Karst database development in Minnesota: design and data assembly

The Karst Feature Database (KFD) of Minnesota is a relational GIS-based Database Management System (DBMS). Previous karst feature datasets used inconsistent attributes to describe karst features in different areas of Minnesota. Existing metadata were modified and standardized to represent a comprehensive metadata for all the karst features in Minnesota. Microsoft Access 2000 and ArcView 3.2 were used to develop this working database. Existing county and sub-county karst feature datasets have been assembled into the KFD, which is capable of visualizing and analyzing the entire data set. By November 17 2002, 11,682 karst features were stored in the KFD of Minnesota. Data tables are stored in a Microsoft Access 2000 DBMS and linked to corresponding ArcView applications. The current KFD of Minnesota has been moved from a Windows NT server to a Windows 2000 Citrix server accessible to researchers and planners through networked interfaces.     

A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis

OpenStreetMap (OSM) is one of the most popular examples of a Volunteered Geographic Information (VGI) project. In the past years it has become a serious alternative source for geodata. Since the quality of OSM data can vary strongly, different aspects have been investigated in several scientific studies. In most cases the data is compared with commercial or administrative datasets which, however, are not always accessible due to the lack of availability, contradictory licensing restrictions or high procurement costs. In this investigation a framework containing more than 25 methods and indicators is presented, allowing OSM quality assessments based solely on the data's history. Without the usage of a reference data set, approximate statements on OSM data quality are possible. For this purpose existing methods are taken up, developed further, and integrated into an extensible open source framework. This enables arbitrarily repeatable intrinsic OSM quality analyses for any part of the world.     

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^?2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^?1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^?1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^?1 [between 0.69–0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^?1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58–0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^?1 for the echo-based model, whereas for the CHM R² was between 0.37–0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^?1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^?2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^?2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^?2.

     

Formal definition of a user-adaptive and length-optimal routing graph for complex indoor environments

Car routing solutions are omnipresent and solutions for pedestrians also exist.Furthermore,public or commercial buildings are getting bigger and the complexity of their internal structure has increased.Consequently,the need for indoor routing solutions has emerged.Some prototypes are available,but they still lack semantically-enriched modelling (e.g.,access constraints,labels,etc.) and are not suitable for providing user-adaptive length-optimal routing in complex buildings.Previous approaches consider simple rooms,concave rooms,and corridors,but important characteristics such as distinct areas in huge rooms and solid obstacles inside rooms are not considered at all,although such details can increase navigation accuracy.By formally defining a weighted indoor routing graph,it is possible to create a detailed and user-adaptive model for route computation.The defined graph also contains semantic information such as room labels,door accessibility constraints,etc.Furthermore,one-way paths inside buildings are considered,as well as three-dimensional building parts,e.g.,elevators or stairways.A hierarchical structure is also possible with the presented graph model.     

The development of computer networks: First results from a microeconomic model

 Computer networks like the Internet are gaining importance in social and economic life. The accelerating pace of the adoption of network technologies for business purposes is a rather recent phenomenon. Many applications are still in the early, sometimes even experimental, phase. Nevertheless, it seems to be certain that networks will change the socioeconomic structures we know today. This is the background for our special interest in the development of networks, in the role of spatial factors influencing the formation of networks, and consequences of networks on spatial structures, and in the role of externalities. This paper discusses a simple economic model – based on a microeconomic calculus – that incorporates the main factors that generate the growth of computer networks. The paper provides analytic results about the generation of computer networks. The paper discusses (1) under what conditions economic factors will initiate the process of network formation, (2) the relationship between individual and social evaluation, and (3) the efficiency of a network that is generated based on economic mechanisms. Received: 5 July 2000 / Accepted: 28 November 2000