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Invasive alien plants (IAPs) threaten biodiversity and critical ecosystem services worldwide. There is, therefore, an urgent need to develop intervention measures to control the spread of IAPs. Efforts to control and monitor the spread of IAPs would require their current and detailed distribution over a large geographic area. Recently launched multispectral instrument on-board Sentinel-2 provides free data with good spatiotemporal and spectral resolution, compared to Landsat datasets. The Sentinel-2 dataset, therefore, can be a useful source of the IAPs spatial information required for detection and monitoring purposes. We combined Sentinel-2 data with a radiative transfer model to discriminate IAPs (Acacia mearnsii and Acacia dealbata) from surrounding native tree species in Van Reenen, KwaZulu-Natal, South Africa. The forward mode of combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL was used to simulate reflectance corresponding to bands of Sentinel-MSI, while the PROSAIL model inversion retrieved leaf area index (LAI) and canopy chlorophyll contents (CCC) of the IAPs and native species. Both reflectance and retrieved properties were used to map the distribution of the species within the study area. Our results showed that A. mearnsii and A. dealbata could be accurately discriminated from the surrounding native trees using integrated PROSAIL Sentinel-2 based model. We found that CCC– and LAI-based (% accuracy = 92.8%, 91.4% for CCC and LAI, respectively) modelling produced a higher classification accuracy than field sampling-based modelling (Accuracy = 90.2% (IAP), 82.2% (NAT) and kappa coefficient = 0.84 (IAP), 0.78 (NAT)). Simulated bands corresponding to Sentinel-2 data, on the other hand, produced species maps comparable to field sampling-based maps. Overall, the integrated PROSAIL Sentinel-2 inversion approach proved suitable for detecting and mapping IAPs over a large area. Due to the high spatiotemporal coverage of Sentinel-2, satellite images, the model developed showed the potential to contribute to the IAPs monitoring systems. [ABSTRACT FROM AUTHOR]

Fresh leaf spectral reflectance is primarily influenced by leaf water content and structural aspects such as the inter-cellular spaces within the spongy mesophyll, which also interfere with the estimation of the leaf nitrogen content. It is therefore essential to identify spectral bands that are least affected by the above perturbing factors for improving leaf nitrogen estimation for fresh leaves across any landscape. Wavelengths selection plays a vital role in identifying the best spectral features for assessing leaf nitrogen concentration from hyperspectral data of dry and fresh leaves. The primary objective of this study was to determine typical optimal bands for leaf nitrogen estimation from spectra (400–2500 nm) of whole fresh and dry leaves for the same specimens of Eucalyptus grandis. This was achieved via the use of competitive adaptive re-weighted sampling (CARS), and Monte Carlo cross-validation-competitive adaptive re-weighted sampling (MCCV-CARS) band selection approaches. Bands selected (931 nm, 1003 nm, 1027 nm, 1036 nm, 1177 nm, and 1180 nm) via the MCCV-CARS approach yielded the highest estimation accuracy for both fresh predicted coefficient of determination (R2cal) = 0.82 and predicted root mean square error (RMSEP) = 0.14) and dry leaves (R2P = 0.88 and RMSEP = 0.13) when compared to CARS (2044 nm, 2107 nm, and 2188 nm) only. The identified spectral features could be relevant for assessing leaf nitrogen concentration for different seasons, for example, wet to dry season. [ABSTRACT FROM AUTHOR]

The tree Acacia mearnsii is native to south-eastern Australia but has become an aggressive invader in many countries. In South Africa, it is a significant threat to the conservation of biomes. Detecting and mapping its early invasion is critical. The current ground-based methods to map A. mearnsii are accurate but are neither economical nor practical. Remote sensing (RS) provides accurate and repeatable spatial information on tree species. The potential of RS technology to map A. mearnsii distributions remains poorly understood, mainly due to a lack of knowledge on the spectral properties of A. mearnsii relative to co-occurring native plants. We investigated the spectral uniqueness of A. mearnsii compared to co-occurring native plant species within the South African landscape. We explored full-range (400–2500 nm), leaf and canopy hyperspectral reflectance of the species. The spectral reflectance was collected biweekly from December 23, 2016 and May 31, 2017. We conducted a time series analysis, to assess the effect of seasonality on species discrimination. For comparison, two classification models were employed: parametric interval extended canonical variate discriminant (iECVA-DA) and nonparametric random forest discriminant classifiers (RF-DA). The results of this paper suggest that phenology plays a crucial role in discriminating between A. mearnsii and sampled species. The RF classifier discriminated A. mearnsii with slightly higher accuracies (from 92% to 100%) when compared with the iECVA-DA (from 85% to 93%). The study showed the potential of RS to discriminate between A. mearnsii and co-occurring plant species. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R2 = 0. 89), LUT (RMSE = 0. 25; R2 = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R2 = 0. 60), LUT (RMSE = 0. 38; R2 = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring. [ABSTRACT FROM AUTHOR]

