000			02657nab|a22003497a|4500
999			_c63317 _d63309
001			63317
003			MX-TxCIM
005			20211006085222.0
008			210211s2019||||xxk|||p|op||||00||0|eng|d
022			_a1010-6049
022			_a1752-0762 (Online)
024	8		_ahttps://doi.org/10.1080/10106049.2019.1669724
040			_aMX-TxCIM
041			_aeng
100	1		_aDhau, I. _918392
245	1	0	_aExamining the prospects of sentinel-2 multispectral data in detecting and mapping maize streak virus severity in smallholder Ofcolaco farms, South Africa
260			_aUnited Kingdom : _bTaylor and Francis, _c2019.
500			_aPeer review
520			_aCrop diseases monitoring is critical in understanding the effects of diseases on crop production and associated implications on food security. The aim of this study was to assess the utility of the 10 m resolution Sentinel 2 data set, in detecting and mapping Maize Streak Virus (MSV) disease in Ofcolaco farms in Tzaneen, South Africa. Specifically, the study sought to spectrally discriminate and map maize infected with MSV from other land-cover classes. To achieve this objective two analysis approaches were used: spectral analysis (Test I: spectral bands; Test II: spectral bands + spectral vegetation indices) using random forest algorithm in a supervised classification approach. The indices combined with spectral bands were EVI, SAVI, NDVI, GNDVI, GLI and MSAVI. Results indicated that infected maize was highly separable from health maize and other land cover classes (TDSI > 1.8). The mapping accuracy was high using spectral data (Overall accuracy = 85.29% and Kappa = 0.79) and even higher when spectral bands were combined with derived vegetation indices (Overall accuracy = 89.43% and Kappa = 0.84). The results of the study show that the 10 m resolution multispectral Sentinel 2 data set can be used to detect and map maize infected by MSV. The findings are important in showing the value of combining 10 m spectral data with derived indices from Sentinel 2 in improving monitoring of maize steak virus in resource-constrained nations.
546			_aText in English
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_aPlant diseases _gAGROVOC _2 _91206
650		7	_aFood security _gAGROVOC _2 _91118
650		7	_aSatellites _2AGROVOC _93815
650		7	_aCosts _2AGROVOC _99694
650		7	_aSemiarid zones _2AGROVOC _95739
651		7	_2AGROVOC _95594 _aSouth Africa
700	1		_aDube, T. _918393
700	1		_aMushore, T.D. _918394
773	0		_gIn press _dUnited Kingdom : Taylor and Francis, 2020. _x1010-6049 _tGeocarto International _wu74640
942			_cJA _n0 _2ddc