Mapping residential density patterns using multi-temporal Landsat data and a decision-tree classifier

McCauley, Stephen; Goetz, Scott J.

doi:10.1080/0143116031000115102

Cited by 42 publications

(17 citation statements)

References 20 publications

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“…2.4). Although the rate of increase in development was realistic, the initial extent of urban development in the 1992 LULC data was likely an underestimation of the actual urban extent because it was difficult to identify and map low-density residential areas using Landsat data (Claggett and others, 2004;McCauley and Goetz, 2004). In addition, the 2001 NLCD data had significantly more urban land mapped than the 1992 NLCD, which was likely due to improved source data and methodologies just as much as actual urban expansion.…”

Section: Baseline Lulc Mapping and Modelingresults For The Western Unmentioning

confidence: 99%

Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of the Western United States

Zhu¹,

Reed²

2012

Professional Paper

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Front of cover:A stand of mixed conifer trees in Glacier National Park, northwestern Montana. The mesic, closed-canopy forests are typically distributed on mountain slopes with high moisture gradients and include western hemlock, western redcedar, Douglas-fir, lodgepole pine, as well as spruce and fir species. These types of forests are affected by natural disturbances such as wildland fires with relatively long fire-return intervals. The major land-management activities affecting forests include fire suppression, timber harvesting, and recreation. For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit

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Section: Baseline Lulc Mapping and Modelingresults For The Western Unmentioning

confidence: 99%

Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of the Western United States

Zhu¹,

Reed²

2012

Professional Paper

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“…Remote sensing imagery provides information on land cover, which does not translate exactly into land use information [51]. To produce valuable information about land, there exist several steps that if supported by computational tools deliver results in short time.…”

Section: Discussionmentioning

confidence: 99%

“…Making effective use of these large data sets needs advances in GIScience [30]. Remote sensing imagery provides information on land cover, which does not translate directly into land use and land change information [51]. Therefore, to extract information about land change, we need to better represent the semantic content of remote sensing imagery [11].…”

Section: Introductionmentioning

confidence: 99%

GeoDMA—Geographic Data Mining Analyst

Körting

Fonseca

Câmara

2013

Computers & Geosciences

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Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.

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“…Most research has quantified tree canopy cover using spaceborne satellites such as the Landsat series and QuickBird (Cauley and Goetz 2004;McPherson et al 2008). This approach provides important and accurate data on tree canopy cover but may not be able to quantify the density of individual trees or quantify the density of trees before the launch of these spaceborne satellites.…”

Section: Introductionmentioning

confidence: 99%