Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape-A Case Study of Taita Hills, Kenya
Received Date: Aug 11, 2014 / Accepted Date: Oct 30, 2014 / Published Date: Nov 10, 2014
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.