Discrimination of peatlands and mineral soil lands using multisource remote sensing data

Kari Lahti, Tuomas Häme

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

he discrimination of peatlands and mineral soil lands and different peatland canopy types was studied using Landsat TM-, Landsat MSS-, NOAA A VHRR-images, aerogeophysical data and data derived from a digital terrain model. The study area, centered at 63° 28' N, 26° 14' E was located in the Middle Finnish Boreal forest. The field data were 2126 temporary sample plots of the National Forest Inventory. The analysis methods were discriminant and clustering analysis and Tukey's studentized range tests. The classifications, based on discriminant analysis (maximum likelihood classification) were tested using external ground truth data. Peatlands and mineral soil lands were separated in an accuracy of 76.6 percent. The best image variables to discliminate peatlands and mineral soil lands were geophysical variables (Gamma ray intensity of Potassium (K40) and out-of-phase component of electromagnetic data). Without geophysical data the classification accuracy was more than 10 percent lower. Open bogs and poor mineral soil lands were separated well but the peatlands and mineral soil lands with abundant growing stock were mixed. The subgroups of peatlands were separated best using Landsat images only. The proportion of correctly classified field plots was 70 percent. Open bogs and spruce dominated peatlands were separated best. The percentage of correctly classified pixels was 67.4 percent, when peatlands were discriminated on basis of peat type (Sphagnum, Carex). The discrimination accuracy of peatlands on basis of ditching stage was 63 percent.
Original languageEnglish
Title of host publication17th Congress, ISPRS
Place of PublicationWashington
Pages452-456
Number of pages5
Publication statusPublished - 1992
MoE publication typeNot Eligible
EventInternational Society for Photogrammetry and Remote Sensing, ISPRS: XVIIth Congress - Washington, United States
Duration: 9 Aug 199215 Aug 1992

Conference

ConferenceInternational Society for Photogrammetry and Remote Sensing, ISPRS
CountryUnited States
CityWashington
Period9/08/9215/08/92

Fingerprint

peatland
remote sensing
mineral
soil
bog
land
Landsat multispectral scanner
digital terrain model
forest inventory
discriminant analysis
Landsat thematic mapper
boreal forest
Landsat
peat
pixel
potassium
canopy

Keywords

  • remote sensing

Cite this

Lahti, K., & Häme, T. (1992). Discrimination of peatlands and mineral soil lands using multisource remote sensing data. In 17th Congress, ISPRS (pp. 452-456). Washington.
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title = "Discrimination of peatlands and mineral soil lands using multisource remote sensing data",
abstract = "he discrimination of peatlands and mineral soil lands and different peatland canopy types was studied using Landsat TM-, Landsat MSS-, NOAA A VHRR-images, aerogeophysical data and data derived from a digital terrain model. The study area, centered at 63° 28' N, 26° 14' E was located in the Middle Finnish Boreal forest. The field data were 2126 temporary sample plots of the National Forest Inventory. The analysis methods were discriminant and clustering analysis and Tukey's studentized range tests. The classifications, based on discriminant analysis (maximum likelihood classification) were tested using external ground truth data. Peatlands and mineral soil lands were separated in an accuracy of 76.6 percent. The best image variables to discliminate peatlands and mineral soil lands were geophysical variables (Gamma ray intensity of Potassium (K40) and out-of-phase component of electromagnetic data). Without geophysical data the classification accuracy was more than 10 percent lower. Open bogs and poor mineral soil lands were separated well but the peatlands and mineral soil lands with abundant growing stock were mixed. The subgroups of peatlands were separated best using Landsat images only. The proportion of correctly classified field plots was 70 percent. Open bogs and spruce dominated peatlands were separated best. The percentage of correctly classified pixels was 67.4 percent, when peatlands were discriminated on basis of peat type (Sphagnum, Carex). The discrimination accuracy of peatlands on basis of ditching stage was 63 percent.",
keywords = "remote sensing",
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Lahti, K & Häme, T 1992, Discrimination of peatlands and mineral soil lands using multisource remote sensing data. in 17th Congress, ISPRS. Washington, pp. 452-456, International Society for Photogrammetry and Remote Sensing, ISPRS, Washington, United States, 9/08/92.

Discrimination of peatlands and mineral soil lands using multisource remote sensing data. / Lahti, Kari; Häme, Tuomas.

17th Congress, ISPRS. Washington, 1992. p. 452-456.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Discrimination of peatlands and mineral soil lands using multisource remote sensing data

AU - Lahti, Kari

AU - Häme, Tuomas

N1 - Project code: INS2507

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N2 - he discrimination of peatlands and mineral soil lands and different peatland canopy types was studied using Landsat TM-, Landsat MSS-, NOAA A VHRR-images, aerogeophysical data and data derived from a digital terrain model. The study area, centered at 63° 28' N, 26° 14' E was located in the Middle Finnish Boreal forest. The field data were 2126 temporary sample plots of the National Forest Inventory. The analysis methods were discriminant and clustering analysis and Tukey's studentized range tests. The classifications, based on discriminant analysis (maximum likelihood classification) were tested using external ground truth data. Peatlands and mineral soil lands were separated in an accuracy of 76.6 percent. The best image variables to discliminate peatlands and mineral soil lands were geophysical variables (Gamma ray intensity of Potassium (K40) and out-of-phase component of electromagnetic data). Without geophysical data the classification accuracy was more than 10 percent lower. Open bogs and poor mineral soil lands were separated well but the peatlands and mineral soil lands with abundant growing stock were mixed. The subgroups of peatlands were separated best using Landsat images only. The proportion of correctly classified field plots was 70 percent. Open bogs and spruce dominated peatlands were separated best. The percentage of correctly classified pixels was 67.4 percent, when peatlands were discriminated on basis of peat type (Sphagnum, Carex). The discrimination accuracy of peatlands on basis of ditching stage was 63 percent.

AB - he discrimination of peatlands and mineral soil lands and different peatland canopy types was studied using Landsat TM-, Landsat MSS-, NOAA A VHRR-images, aerogeophysical data and data derived from a digital terrain model. The study area, centered at 63° 28' N, 26° 14' E was located in the Middle Finnish Boreal forest. The field data were 2126 temporary sample plots of the National Forest Inventory. The analysis methods were discriminant and clustering analysis and Tukey's studentized range tests. The classifications, based on discriminant analysis (maximum likelihood classification) were tested using external ground truth data. Peatlands and mineral soil lands were separated in an accuracy of 76.6 percent. The best image variables to discliminate peatlands and mineral soil lands were geophysical variables (Gamma ray intensity of Potassium (K40) and out-of-phase component of electromagnetic data). Without geophysical data the classification accuracy was more than 10 percent lower. Open bogs and poor mineral soil lands were separated well but the peatlands and mineral soil lands with abundant growing stock were mixed. The subgroups of peatlands were separated best using Landsat images only. The proportion of correctly classified field plots was 70 percent. Open bogs and spruce dominated peatlands were separated best. The percentage of correctly classified pixels was 67.4 percent, when peatlands were discriminated on basis of peat type (Sphagnum, Carex). The discrimination accuracy of peatlands on basis of ditching stage was 63 percent.

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