AbstractUnderstanding how vegetation growth and seasonality will change as a result of climate change is imperative for the development of targeted mitigation strategies by governments and non-governmental organizations (NGOs). Iraq is a country that is teetering precariously under the current climate regime and political instability. However, little environmental research has been carried out, and as such relatively little is known about the interaction between Iraq’s vegetation and climate. The United Kingdom (UK), although a more economically and politically stable country, too, has areas that will be affected by future climate change, including increased risk of flooding and extreme temperatures and sea level rise. The main aim of this thesis is to develop a robust model that provides high-resolution data, which can predict future phenological growth in regions with different climate regimes (Iraq and the UK).
This thesis sets out to test a modified Growing Season Index (GSI) model and to answer the following questions: RQ1) What is the relationship between the Normalized Difference Vegetation Index (NDVI) from the MODerate-resolution Imaging Spectroradiometer (MODIS) and two climatic factors (precipitation and air temperature)?: RQ2) Does the use of precipitation as a variable make the GSI phenological model more robust? And RQ3) Can the model be used to predict future phenological changes? The study areas (Iraq and the UK) contain a number of climatic and environmental zones. The proposed model is tested across the whole of Iraq and in three climatically and environmentally different regions: Sulaymaniyah, Wasit and Basrah. The model is also tested in the UK for comparison with Iraq.
First, the relationship was investigated between the MODIS NDVI and two climatic variables, precipitation and air temperature, over the last decade (RQ1). The results show that there is a strong link between temporal patterns of NDVI and precipitation, and a weak link with air temperature, thus indicating that precipitation is the primary factor in germination with air temperature acting as a secondary driver. Further, an extant phenological model, the GSI, is modified by adding the new precipitation variable, to better quantify relationships between weather and vegetation canopy dynamics across the various semi-arid regions of Iraq (RQ2). It is found that the correlations are more robust with the modified model. The model is then used to test the applicability of the GSI model in predicting future phenological changes (RQ3) using climate change scenario datasets for the period (1951-2098). The results show that the modified GSI model performs well in predicting future phenological changes.
The model is tested across the UK for comparison with Iraq. The results show that the modified model is far more robust when the new variable of precipitation is added. It performs well in comparison with the past NDVI datasets. It also simulates well in comparison with other climate scenario models and can be confidently used to predict future climate change, particularly in areas with insufficient infrastructure and political stability leading to a dearth of ground survey data. In addition, the thesis investigates the duration of maturity of the vegetation and monitoring wheat cropland growth in a specific region of the UK (Duxford) over a limited time span, using a new remote sensing dataset (Sentinel S-1 images) and applying the Differential Interferometry Synthetic-Aperture Radar (DInSAR) technique, with a comparison with MODIS NDVI data. The results show that there is real potential in using the DInSAR technique and remotely sensed data (MODIS dataset) to estimate the crop height and to calculate the area of crop distribution.
|Date of Award||23 Jan 2020|
|Supervisor||Dawei Han (Supervisor) & Miguel A Rico-Ramirez (Supervisor)|