two of the most commonly used ‘companion’ crops (i.e. greenleaf Desmodium (Desmodium intortum) and Brachiaria (Brachiaria cv Mulato) with co-occurring soil, green maize, and maize stover. We used FieldSpec® Handheld 2™ analytical spectral device to collect in situ hyperspectral data in the visible and near-infrared region of the electromagnetic spectrum. Random forest was then used to discriminate among the different companion crops, green maize, maize stover and the background soil.
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    A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data
    (Elsevier, 2021) Mudereri, B.T; Abdel-Rahman, Elfatih Mohamed; Dube, T
    Information on weed occurrence within croplands is vital but is often unavailable to support weeding practices and improve cropland productivity assessments. To date, few studies have been conducted to estimate and map weed abundances within agroecological systems from spaceborne images over wide-area landscapes, particularly for the genus Striga. Therefore, this study attempts to increase the detection capacity of Striga at subpixel size using spaceborne high-resolution imagery. In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. Firstly, multidate and multiyear Sentinel-2 (S2) data (2017 to 2018) were utilized to map cropland and non-cropland areas using the random forest algorithm within the Google Earth Engine. The non-cropland class was thereafter masked out from a single date S2 image of the 13th of December 2017. The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ hyperspectral data. The gathered in-situ hyperspectral data were resampled to the spectral waveband configurations of S2 and three representative EMs were inferred, namely: (1) Striga, (2) crop and other weeds, and (3) soil. Overall classification accuracies of 88% and 78% for the pixel-based cropland mapping and subpixel Striga detection were achieved, respectively.