Optimal season for discriminating C3 and C4 grass functional types using multi-date Sentinel 2 data

ABSTRACT

The ability of remote sensing systems to optimally discriminate and map C3 and C4 grass species varies over time, due to environmental changes, which influence their phenological, physiological and morphological characteristics. In this regard, the discrimination of C3 and C4 grasses is insufficient when using a single image acquired at a specific period. In this study, multi-date Sentinel 2A MultiSpectral Instrument (MSI) data were explored to determine the optimal period for classifying and mapping Festuca costata, C3 and Themeda Triandra, C4 grasses in the montane grasslands of South Africa. The study further assessed how seasonal variations in species classification can be explained by climatic variability (rainfall and temperature). Results showed that image acquisition dates influence the discrimination accuracy, spatial representation of the two grass species, as well as the performance of spectral bands. The winter period also presents a better temporal window for discriminating C3 and C4 target grass species, with higher overall classification accuracies (between 91.8% and 95.3%), than summer (between 81.4% and 90.3%). Lower omission (between 2.8% and 11.6%) and commission (between 2.5% and 14.2%) errors were also observed when discriminating using winter images, as compared to those acquired in summer. Summer images showed large grass species areal coverage (e.g. in November and March, C3 and C4 covered ±25%), whereas in winter (mainly August), a notable decrease was observed. Overall, findings of the study have demonstrated the relevance of multi-date Sentinel data in discriminating C3 and C4 grass species. There is, however, a need to explore the classification ability of Sentinel 2 derivatives, especially during early summer and winter fall.     

Predicting the distribution of C3 (Festuca spp.) grass species using topographic variables and binary logistic regression model

The spatial distribution of different C3 and C4 grass species in tropical montane areas is commonly influenced by a number of factors that include site-specific topography. Hence, the distribution of these grasses across topographic gradients can vary significantly. In this study, we investigate the influence of topographic factors (elevation, slope and aspect) on the spatial distribution of Festuca grass species in a commonage area, comprising agro-biodiversity conservation land use. Integration of the topographic variables using GIS and binary logistic regression (LR) modelling showed that C3, Festuca grass species distribution can be predicted or mapped with an accuracy of 80% in the landscape under study. The study contributes to understanding the spatial distribution of C3 grass species and provides valuable information for designing and optimizing rangeland conservation in the subtropical montane landscapes.     

Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor

Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R² of 0.67 and a RMSE-CV of 2.2 t ha^?1) when compared to when the red-edge band was included as a co-variate (R² of 0.73 and a RMSE-CV of 2.04 t ha^?1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R² of about 0.67 and a RMSE-CV of 2.20 t ha^?1), when compared to those obtained using all indices and RE-computed indices(R² of 0.76 and a RMSE-CV of 1.88 t ha^?1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint.     

Assessing the potential of integrated Landsat 8 thermal bands,with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.     

Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley,Zimbabwe using Geographic Information Systems and remote sensing

The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa.     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.     

Detection and mapping of maize streak virus using RapidEye satellite imagery

The aim of this study was to detect and map MSV using RapidEye multispectral sensor in Ofcolaco farm. To achieve this objective, the acquired RapidEye sensor was classified using the robust Random Forest algorithm. Furthermore, the variable importance technique was used to determine the influence of each spectral band and indices on the mapping accuracy. For better performance of image data, the value of the commonly used vegetation indices in improving the classification accuracy was tested. The results revealed that the use of RapidEye spectral bands in detection and mapping of MSV yielded good classification results with an overall accuracy of 82.75%. The inclusion of vegetation indices computed from RapidEye sensor improved the classification accuracies by 3.4%. The most important RapidEye spectral bands in classifying MSV were near infrared, blue and red-edge. On the other hand, the most important vegetation indices were the Soil adjusted vegetation index, Enhanced vegetation index, Red index and Normalized Vegetation Index. The current study recommends future studies to assess the importance of multi-temporal remote sensing applications in detecting and monitoring the spread of MSV.     

Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands

This study assessed the strength of Sentinel-2 multispectral instrument (MSI) derived Red Edge (RE) bands in estimating Leaf Area Index (LAI) and mapping canopy storage capacity (CSC) for hydrological applications in wattle infested ecosystems. To accomplish this objective, this study compared the estimation strength of models derived, using standard bands (all bands excluding the RE band) with those including RE bands, as well as different vegetation indices. Sparse Partial Least Squares (SPLSR) and Partial Least Squares Regression (PLSR) ensembles were used in this study. Results showed that the RE spectrum covered by the Sentinel-2 MSI satellite reduced the estimation error by a magnitude of 0.125 based on simple ratio (RE SR) vegetation indices from 0.157 m²· m^?2 based on standard bands, and by 0.078 m²· m^?2 based on red edge normalised difference vegetation (NDVI-RE). The optimal models for estimating LAI to map CSC were obtained based on the RE bands centered at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a). A root mean square error of prediction (RMSEP) of 0.507 m²· m^?2 a relative root mean square error of prediction (RRMSEP) of 11.3% and R² of 0.91 for LAI and a RMSEP of 0.246 m²/m² (RRMSEP = 7.9%) and R² of 0.91 for CSC were obtained. Overall, the findings of this study underscore the relevance of the new copernicus satellite product in rapid monitoring of ecosystems that are invaded by alien invasive species.     

Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry

Techniques for mapping and monitoring wetland species are critical for their sustainable management. Papyrus (Cyperus papyrus L.) is one of the most important species-rich habitats that characterize the Greater St. Lucia Wetlands Park (GSWP) in South Africa. This paper investigates whether papyrus could be discriminated from its co-existing species using ASD field spectrometer data ranging from 300 nm to 2500 nm, yielding a total of 2151 bands. Canopy spectral measurements from papyrus and three other species were collected in situ in the Greater St. Lucia Wetlands Park, South Africa. A new hierarchical method based on three integrated analysis levels was proposed and implemented to spectrally discriminate papyrus from other species as well as to reduce and subsequently select optimal bands for the potential discrimination of papyrus. In the first level of the analysis using ANOVA, we found that there were statistically significant differences in spectral reflectance between papyrus and other species on 412 wavelengths located in different portions of the electromagnetic spectrum. Using the selected 412 bands, we further investigated the use of classification and regression trees (CART) in the second level of analysis to identify the most sensitive bands for spectral discrimination. This analysis yielded eight bands which are considered to be practical for upscaling to airborne or space borne sensors for mapping papyrus vegetation. The final sensitivity analysis level involved the application of Jeffries-Matusita (JM) distance to assess the relative importance of the selected eight bands in discriminating papyrus from other species. The results indicate that the best discrimination of papyrus from its co-existing species is possible with six bands located in the red-edge and near-infrared regions of the electromagnetic spectrum. Overall, the study concluded that spectral reflectance of papyrus and its co-existing species is statistically different, a promising result for the use of airborne and satellite sensors for mapping papyrus. The three-step hierarchical approach employed in this study could systematically reduce the dimensionality of bands to manageable levels, a move towards operational implementation with band specific sensors.     

Modeling the Potential Distribution of Pine Forests Susceptible to Sirex Noctilio Infestations in Mpumalanga,South Africa

Reducing the impact of the siricid wasp, Sirex noctilio is crucial for the future productivity and sustainability of commercial pine resources in South Africa. In this study we present a machine learning model that serves as a spatial guide and allows forest managers to focus their existing detection and monitoring efforts on key areas and proactively adopt the most appropriate course of intervention. We implemented the random forest model within a spatial framework to determine which pine forests in Mpumalanga are highly susceptible to S. noctilio infestations. Results indicate that a majority (63%) of pine forest plantations located in Mpumalanga have a high susceptibility (>70%) to S. noctilio infestation. A KHAT value of 0.84 and F measures above 0.87 indicate that the random forest model is a robust classifier that produces accurate results. Additionally, the use of the backward variable selection method enabled us to simplify the random forest modeling process and identify the minimum number of explanatory variables that offer the best discriminatory power and help in the empirical interpretation of the final random forest model. Overall, the results show that pine forests that experience stress caused by evapotranspiration and evaporation followed by rainfalls, especially during the summer months are more susceptible to S. noctilio infestations.