Influence of stratigraphy and heterogeneity on simulated microwave brightness temperatures of shallow snowpacks

Snow accumulation has potential climatological, hydrological and ecological impacts at a global scale. Satellite passive microwave radiometers have the potential to provide snow accumulation data with a historical record of over 30 years, however, current data products contain unknown uncertainty and error. Snowpack stratigraphy is the spatial variation in snowpack properties caused by the layered nature of the snowpack. Snowpack stratigraphy influences the accuracy and increases uncertainty in simulations of microwave emission from snow which in turn increases uncertainty in satellite derived estimates of snow water equivalent using microwave radiometers. Two methods were developed to help better quantify snowpack stratigraphy. An improved technique for characterising snowpack stratigraphy within a snow trench was developed. Secondly a new method was developed to quantify the density of ice layers that form in snowpacks with known error and uncertainty. Snowpack stratigraphy was characterised using the improved technique across the Trail Valley Creek watershed in the Canadian Northwest Territories. Two 50 m trenches and eleven 5 m trenches were dug across the range of landcover types found in the watershed. This dataset allowed layer boundary roughness to be characterised and the properties of snow layers to be mapped with an unprecedented level of accuracy. Ice lens density was measured 60 times at three locations in the Arctic and midlatitudes at locations with coincident ground based radiometer measurements. The impact that accurate parameterisation of density has on modelled estimates of brightness temperature was quantified. Simulations of microwave brightness temperatures were conducted using snow emission models at all locations. The output of these simulations, and comparison to ground based observations where available, allowed for the characterisation of variability in brightness temperature simulations caused by stratigraphic heterogeneity. The findings presented in this thesis will inform research aiming to better characterise the satellite error budget. Improvements in this area helps improve global snow mass and snow accumulation estimates

Automated temporal NDVI analysis over the Middle East for the period 1982 - 2010

The NDVI time-series consist of trend, season and noise. Changes in the season component are related to climate factors and they happen gradually over long period of time. The changes in the trend component are often due to human activities, fires and etc. This paper implements two algorithms (PolyTrend and DBEST) in R language, in order to examine the vegetation changes in the Middle East and to give more possibilities in the hands of the remote sensing communities. DBEST can analyse the gradual and the abrupt changes by decomposing the data, while PolyTrend classifies the inter-annual change between the picks of the green season. PolyTrend and DBEST were adapted for R language environment. Two additional algorithms were developed to apply both algorithms over NDVI3g data set of the Middle East. A third algorithm discovered the affected land-cover through an overlay analysis by the use of the UMD land-cover classification data set. PolyTrend showed linear (4%), quadratic (2%) and cubic (3%) trends. The different trend types were often found to be grouped in clusters. The largest clusters of trends were found near the south-eastern corner of the Arabian Peninsula and in the central regions of Saudi Arabia. More than 10% of all mixed forests were affected by these trends, most of which were in negative direction. DBEST showed that 1% of the vegetation experienced a higher magnitude of change. Clusters of these changes were mainly located in the south-eastern and the western part of Turkey, the northern regions of Iraq and Syria, as well as along the coastlines of the Black Sea and the Caspian Sea. The changes were mainly related to the cropland and the grassland and were more in positive directions. The study demonstrated the potential of PolyTrend and DBEST in R language for the remote sensing. It concludes that probably climatic factors affected the forests in Turkey and Iran. The high magnitude of changes of the cropland and grassland indicates that in some regions the agriculture expanded, while in others it declined.The constant Earth observations from space allow monitoring of the vegetation changes on a regional scale. The changes in vegetation can be long and gradual (e.g. due to climatic factors) or more sudden and abrupt (e.g. due to fires, diseases and etc.). In order to estimate the changes of the vegetation, researchers use algorithms that decompose the observed data to seasonal, trend and remainder (e.g. noise). The algorithms that can distinguish these changes are of limited number, often hard to be accessed and most of the existing ones could be applied only to specific situations. This paper implements two such algorithms (PolyTrend and DBEST) in R language, in order to give more possibilities in the hands of the remote sensing communities, and both are used to examine the vegetation changes in the Middle East. DBEST can analyse the gradual and the abrupt changes by decomposing the data, while PolyTrend classifies the inter-annual change between the picks of the green season. PolyTrend and DBEST were re-coded and adapted for R language environment. Two other algorithms were developed to apply both algorithms over imagery data of the Middle East for the period between 1982 and 2010. A third algorithm related the results to a specific class of vegetation by comparing the results from the last two and a land-cover data set. PolyTrend showed linear (4%), quadratic (2%) and cubic (3%) trends. The different trend types were often found to be grouped in clusters. The largest clusters of trends were found near the south-eastern corner of the Arabian Peninsula and in the central regions of Saudi Arabia. More than 10% of all mixed forests were affected by these trends, most of which were in negative direction. DBEST showed that 1% of the vegetation experienced a higher magnitude of change. Clusters of these changes were mainly located in the south-eastern and the western part of Turkey, the northern regions of Iraq and Syria, as well as along the coastlines of the Black Sea and the Caspian Sea. The changes were mainly related to the cropland and the grassland and were more in positive directions. The study demonstrated the potential of PolyTrend and DBEST in R language for the remote sensing. The obtained results showed that long gradual inter-annual changes affected the forests in Turkey and Iran. The reasons for these changes should be further investigated, but are probably related to climatic factors. The land-cover associated with high magnitude of more sudden changes was related to grassland and cropland. This leads to the suggestion that in some regions the agriculture expanded, while in others it declined