Mapping leaf nitrogen and carbon concentrations of intact and fragmented indigenous forest ecosystems using empirical modeling techniques and WorldView-2 data

Forest nitrogen (N) and carbon (C) are among the most important biochemical components of tree organic matter, and the estimation of their concentrations can help to monitor the nutrient uptake processes and health of forest trees. Traditionally, these tree biochemical components are estimated using costly, labour intensive, time-consuming and subjective analytical protocols. The use of very high spatial resolution multispectral data and advanced machine learning regression algorithms such as support vector machines (SVM) and artificial neural networks (ANN) provide an opportunity to accurately estimate foliar N and C concentrations over intact and fragmented forest ecosystems. In the present study, the utility of spectral vegetation indices calculated from WorldView-2 (WV-2) imagery for mapping leaf N and C concentrations of fragmented and intact indigenous forest ecosystems was explored. We collected leaf samples from six tree species in the fragmented as well as intact Dukuduku indigenous forest ecosystems. Leaf samples (n = 85 for each of the fragmented and intact forests) were subjected to chemical analysis for estimating the concentrations of N and C. We used 70% of samples for training our models and 30% for validating the accuracy of our predictive empirical models. The study showed that the N concentration was significantly higher (p = 0.03) in the intact forests than in the fragmented forest. There was no significant difference (p = 0.55) in the C concentration between the intact and fragmented forest strata. The results further showed that the foliar N and C concentrations could be more accurately estimated using the fragmented stratum data compared with the intact stratum data. Further, SVM achieved relatively more accurate N (maximum R2 Val = 0.78 and minimum RMSEVal = 1.07% of the mean) and C (maximum R2 Val = 0.67 and minimum RMSEVal = 1.64% of the mean) estimates compared with ANN (maximum R2Val = 0.70 for N and 0.51 for C and minimum RMSEVal = 5.40% of the mean for N and 2.21% of the mean for C). Overall, SVM regressions achieved more accurate models for estimating forest foliar N and C concentrations in the fragmented and intact indigenous forests compared to the ANN regression method. It is concluded that the successful application of the WV-2 data integrated with SVM can provide an accurate framework for mapping the concentrations of biochemical elements in two indigenous forest ecosystems.