An assessment of landsat MSS and TM data for urban and near-urban land-cover digital classification

Haack, Barry

doi:10.1016/0034-4257(87)90053-8

Cited by 105 publications

(41 citation statements)

References 6 publications

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“…The major reason for the superior performance of Landsat TM images was attributed to the higher spatial resolution, addition of more spectral bands and the increase of radiometric resolution from 6 bit for Landsat MSS images to 8 bit for Landsat TM images [4,125]. In a separate study on the performance of Landsat MSS and TM by Haack, et al [126]; it was reported that Landsat TM images were more useful in separating more homogenous near-urban land cover types as compared to heterogeneous urban areas. Most research has indicated superior performance of Landsat TM as compared to Landsat MSS [125,126] with a difference in accuracy of between 5 and 7% [127].…”

Section: Comparative Performance Of Different Landsat Images In Land mentioning

confidence: 99%

“…In a separate study on the performance of Landsat MSS and TM by Haack, et al [126]; it was reported that Landsat TM images were more useful in separating more homogenous near-urban land cover types as compared to heterogeneous urban areas. Most research has indicated superior performance of Landsat TM as compared to Landsat MSS [125,126] with a difference in accuracy of between 5 and 7% [127].…”

Section: Comparative Performance Of Different Landsat Images In Land mentioning

confidence: 99%

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Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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Section: Comparative Performance Of Different Landsat Images In Land mentioning

confidence: 99%

Section: Comparative Performance Of Different Landsat Images In Land mentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

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“…Remotely sensed derived variables, GIS thematic layers, and census data are three essential data sources for urban analyses, and their integration is thus a central theme in urban analysis. Since census data collected within spatial units can be stored as GIS attributes, the combination of census and remote sensing data combined with a GIS can be envisaged in three main ways [62] that relate to urban analyses: (i) remote sensing imagery have been used in extracting and updating transportation networks [63][64][65][66] and buildings [67][68][69][70], providing land use/cover data and biophysical attributes [17,58,59,[71][72][73], and detecting urban expansion [61,74,75]; (ii) Census data have been used to improve image classification in urban areas [60,76,77]; (iii) The integration of remote sensing and census data has been applied to estimate population and residential density [78][79][80][81][82][83][84][85][86][87][88], to assess socioeconomic conditions [89,90], and to evaluate the quality of life [91][92][93][94]. We note that census data are available at a number of different scales, as determined by independent (not remote sensing-based) spatial areas, typically down to census block levels.…”

Section: Integrating Remote Sensing and Gis For Urban Analysismentioning

confidence: 99%

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

et al. 2011

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Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

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“…The training and simulation were conducted using the MATLAB software (www.mathworks.com). Landsat TM images have been widely used for environmental monitoring, resource management, and land cover/land use classification (Haack et al, 1987;Park and Stenstrom, 2006). The images obtained from the 2009 -2010 wet and dry seasons showed only little residual haziness with much more improvement after haze removal and atmospheric corrections.…”

Section: Data Compilation and Processingmentioning

confidence: 99%

Operational Prediction of Groundwater-phosphorous Interaction Over Surficial Aquifers of South Florida

Chebud¹

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An assessment of landsat MSS and TM data for urban and near-urban land-cover digital classification

Cited by 105 publications

References 6 publications

Developments in Landsat Land Cover Classification Methods: A Review

Developments in Landsat Land Cover Classification Methods: A Review

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

Operational Prediction of Groundwater-phosphorous Interaction Over Surficial Aquifers of South Florida

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