The objective of this research is to explore the information content of repeat-pass cross-track Interferometric SAR (InSAR) with regard to snow, in particular Snow Water Equivalent (SWE) and snow depth. The study is an outgrowth of earlier snow cover modeling and radar interferometry experiments at Schefferville, Quebec, Canada and elsewhere which has shown that for reasons of loss of coherence repeat-pass InSAR is not useful for the purpose of snow cover mapping, even when used in differential InSAR mode. Repeat-pass cross-track InSAR would overcome this problem. As at radar wavelengths dry snow is transparent, the main reflection is at the snow/ground interface. The high refractive index of ice creates a phase delay which is linearly related to the water equivalent of the snow pack. When wet, the snow surface is the main reflector, and this enables measurement of snow depth. Algorithms are elaborated accordingly. Field experiments were conducted at two sites and employ two different types of digital elevation models (DEM) produced by means of cross track InSAR. One was from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), flown in February 2000. It was compared to the photogrammetrically produced Canadian Digital Elevation Model (CDEM) to examine snow-related effects at a site near Schefferville, where snow conditions are well known from half a century of snow and permafrost research. The second type of DEM was produced by means of airborne cross track InSAR (TOPSAR). Several missions were flown for this purpose in both summer and winter conditions during NASA's Cold Land Processes Experiment (CLPX) in Colorado, USA. Differences between these DEM's were compared to snow conditions that were well documented during the CLPX field campaigns. The results are not straightforward. As a result of automated correction routines employed in both SRTM and AIRSAR DEM extraction, the snow cover signal is contaminated. Fitting InSAR DEM's to known topography distorts the snow information, just as the snow cover distorts the topographic information. The analysis is therefore mostly qualitative, focusing on particular terrain situations. At Schefferville, where the SRTM was adjusted to known lake levels, the expected dry-snow signal is seen near such lakes. Mine pits and waste dumps not included in the CDEM are depicted and there is also a strong signal related to the spatial variations in SWE produced by wind redistribution of snow near lakes and on the alpine tundra. In Colorado, cross-sections across ploughed roads support the hypothesis that in dry snow the SWE is measurable by differential InSAR. They also support the hypothesis that snow depth may be measured when the snow cover is wet. Difference maps were also extracted for a 1 km2 Intensive Study Area (ISA) for which intensive ground truth was available. Initial comparison between estimated and observed snow properties yielded low correlations which improved after stratification of the data set.In conclusion, the study shows that snow-related signals are measurable. For operational applications satellite-borne cross-track InSAR would be necessary. The processing needs to be snow-specific with appropriate filtering routines to account for influences by terrain factors other than snow.

This research assesses the accuracy of SRTM and AIRSAR DEMs acquired over the mountainous-hillands of Cameron Highlands with DEMs generated from Digital Aerial Photograph (DAP) with a fine (2 m) spatial resolution and height resolution of about 0.5 m. The ground control points used for generating stereo models from the DAP were acquired during field work using GPS which achieved accuracy better than 2 cm in most cases. To overcome the difficulty of overlaying the DEMs with the DAP DEM as no features can be easily identified on both the images, therefore a technique of using transects and contours generated from the DEMs were used to correct the horizontal displacement. For AIRSAR DEM, comparing the accompanying AIRSAR composite images was also employed. These then allowed an analysis of the height accuracy to be undertaken. The height of both the AIRSAR and SRTM DEMs were also corrected by applying Linear Regression Models. These models were produced by comparing pixels obtained from points, profiles and an area. Once again the corrected DEMs were assessed. Finally the extracted profiles and contours from the corrected SRTM and AIRSAR were compared with the reference DEM. From the comparisons, the horizontal errors were found to be about one and the half pixels (138.72 m: for SRTM) to the east and 1 pixel (10 m: for AIRSAR) to the south. The SD of height differences of the SRTM and AIRSAR DEMs using 90% data were 9.2 m and 5.2 m with profiles comparison; 10.4 m and 5.4 m with area comparison; 10.8 m and 2.4 m with GPS GCPs comparison respectively. From the three comparisons, the means of height differences are 5.2 m, 6.1 m and 15.2 m for SRTM and 8.1 m, 8.3 m and 7.9 m for AIRSAR DEM. The results suggest there is height offset in the AIRSAR DEM. When both heights of DEMs were corrected, the generated contours are close to each other and to reference contours. Using contour colours images and height modelling, the corrected DEM was found to have the potential to detect areas that prone to flash floods and mudslides.

This research assesses the accuracy of SRTM and AIRSAR DEMs acquired over the mountainous-hillands of Cameron Highlands with DEMs generated from Digital Aerial Photograph (DAP) with a fine (2 m) spatial resolution and height resolution of about 0.5 m. The ground control points used for generating stereo models from the DAP were acquired during field work using GPS which achieved accuracy better than 2 cm in most cases. To overcome the difficulty of overlaying the DEMs with the DAP DEM as no features can be easily identified on both the images, therefore a technique of using transects and contours generated from the DEMs were used to correct the horizontal displacement. For AIRSAR DEM, comparing the accompanying AIRSAR composite images was also employed. These then allowed an analysis of the height accuracy to be undertaken. The height of both the AIRSAR and SRTM DEMs were also corrected by applying Linear Regression Models. These models were produced by comparing pixels obtained from points, profiles and an area. Once again the corrected DEMs were assessed. Finally the extracted profiles and contours from the corrected SRTM and AIRSAR were compared with the reference DEM. From the comparisons, the horizontal errors were found to be about one and the half pixels (138.72 m: for SRTM) to the east and 1 pixel (10 m: for AIRSAR) to the south. The SD of height differences of the SRTM and AIRSAR DEMs using 90% data were 9.2 m and 5.2 m with profiles comparison; 10.4 m and 5.4 m with area comparison; 10.8 m and 2.4 m with GPS GCPs comparison respectively. From the three comparisons, the means of height differences are 5.2 m, 6.1 m and 15.2 m for SRTM and 8.1 m, 8.3 m and 7.9 m for AIRSAR DEM. The results suggest there is height offset in the AIRSAR DEM. When both heights of DEMs were corrected, the generated contours are close to each other and to reference contours. Using contour colours images and height modelling, the corrected DEM was found to have the potential to detect areas that prone to flash floods and mudslides.

Tropical forests provide important habitat for a tremendous diversity of plant and animal species. However, limitations in measuring and monitoring the structure and function of tropical forests has caused these systems to remain poorly understood. Remote-sensing technology has provided a powerful tool for quantification of structural patterns and associating these with resource use. Satellite and aerial platforms can be used to collect remotely sensed images of tropical forests that can be applied to ecological research and management. Chapter 1 of this article highlights the resources available for tropical forest remote sensing and presents a case-study that demonstrates its application to a neotropical forest located in the Petén region of northern Guatemala. The ancient polity of Tikal has been extensively studied by archaeologists and soil scientists, but little is known about the subsistence and ancient farming techniques that sustained its inhabitants. The objective of chapter 2 was to create predictive models for ancient maize (Zea mays L.) agriculture in the Tikal National Park, Petén, Guatemala, improving our understanding of settlement patterns and the ecological potentials surrounding the site in a cost effective manner. Ancient maize agriculture was described in this study as carbon (C) isotopic signatures left in the soil humin fraction. Probability models predicting C isotopic enrichment and carbonate C were used to outline areas of potential long term maize agriculture. It was found that the Tikal area not only supports a great variety of potential food production systems but the models suggest multiple maize agricultural practices were used.