Performance of Support Vector Machines and Artificial Neural Network for Mapping Endangered Tree Species Using WorldView-2 Data in Dukuduku Forest, South Africa

Abstract

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.

Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images

“…A study evaluating riparian vegetation using six bands of the WV2 achieved high overall accuracies of 93% [51]. Research mapping endangered tree species using WV imagery resulted in an accuracy of 77% [52]. In addition, hyperspectral imagery coupled with SVM showed high overall accuracies of 90% for delineating boreal forest areas that contain similar tree species to forests in our research [53].…”

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

“…Likewise, inaccurate universal LAI prediction model was obtained when the data were combined across the six endangered tree species and the two forests (fragmented and intact). That is expected, since our six endangered tree species have distinguishable spectral features [66] that would have confounded the establishment of accurate LAI models across the tree species. The future studies aiming at predicting LAI in our study area or other areas with similar conditions should classify areas into different tree species and separate between the fragmented and intact forests when WV-2 spectral variables and SVM as well as ANN regression algorithms are employed.…”

“…However, it is observed that six other tree species; namely Albizia adianthifolia, Ekebergia capensis, Harpephyllum caffrum, Hymenocardia ulmoides, Sclercarya birrea and Trichilia dregeana in the Dukuduku forest are under severe threat and endangered in both the fragmented and intact forest strata as they face rapid harvesting for woodcarving and traditional medicine [63][64][65]. In a previous study, these six endangered tree species were accurately mapped (overall accuracy = 77%) and distinguished from other land use/cover classes in the Dukuduku area [66]. It is therefore of some interest to monitor the growth and health of these six endangered tree species through the prediction of key biophysical traits (e.g., LAI).…”

“…Moreover, in a few cases in the intact forest and due to the difficulty of taking measurements close to the stem area of the individual trees, we collected LAI measurements from sites where two or more tree species were overlapped. The spectral features from the overlapped sites could have also resulted in mixed spectral features due to different trees structural and biophysical traits [66]. It is also interesting to note that in intact forest stratum, we sampled trees along the roads and open paths, hence it is expected that one side of trees could have received more sunlight than other side of the trees.…”

Balanced vs Imbalanced Training Data: Classifying Rapideye Data With Support Vector Machines

“…Accurate crop identification is significant for national food policy and sustainable crop production in local scale (Forkuor et al, 2014;Wang et al, 2015). Therefore the number of earth observation satellites incorporating the sensor sensitive to chlorophyll content of vegetation as well as its related environmental applications have been increasing over the last few years (Omer et al, 2015;Gärtner et al, 2016). As an such example of recently launched satellite Sentinel-2A (2015) which is a European high resolution and multispectral imaging system offers 13-multispectral bands with spatial resolutions of 10,20 and 60 meters including three different red-edge and one nearinfrared bands as those are particularly useful for agricultural, ecological, and forestry applications (Immitzer et al, 2016).…”