Framework for semi-automated object-based image classification of invasive alien plant species in South Africa: Harrisia Pomanensis as a case study

Invasive alien plants (IAPs) not only pose a serious threat to biodiversity and water resources but also have impacts on human and animal wellbeing. An important step in IAPs management is to map their location as there is a strong correlation between the spatial extent of an invaded area and the effort required for clearing the plant invasion. However, the traditional GPS based IAPs mapping field campaigns are costly, time consuming and labour intensive. The developments in the Unmanned Aerial Vehicle (UAV) technology have afforded the remote sensing (RS) community the opportunity to map IAPs at enhanced temporal and spatial resolutions. As a result, this framework synthesises a UAV-RS approach for mapping invasive alien plants in South African semi-arid woodlands using Harrisia pomanensis (the Midnight lady) as a case study. In particular, this framework outlines procedures for geometric and radiometric calibration of UAV-derived orthomosaics as well a semi-automated object-based image classification technique for mapping IAPs. The geometric calibration was conducted in the Agisoft Lens software package to determine the camera interior orientation parameters. Since sample photos of the LCD screen were taken from a short-range, there were more radial than tangential distortions. In addition, a scene illumination uniformity statistical inference allowed for the radiometric calibration of the entire scene using parameters derived from radiometric calibration targets placed only in one spot within the study area using the empirical line method (ELM). In particular, accuracy assessment of the radiometric calibration resulted in a correlation coefficient (r) value of 0.977 between in situ measured reflectance and the reflectance values derived from the calibrated image wavebands. This strong correlation validated the proposed UAV-RS ELM based radiometric calibration method for applications in semi-arid woodlands. Furthermore, out of the five evaluated image classifiers, the case study demonstrated that the object-based supervised Bhattacharya classifier which gave 90% and 95.7% producer and user accuracies, respectively, produced more accurate results for mapping Harrisia pomanensis. Even more so, an area based accuracy assessment showed that the Bhattacharya classifier mapped Harrisia pomanensis better than the Maxver classifier (i.e. the second best algorithm) with mapping accuracy averages of 86.1% and 65.2%, respectively, for all the different polygon area sizes. Future research should ascertain whethe radiometric calibration increases mapping accuracy in large scale (>100ha) UAV-RS applications.Dissertation (MSc)--University of Pretoria, 2018.Geography, Geoinformatics and MeteorologyMScUnrestricte

Johannesburg’s inner city private schools: the teacher’s perspective

This study contributes to the literature by documenting the working conditions as well as the socio-economic and demographic profile of teachers employed in Johannesburg’s inner city low-fee private schools. A total of 42 teachers, working in 10 randomly selected inner city private schools, participated in a self-administered questionnaire survey. It was found that most were under 50 years of age, Black-African and foreign born (as were many of the owners of the schools). There were three distinct groupings: South African citizens, Zimbabwean nationals and other foreign nationals. Some were found to be underqualified; others had tertiary qualifications but not in education. Most were working there as a stop gap measure until they had completed their degrees or had a better job offer, either in a public school or in the private non-educational sector. Most expressed unhappiness with their low salaries, long working hours and poor working conditions. They lamented the lack of adequate teaching and learning materials, as well as negligible educational infrastructure such as libraries, laboratories and sports fields. Many wanted the South African State to support low-fee private schools better, both financially and managerially. The paper concludes that the embedded apartheid resource backlog of poor infrastructure and under-qualified teachers cuts across both public and at least some private schools.Keywords: Johannesburg; low fee private schools; migrant teachers; quality education; South Afric

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

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.The South African National Department of Environment Affairs through its funding of the South African National Biodiversity Institute Invasive Species Programme, project number P038.http://www.elsevier.com/ locate/ isprsjprs2018-07-30hj2018Geography, Geoinformatics and Meteorolog

An assessment of image classifiers for generating machine-learning training samples for mapping the invasive Campuloclinium macrocephalum (Less.) DC (pompom weed) using DESIS hyperspectral imagery

Machine-learning algorithms may require large numbers of reference samples to train depending on the spatial and spectral heterogeneity of the mapping area. Acquiring these reference samples using traditional field data collection methods is a challenge due to time constraints, logistical limitations, and terrain inaccessibility. The aim of study was to assess how parametric, nonparametric, and spectral matching image classifiers can be used to generate a large number of accurate training samples from minimal ground control points to train machine-learning algorithms for mapping the invasive pompom weed using 30 m DESIS hyperspectral data. Three image classifiers, namely, maximum likelihood classifier (MLC), support vector machine (SVM) and spectral angle mapper (SAM) were selected to represent each of the three types of image classifiers under investigation in this study. Results show that the SAM, MLC and SVM classifiers had pixel-based classification accuracies of 87%, 73% and 67% for the pompom-containing pixels class, respectively. Furthermore, an independent field verification for the SAM classification was conducted yielding a 92% overall mapping accuracy for the pompom-containing pixels class. A total of 4000 pompom-containing and 8000 non-pompom-containing training samples were generated from an SAM classification that was trained using only 20 endmembers. Overall, this study presents a potential solution strategy that has significant implications for generating large numbers of reference training samples for mapping invasive alien plants from new generation spaceborne hyperspectral imagery using machine-learning algorithms.The South African Department of Environment, Forestry, and Fisheries (DEFF).https://www.elsevier.com/locate/isprsjprshj2023Geography, Geoinformatics and MeteorologyZoology and Entomolog

