Seasonal patterns of forest canopy and their relevance for the global carbon cycle

In the terrestrial biosphere forests have a significant role as a carbon sink. Under recent climate change, it is increasingly important to detect seasonal change or ‘phenology’ that can influence the global carbon cycle. Monitoring canopies using camera systems has offered an inexpensive means to quantify the phenological changes. However, the reliability is not well known. In order to examine the usefulness of cameras to observe forest phenology, we analysed canopy images taken in two deciduous forests in Japan and England and investigate which colour index is best for tracking forest phenology and predict carbon uptake by trees. A camera test using model leaves under controlled conditions has also carried out to examine sensitivity of colour indices for discriminating leaf colours. The main findings of the present study are: 1) Time courses of colour indices derived from images taken in deciduous forests showed typical patterns throughout the growing season. Although cameras are not calibrated instrument, analysis of images allowed detecting the timings of phenological events such as leaf onset and leaf fall; 2) The strength of the green channel (or chromatic coordinate of green) was useful to observe leaf expansion as well as damage by spring late frost. However, the results of the camera test using model leaves suggested that this index was not sufficiently sensitive to detect leaf senescence. Amongst colour indices, Hue was the most robust metric for different cameras, different atmospheric conditions and different distances. The test also revealed Hue was useful to track nitrogen status of leaves; 3) Modelling results using a light use efficiency model for GPP showed a strong relationship between GPP and Hue, which was stronger than the relationships using alternative traditional indices

ENHANCING CONSERVATION WITH HIGH RESOLUTION PRODUCTIVITY DATASETS FOR THE CONTERMINOUS UNITED STATES

Human driven alteration of the earth’s terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth’s terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production across the CONUS domain. The main results of this work are three publically available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices

Validation and application of the MERIS Terrestrial Chlorophyll Index.

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity