Endangered tree species (ETS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, and security-based and well-being benefits. The development of techniques for mapping and monitoring ETS is thus critical for understanding functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to map ETS over fragmenting areas. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vector machines (SVM) and artificial neural network (ANN). However, delineation of ETS in a fragmenting ecosystem using high-resolution imagery has largely remained elusive due to the complexity of the species structure and their distribution. Therefore, the aim of the present study was to examine the utility of the advanced WV-2 data for mapping ETS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. Specifically, the study looked at testing the advent of the additional WV-2 bands in mapping six ETS. WV-2 image was spectrally resized to separate four standard bands (SB) and four additional bands (AB). WV-2 image (8 bands: 8B) together with the SB and AB subsets was classified using SVM and ANN methods. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% for SVM and 75.00% for ANN using 8B. The SB produced OA of 65.00% for SVM and 64.00% for ANN. The AB produced almost the same OA of 70.00% for both SVM and ANN. There were significant differences between the performances of the two algorithms as demonstrated by the results of McNemar's test (Z score ≥ 1.96). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to map ETS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of ETS.
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 groundbased 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 (R 2 Val = 0.75, RMSE Val = 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.
This work explores the potential of the high‐resolution WorldView‐2 sensor in quantifying edge effects on the spatial distribution of selected forest biochemical properties in fragmented Dukuduku forest in KwaZulu‐Natal, South Africa. Specifically, we sought to map fragmented patches within forested areas in Dukuduku area, using very high spatial resolution WorldView‐2 remotely sensed data and to statistically determine the effect of these fragmented patches on the spatial distribution of selected forest biochemical properties. Edge effects on carbon, LAI and foliar nitrogen were quantified based on the models derived by Omer et al. (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8, 4825). Edge effect statistical results on the spatial distribution of carbon, LAI and nitrogen showed significant (α = 0.05) variations with change in distance from fragmented patches (>150 m2). Forest foliar carbon concentrations significantly (p‐value = 0.016) increased from 44.8% to 45.3% with increasing distance (25–375 m) from fragmented patches. A similar trend was observed for LAI. Nevertheless, for nitrogen the results show that its concentration significantly (p = 0.016) decreased with increase in distance from the fragmented patches. Overall, the findings of this work underscore the invaluable potential and strength of WorldView‐2 data set in assessing edge effect on the spatial distribution of selected forest biochemical properties.
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