Terrestrial laser scan error in the presence of dense ground vegetation

Terrestrial Laser Scan (TLS) data are seeing increasing use in geology, geomorphology, forestry and urban mapping. The ease of use, affordability and operational flexibility of TLS suggests that demand for it is likely to increase in large scale mapping studies. However, its advantages may remain restricted to specific environments, due to difficulties in defining bare-ground level in the presence of ground level vegetation. This paper seeks to clarify the component contributions to TLS elevation error deriving from vegetation occlusion, scan co-registration error, point-cloud georeferencing error and target position-definition in TLS point-cloud data. A very high-resolution (c.250 points/m2) multi-scan single-returns TLS point-cloud data-set is acquired for an 11-hectare area of open, substantially flat and 100% vegetated coastal saltmarsh, providing data for the empirical quantification of TLS error. Errors deriving from the sources discussed are quantified, clarifying the potential proportional contribution of vegetation to other error sources. Initial data validation is applied to the TLS point-cloud data after application of a local-lowest-point selection process, and repeat validation tests are applied to the resulting filtered point-cloud after application of a kriging-based error-adjustment using a data fusion with GPS. The final results highlight the problem of representing bare-ground effectively within TLS data captured in the presence of dense ground vegetation and clarify the component contributions of elevation error deriving from surveying and data processing

A Geographer Looks at Spatial Information Theory

Abstract. Geographic information is defined as a subset of spatial information, specific to the spatiotemporal frame of the Earth’s surface. Thus geographic information theory inherits the results of spatial information theory, but adds results that reflect the specific properties of geographic information. I describe six general properties of geographic information, and show that in some cases specialization has assumed other properties that are less generally observed. A recognition of the distinction between geographic and spatial would allow geographic information theory to achieve greater depth and utility.

Geographically Weighted Local Statistics Applied to Binary Data

This paper considers the application of geographically weighting to summary statistics for binary data. We argue that geographical smoothing techniques that are applied to descriptive statistics for ratio and interval scale data may also be applied to descriptive statistics for binary categorical data. Here we outline how this may be done, focussing attention on the odds ratio statistic used for summarising the linkage between a pair of binary variables. An example of this is applied to data relating to house sales, based on over 30,000 houses in the United Kingdom. The method is used to demonstrate that time trends in the building of detached houses vary throughout the country