Assessing the effect of band selection on accuracy of pansharpened imagery: application to young woody vegetation mapping

Expansion of woody vegetation has adverse effects on ecosystem services, and thus it is desirable to contain the problem at the early developmental stages. This can be aided by using high spatial resolution remotely-sensed data. The study investigated the effect of band selection during pansharpening on the ability to discriminate young woody vegetation from coexisting land cover types. Red-green-blue (RGB) spectral bands (30 m) of Landsat 8 imagery was pansharpened using the panchromatic band (15 m) of the same image to improve spatial resolution. Near-infrared (NIR), shortwave-infrared 1 (SWIR1) and shortwave-infrared 2 (SWIR2), bands were used respectively as the fourth spectral band during pansharpening, resulting in three pansharpened images. Unsupervised classification was performed on each pansharpened image as well as non-pansharpened multispectral image. The overall accuracies of classification derived from the pansharpened image was higher (87% − 89%) than that derived from the non-pansharpened multispectral image (83%). The study shows that band selection did not affect the classification accuracy of woody vegetation significantly. In addition, the study shows the potential of pansharpened Landsat data in detecting woody vegetation encroachment at the early growth stage.Keywords: Young woody vegetation, Landsat, pansharpening, unsupervised classificatio

Modelling the relationship between groundwater depth and NDVI using time series regression with Distributed Lag M

Groundwater plays a key role in hydrological processes, including in determining aboveground vegetal growth characteristics and species distribution. This study aimed at estimating time-series data of Normalized Difference Vegetation Index (NDVI) using groundwater depth as a predictor in two land cover types: grassland and shrubland. The study also investigated the significance of past (lagged) groundwater and NDVI in estimating the current NDVI. Results showed that lagged groundwater depth and vegetation conditions influence the amount of current NDVI. It was also observed that first lags of groundwater depth and NDVI were significant predictors of NDVI in grassland. In addition, first and second lags of NDVI were consistently significant predictors of NDVI in shrubland. This shows the importance of vegetation type when modelling the relationship between groundwater depth and NDVI.Keywords: Groundwater depth; Landsat NDVI; Time-series analysis; Distributed Lag Model

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

Abstract: Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R2: 0.41−0.46; Root Mean Square Error (RMSE): 0.60−0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three–five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three–five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery

Weed detection in rainfed maize crops using UAV and planetscope imagery

DATA AVAILABILITY STATEMENT : The PlanetScope data were obtained from the Planet website for academic research.Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.The Agricultural Research Council-Natural Resources and Engineering (ARC-NRE), Department of Science and Innovation, Council for Scientific and Industrial Research; the National Research Foundation; the Department of Agriculture, Land Reform and Rural Development (DALRRD); and the University of Pretoria.https://www.mdpi.com/journal/sustainabilityam2024Geography, Geoinformatics and MeteorologySDG-02:Zero HungerSDG-12:Responsible consumption and productio

Socio-demographic determinants of environmental attitudes, perceptions, place attachment, and environmentally responsible behaviour in Gauteng province, South Africa

This study attempts to determine the socio-demographic determinants of environmental attitudes, perceptions, place attachment, and resultant environmental behaviour. Drawing on secondary data collected in the Gauteng province, South Africa, a model was developed to test the relationships between these constructs, using critical ratios and a structural equation model approach. Critical ratio analysis showed that employment status was a positive predictor of perceptions, while none of the other socio-demographic variables tested positively predicted environmental attitudes. Population group, education level, and migration status were positive predictors of place attachment. Results from structural equation modelling indicated that people's satisfaction with amenities like water and waste services in the province were some of the important determinants of environmental attitudes. Results also indicated that perceptions, attitudes and attachment played a positive role in determining environmentally responsible behaviour. This has implications for environmental planning in the province

A bi-seasonal classification of woody plant species using Sentinel-2A and SPOT-6 in a localised species-rich savanna environment

Sustainable management of biodiversity benefit from cost-effective multi-temporal classification schemes afforded by remote sensing techniques. This study compared classification accuracies of woody plant species (n = 27) and three coexisting land cover types using dry and wet seasons data. Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN), were applied to Sentinel-2A and SPOT-6 images. The results showed higher overall classification accuracies for wet season data (65%–72%) for both images and classifiers (DNN, RF and SVM), compared to dry season classification (52%–59%). Near infrared region bands, available in both Sentinel-2A and SPOT-6 imagery, produced high performance for both wet (83%) and dry (80%) seasons. Overall, the findings show potential of multispectral remote-sensing for woody plant species diversity in different seasons. Such a study should be extended to higher frequency species diversity classification, to capture dynamics that may manifest at short time intervals of the year

Monthly geographically weighted regression between climate and vegetation in the Eastern Cape Province of South Africa: Clustering pattern shifts and biome-dependent accuracies

Remote sensing provides cost-effective and unbiased data and thus is ideal for assessing climate–vegetation relationships. Such relationships can be quantified using geographically weighted regression (GWR) approach to account for variations of the relationships across space. This approach was applied in the Eastern Cape province of South Africa that is rich in biodiversity hosting 10 of the country's 11 biomes. The study aimed to determine if the GWR accuracy for relating Enhanced Vegetation Index (EVI) with rainfall and Land Surface Temperature (LST) shows an optimal pattern with time and space. and to explore if the correlation of EVI with rainfall and LST varies with biome type. Monthly data covering February 2000 to December 2017 were used for the three variables. The coefficient of determination (R2) was greater than 0.5 for 75% of the locations, with month-to-month change of R2 exceeding 25% for many locations. Optimized Hot Spot Analysis returned well-defined broad clusters of high and low R2 values separated by clusters of randomly distributed R2 values. These clusters shifted with month, further stressing the benefit of modelling at the monthly scale. Assessment of R2 by biome showed the importance of biomes in characterizing GWR of climate and vegetation, with better correlations found in low biodiversity (Succulent Karoo and Nama-Karoo biomes) than in higher biodiversity (Forest and Indian Ocean Coastal Belt biomes) zones. Further, the estimation residuals of the Forest Biome varied significantly from 3 to 5 other biomes across the year indicating the complex interaction of this biome with rainfall and LST. The study encourages further research by using high temporal resolution data for detailed monitoring within the GWR framework

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche

Discriminating pure Tamarix species and their putative hybrids using field spectrometer

South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, non-native and hybrid Tamarix species recorded in South Africa. Leaf- and canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species

Predicting the effect of climate change on a range-restricted lizard in southeastern Australia

Climate change is ranked as one of the most severe threats to global biodiversity. This global phenomenon is particularly true for reptiles whose biology and ecology are closely linked to climate. In this study, we used over 1,300 independent occurrence points and different climate change emission scenarios to evaluate the potential risk of changing climatic conditions on the current and future potential distribution of a rock-dwelling lizard; the velvet gecko. Furthermore, we investigated if the current extent of protected area networks in Australia captures the full range distribution of this species currently and in the future. Our results show that climate change projections for the year 2075 have the potential to alter the distribution of the velvet gecko in southeastern Australia. Specifically, climate change may favor the range expansion of this species to encompass more suitable habitats. The trend of range expansion was qualitatively similar across the different climate change scenarios used. Additionally, we observed that the current network of protected areas in southeast Australia does not fully account for the full range distribution of this species currently and in the future. Ongoing climate change may profoundly affect the potential range distribution of the velvet gecko population. Therefore, the restricted habitat of the velvet geckos should be the focus of intensive pre-emptive management efforts. This management prioritization should be extended to encompass the increases in suitable habitats observed in this study in order to maximize the microhabitats available for the survival of this species