Remote sensing has the potential to complement and perhaps replace some wet analytical chemistry measurements of some plant traits, a mechanism that can facilitate quick and non-destructive estimation. The present study reports on the use of hyperspectral data in estimating total phenolic and flavonoid concentration in Moringa oleifera, a plant with great nutraceutical properties. A completely randomized design (CBD) was used in the planting of four M. oleifera cultivars in a greenhouse setup. A hand-held spectroradiometer was used to collect canopy reflectance on two-month-old plants. This was followed by the analytical quantification of total phenolic and flavonoid concentrations in the leaf extracts. Various pre-processing techniques were carried out on the reflectance spectra such as Savitzky-Golay filter and continuum removal (convex and segmented upper hull). Analysis of variance (ANOVA) and random forest (RF) regression algorithms were then conducted to analyse the data. The ANOVA indicated that the spectral features of the cultivars differed significantly (p ≤ 0.01) from each other in some sections of the visible, near-infrared (NIR) and shortwave-infrared (SWIR) regions of the electromagnetic spectrum (EMS). The band depth values created from the segmented hull were used in the selection of phytochemicals absorption features. The results indicated that phytochemical concentration can be accurately estimated for individual cultivars as shown by validation models having R 2 values of between 0.58 and 0.79 and relative root mean square error (RMSErel) of between 5% and 17%. However, when data were aggregated across cultivars, the RMSErel ranged from 16% to 21%. The most important wavebands for predicting the concentration of these phytochemicals were found in the visible and SWIR regions of the EMS. Overall, accurately estimating the concentration of the phytochemicals demonstrates an insight in the potential of hyperspectral data to be extrapolated to landscape scale using hyperspectral reflectance sensing technique. [ABSTRACT FROM AUTHOR]

Weeds can significantly reduce productivity in agriculture (Roslim et al., 2021). Mechanical weeding and pesticides for tomatoes under irrigation are some of the weed management methods which are time-consuming and environmentally hazardous (Gerhards et al., 2022). Remote sensing and machine learning techniques offer more precise weed detection and management, using high-resolution satellite data and drones to identify early weed infestations and employ precision agriculture approaches (Su et al., 2022). This study uses machine learning to analyze drone images and map weeds in the Ha-Mphaila tomato irrigation scheme in South Africa, showcasing the potential of drones and machine learning for sitespecific weed management in agriculture. [ABSTRACT FROM AUTHOR]

The study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) when simulating an urban heat island (UHI) over the city of eThekwini, located along the southeast coast of South Africa. The CCAM is applied at a grid length of 1 km on the panel with eThekwini, in a stretched-grid mode. The CCAM is coupled to the urban climate model called the Australian Town Energy Budget (ATEB). The ATEB incorporates measured urban parameters including building characteristics, emissions, and albedo. The ATEB incorporates the land-cover boundary conditions obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The CCAM configuration applied realistically captured the orientation of the city and land-cover types. Simulations of meteorological variables such as temperatures and longwave radiation reproduced the spatial distribution and intensity of the UHI. Results show that the UHI is stronger during summer and weaker in all other seasons. The UHI developed because of natural factors (e.g., distribution of longwave radiation) and human factors (e.g., urban expansion, an increase in anthropogenic emissions, and additional heating). Because of the city's location along the coast, the UHI simulation could be weakened by atmospheric circulations resulting from land and sea breezes. Mitigation methods such as applying reflective paints and revegetation of the city may increase albedo and latent heat fluxes but reduce the sensible heat fluxes and weaken the UHI. However, the UHI may not be completely eliminated since natural factors and emissions constantly influence its development. Significance Statement: The outcome of this study could be particularly valuable for municipalities in their disaster management planning since the occurrence of UHIs can cause heat-related diseases such as heatstrokes and even fatalities, especially for the elderly, in cities. Increases in temperatures also lead to higher demand for air conditioners, which in the long term lead to higher demand and pressure on the electricity grid system as well as increased costs for the individual. As higher temperatures increase heatwave events, increases in anthropogenic emissions also result in degraded air quality that impacts health. UHIs impact human lives and can cause deterioration in health when individuals experience high temperatures in summer. Warmer temperatures also reduce energy demand (and in the long term assist with global environmental restoration). [ABSTRACT FROM AUTHOR]