AVHRR-based forest proportion map of the Pan-European area

Tuomas Häme (Corresponding Author), Pauline Stenberg, Kaj Andersson, Yrjö Rauste, Pamela Kennedy, Sten Folving, Janne Sarkeala

Research output: Contribution to journalArticleScientificpeer-review

57 Citations (Scopus)

Abstract

A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors (reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the European Commission's Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12 countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when compared with EUROSTAT's statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to CORINE) was in France (5.9%). The largest overestimation was found in Ireland, 15.6%.
Original languageEnglish
Pages (from-to)76-91
Number of pages16
JournalRemote Sensing of Environment
Volume77
Issue number1
DOIs
Publication statusPublished - 2001
MoE publication typeA1 Journal article-refereed

Fingerprint

Advanced very high resolution radiometers (AVHRR)
radiometers
AVHRR
Pixels
Statistics
Probability density function
pixel
Satellites
Spectral density
forest statistics
statistics
Sampling
Economics
United Nations
methodology
ground cover
land cover
Ireland
Food and Agricultural Organization
European Commission

Cite this

Häme, Tuomas ; Stenberg, Pauline ; Andersson, Kaj ; Rauste, Yrjö ; Kennedy, Pamela ; Folving, Sten ; Sarkeala, Janne. / AVHRR-based forest proportion map of the Pan-European area. In: Remote Sensing of Environment. 2001 ; Vol. 77, No. 1. pp. 76-91.
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abstract = "A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors (reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the European Commission's Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12 countries of the European Union was underestimated by 1.8{\%} compared to the CORINE data. It was underestimated by 4.2{\%} when compared with EUROSTAT's statistics and 6.0{\%} when compared to United Nations Economic Commission for Europe/Food and Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to CORINE) was in France (5.9{\%}). The largest overestimation was found in Ireland, 15.6{\%}.",
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Häme, T, Stenberg, P, Andersson, K, Rauste, Y, Kennedy, P, Folving, S & Sarkeala, J 2001, 'AVHRR-based forest proportion map of the Pan-European area', Remote Sensing of Environment, vol. 77, no. 1, pp. 76-91. https://doi.org/10.1016/S0034-4257(01)00195-X

AVHRR-based forest proportion map of the Pan-European area. / Häme, Tuomas (Corresponding Author); Stenberg, Pauline; Andersson, Kaj; Rauste, Yrjö; Kennedy, Pamela; Folving, Sten; Sarkeala, Janne.

In: Remote Sensing of Environment, Vol. 77, No. 1, 2001, p. 76-91.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Häme, Tuomas

AU - Stenberg, Pauline

AU - Andersson, Kaj

AU - Rauste, Yrjö

AU - Kennedy, Pamela

AU - Folving, Sten

AU - Sarkeala, Janne

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N2 - A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors (reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the European Commission's Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12 countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when compared with EUROSTAT's statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to CORINE) was in France (5.9%). The largest overestimation was found in Ireland, 15.6%.

AB - A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors (reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the European Commission's Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12 countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when compared with EUROSTAT's statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to CORINE) was in France (5.9%). The largest overestimation was found in Ireland, 15.6%.

U2 - 10.1016/S0034-4257(01)00195-X

DO - 10.1016/S0034-4257(01)00195-X

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