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Remote Sens. 2016, 8(4), 324; doi:10.3390/rs8040324

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

1
School of Agricultural, Earth and Environmental Sciences, Pietermaritzburg Campus, University of KwaZulu-Natal, Scottsville P/Bag X01, Pietermaritzburg 3209, South Africa
2
Faculty of Forestry, University of Khartoum, Khartoum North 13314, Sudan
3
Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan
4
Schools of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa
*
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 12 December 2015 / Revised: 25 March 2016 / Accepted: 4 April 2016 / Published: 13 April 2016
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Abstract

Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The ground-based methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R2Val = 0.75, RMSEVal = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species. View Full-Text
Keywords: leaf area index; tree species; indigenous forest; WorldView-2; support vector machines; artificial neural networks leaf area index; tree species; indigenous forest; WorldView-2; support vector machines; artificial neural networks
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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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MDPI and ACS Style

Omer, G.; Mutanga, O.; Abdel-Rahman, E.M.; Adam, E. Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms. Remote Sens. 2016, 8, 324.

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