Impacts of Spatial Resolution and Viewing Angle on Remotely Sensed Estimates of Spartina alterniflora Aboveground Biomass

Coastal salt marshes sequester large quantities of “blue carbon” in plant biomass and sediments, and provide numerous other valuable ecosystem functions and services. However, these ecosystems are increasingly threatened by external stressors, including rising sea levels and a changing climate, which have resulted in large losses of tidal marsh habitat. Measuring plant biomass is critical for understanding how carbon storage may be affected as stressors continue to cause marsh losses, and for improving conservation and management efforts. A number of studies have quantified aboveground biomass (AGB) in salt marshes using remote sensing techniques, and with the development of high resolution sensors there is excellent potential to improve estimates over large scales. However, few studies have evaluated how variability in spatial resolution and viewing angle across platforms impacts AGB estimates, despite the large range of potential imaging systems available. Using 3 cm and 6 cm resolution nadir hyperspectral drone imagery, and 0.5-3 cm oblique imagery collected from a ground-based camera at three viewing angles from two different-aged barrier island salt marshes in Virginia, USA, I evaluated the accuracy of regression models predicting S. alterniflora AGB from vegetation indices across resolution and viewing angle. The overall best performing linear regression models were obtained using the 3 cm nadir drone imagery. However, the best 6 cm regression models demonstrated only minor losses in accuracy relative to 3 cm. AGB estimates from obliquely angled imagery were less accurate than either nadir resolution. The most accurate oblique models were obtained at the highest viewing angle, with performance decreasing as the viewing angle became shallower. These results suggest that not all platforms perform similarly within salt marsh ecosystems, and that both spatial resolution and viewing angle must be considered in choice of imaging systems

Exploring open-source multispectral satellite remote sensing as a tool to map long-term evolution of salt marsh shorelines

From an ecological and socio-economic perspective, salt marshes are one of the most valuable natural assets on Earth. As external pressures are causing their extensive degradation and loss globally, the ability to monitor salt marshes on a long-term scale and identify drivers of change is essential for their conservation. Remote sensing has been demonstrated to be one of the most adept methods for this purpose and open-source multispectral satellite remote sensing missions have the potential to provide worldwide long-term time-series coverage that is non-cost-prohibitive. This study derives the long-term lateral evolution of four salt marsh patches in the Ria Formosa coastal lagoon (Portugal) using data from the Sentinel-2 and Landsat missions as well as from aerial photography surveys to quantitatively examine the accuracy and associated uncertainty in using open-source multispectral satellite remote sensing for this purpose. The results show that these open-source satellite archives can be a useful tool for tracking long-term salt marsh extent dynamics. During 1976-2020, there was a net loss of salt marsh in the study area, with erosion rates reaching an average of-3.3 m/yr opposite a tidal inlet. The main source of error in the satellite results was the dataset spatial resolution limits, but the specific salt marsh shoreline environment contributed to the relative magnitude of that error. The study notes the influence of eco-geomorphological dynamics on the mapping of sedimentary environments, so far not extensively discussed in scientific literature, highlighting the difference between mapping a morphological process and a sedimentary environment.info:eu-repo/semantics/publishedVersio

Modelling leaf area index in a tropical grassland using multi-temporal hyperspectral data.

Master of Science in Environmental science.Abstract available in PDF file

Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way