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- Title
- Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery.
- Creator
- Johnson, Brian A., Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of...
Show moreFine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods., Since the two dissertation methods used an automated algorithm (Random Forest) for image classification, they are also less subjective and easier to apply to other study areas than most existing multi-scale object-based methods that rely on expert knowledge (i.e. decision rules developed based on detailed visual inspection of image segments) for classifying each type of land cover.
Show less - Date Issued
- 2012
- PURL
- http://purl.flvc.org/FAU/3342110
- Subject Headings
- Image processing, Digital techniques, Remote sensing, Mathematics, Remote-sensing images, Computational intelligence, Cities and towns, Remote sensing, Environmental sciences, Remote sensing, Spatial analysis (Statistics)
- Format
- Document (PDF)
- Title
- Object Oriented Classification of Australian Pine (Casuarina equisetifolia), an Invasive Exotic Species in South Florida.
- Creator
- Johnson, Brian A., Xie, Zhixiao, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
Invasive exotic plant species cause a number of problems in native south Florida ecosystems, and a great deal of effort is being put into controlling the populations ofthese species. Control efforts require updated information on the locations of exotic species. This information can be obtained from high resolution remotely sensed data such as digital orthoimagery and LIDAR. Extraction of information from these data sources is often problematic using traditional pixel-based image processing...
Show moreInvasive exotic plant species cause a number of problems in native south Florida ecosystems, and a great deal of effort is being put into controlling the populations ofthese species. Control efforts require updated information on the locations of exotic species. This information can be obtained from high resolution remotely sensed data such as digital orthoimagery and LIDAR. Extraction of information from these data sources is often problematic using traditional pixel-based image processing techniques. An object oriented method of image analysis, however, has been shown to be better suited to this task. One invasive exotic species that has become widespread in south Florida is Casuarina equisetifolia, also known as Australian pine. This study develops a semiautomated procedure for detecting Australian pine over a large, diverse area with high resolution remotely sensed data using the object oriented method of analysis.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00000775
- Subject Headings
- Ecology--Remote sensing, Aerial photogrammetry
- Format
- Document (PDF)