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Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape-A Case Study of Taita Hills, Kenya | OMICS International| Abstract
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
Open Access

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  • Research Article   
  • J Earth Sci Clim Change 2014, Vol 5(9): 232
  • DOI: 10.4172/2157-7617.1000232

Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape-A Case Study of Taita Hills, Kenya

Boitt M1*, Ndegwa C2 and Pellikka P3
1Department of Geomatic Engineering and GIS, Faculty of Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
2Senior Lecturer, Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, Nyeri, Kenya
3Department of Geosciences and Geography, Faculty of Science, University of Helsinki, Finland
*Corresponding Author : Boitt M, Department of Geomatic Engineering and GIS, Faculty of Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya, Tel: +254 (067)52711, Email: [email protected]

Received Date: Aug 11, 2014 / Accepted Date: Oct 30, 2014 / Published Date: Nov 10, 2014

Abstract

Recent advances in hyperspectral remote sensing techniques and technologies allow us to more accurately identify larger range of crop species from airborne measurements. This study employs hyperspectral AISA Eagle VNIR imagery acquired with 9 nm spectral and 0.6 m spatial resolutions over a spectral range of 400 nm to 1000 nm. The area of study is the Taita hills in Kenya. Various crops are grown in this region basically for food and as an economic activity. The crops addressed are: maize, bananas, avocados, and sugarcane and mango trees. The main objectives of this study were to study what crop species can be distinguished from the cultivated population crops in the agricultural landscape and what feature space discriminates most effectively the spectral signatures of different species. Spectral Angle Mapper (SAM) algorithm together with some dissimilarity concepts was applied in this work. The spectral signatures for crops were collected using accurate field plot maps. Accuracy assessment was done using independent training vector data. We achieved an overall accuracy of 77% with a kappa value of 0.67. Various crops in different locations were identified and shown.

Keywords: Hyperspectral imaging; Spectral signatures; Spectral variation; Crop identification; Spectral angle mapper

Citation: Boitt M, Ndegwa C, Pellikka P (2014) Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape-A Case Study of Taita Hills, Kenya. J Earth Sci Clim Change 5: 232. Doi: 10.4172/2157-7617.1000232

Copyright: ©2014 Boitt M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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