Studying the Influence of Nitrogen Deposition, Precipitation, Temperature, and Sunshine in Remotely Sensed Gross Primary Production Response in Switzerland

Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn’t been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: Grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics

The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa

Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability.Thesis (MSc) -- Faculty of Science and Agriculture, 202

The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa

Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability.Thesis (MSc) -- Faculty of Science and Agriculture, 202

Road Verges for Bumblebee Conservation: A Green Infrastructure Opportunity or an Ecological Trap?

My thesis discusses the potential of road verges as a tool for bumblebee conservation, focusing on Bombus terrestris and roadsides within the UK. Chapter 1 is an extensive literature review on the topic of road verges as a tool for pollinator conservation. The benefits and drawbacks of roadsides are thoroughly discussed, highlighting areas requiring further investigation. The concept of, and how verges may represent, ecological traps is also covered. Chapter 2 addresses how distance from a major road impacts the development of B. terrestris colonies. The reproductive success of colonies located on the verge is compared to those positioned in the surrounding landscape. The results are discussed in the context of bumblebee conservation, and the importance of large-scale ecologically realistic field studies is highlighted. Chapter 3 investigates the impacts of two common roadside metals (copper and cadmium) on the development of B. terrestris micro-colonies. Micro-colonies were exposed to different levels of metal contamination via a pollen or nectar source. The main findings are presented, with emphasis on the future of the transport sector. Chapter 4 explores the pollen collection of B. terrestris colonies located on verges compared to those in the surrounding landscape. Pollen loads collected from foraging workers from colonies either on the roadside or in the surrounding area were identified using microscopy. Flowers visited by bumblebees were compared to floral resources within the landscape, and the potential for roadsides as a viable forage source for bumblebees is discussed in depth. I conclude with a summary of all three data chapters, highlighting the knowledge gaps which have been addressed. Areas of research still requiring further investigation are discussed along with conservation applications/recommendations identified by my thesis. The future of the transport industry and the likely impacts this will have on pollinator conservation along verges is also discussed