alexa Simulation and Prediction of Land Surface Temperature (
ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Simulation and Prediction of Land Surface Temperature (LST) Dynamicswithin Ikom City in Nigeria Using Artificial Neural Network (ANN)

Maduako ID1*, Yun Z1, Patrick B2
1Department of Geodesy and Geomatics Engineering, University of New Brunswick, Canada
2Department of Geoinformatics and Surveying, University of Nigeria, Nsukka, Nigeria
*Corresponding Author : Maduako I
Department of Geodesy and Geomatics Engineering
University of New Brunswick, Canada
Tel: 734-763-4264
E-mail: [email protected]
Received date: Feb 06, 2016; Accepted date: Feb 22, 2016; Published date: Feb 28, 2016
Citation: Maduako ID, Yun Z, Patrick B (2016) Simulation and Prediction of Land Surface Temperature (LST) Dynamics within Ikom City in Nigeria Using Artificial Neural Network (ANN). J Remote Sensing & GIS 5:158. doi:10.4172/2469-4134.1000158
Copyright: © 2016 Maduako ID, 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.


Land Surface Temperature (LST) is one of the factors associated to urban heat rise and micro climatic warming within a city. Researches relating to the development new technologies or the improvement on the existing ones are very important in urban climate studies. This paper expounds our study on the simulation and prediction of specific future time LST quantitative trend in Ikom city of Nigeria using Feed Forward Back Propagation Artificial Neural Network technology. This study was based on time series ANN model that takes a sequence of past LST values, understand the pattern of change within the dataset and further predict or future time values. Similar studies have been carried out in this manner from our literature review but none used earth observation time series satellite data of a coarse resolution epoch interval for LST time series prediction using ANN. The novelty of this study centers on the attempt to predict some specific future time LST values city-wide using ANN from past LST values derived from earth observation remote sensing imagery (Landsat 7 ETM). The results derived from this study reaffirms the efficiency of ANN (part of deep learning technologies) in learning, understanding and making accurate predictions from a non-linear chaotic real world complex datasets.


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