Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient, producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83. MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8 products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying different crop and landcover types though at varying degrees of accuracies.

Cassava; Crop Systems; Farming Systems; Food Security; Land Use; Maize

Cassava; Maize; Neural Network

West Africa

Nigeria