Modelling potential distribution of bramble (rubus cuneifolius) using topographic, bioclimatic and remotely sensed data in the KwaZulu-Natal Drakensberg, South Africa

Ndlovu, Phindile Faith; Mutanga, Onisimo; Sibanda, Mbulisi; Odindi, John; Rushworth, Ian

doi:10.1016/j.apgeog.2018.07.025

Cited by 19 publications

(16 citation statements)

References 57 publications

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“…Perspectives of supplementing DM with RS and light detection and ranging (LiDAR) may turn out advantageous for vegetation prediction over large spatial domains, when these products become available at desired extents (Álvarez-Martínez et al, 2018;Assal, Anderson, & Sibold, 2015). Predictor variables derived from remote sensing data, such as NDVI (or similar vegetation indices), have been successfully used to classify forests by dominating species based on spectral reflectance data (Chen, Bian, Li, Tang, & Wu, 2015), and also to improve the predictive power of DMs on a species level (Ndlovu, Mutanga, Sibanda, Odindi, & Rushworth, 2018) and on vegetation-type level (Álvarez-Martínez et al, 2018). Research to further explore the potential of RS and LiDAR, for development of wall-to-wall covering variables that are good proxies for important explanatory variables which are hard to measure directly, should be strongly encouraged.…”

Section: Dataset Propertiesmentioning

confidence: 99%

Distribution modelling of vegetation types based on area frame survey data

Horvath

Halvorsen

Stordal

et al. 2019

Applied Vegetation Science

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Aim Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location Mainland Norway, covering ca. 324,000 km2. Methods We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

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Section: Dataset Propertiesmentioning

confidence: 99%

Distribution modelling of vegetation types based on area frame survey data

Horvath

Halvorsen

Stordal

et al. 2019

Applied Vegetation Science

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“…The freely available Maxent approach (version 3.4.0) is developed for species distribution modelling (SDM) and was used in this study for modelling the probability of the occurrence of the C. tristis (http://biodiversityinformatics.amnh.org/open_source/maxent/) [35]. Maxent is a machine learning technique that uses presence-only data to determine the potential spatial suitability preference of species [35,36]. The model evaluates the probability of the occurrence from a number of spatial environmental variables [37][38][39].…”

Section: Maxent Modelling Approachmentioning

confidence: 99%

“…The background dataset definition contributes to the model's output significantly and requires the species full environmental distribution of those areas that have been searched [41]. As a result, Maxent establishes a model with a maximum entropy in relation to the data of presence locations and variables to similar interactions with background locations [36,41].…”

Section: Maxent Modelling Approachmentioning

confidence: 99%

“…A sub-sample was used as the replicate run and iterations were fixed to 500. The regularization multiplier was maintained at 4 to avoid overfitting of the test data [36]. The remaining model values were set to default values.…”

Section: Model Accuracy Assessmentmentioning

confidence: 99%

“…A complementary log-log (clog log) output was utilized as it strongly predicts areas of moderately high output [35]. The regularization multiplier was set at 4 to avoid overfitting the test data [36]. Model parameters were set to default replication of 1 with 500 iterations using cross-validation run type.…”

Section: Mapping C Tristis Occurrencementioning

confidence: 99%

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Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa

et al. 2019

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Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control.

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