The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

Remote Sens. 2015, 7(10), 13298-13318; doi:10.3390/rs71013298

Author

Elfatih M. Abdel-Rahman ^1,2,* , David M. Makori ^1,3

, Tobias Landmann ¹

, Rami Piiroinen ⁴

, Seif Gasim ²

, Petri Pellikka ⁴

 and Suresh K. Raina ¹

¹

International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772, Nairobi 00100, Kenya

²

Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan

³

Department of Geosciences and Geography, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki 00560, Finland

⁴

Department of Geography, School of Agricultural, Environment and Earth Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa

^*

Author to whom correspondence should be addressed. 

Academic Editors: Ioannis Gitas and Prasad S. Thenkabail

Received: 5 August 2015 / Revised: 18 September 2015 / Accepted: 25 September 2015 / Published: 12 October 2015

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Abstract 

Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%–88.15% overall accuracy for the peak flowering season imagery versus 80.82%–83.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees’ foraging behavior. View Full-Text

Keywords: AISA Eagle hyperspectral data;  random forest classifier;  flowering plants

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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