Remote sensing bio-control damage on aquatic invasive alien plant species

Agjee, Na’eem Hoosen; Mutanga, Onisimo; Ismail, Riyad

doi:10.4314/sajg.v4i4.8

Cited by 4 publications

(4 citation statements)

References 67 publications

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“…Further, Williams and Hunt [14] reported a 95% overall accuracy for mapping leafy spurge (Euphorbia esula L.) using the Airborne Visible/Infrared Spectrometer (AVIRIS) hyperspectral data. Notwithstanding these successful attempts, currently, using hyperspectral data in mapping IAPs has several limitations such as the high cost of satellite hyperspectral data, airborne and handheld sensors as well as the resultant large volumes of data that require high computing power for processing [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

Mafanya

Tsele

Botai

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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Invasive alien plants (IAPs) not only pose a serious threat to biodiversity and water resources but also have impacts on human and animal wellbeing. To support decision making in IAPs monitoring, semi-automated image classifiers which are capable of extracting valuable information in remotely sensed data are vital. This study evaluated the mapping accuracies of supervised and unsupervised image classifiers for mapping Harrisia pomanensis (a cactus plant commonly known as the Midnight Lady) using two interlinked evaluation strategies i.e. point and area based accuracy assessment. Results of the point-based accuracy assessment show that with reference to 219 ground control points, the supervised image classifiers (i.e. Maxver and Bhattacharya) mapped H. pomanensis better than the unsupervised image classifiers (i.e. K-mediuns, Euclidian Length and Isoseg). In this regard, user and producer accuracies were 82.4 % and 84% respectively for the Maxver classifier. The user and producer accuracies for the Bhattacharya classifier were 90% and 95.7%, respectively. Though the Maxver produced a higher overall accuracy and Kappa estimate than the Bhattacharya classifier, the Maxver Kappa estimate of 0.8305 is not significantly (statistically) greater than the Bhattacharya Kappa estimate of 0.8088 at a 95% confidence interval. The area based accuracy assessment results show that the Bhattacharya classifier estimated the spatial extent of H. pomanensis with an average mapping accuracy of 86.1% whereas the Maxver classifier only gave an average mapping accuracy of 65.2%. Based on these results, the Bhattacharya classifier is therefore recommended for mapping H. pomanensis. These findings will aid in the algorithm choice making for the development of a semi-automated image classification system for mapping IAPs.

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Section: Introductionmentioning

confidence: 99%

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

Mafanya

Tsele

Botai

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The normalized mean absolute error (NMEA) [28], root mean square error (RMSE), reliability index (RI) [21] and correlation coefficient (r) are given in Eqs. (3)-(6) [17].…”

Section: Test Statistics For Validating Transformation Parametersmentioning

confidence: 99%

Realization of time-dependent geocentric datum transformation parameters for Nigeria

et al. 2019

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Geodetic reference frames were established decades ago by classical surveying techniques; however, due to biases caused by poor observation techniques and effects of plate tectonic, the origins are poorly defined and adopted. Due to plate tectonics, the relative position of points changes with time and therefore, datum such as MINNA requires redefinition at regular intervals to be in consonant with geodetic reference frame in use. Therefore, with the Continuously Operating Reference Stations (CORS) network in Nigeria, these biases can be mitigated and a more accurate datum transformation parameter between MINNA datum and ITRF can be developed and adopted. Therefore, this study presents the results of time-dependent datum transformation parameters proposed for Nigeria using 5-years GNSS data (from 2011 to 2015) obtained from the NigNet CORS. GAMIT GNSS scientific processing software was deployed in processing, while GLOBK was used for frame definition and datum transformation parameters development. Statistical assessments show the validity of the transformation parameters. The correlation value was found to be 1, root mean square error 0.00411, normalized mean absolute error 1.267E−10 and reliability index 1.0.

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“…The performance of both models in estimating T m remains widely unknown on the African continent. Nevertheless, a study by Isioye et al (2015b) on the performance of the UNB3m and GPT2w models revealed the performances of the duo models in estimating ZTD on the African continent.…”

Section: Introductionmentioning

confidence: 99%

“…Isioye et al (2015b) have demonstrated the efficacy of these model performance indicators and their mathematical formulations are contained therein.The NMAE measures the absolute difference between the forecasted values (from NWMTE-I, NWMTE-II, NWMTE-III, Bevis, UNB3m, GPT2w and GWMT-IV models) and the reference (integral method), normalized to the mean. AnNMAE value of zero indicates a perfect agreement.…”

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confidence: 99%

Modelling weighted mean temperature in the West African region: implications for GNSS meteorology

2016

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Weighted mean temperature (Tm) is a critical parameter in the estimation of precipitable water vapour from ground‐based Global Navigation Satellite System (GNSS) receivers. In the present study, three models of Tm are developed for GNSS meteorological applications in the West African region in general and Nigeria in particular. The first is a least squares linear regression model based on National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis II data for Nigeria from 2010 to 2012. A regression of 37 330 data pairs of surface temperature and Tm calculated from the vertical profiles of temperature above each grid node of the reanalysis model was used to produce the Nigerian Weighted Mean Temperature Equation‐I (NWMTE‐I) model after outlier data were removed. By using the same approach, NWMTE‐II was obtained from 11 433 radiosonde profiles from 24 sounding stations in the West African region for the period 2009–2013. NWMTE‐III was produced from a combination of the data used to build NWMTE‐I and NWMTE‐II. To evaluate the accuracy of these three models, they were compared with four global models based on Tm obtained by using the integral method, which was the reference model for this study. The normalized mean absolute error, root‐mean‐square error, model efficiency, reliability index and correlation co‐efficient were used as performance indicators. The performances of the developed models were promising compared with the global models, justifying the importance of using measured meteorological parameters when estimating Tm and fine tuning Tm to specific areas or regions. Finally, NWMTE‐III is recommended for Nigerian users and NWMTE‐II for users in the West African region.

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Remote sensing bio-control damage on aquatic invasive alien plant species

Abstract: Abstract

Cited by 4 publications

References 67 publications

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

Realization of time-dependent geocentric datum transformation parameters for Nigeria

Modelling weighted mean temperature in the West African region: implications for GNSS meteorology

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