Improved mapping of tropical forests with optical and SAR imagery, Part I: Forest cover and accuracy assessment using multi-resolution data

Tuomas Häme, Jorma Kilpi, Heikki Ahola, Yrjö Rauste, Oleg Antropov, M. Rautiainen, Laura Sirro, Sengthong Bounepone

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. A simulation study indicated that the sample of VHR images should be collected in a stratified manner using small (25 km) images. The VHR images should cover approximately one percent of the total area of interest, depending on the accuracy requirement. The recommended size of the reference plots (population units) that are selected within the VHR imagery is in the order of 50 m by 50 m. In a systematic selection the plots should be located at a distance of several hundred meters from each other. The forest variables were predicted with an unsupervised fuzzy classification method. The ALOS AVNIR-based forest/non-forest mapping accuracies varied between 68% and 97% of the areas of the VHR images. The corresponding ALOS PALSAR mapping accuracies were poorer. At AVNIR resolution, the area of natural forest was over-estimated, and the degree of disturbance underestimated in humid, heavily disturbed parts of the study area in Laos. The three predictions for the total forest fraction from VHR, AVNIR and PALSAR data over the area that was covered by the VHR images were 55.1%, 53.6%, and 52.8%, respectively.
Original languageEnglish
Pages (from-to)74-91
Number of pages18
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume6
Issue number1
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

Fingerprint

AVNIR
accuracy assessment
forest cover
ALOS
tropical forest
image resolution
Image resolution
synthetic aperture radar
imagery
PALSAR
Satellites
Sampling
disturbance
sampling
prediction
simulation

Keywords

  • ALOS AVNIR
  • ALOS PALSAR
  • accuracy assessment
  • REDD
  • SAR
  • VHR
  • optical
  • sampling
  • tropical fores

Cite this

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title = "Improved mapping of tropical forests with optical and SAR imagery, Part I: Forest cover and accuracy assessment using multi-resolution data",
abstract = "This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. A simulation study indicated that the sample of VHR images should be collected in a stratified manner using small (25 km) images. The VHR images should cover approximately one percent of the total area of interest, depending on the accuracy requirement. The recommended size of the reference plots (population units) that are selected within the VHR imagery is in the order of 50 m by 50 m. In a systematic selection the plots should be located at a distance of several hundred meters from each other. The forest variables were predicted with an unsupervised fuzzy classification method. The ALOS AVNIR-based forest/non-forest mapping accuracies varied between 68{\%} and 97{\%} of the areas of the VHR images. The corresponding ALOS PALSAR mapping accuracies were poorer. At AVNIR resolution, the area of natural forest was over-estimated, and the degree of disturbance underestimated in humid, heavily disturbed parts of the study area in Laos. The three predictions for the total forest fraction from VHR, AVNIR and PALSAR data over the area that was covered by the VHR images were 55.1{\%}, 53.6{\%}, and 52.8{\%}, respectively.",
keywords = "ALOS AVNIR, ALOS PALSAR, accuracy assessment, REDD, SAR, VHR, optical, sampling, tropical fores",
author = "Tuomas H{\"a}me and Jorma Kilpi and Heikki Ahola and Yrj{\"o} Rauste and Oleg Antropov and M. Rautiainen and Laura Sirro and Sengthong Bounepone",
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language = "English",
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journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
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Improved mapping of tropical forests with optical and SAR imagery, Part I : Forest cover and accuracy assessment using multi-resolution data. / Häme, Tuomas; Kilpi, Jorma; Ahola, Heikki; Rauste, Yrjö; Antropov, Oleg; Rautiainen, M.; Sirro, Laura; Bounepone, Sengthong.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, No. 1, 2013, p. 74-91.

Research output: Contribution to journalArticleScientificpeer-review

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T2 - Forest cover and accuracy assessment using multi-resolution data

AU - Häme, Tuomas

AU - Kilpi, Jorma

AU - Ahola, Heikki

AU - Rauste, Yrjö

AU - Antropov, Oleg

AU - Rautiainen, M.

AU - Sirro, Laura

AU - Bounepone, Sengthong

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PY - 2013

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N2 - This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. A simulation study indicated that the sample of VHR images should be collected in a stratified manner using small (25 km) images. The VHR images should cover approximately one percent of the total area of interest, depending on the accuracy requirement. The recommended size of the reference plots (population units) that are selected within the VHR imagery is in the order of 50 m by 50 m. In a systematic selection the plots should be located at a distance of several hundred meters from each other. The forest variables were predicted with an unsupervised fuzzy classification method. The ALOS AVNIR-based forest/non-forest mapping accuracies varied between 68% and 97% of the areas of the VHR images. The corresponding ALOS PALSAR mapping accuracies were poorer. At AVNIR resolution, the area of natural forest was over-estimated, and the degree of disturbance underestimated in humid, heavily disturbed parts of the study area in Laos. The three predictions for the total forest fraction from VHR, AVNIR and PALSAR data over the area that was covered by the VHR images were 55.1%, 53.6%, and 52.8%, respectively.

AB - This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. A simulation study indicated that the sample of VHR images should be collected in a stratified manner using small (25 km) images. The VHR images should cover approximately one percent of the total area of interest, depending on the accuracy requirement. The recommended size of the reference plots (population units) that are selected within the VHR imagery is in the order of 50 m by 50 m. In a systematic selection the plots should be located at a distance of several hundred meters from each other. The forest variables were predicted with an unsupervised fuzzy classification method. The ALOS AVNIR-based forest/non-forest mapping accuracies varied between 68% and 97% of the areas of the VHR images. The corresponding ALOS PALSAR mapping accuracies were poorer. At AVNIR resolution, the area of natural forest was over-estimated, and the degree of disturbance underestimated in humid, heavily disturbed parts of the study area in Laos. The three predictions for the total forest fraction from VHR, AVNIR and PALSAR data over the area that was covered by the VHR images were 55.1%, 53.6%, and 52.8%, respectively.

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KW - REDD

KW - SAR

KW - VHR

KW - optical

KW - sampling

KW - tropical fores

U2 - 10.1109/JSTARS.2013.2241019

DO - 10.1109/JSTARS.2013.2241019

M3 - Article

VL - 6

SP - 74

EP - 91

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

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