Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing
TL;DR: In this paper, the authors developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) data in the seven-county Twin Cities metropolitan area of Minnesota for 1986, 1991, 1998, and 2002.
About: This article is published in Remote Sensing of Environment.The article was published on 2005-10-15. It has received 1047 citations till now. The article focuses on the topics: Land cover & Thematic Mapper.
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1,327 citations
TL;DR: In this article, the authors compared the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), Percent Impervious Surface area (%ISA), and the NDVI.
1,289 citations
Cites background or methods from "Land cover classification and chang..."
...Our previous study (Yuan et al., 2005) showed that in 2002, approximately 33% of the total land was urban, 40% agriculture, and about 14% forest....
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...The emissivities were based on our land cover classification (Yuan et al., 2005) and emissivity values from Snyder et al. (1998)....
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TL;DR: This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context, followed by a review of object-basedchange detection techniques.
Abstract: The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.
1,159 citations
Additional excerpts
...…set Final accuracy is dependent on classification accuracy of individual image Land use land cover classification and change (Miller et al., 1998; Yuan et al., 2005) Urban Sprawl measuring (Ji et al., 2006) Change detection by unsupervised classification (2000; Ghosh et al., 2011) Multi-date…...
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TL;DR: In this paper, the authors evaluated land use/cover changes and urban expansion in Greater Dhaka, Bangladesh, between 1975 and 2003 using satellite images and socio-economic data and found that substantial growth of built-up areas in greater Dhaka over the study period resulted significant decrease in the area of water bodies, cultivated land, vegetation and wetlands.
863 citations
TL;DR: In this article, the authors present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.
Abstract: Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.
784 citations
Cites background from "Land cover classification and chang..."
...In theory, validation of a time series of land cover and change detection results can be achieved with independent error matrices (Mertens and Lambin, 2000; Yuan et al., 2005)....
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References
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Book•
01 Dec 1995
TL;DR: Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications.
Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.
5,478 citations
17 Sep 1998
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.
Abstract: Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference Data Collection Basic Collection Forms Basic Analysis Techniques Non-Site Specific Assessments Site Specific Assessments Area Estimation/Correction Practicals Impact of Sample Design on Cost Recommendations for Collecting Reference Data ASources of Variation in Reference Data Photo Interpretation vs. Ground Visitation Interpreter Variability Observations vs. Measurements What is Correct? Labeling Map vs. Labeling the Reference Data Qualitative vs. Quantitative Analysis Local vs. Regional vs. Global Assessments Advanced Topics Beyond the Error Matrix Modifying the Error Matrix Fuzzy Set Theory Measuring Variability Complex Data Sets Change Detection Multi-Layer Assessments California Hardwood Rangeland Monitoring Project Case Study Balancing Statistical Validity with Practical Reality Bibliography
4,586 citations
"Land cover classification and chang..." refers background or methods in this paper
...Change detection presents unique problems for accuracy assessment since it is difficult to sample areas that will change in the future before they change (Congalton & Green, 1999)....
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...Error matrices as cross-tabulations of the mapped class vs. the reference class were used to assess classification accuracy (Congalton & Green, 1999)....
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01 Jan 1976
TL;DR: The framework of a national land use and land cover classification system is presented for use with remote sensor data and uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources.
Abstract: The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The pro-posed system uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in US Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.
4,154 citations
"Land cover classification and chang..." refers methods in this paper
...Our classification scheme, with seven level 1 classes (Table 1), was based on the land cover and land use classification system developed by Anderson et al. (1976) for interpretation of remote sensor data at various scales and resolutions....
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...Our classification scheme, with seven level 1 classes (Table 1), was based on the land cover and land use classification system developed by Anderson et al. (1976) for interpretation of remote sensor data at various scales and resolutions....
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TL;DR: An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.
Abstract: A variety of procedures for change detection based on comparison of multitemporal digital remote sensing data have been developed. An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.
3,361 citations
"Land cover classification and chang..." refers background in this paper
...These techniques locate changes but do not provide information on the nature of change (Ridd & Liu, 1998; Singh, 1989; Yuan et al., 1998)....
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...These techniques locate changes but do not provide information on the nature of change (Ridd & Liu, 1998; Singh, 1989; Yuan et al., 1998)....
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TL;DR: This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today.
Abstract: Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.
2,043 citations
"Land cover classification and chang..." refers background in this paper
...The post-classification comparison approach also compensates for variation in atmospheric conditions and vegetation phenology between dates since each classification is independently produced and mapped (Coppin et al., 2004; Yuan et al., 1998)....
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