Data Collection, Processing, and Applications for Geospatial Analysis

Abstract

Geospatial data collection is an important task for many spatial information users. Geospatial data collection may include field data collection, remote sensing data processing, and in-house geographical information science (GIS) data conversion. Nowadays, geospatial data are available from various sources. Among these, remote sensing data (i.e., optical, radio detection and ranging (RADAR), light detection and ranging (LIDAR), etc.) are among the primary data sources in many GIS analyses. For example, high-resolution satellite images such as QuickBird, IKONOS, and aerial photographs are the basis for the generation of qualitative land-use maps (i.e., land-use zoning maps) and the delineation of transportation networks. Medium-resolution satellite images such as ALOS, SPOT, and Landsat TM/ETM are used in the generation of quantitative land-use maps (i.e., land cover maps) for regional-scale studies of changes in land use. The shuttle radar topography mission (SRTM) and LIDAR provide topographical characteristics for GIS analysis. Moreover, remote sensing data are important for environmental studies such as deforestation, global warming, and natural resource management. This technology captures the real-world information with various sophisticated sensors and platforms. However, building a GIS database is required for further geospatial analysis and mapping purposes. GIS converts the real-world information into a geodatabase in order to retrieve, analyze, and allow further geocomputations. On the other hand, field data collection is important for spatial information users in order to collect spatially distributed objects with their associated attribute information. In this chapter, we discuss geospatial data collection methods and processing, and their applications in GIS.