Estimating the road edge effect on adjacent<i>Eucalyptus grandis</i>forests in KwaZulu-Natal, South Africa, using texture measures and an artificial neural network

Lottering, Romano; Mutanga, Onisimo

doi:10.1080/14498596.2012.733617

Cited by 27 publications

(24 citation statements)

References 48 publications

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“…This was expected since we employed empirical statistical approaches for deriving the predictive models under two different forest ecosystems. This result is in conformity with other studies that reported different optimal settings for SVM and ANN under different levels and complexities of landscapes [54,68,101,121,122].…”

Section: Support Vector Machines (Svm) and Artificial Neural Networksupporting

confidence: 93%

“…The networks of road and open paths were used to assist in selecting the endangered tree species by walking in various directions in the intact forest. A handheld LAI-2200 plant canopy analyzer was used to estimate LAI of each sample tree under overcast sky conditions at low solar elevation, i.e., around early morning (8:00-10:00 a.m., Greenwich Mean Time: GMT +2) and late afternoon (3:00-6:00 p.m., GMT +2) with 180˝view restrictor on the sensor [38,68]. To avoid direct sunlight on the sensor, it was required for the operator to take samples of below and above canopy radiation in the opposite direction to the sun.…”

Section: Sampling Procedures and Field Data Collectionmentioning

confidence: 99%

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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

et al. 2016

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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.

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Section: Support Vector Machines (Svm) and Artificial Neural Networksupporting

confidence: 93%

Section: Sampling Procedures and Field Data Collectionmentioning

confidence: 99%

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

et al. 2016

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“…However, this statistical technique was criticized for its limitations. For example, Gebreslasie et al [52], Dye et al [88] and Lottering and Mutanga [89] documented that multiple linear regression assumed both linearity and independence between variables, which was seldom observed in forest and remotely sensed data. Furthermore, linear regression also required the absence of collinearity amongst input variables [88,90].…”

Section: Discussionmentioning

confidence: 99%

“…VIF was normally employed to analyze multicollinearity and some variables indicating on collinearity (multicollinearity) might be removed, which resulted in model that explained less variance than the best possible full model with more variables. Therefore, more robust statistical methods, which did not need to make any assumptions about the data, such as artificial Neural Networks (ANN) [89][90][91], Classification and Regression Tree Analysis (CART) [22,92,93], and Random forests (RF) [88,94,95] were widely used to investigate complex relationship between forests stand variables and remotely sensed data. These robust statistical techniques should be given first priority in future remote sensing studies as many researches have already demonstrated that nonlinear interactions might exist between the observed data and remotely sensed data [88,90,96].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

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“…Various models of ANN have been used in remote sensing studies such as RBF, back propagation (BP), and multilayer perceptron (MLP) [34], [37], [58], [82]- [84]. The MLP is a commonly used ANN structure that comprises an input layer and an output layer and one or more hidden layers of nonlinearly activating nodes [34], [78], [85].…”

Section: B Ann Classification Algorithm: Multilayer Perceptronmentioning

confidence: 99%

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

Omer

Mutanga

Abdel‐Rahman

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Estimating the road edge effect on adjacentEucalyptus grandisforests in KwaZulu-Natal, South Africa, using texture measures and an artificial neural network

Cited by 27 publications

References 48 publications

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

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

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

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

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