Browsing by Author "Arogoundade, Mariama Adeola."

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Mapping the spatial variability of foliar C:N ratio in a communal rangeland using remote sensing.
(2024) Arogoundade, Mariama Adeola.; Mutanga, Onisimo.; Odindi, John Odhiambo.
Rangelands contribute significantly to livelihoods by providing grazing land, as well as an array of ecological goods and services. However, they are increasingly threatened by among others, alien invasive plant species, climatic variability and injudicious land management. Hence, sustainable use and optimization of rangelands has recently gained attention. Forage nutrients, such as the C:N ratio are valuable indicators of rangeland quality and quantity, and influence rangeland’s carrying capacity and grazing distribution. Therefore, understanding the spatial distribution of foliar C:N ratio in rangelands is valuable for implementing strategic grazing plans and management strategies. Recently, remotely sensed data, specifically the readily available multispectral sensors with improved spectral properties have gained popularity in foliar nutrients modelling. Consequently, this study sought to model fine scale foliar C:N ratio in a heterogeneous communal rangeland using the new generation multispectral sensors. Thus, five objectives were established, firstly; a review of remote sensing applications in mapping foliar nutrients in tropical grasslands. The findings show that the monitoring of foliar nutrients in grasslands, particularly in Sub- Sahara Africa, using high spatial resolution sensors has been hindered by prohibitive costs. Hence, readily available multispectral sensors remain the most viable option in mapping forage nutrients in heterogeneous landscapes. Secondly; to leverage on Google Earth Engine cloud computing platform to monitor the foliar C:N ratio in a heterogeneous landscape using Sentinel 2 data and the random forest algorithm. The results show an estimated R2 accuracy of 74, with RMSE of 2.68 for the validation datasets of the C:N ratio model established by integrating the spectral bands and vegetation indices. Thirdly, the study sought to test the efficacy of fusing Sentinel 2 and Superdove Planetscope datasets in enhancing the rangeland foliar C:N ratio prediction at a landscape scale. The results demonstrate that freely available new generation multispectral sensors with unique spectral settings offer new opportunities for improving forage C:N ratio mapping in resource-poor countries. Using Sentinel 2 data, the study established that the visible, red edge and near infrared regions of the electromagnetic spectrum were influential in predicting the foliar C:N ratio. The study also established that fusing the spatial resolution of Planet scope with the Sentinel 2’s spectral properties enhanced foliar C:N ratio estimation within a heterogeneous landscape (R2 of 0.79 and RMSE of 2.36).Furthermore, the study noted that both Planetscope's high spatial resolution and Sentinel 2 MSI's high spectral resolutions were valuable in determining the spatial variability of foliar C:N ratio and the inclusion of the red edge spectral settings, combining fused datasets with ancillary variables and the adoption of robust algorithms such as Random Forest improved foliar C:N ratio modelling accuracy. Other variables such as wind effect, topographic wetness index, and the sky view factor also influence the foliar C:N ratio spatial variability . Overall, the findings of this study offer new insights on reliable and cost-efficient approaches for mapping forage nutrients in resource-constrained regions such as South Africa. Using freely available advanced multispectral sensors, the study provides valuable information necessary for optimal rangeland management.
Modelling susceptibility to Parthenium hysterophorus invasion in KwaZulu-Natal Province, South Africa using physical, climatic and remotely sensed derived variables.
(2018) Arogoundade, Mariama Adeola.; Odindi, John Odhiambo.; Mutanga, Onisimo.
Invasive alien plants (IAP) are considered as one of the major causes of global change. Parthenium hysterophorus is recognized as one of the world’s most aggressive, harmful and extremely resilient invasive plant species. It has adverse impacts on the environment, economies, biodiversity, human health and agriculture. Identification and modelling of areas vulnerable to Parthenium invasion is critical for proactive control and site- specific management of its spread. This study sought to test the performance of Maxent algorithm in modelling habitats susceptible to Parthenium invasion using selected environmental and physical variables and remotely sensed data. Specifically, the study sought to identify key physical and bio-climatic variables that influence the distribution of Parthenium. Furthermore, the study sought to determine the value of the freely available Sentinel 2 multispectral instrument (MSI) datasets in concert with environmental variables in modelling habitat susceptible to Parthenium invasion. The Maximum Entropy model (MaxEnt) machine learning algorithm was used to model Parthenium invasion using presence - only records (n = 274). Results showed that landscapes characterized by low elevation, close proximity to roads and high precipitation were the most susceptible to Parthenium invasion. An Area under curve (AUC) value of 0.946 was attained, indicating that the model derived using the aforementioned optimal physical and bio-climatic variables performed better than random. Based on the high AUC values, results also showed that all the model scenarios derived from spectral bands and environmental variables, vegetation indices and environmental variables and a combination of spectral bands, vegetation indices and environmental variables performed better than random, with AUC values of 0.976, 0.970 and 0.974, respectively. The higher accuracy exhibited by the optimal model (bands and environmental variables) can be attributed to the integration of red edge band centered at 705 nm in Sentinel 2 MSI and environmental variables in predicting areas susceptible to Parthenium. Overall, these results demonstrate the potential of integrating the freely available Sentinel 2 MSI data and environmental variables to improve the mapping of habitat susceptibility to Parthenium invasion. These results could be beneficial for early detection, site -specific weed management and long-term monitoring.