Procedures for change detection using Landsat digital data

Howarth, Philip J.; Wickware, Gregory M.

doi:10.1080/01431168108948362

Cited by 243 publications

(83 citation statements)

References 3 publications

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“…Colwell and Weber (1981) used post-classification comparison, change vector analysis and visual estimate of change from two dates' images for forest change detection, but no ground reference accuracy assessment was done. Howarth and Wickware (1981) Ingram et al (1981) have provided a quantitative comparison of results obtained by using image differencing and image regression techniques for urban change detection. They concluded that the regression procedure performed slightly better than univariate image differencing.…”

Section: Evaluation Of Digital Change Detection Techniquementioning

confidence: 99%

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Review Article Digital change detection techniques using remotely-sensed data

Singh

1989

International Journal of Remote Sensing

3,229

1,737

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Section: Evaluation Of Digital Change Detection Techniquementioning

confidence: 99%

“…Ratioing is considered to be a relatively rapid means of identifying areas of change (Howarth and Wickware 1981, Howarth and Boasson 1983, Nelson 1983, Todd 1977, Wilson et al 1976. In ratioing two registered images from different dates with one or more bands in an image are ratioed, band by band.…”

Section: Image Ratioingmentioning

confidence: 99%

Review Article Digital change detection techniques using remotely-sensed data

Singh

1989

International Journal of Remote Sensing

3,229

1,737

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show abstract

“…The simplest direct comparison techniques are the image difference (Lu et al, 2005) and image rationing (Howarth, Wickware, 1981). Image regression represents second image as a linear function of first (Lunetta, 1999).…”

Section: Related Workmentioning

confidence: 99%

Change Detection via Morphological Comparative Filters

Vizilter

Rubis

Zheltov

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.

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“…Now managed by the U.S. Change detection is the analysis of spatial differences from two satellites scenes of acquired over the same location at different times. Scientists have used Landsat for detection of land cover change not long after the launch of the first satellite (Howarth and Wickware 1981). Landsat change detection encompasses a wide range of algorithms, including generation of difference images, principal components analysis, spectral mixture analysis, and artificial neural networks (Lu et al 2004 To estimate future change in natural closed forest, we projected into the future only as many years as we possessed observations of the past.…”

Section: Extrapolation From Landsat Change Detectionmentioning

confidence: 99%

Comparison of Three Methods to Project Future Baseline Carbon Emissions in Temperate Rainforest, Curinanco, Chile

González¹,

Lara²,

Gayoso³

et al. 2005

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Deforestation of temperate rainforests in Chile has decreased the provision of ecosystem services, including watershed protection, biodiversity conservation, and carbon sequestration. Forest conservation can restore those ecosystem services. Greenhouse gas policies that offer financing for the carbon emissions avoided by preventing deforestation require a projection of future baseline carbon emissions for an area if no forest conservation occurs. For a proposed 570 km 2 conservation area in temperate rainforest around the rural community of Curiñanco, Chile, we compared three methods to project future baseline carbon emissions: extrapolation from Landsat observations, Geomod, and Forest Restoration Carbon Analysis (FRCA). Analyses of forest inventory and Landsat remote sensing data show 1986-1999 net deforestation of 1900 ha in the analysis area, proceeding at a rate of 0.0003 y -1 . The gross rate of loss of closed natural forest was 0.042 y -1 . In the period 1986-1999, closed natural forest decreased from 20 000 ha to 11 000 ha, with timber companies clearing natural forest to establish plantations of non-native species. Analyses of previous field measurements of species-specific forest biomass, tree allometry, and the carbon content of vegetation show that the dominant native forest type, broadleaf evergreen (bosque siempreverde), contains 370 ± 170 t ha -1 carbon, compared to the carbon density of non-native Pinus radiata plantations of 240 ± 60 t ha -1 . The 1986-1999 conversion of closed broadleaf evergreen forest to open broadleaf evergreen forest, Pinus radiata plantations, shrublands, grasslands, urban areas, and bare ground decreased the carbon density from 370 ± 170 t ha -1 carbon to an average of 100 t ha -1 (maximum 160 t ha -1 , minimum 50 t ha -1 ). Consequently, the conversion released 1.1 million t carbon. These analyses of forest inventory and Landsat remote sensing data provided the data to evaluate the three methods to project future baseline carbon emissions. Extrapolation from Landsat change detection uses the observed rate of change to estimate change in the near future. Geomod is a software program that models the geographic distribution of change using a defined rate of change. FRCA is an integrated spatial analysis of forest inventory, biodiversity, and remote sensing that produces estimates of forest biodiversity and forest carbon density, spatial data layers of future probabilities of reforestation and deforestation, and a projection of future baseline forest carbon sequestration and emissions for an ecologically-defined area of analysis. For the period 1999-2012, extrapolation from Landsat change detection estimated a loss of 5000 ha and 520 000 t carbon from closed natural forest; Geomod modeled a loss of 2500 ha and 250 000 t; FRCA projected a loss of 4700 ± 100 ha and 480 000 t (maximum 760 000 t, minimum 220 000 t). Concerning labor time, extrapolation for Landsat required 90 actual days or 120 days normalized to Bachelor degree level wages; Geomod required 240 actual da...

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Procedures for change detection using Landsat digital data

Cited by 243 publications

References 3 publications

Review Article Digital change detection techniques using remotely-sensed data

Review Article Digital change detection techniques using remotely-sensed data

Change Detection via Morphological Comparative Filters

Comparison of Three Methods to Project Future Baseline Carbon Emissions in Temperate Rainforest, Curinanco, Chile

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