Hybrid retrieval of grass biophysical variables based-on radiative transfer, active learning and regression methods using Sentinel-2 data in Marakele National Park

DATA AVAILABILITY STATEMENT : We understand that the publication of the data is becoming a good practice in research.Biophysical variables such as leaf area index (LAI) and leaf chlorophyll content (LCC) are cited as essential biodiversity variables. A comprehensive comparison and integration of retrieval methods is needed for the estimation of biophysical variables such as LAI and LCC over a multispecies grass canopy. This study tested an assortment of five potentially robust, nonparametric regression methods (NPRMs) for inversion of radiative transfer model (RTM) to retrieve grass LAI and LCC in the Marakele National Park (MNP) of South Africa. The NPRMs used were, namely (i) Partial least squares regression (PLSR), (ii) Principle components regression (PCR), (iii) Kernel ridge regression (KRR), (iv) Random forest regression (RFR), and (v) K-nearest neighbours regression (KNNR). Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a large pool of RTM simulations. Results show the most accurate grass LAI and LCC retrievals had lower relative root mean squared errors (RRMSEs) of 39.87% and 16.58% respectively. These findings have significant implications for the development of transferable rangeland monitoring systems in protected mountainous regions.Research development programme of the University of Pretoria; National Research Foundation (NRF) of South Africa AND Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL).https://www.tandfonline.com/journals/TGEIhj2024Geography, Geoinformatics and MeteorologySDG-13:Climate actionSDG-15:Life on lan

Analysis of thermally-induced displacements of the HartRAO lunar laser ranger optical tube : its impact on pointing

The Hartebeesthoek Radio Astronomy Observatory (HartRAO) of South Africa is developing a Lunar Laser Ranging (LLR) system to achieve sub-centimetre range precision to the Moon. Key to this high precision expectation, which includes improving the overall operational performance of its telescope, is the thermal analysis of the telescope structure. In this study, thermal sensors were mounted on the thermally- important areas of the tube structure to measure the tube displacements emanating from the varying ambient air temperatures. A laser distance-measurement system was used for this purpose. Results showed that while the optical tube undergoes structural changes with changes in temperature, the tube position closer to the place where the spider assembly is mounted is unevenly displaced in three directions. In particular, for the time period considered in this study, it was found that the relative displacements on average at prisms 1, 2 and 3 in the vertical direction were 2.5540 ± 0.0007 m, 1.3750 ± 0.0008 m and 1.9780 ± 0.0007 m, respectively. The corresponding standard deviation (SD) values of ±0.0007 m, ±0.0008 m and ±0.0007 m denotes the average deviations that occurred in the vertical direction at the centre of prisms 1, 2 and 3, respectively. The generally higher SD of relative displacements in the vertical direction rather than in the easting and northing directions, suggest that the tube experienced greater variations in the vertical direction. Furthermore, the lower arc of the tube front, was found to have increased variability, and therefore it was hypothesised to introduce more elevation pointing offsets than azimuth for the LLR. This information constitutes an important input for guiding the efforts to determine the extent of the correction needed to be fed into the LLR telescope pointing model to counteract expected thermally induced pointing offsets.The National Research Foundation of South Africa.https://www.sajg.org.za/index.php/sajgam2024Geography, Geoinformatics and MeteorologyNon

Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa

Please read abstract in article.The Council for Scientific and Industrial Research (CSIR), The Southern Africa Science Service Centre for Climate and Adaptive Land Management (SASSCAL), The National Research Foundation of South Africa (NRF), University of Pretoria.https://www.springer.com/journal/12518Geography, Geoinformatics and Meteorolog

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

Towards development of a thermal monitoring system and analysis of meteorological parameters for the HartRAO lunar laser ranging telescope

