GML-Based nD Data Management With a Big Geo Data Semantic World Modeling Approach

GML-Based nD Data Management With a Big Geo Data Semantic World Modeling Approach

Juergen Rossmann (RWTH Aachen University, Germany), Martin Hoppen (RWTH Aachen University, Germany) and Arno Buecken (RWTH Aachen University, Germany)
Copyright: © 2018 |Pages: 33
DOI: 10.4018/978-1-5225-5625-1.ch008

Abstract

3D simulation applications benefit from realistic and exact forest models. They range from training simulators like flight or harvester simulators to economic and ecological simulations for tree growth or succession. The nD forest simulation and information system integrates the necessary methods for data extraction, modeling, and management of highly realistic models. Using semantic world modeling, tree data can efficiently be extracted from remote sensing data – even for very large areas. Data is modeled using a GML-based modeling language and a flexible data management approach is integrated to provide caching, persistence, a central communication hub, and a versioning mechanism. Combining various simulation techniques and data versioning, the nD forest simulation and information system can provide applications with historic 3D data in multiple time dimensions (hence nD) as well as with predicted data based on simulations.
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Introduction

At 3D GeoInfo 2012, we presented an innovative and efficient way to generate “Virtual Forests” from remote sensing data (Bücken & Rossmann, 2013). Individual trees are delineated from normalized digital surface models and annotated with height and species. This approach is the first step towards various forestall simulation applications based on real-world data like the simulation of forest machines (Figure 1), a flight simulator, a tree growth or a succession simulation. To provide a basis for an efficient and modern data management of such vast datasets, a database-driven method for 3D simulation systems previously presented at 3D GeoInfo 2010 is used (M Hoppen, Rossmann, Schluse, & Waspe, 2010). It provides a persistence layer and a common data schema for simulation systems. Now, it is enhanced by techniques for database-driven, distributed data management and simulation, for data versioning and for the use of big, heterogeneous geo data.

Figure 1.

Driver training by means of an advanced forest machine simulator

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In this revised work, we focus on the integration, enhancement, and on future trends regarding these two core technologies of a large-scale nD forest simulation and information system. In particular, algorithms for the attribution of the individual tree, details on the GML-based (Open Geospatial Consortium (OGC), n.d.), object-oriented schema family ForestGML for forestry data, and the concept of database-driven communication are presented. Overall, a shared world model is efficiently managed in a geo database and filled using modern techniques of semantic world modeling. The latter transform remote sensing data into a semantic object representation that can be used for the various simulation scenarios as mentioned above. Furthermore, data versioning can be used to analyze past scenarios like a windthrow, where the corresponding storm loss must be calculated. In this context, multiple time dimensions (hence nD) are introduced to the system. Furthermore, even simulated or predicted future states can be managed in a database for conservation, analysis, and comparison. These two concepts – simulation and versioning – add multiple time dimensions yielding an nD forest simulation and information system. Furthermore, given the performance of today’s database systems, it even becomes feasible to use the presented system for a multi-client simulation. Here, different clients are simultaneously working with the shared world model, while their actions’ effects are distributed over the very same active geo database system.

The work at hand is organized as follows. In the next section, we give an overview of related work. In Section “Single Tree Delineation and Attribution”, the tree extraction approach is introduced and current results are presented. Subsequently in Section “Database Interface”, the database interface is introduced, including database versioning and data streaming. Here, we give an insight into the systems nD capabilities as well as current developments, and show how the database interface and the tree extraction interact. Subsequently, we give details on the new ForestGML schema family used for data modeling in the Virtual Forest in Section “Data Modeling”. A selection of applications benefitting from realistic tree data is presented in Section “Applications”. Finally, we conclude this work in the last section.

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There are several approaches for the delineation and attribution of individual trees from remote sensing data documented in literature. (Garcia, Suárez, & Patenaude, 2007) compare four common algorithms for single tree delineation from nDSM (normalized digital surface model) datasets and point out their advantages and disadvantages. (Reitberger, 2010) provides algorithms that work on full waveform data. The volumetric algorithm used in this paper focuses on nDSM data. It can detect up to 90 percent of the trees in a spruce forest that is ready to harvest. (Hyyppä & Inkinen, 1999) estimate the diameter at breast height (DBH) of a tree depending on its height and crown diameter, but do not specify the parameters of their heuristic as it might vary for different areas.

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