Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data

Tunrayo Alabi, Michael Haertel, Sarah Chiejile

2016

Abstract

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.

Paper Citation

in Harvard Style

Alabi T., Haertel M. and Chiejile S. (2016). Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data . In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-188-5, pages 109-120. DOI: 10.5220/0005767301090120

in Bibtex Style

@conference{gistam16,
author={Tunrayo Alabi and Michael Haertel and Sarah Chiejile},
title={Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data},
booktitle={Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2016},
pages={109-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005767301090120},
isbn={978-989-758-188-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data
SN - 978-989-758-188-5
AU - Alabi T.
AU - Haertel M.
AU - Chiejile S.
PY - 2016
SP - 109
EP - 120
DO - 10.5220/0005767301090120