Remote Sensing of Invasive Lantana camara (Verbenaceae) in Semiarid Savanna Rangeland Ecosystems of South Africa

Dube, Timothy; Shoko, Cletah; Sibanda, Mbulisi; Madileng, Paschaline; Maluleke, Xivutiso Glenny.; Mokwatedi, Velma R.; Tibane, Lorencia; Tshebesebe, Tebogo

doi:10.1016/j.rama.2020.01.003

Cited by 14 publications

(14 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The freely available S2 data with its high spatial, spectral, and temporal resolutions provides important information for species-level monitoring and management (Immitzer et al 2016;Rapinel et al 2019). Several studies also used S2-derived variables for mapping and modelling of invasive species distribution (Arogoundade et al 2020;Dube et al 2020;Ng et al 2017;Rajah et al 2019). Dube et al (2020) compared S2 with Landsat-8 OLI for mapping the distribution of invasive Lantana camara (Verbenaceae) and conclude that the higher performance of S2derived variables is due to its higher spectral and spatial resolution.…”

Section: Potential Of Sentinel-2 For Invasive Species Predictionmentioning

confidence: 99%

“…Several studies also used S2-derived variables for mapping and modelling of invasive species distribution (Arogoundade et al 2020;Dube et al 2020;Ng et al 2017;Rajah et al 2019). Dube et al (2020) compared S2 with Landsat-8 OLI for mapping the distribution of invasive Lantana camara (Verbenaceae) and conclude that the higher performance of S2derived variables is due to its higher spectral and spatial resolution. Moreover, a study by Arogoundade et al…”

Section: Potential Of Sentinel-2 For Invasive Species Predictionmentioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Potential Of Sentinel-2 For Invasive Species Predictionmentioning

confidence: 99%

Section: Potential Of Sentinel-2 For Invasive Species Predictionmentioning

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

View full text Add to dashboard Cite

show abstract

“…0 representing strong disagreement and 1 representing strong agreement. To test the difference in the classification accuracy of the two sensors, a T test was used following Dube, et al [56].…”

Section: Plos Onementioning

confidence: 99%

Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Ngadze¹,

Mpakairi

Kavhu

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

When wildfires are controlled, they are integral to the existence of savannah ecosystems and play an intrinsic role in maintaining their structure and function. Ample studies on wildfire detection and severity mapping are available but what remains a challenge is the accurate mapping of burnt areas in heterogenous landscapes. In this study, we tested which spectral bands contributed most to burnt area detection when using Sentinel-2 and Landsat 8 multispectral sensors in two study sites. Post-fire Sentinel 2A and Landsat 8 images were classified using the Random Forest (RF) classifier. We found out that, the NIR, Red, Red-edge and Blue spectral bands contributed most to burned area detection when using Landsat 8 and Sentinel 2A. We found out that, Landsat 8 had a higher classification accuracy (OA = 0.92, Kappa = 0.85 and TSS = 0.84)) in study site 1 as compared to Sentinel-2 (OA = 0.86, Kappa = 0.74 and TSS = 0.76). In study site 2, Sentinel-2 had a slightly higher classification accuracy (OA = 0.89, Kappa = 0.67 and TSS = 0.64) which was comparable to that of Landsat 8 (OA = 0.85, Kappa = 0.50 and TSS = 0.41). Our study adds rudimentary knowledge on the most reliable sensor allowing reliable estimation of burnt areas and improved post-fire ecological evaluations on ecosystem damage and carbon emission.

show abstract

“…Possibly, the use of the robust algorithms is required for the detection and monitoring of the IAPs and the use of the combination of both the VIs and spectral bands to improve classification, when using Landsat data series (Matongera, Mutanga, Dube, & Sibanda, 2017;Thamaga & Dube, 2018). (Dube et al, 2020;Mudereri et al, 2020). Mapping the spatial distribution of the IAPs is also important for conservation, and the allocation of resources for management and planning purposes Masocha & Dube, 2018;Mungate et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…But the improved spatial and spectral resolutions of the S2 have provided the opportunity for more accurate detection and quantification of the areas invaded by the IAPs (Sanchez‐Espinosa & Schroder, 2019). The detection and determination of the spatial extent of the IAPs is valuable as it provides the requisite baseline information for mitigating and rehabilitating the invaded landscapes (Dube et al, 2020; Mudereri et al, 2020). Mapping the spatial distribution of the IAPs is also important for conservation, and the allocation of resources for management and planning purposes (Masocha et al, 2017; Masocha & Dube, 2017; Masocha & Dube, 2018; Mungate et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Use of multispectral satellite datasets to improve ecological understanding of the distribution of Invasive Alien Plants in a water‐limited catchment, South Africa

et al. 2020

Self Cite

View full text Add to dashboard Cite

Invasive Alien Plants (IAPs) pose major threats to biodiversity, ecosystem functioning and services. The availability of moderate resolution satellite data (e.g. Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Imager) offers an opportunity to map and monitor the occurrence and spatial distribution of IAPs. The use of two multispectral remote sensing data sets to map and monitor IAPs in the Heuningnes Catchment, South Africa, was therefore investigated using the maximum likelihood classification algorithm. It was possible to identify areas infested with IAPs using remote sensing data. Specifically, IAPs were mapped with a higher overall accuracy of 71%, using Sentinel-2 MSI as compared to using Landsat 8 OLI, which produced 63% accuracy. However, both sensors showed similar patterns in the spatial distribution of IAPs within the hillslopes and riparian zones of the catchment. This work demonstrates the utility of the two multispectral data sets in mapping and monitoring the occurrence and distribution of IAPs, which contributes to improved ecological modelling and thus to improved management of invasions and biodiversity in the catchment.

show abstract

Remote Sensing of Invasive Lantana camara (Verbenaceae) in Semiarid Savanna Rangeland Ecosystems of South Africa

Cited by 14 publications

References 41 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

Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Use of multispectral satellite datasets to improve ecological understanding of the distribution of Invasive Alien Plants in a water‐limited catchment, South Africa

Contact Info

Product

Resources

About