The Hartebeesthoek Radio Astronomy Observatory (HartRAO) of South Africa is currently developing a Lunar Laser Ranger (LLR) system based on a 1-metre aperture telescope in collaboration with the National Aeronautics and Space Administration (NASA) and the Observatoire de la Côte d'Azur (OCA). This LLR will be an addition to a small number of operating LLR stations globally and it is expected to achieve sub-centimetre range precision to the Moon. Key to this achievement, requires thermal analysis of the composite telescope structure, based on thermal properties of the telescope component materials and their interaction with the environment through conventional heat transfer mechanisms. This analysis includes a thermal monitoring system that will feed temperature measurements to a model that will assist the steering and pointing software of the telescope in order to minimize tracking errors. In particular, no study has been reported previously on the thermal behaviour and related structural changes coupled with displacements of the HartRAO LLR composite structure with respect to ambient air temperature at the observatory site. Furthermore, a prototype pointing and steering software package developed for the HartRAO LLR, has so far only been tested on a 125 mm dual refractor testbed telescope (under room temperature conditions) and achieved root mean squared error values at the 0.5 arcsecond level. The extent of variation of the achieved error values is currently not known, particularly when the pointing model will be tested on the actual LLR telescope, which will be exposed to the varying thermal environment during operation. Therefore, in this study the thermal behaviour and related distortion dynamics of the HartRAO LLR telescope composite structure were modelled for possible adverse impact on pointing. Key findings of this research study were that the thermal response time varies per LLR telescope material component, primarily due to their respective thermal properties. The spider assembly and outer tube surface had the largest range of thermal variations, and thus were identified as the main areas on the telescope where most thermal variations can be expected. However, the primary mirror surface including its mount as well as the fork assembly had the lowest range of thermal variations. The total deformations of the tube assembly were found to be in the range 2.9 μm to 40.7 μm from night (00h00) until approximately midday (11h30). The primary mirror had virtually zero localised deformations due to its resistance against temperature change. The LLR thermal dynamic model was proposed and several test results of the proposed model were presented which covered the placement of RTD sensors on thermally-important areas of the tube structure; measurement and interpolation of the optical tube temperatures; and tube displacements due to assumed thermal deformations were reported using a laser distance-measurement system. In particular, the smallest variations in relative displacements of the tube were found to be 0.418 mm (east), 0.512 mm (north) and 0.670 mm (height) whereas, the largest variations were 0.523 mm (east), 0.691 mm (north) and 0.751 mm (height) during the time period considered. This period was characterized by ambient temperatures that varied between 11.20 °C and 29.90 °C and corresponding tube temperatures that varied between 13.75 °C and 33.84 °C. This information constitutes an important input for guiding the efforts to determine the amount of correction needed to be fed into the LLR telescope pointing model to counteract expected thermally-induced pointing offsets. Overall these results are a step towards the development of a real-time thermal monitoring system for the HartRAO LLR telescope, which is imperative in maximizing the pointing accuracy of the telescope, thereby increasing the chance of being on-target with the retroreflectors located on the lunar surface. Efforts to maximize pointing accuracy for the HartRAO LLR would support the global effort of high-accuracy laser ranging, which currently provides millimetre precision. Lastly, these findings have significant implications in exploring strategies and options for developing thermal dynamic models and monitoring systems for current and future LLR optical telescopes.Thesis (PhD)--University of Pretoria, 2019.Geography, Geoinformatics and MeteorologyPhDUnrestricte

Hybrid retrieval of grass biophysical variables based-on radiative transfer, active learning and regression methods using Sentinel-2 data in Marakele National Park

Biophysical variables such as leaf area index (LAI) and leaf chlorophyll content (LCC) are cited as essential biodiversity variables. A comprehensive comparison and integration of retrieval methods is needed for the estimation of biophysical variables such as LAI and LCC over a multispecies grass canopy. This study tested an assortment of five potentially robust, nonparametric regression methods (NPRMs) for inversion of radiative transfer model (RTM) to retrieve grass LAI and LCC in the Marakele National Park (MNP) of South Africa. The NPRMs used were, namely (i) Partial least squares regression (PLSR), (ii) Principle components regression (PCR), (iii) Kernel ridge regression (KRR), (iv) Random forest regression (RFR), and (v) K-nearest neighbours regression (KNNR). Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a large pool of RTM simulations. Results show the most accurate grass LAI and LCC retrievals had lower relative root mean squared errors (RRMSEs) of 39.87% and 16.58% respectively. These findings have significant implications for the development of transferable rangeland monitoring systems in protected mountainous regions

Development of the grass LAI and CCC remote sensing-based models and their transferability using sentinel-2 data in heterogeneous grasslands

Please read abstract in the article.Research development programme of the University of Pretoria, as well as the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) of the National Research Foundation (NRF) of South Africa.http://www.tandfonline.com/loi/tres202023-05-03hj2023Geography, Geoinformatics and MeteorologySDG-15:Life on lan

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

Machine learning algorithms for mapping Prosopis glandulosa and land cover change using multi-temporal Landsat products : a case study of Prieska in the Northern Cape Province, South Africa

Invasive alien plants (IAPs) are responsible for loss in biodiversity and the depletion of water resources in natural ecosystems. Prosopis species are IAPs previously introduced by farmers to provide shade and fodder for livestock. In the Northern Cape, Prosopis spp. invasions are associated with the loss of native species resulting in overgrazing and degrading rangelands. Mapping Prosopis glandulosa is essential for management initiatives to assist the government in minimising the spread and impact of IAPs. This study aims to evaluate the performance of two machine learning algorithms i.e., Support Vector Machine (SVM) and Random Forest (RF) to map the spatial dynamics of P. glandulosa in Prieska. The spatial invasion extent of P. glandulosa was mapped using multitemporal Landsat data spanning the period from 1990 to 2018. Validation of the results was done through an estimated error matrix with the use of the proportion of area and the estimates of overall accuracy, user’s accuracy and producer’s accuracy with a 95% confidence interval. The performance of the SVM and RF classifiers showed similar results in the overall accuracy and Kappa statistics throughout the years. These methods showed an overall increase of at least 3.3% of the area invaded by P. glandulosa from 1990 to 2018. The study indicates the importance of Landsat imagery for mapping historical and current land cover change of IAPs. The spread of P. glandulosa was confirmed by an increase in the total area of invasion, which enables decision-makers to improve monitoring and eradication initiatives.The Geoinformatics Division at the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW).http://www.sajg.org.za/index.php/sajgam2021Geography, Geoinformatics and Meteorolog