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.
Highlights
Indigenous forests in South Africa cover about 0.2% of the country’s land surface [1]
With regard to the individual tree species, there is a great variability in Leaf area index (LAI) among the species and the highest LAI mean values were achieved for Albizia adianthifolia (4.19) and Trichilia dregeana (3.94) in intact forest stratum, while the lowest mean values were obtained for Albizia adianthifolia (2.03) in fragmented forest stratum dregeana (3.94) in intact forest stratum, while the lowest mean values were obtained for Albizia adianthifolia in fragmented forest stratum
This study shows a successful application of high spatial resolutions WV-2 spectral variables and the machine learning Support Vector Machines (SVM) and Artificial Neural Networks (ANN) regression methods for predicting LAI of six endangered tree species in fragmented and intact Dukuduku indigenous forest ecosystems in South Africa
Summary
Indigenous forests in South Africa cover about 0.2% of the country’s land surface [1]. In. KwaZulu-Natal Province, coastal lowland indigenous forests occur in small, fragmented and largely scattered patches in relatively dry landscapes [1,2]. KwaZulu-Natal Province, coastal lowland indigenous forests occur in small, fragmented and largely scattered patches in relatively dry landscapes [1,2] 2016, 8, 324 is the Dukuduku indigenous forest, which is one of the largest and the best preserved remnants of. South African coastal forests [3]. As one of the key features of landscape in South Africa, careful indigenous forest monitoring, management and conservation is critical. One way to manage and monitor indigenous forest ecosystems is to estimate the trees’
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
