Abstract
Abstract. Geoinformation systems (GIS) and other spatial databases containing land-use data are usually subjected to intensive change processes that impact the quality of their inherent classification and diminish its relevance. Consequently, with time, databases accumulate various types of erroneous information. The combination of the satellite data with the thematic land-use data from a core national GIS, provides an excellent case for GIS-driven analysis of land-use changes. The aim of this research was to assess the land-use changes using time-series of optical Ikonos satellite data. An area of ∼35 km2 in the north of Israel served as the study case of the research. Seven land-use classes were detected in the relevant National GIS spatial database layers updated in the year 2000 and further in the year 2009. These seven type-classes were: water bodies, residential areas, agricultural fields, badlands, natural forests, build-up areas, and plantations. The Iterative discriminant analysis (IDA) algorithm was applied on both GIS datasets using corresponding Ikonos images acquired in 2002 and 2010, respectfully. The IDA process resulted with a re-classification of the initial land-use polygons. It was assessed by validating the classification of all the land-use polygons. Comparing with Ikonos image from the year 2002, the fraction of the polygons that were correctly detected as consistent with the corresponding GIS dataset (77.9%) was relatively close to the fraction of polygons correctly detected as discrepant (75.5%). Classification of Ikonos image from the year 2010 showed that 81.9% of the land-use polygons were correctly detected as consistent whereas the fraction of polygons that were correctly detected as discrepant was about (78.3%). The main advantage of the proposed GIS-driven methodology for detection of changes in land-use classification is its analytical simplicity that allows for straightforward employment of spectral and spatial data in the classification process.
Highlights
Statistical methods of image analysis in remote sensing have served as most common approach to classification of the investigated phenomena
In this study we present the framework method for incorporation of knowledge about land cover types from the Israeli National Geoinformation systems (GIS) database into classification procedure of remote sensing data
The iterative discriminant analysis (IDA) process was successful in re-classification of the polygons into spectrally homogenous groups that followed the reduction in within-class spectral variability of land-use classes
Summary
Statistical methods of image analysis in remote sensing have served as most common approach to classification of the investigated phenomena. The GIS-driven methodology was proposed by Peled (1994) for integration of spatial information from GIS databases for national-wide classification of remote sensing data. Compared to more traditional mapping approaches such as terrestrial survey and basic aerial photo interpretation, GISdriven classification of land cover has the advantages of lower cost, area-wide coverage, and a possibility for frequent updating. Data about land cover stored in GIS database are usually subjected to an intensive change processes that diminish their relevance and include different types of discrepant information. Classification of land cover by up-to-date satellite imagery and automatic updating of GIS database allows revision of discrepant or erroneous data. Having assumed that the number of wrongly captured GIS objects (or classification types) are substantially less than the number of all GIS objects of the data set, the training areas can be derived automatically from the already existing GIS data (Walter, 2000)
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