Validation of LAI, chlorophyll and FVC biophysical estimates from sentinel-2 level 2 prototype processor over a heterogeneous savanna and grassland environment in South Africa

The Sentinel-2 Level 2 Prototype Processor (SL2P) allows the generation of biophysical estimates at high spatiotemporal resolution from Sentinel-2 imagery and could be a solution for generating products in natural environments. This study validated the SL2P estimates of leaf area index (LAI), fractional vegetation cover (FVC) and canopy chlorophyll content (CCC) over the savanna and grassland environments using field measurements. The performance of the SL2P estimates in Marakele and Golden Gate Highlands National Parks were comparatively poor and linearly biased coupled with moderate-to-high errors. The SL2P estimates in the two study sites had low accuracy with relative root mean squared error’s in the range 61.63% to 85.26% and possible systematic underestimations with pBias's ranging from 32.17% to 63.16%. These findings gave insight about the performance of the SL2P estimates over the considered heterogenous environments, and suggest the need for extensive validation and re-calibration of the system using long-term field measurements

Validation of LAI, chlorophyll and FVC biophysical estimates from sentinel-2 level 2 prototype processor over a heterogeneous savanna and grassland environment in South Africa

DATA AVAILABILITY STATEMENT : We understand that the publication of the data is becoming a good practice in research. However, we plan to share all our data in future, but at this stage we are still going to further analyse it for locally par- ameterized types of models, looking at both empirical and the inversion of the physically-based models.The Sentinel-2 data used in this study were downloaded from the European Space Agency Copernicus Open Access Hub.The Sentinel-2 Level 2 Prototype Processor (SL2P) allows the generation of biophysical estimates at high spatiotemporal resolution from Sentinel-2 imagery and could be a solution for generating products in natural environments. This study validated the SL2P estimates of leaf area index (LAI), fractional vegetation cover (FVC) and canopy chlorophyll content (CCC) over the savanna and grassland environments using field measurements. The performance of the SL2P estimates in Marakele and Golden Gate Highlands National Parks were comparatively poor and linearly biased coupled with moderate-to-high errors. The SL2P estimates in the two study sites had low accuracy with relative root mean squared error’s in the range 61.63% to 85.26% and possible systematic underestimations with pBias's ranging from 32.17% to 63.16%. These findings gave insight about the performance of the SL2P estimates over the considered heterogenous environments, and suggest the need for extensive validation and re-calibration of the system using long-term field measurements.https://www.tandfonline.com/loi/tgei202023-06-17hj2023Geography, Geoinformatics and Meteorolog

Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands : Harrisia pomanensis as a case study

Orthomosaics derived from consumer grade digital cameras on board unmanned aerial vehicles (UAVs) are increasingly being used for biodiversity monitoring and remote sensing of the environment. To have lasting quantitative value, remotely sensed imagery should be calibrated to physical units of reflectance. Radiometric calibration improves the quality of raw imagery for consistent quantitative analysis and comparison across different calibrated imagery. Moreover, calibrating remotely sensed imagery to units of reflectance improves its usefulness for deriving quantitative biochemical and biophysical metrics. Notwithstanding the existing radiometric calibration procedures for correcting single images, studies on radiometric calibration of UAV-derived orthomosaics remain scarce. In particular, this study presents a cost- and time-efficient radiometric calibration framework for designing calibration targets, checking scene illumination uniformity, converting orthomosaic digital numbers to units of reflectance, and accuracy assessment using in situ mean reflectance measurements (i.e. the average reflectance in a particular waveband). The empirical line method was adopted for the development of radiometric calibration prediction equations using mean reflectance values measured in only one spot within a 97 ha orthomosaic for three wavebands, i.e. red, green and blue of the Sony NEX-7 camera. A scene illumination uniformity check experiment was conducted to establish whether 10 randomly distributed regions within the orthomosaic experienced similar atmospheric and illumination conditions. This methodological framework was tested in a relatively flat terrain semi-arid woodland that is invaded by Harrisia pomanensis (the Midnight Lady). The scene illumination uniformity check results showed that at a 95% confidence interval, the prediction equations developed using mean reflectance values measured from only one spot within the scene can be used to calibrate the entire 97 ha RGB orthomosaic. Furthermore, the radiometric calibration accuracy assessment results showed a correlation coefficient r value of 0.977 (p < 0.01) between measured and estimated reflectance values with an overall root mean square error of 0.063. These findings suggest that given the entire scene being mapped is experiencing similar atmospheric and illumination conditions, then prediction equations developed using mean reflectance values measured in only one spot within the scene can be used to calibrate the entire orthomosaic in semi-arid woodlands. The proposed methodological framework can potentially be tested and adapted for use in large-scale crop mapping and monitoring in precision agriculture, land-use/land-cover classification as well as plant species delimitation, particularly for mapping widespread invasive alien plants such as H. pomanensis.The South African National Department of Environmental Affairs through its funding of the South African National Biodiversity Institute Directorate: Biological Invasions, project number [P038].http://www.tandfonline.com/loi/tres202019-07-03hj2018Geography, Geoinformatics and Meteorolog