Classifying increaser species as an indicator of different levels of rangeland degradation using WorldView-2 imagery

Mansour, Khalid; Mutanga, Onisimo

doi:10.1117/1.jrs.6.063558

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…Each tree contributes a single vote for the assignment of the most frequent class to the input data. 28,30 The default number of trees (ntree) is usually 500, while the default value for the number of variables (mtry) is p P, where P equals the number of predictor variables within a data set. 29 First, RF builds many binary classification trees (ntree) using several bootstrap samples with replacements drawn from the original observations.…”

Section: Random Forest Classifiermentioning

confidence: 99%

Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm

Odindi

Adam

Ngubane

et al. 2014

J. Appl. Remote Sens

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Plant species invasion is known to be a major threat to socioeconomic and ecological systems. Due to high cost and limited extents of urban green spaces, high mapping accuracy is necessary to optimize the management of such spaces. We compare the performance of the newgeneration WorldView-2 (WV-2) and SPOT-5 images in mapping the bracken fern [Pteridium aquilinum (L) kuhn] in a conserved urban landscape. Using the random forest algorithm, gridsearch approaches based on out-of-bag estimate error were used to determine the optimal ntree and mtry combinations. The variable importance and backward feature elimination techniques were further used to determine the influence of the image bands on mapping accuracy. Additionally, the value of the commonly used vegetation indices in enhancing the classification accuracy was tested on the better performing image data. Results show that the performance of the new WV-2 bands was better than that of the traditional bands. Overall classification accuracies of 84.72 and 72.22% were achieved for the WV-2 and SPOT images, respectively. Use of selected indices from the WV-2 bands increased the overall classification accuracy to 91.67%. The findings in this study show the suitability of the new generation in mapping the bracken fern within the often vulnerable urban natural vegetation cover types.

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Section: Random Forest Classifiermentioning

confidence: 99%

Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm

Odindi

Adam

Ngubane

et al. 2014

J. Appl. Remote Sens

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“…Among these satellites, WV-2 and WV-3 offer key spectral bands like yellow, coastal blue, and red edge that help in depicting tree characteristics. The utility of WV-2 image, for instance, has been demonstrated in various studies that include among others, predicting and mapping forest structural parameters [28], mapping of tree species [29], monitoring plantation forest [30], mapping increaser and decreaser grass species in degraded rangelands [31], and the detection of invasive alien plants [32]. These studies discussed the utility of the eight available spectral bands of WV-2 imagery and concluded that the WV-2 data have considerably improved the classification and prediction accuracies of features of interest compared to other multispectral data.…”

Section: Introductionmentioning

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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Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors

Mansour

Mutanga

Adam

et al. 2015

Geocarto International

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Land degradation is believed to be one of the most severe and widespread environmental problems. In South Africa, large areas of land have been identified as degraded, as shown by the lower vegetation cover. One of the major causes of grassland degradation is change in plant species composition that leads to presence of unpalatable grass species. Some grass species have been successfully used as indicators of different levels of grassland degradation in the country. This paper, therefore explores the possibility of mapping grassland degradation in Cathedral Peak, South Africa, using indicators of grass species and edaphic factors. Multispectral SPOT 5 data were used to produce a grassland degradation map based on the spatial distribution of decreaser (Themeda triandra) and increaser (Hyparrhenia hirta) species. To improve mapping accuracy, soil samples were collected from each species site and analysed for nutrient content. A t-test and machine learning random forest classification algorithm were applied for variable selection and classification using SPOT 5 data and edaphic variables. Results indicated that the decreaser and increaser grass species can be mapped with modest accuracy using SPOT 5 data (overall accuracy of 75.30%, quantity disagreement = 2 and allocation disagreement = 23). The classification accuracy was improved to 88.60%, 1 and 11 for overall accuracy, quantity and allocation disagreements, respectively, when SPOT 5 bands and edaphic factors were combined. The study demonstrated that an approach based on the integration of multispectral data and edaphic variables, which increased the overall classification accuracy by about 13%, is a suitable when adopting remote sensing to monitor grassland degradation.

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Classifying increaser species as an indicator of different levels of rangeland degradation using WorldView-2 imagery

Cited by 5 publications

References 55 publications

Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm

Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm

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

Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors

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