Modelling <i>Parthenium hysterophorus</i> invasion in KwaZulu-Natal province using remotely sensed data and environmental variables

Arogoundade, Adeola M.; Odindi, John; Mutanga, Onisimo

doi:10.1080/10106049.2019.1581268

Cited by 14 publications

(6 citation statements)

References 74 publications

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“…In this regard, the use of Species Distribution Medelling (SDM) and Geographic Information System (GIS) are among the widely used prediction tools (Bradley 2014). SDM's had been used by ecologists for a long time to predict species distribution (Allouche et al 2006; Jiménez-Valverde 2014; Lemke and Brown 2012;Wisz et al 2008). However, models have shown varied performance and no single best model has been identified by studies for different species and environments (Reside et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

2021

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Background Species Distribution Modelling (SDM) coupled with freely available multispectral imagery from Sentinel-2 (S2) satellite provides an immense contribution in monitoring invasive species. However, attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices (S2-RIs) and biophysical variables, in particular, were limited. Hence, this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin, Ethiopia. Thirty-five variables were computed from Sentinel-2B level-2A, and out of the variables, twelve significant variables were selected using Variable Inflation Factor (VIF). A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software “sdm” package. The performance of the models was evaluated using sensitivity, specificity, True Skill Statistics (TSS), kappa coefficient, area under the curve (AUC), and correlation. Results Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction. Among the tested models, Random Forest (RF) performed better with 93% TSS and 99% AUC followed by Boosted Regression Trees (BRT), ensemble, Generalized Additive Model (GAM), Support Vector Machine (SVM), and Generalized Linear Model (GLM) in decreasing order. The relative influence of vegetation indices was the highest followed by soil indices, biophysical variables, and water indices in decreasing order. According to RF prediction, 16.14% (1553.5 km2) of the study area was invaded by the alien species. Conclusions Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution. Besides, the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.

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Section: Introductionmentioning

confidence: 99%

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

2021

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“…However, there is no consensus among scientists whether the integration of both datasets with SDM can enhance the prediction of species distribution or not. Some researchers argue that integration of both datasets with SDM has huge potential for efficient mapping of species distribution, compared to using remotely sensed or climate data alone (Arogoundade et al 2019;Buermann et al 2008;Prates-Clark et al 2008). Other papers revealed that the integration of both datasets could even decrease the accuracy of modeling due to the quality of remotely sensed environmental variables (land cover) (Engler et al 2013;Truong et al 2017;Zimmermann et al 2007).…”

Section: Integration Of Remote Sensing and Bioclimatic Variablesmentioning

confidence: 99%

Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

2020

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Prediction and modeling using integrated datasets and expertise from various disciplines greatly improve the management of invasive species. So far several attempts have been made to predict, handle, and mitigate invasive alien species impacts using specific efforts from various disciplines. Yet, the most persuasive approach is to better control its invasion and subsequent expansion by making use of cross-disciplinary knowledge and principles. However, the information in this regard is limited and experts from several disciplines have sometimes difficulties understanding well each other. In this respect, the focus of this review was to overview challenges and opportunities in integrating bioclimatic, remote sensing variables, and species distribution models (SDM) for predicting invasive species in data-poor regions. Google Scholar search engine was used to collect relevant papers, published between 2005–2020 (15 years), using keywords such as SDM, remote sensing of invasive species, and contribution of remote sensing in SDM, bioclimatic variables, invasive species distribution in data-poor regions, and invasive species distribution in Ethiopia. Information on the sole contribution of remote sensing and bioclimatic datasets for SDM, major challenges, and opportunities for integration of both datasets are systematically collected, analyzed, and discussed in table and figure formats. Several major challenges such as quality of remotely sensed data and its poor interpretation, inappropriate methods, poor selection of variables, and models were identified. Besides, the availability of Earth Observation (EO) data with high spatial and temporal resolution and their capacity to cover large and inaccessible areas at a reasonable cost, as well as progress in remote sensing data integration techniques and analysis are among the opportunities. Also, the impacts of important sensor characteristics such as spatial and temporal resolution are crucial for future research prospects. Similarly important are studies analyzing the impacts of interannual variability of vegetation and land use patterns on invasive SDM. Urgently needed are clearly defined working principles for the selection of variables and the most appropriate SDM.

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“…This is because, among other factors, the spectral signature of herbaceous weeds is similar to that of the surrounding herbaceous plant species, such as grasses, resulting in low overall classification accuracies of infested landscapes [3], [43]. Nevertheless, strategic bands (e.g., bands in the red-edge, near-infra (NIR) and short-wave (SWIR) regions) were found to contribute the most in developing more accurate models of the spatial distribution of Parthenium weed [26], [44]. Although multidate images of Sentinel-2 provide additional spectral information due to the combination of several images, without the implementation of an efficient feature selection method, the contributing effects of these bands may be suppressed by redundant spectral bands in mapping Parthenium weed.…”

Section: Ecology Of the Parthenium Weedmentioning

confidence: 99%

A Hybrid Feature Method for Handling Redundant Features in a Sentinel-2 Multidate Image for Mapping Parthenium Weed

Kiala

Mutanga

Odindi

et al. 2020

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

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Multidate images provide additional spectral information valuable for mapping plant species. However, correlated or redundant variables created from multiple image data and a large set of variables impede accurate and efficient landscape classification. Nevertheless, with the implementation of an appropriate feature selection method, the full potential of multidate images and subsequent increased classification accuracies may be achieved. Feature selection is a process of automatically selecting features in a dataset that contribute the most to the prediction of a target variable. It improves the classification process in terms of computation cost and predictive accuracy. Feature selection algorithms are typically classified into three groups: filters, wrappers, and embedded. Due to the inherent tradeoffs provided by different feature selection approaches, we hypothesize that a hybrid approach could optimize landscape delineation by leveraging on their complementary strengths. In this regard, we propose a new feature selection method that combines a filter ReliefF, a wrapper Support vector machines-backward (SVM-b), and the embedded random forest in mapping the noxious Parthenium weed using a Sentinel-2 multidate image. The new approach was compared to its three constituents based on the size of optimal feature subsets and classification accuracies. To make comparison and assessment of developed models from selected variables more valuable, the split between training and test datasets was implemented in three different ratios, namely, 3:1(Dataset 1), 1:1(Dataset 2); 1:3 (Dataset 3). The new approach was also evaluated against the multidate image without feature selection and a single-date image. Results showed that the new approach yielded the highest overall accuracies with the smallest optimal feature subsets. For instance, on Dataset 3, the OA was 86.6% with 22 optimal features, whereas it was 84.7% with 35 optimal features using SVM-b, which was the second most performing feature selection algorithm. The new approach yielded higher classification accuracies than the multidate image without feature selection and the single-date image. The findings of this study underscore the capability of hybrid methods to select fewer features from multidate images, with higher predictive accuracies than individual feature selection methods.

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Modelling Parthenium hysterophorus invasion in KwaZulu-Natal province using remotely sensed data and environmental variables

Cited by 14 publications

References 74 publications

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

A Hybrid Feature Method for Handling Redundant Features in a Sentinel-2 Multidate Image for Mapping Parthenium Weed

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