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Düzlerçamı Kızılçam Ormanında (Antalya) Toprak Üstü Orman Biyokütlesinin Modellenmesi

Year 2017, Volume: 26 Issue: 2, 151 - 161, 26.12.2017

Doğukan Doğu Yavaşlı M. Kirami Ölgen

Abstract

Estimation of forest biomass is
needed for monitoring the changes in carbon stocks as well as other purposes.
This study reports on a test of the ability to estimate above ground biomass of
Calabrian pine forests of Düzlerçamı, Antalya, Turkey using Landsat and
ICESat/GLAS data. The field data has been collected in 2017 and plot-level
estimates were calculated using the allometric equations. GLAS parameters and
various Landsat vegetation indices were modeled using multiple regression
analysis to estimate above ground biomass. In the first model (ModelA) height
of median energy (HOME) and the ratio of HOME to maximum vegetation height
(%HOME) parameter of GLAS showed relation with field based estimates of above
ground biomass with a coefficient of determination (R2) of 0.87. Above ground
biomass derived from ModelA and the variables obtained from Landsat indices has
been used at the second model (ModelB) had a R2 of 0.52 meaning the GLAS data
is poorly correlated with Landsat at the study area. A better statistical
relationship has been found with Landsat data and AGB with a R2 of 0.91 in
ModelC that uses Landsat pixel values of each bands and pixel values of the
indices are used as independent variable to explain above ground biomass. The
results demonstrate a current potential for above ground biomass estimation
of forests using optical sensor data and satellite lidar where airborne
lidar data is not widely available.

Keywords

Above ground biomass forest ICESat/GLAS Landsat

References

  • Baccini A., Friedl M.A., Woodcock C.E., Warbington R., 2004. Forest biomass estimation over regional scales using multisource data. Geophysical Research Letters, 31, L10501, doi: 10.1029.
  • Baccini A., Laporte N., Goetz S. J., Sun M., Dong H., 2008. A First Map of Tropical Africa’s Above-ground Biomass Derived from Satellite Imagery, Environmental Research Letters, 3(4).
  • Birth, G. S., & McVey, G. R., 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640-643.
  • Brown L., Chen J. M., Leblanc S. G., Cihlar J., 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis. Remote sensing of environment, 71(1), 16-25.
  • Brown, S., 1997. Estimating biomass and biomass change of tropical forests: a primer (Vol. 134). Food & Agriculture Org..
  • Chambers, J. Q., Higuchi N., Teixeira L. M., Santos J. D., Laurance S. G., Trumbore S. E., 2004. Response of tree biomass and wood litter to disturbance in a Central Amazon forest, Oecologia, 141, 596 – 614.
  • Chave, J., Réjou‐Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B., ... & Henry, M. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global change biology, 20(10), 3177-3190.
  • Crist E. P., Cicone R,. 1984. Application of the Tasseled Cap Concept to Simulated Thematic Mapper Data, Photogrammetric Engineering and Remote Sensing,50, 343-352.
  • Dong J., Kaufmann R.K., Myneni R.B., Tucker C.J., Kauppi P.E., Liski J., Buermann W., Alexeyev V., Hughes M.K., 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84, 393–410.
  • Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Prince, S., 2002. Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment. 79 (2), 305–319.
  • Foody G. M., Boyd D. S. Cutler M. E. J., 2003. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85(4), 463-474.
  • Huete A. R., Liu H., Batchily K., van Leeuwen W., 1997. A Comparison of Vegetation Indices Over a Global Set of TM Images for EOS-MODIS. Remote Sensing of Environment, 59 (3), 440-451.
  • Huete A.R, 1998., A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25 (3), 295-309.
  • Kaufman Y. J., Tanre D., 1996. Strategy for Direct and Indirect Methods for Correcting the Aerosol Effect on Remote Sensing: from AVHRR to EOSMODIS. Remote Sensing of Environment. 55, 65-79.
  • Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Espirito-Santo, D.B., F, Hunter, M.O., de Oliveira, R., Jr., 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32.
  • Lu D., Mausel P., Brondizio E., Moran E., 2002. Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin. Symposium on Geospatial Theory, Processing and Applications. Lu D., Mausel P., Brondizio E., Moran E., 2004. Relationships Between Forest Stand Parameters and Landsat TM Spectral Responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198 (1-3), 149–167.
  • Pinty B., Verstraete M. M., 1992. GEMI: a Non-linear Index to Monitor Global Vegetation from Satellites, Plant Ecology, 101 (1), 15-20.
  • Popescu, S. C., 2007. Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655.
  • Qi J., Chehbouni A., Huete A. R., Kerr Y. H., Sorooshian S., 1994a. A Modified Soil Adjusted Vegetation Index, Remote Sensing of Environment, 48 (2), 119-126.
  • Qi J., Kerr Y., Chehbouni A., 1994b. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 723-730.
  • Rosette, J.A.B., North, P.R.J., Suarez, J.C., 2008. Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. International Journal of Remote Sensing. 29 (5), 1475–1493.
  • Saatchi S. S., Houghton R. A., Dos Santos Alvalá R. C., Soares J. V., Yu Y., 2007. Distribution of Aboveground Live Biomass in the Amazon Basin, Global Change Biology, 13 (4), 816–37.
  • Saatchi S. S., Moghaddam M., 1995. Biomass of Boreal Forest Using Polarimetric SAR Imagery. Geoscience and Remote Sensing, IEEE Transactions, 38(2), 697-709.
  • Steininger M. K., 2000. Satellite estimation of tropical secondary forest aboveground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing, 21, 1139–1157.
  • Sun O., Uğurlu S., Özer E., 1980. Kızılçam (Pinus brutia Ten.) türüne ait biyolojik kütlenin saptanması. Ormancılık Araştırma Enstitüsü Teknik Bülteni, Teknik Bülten Serisi No: 104.
  • Tucker C. J., 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8, 127-150.
  • Yavaşlı, D. D., 2016. Estimation of above ground forest biomass at Muğla using ICESat/GLAS and Landsat data. Remote Sensing Applications: Society and Environment, 4, 211-218.
  • Zolkos, S.G., Goetz, S.J., Dubayah, R., 2013. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment. 128, 289–298.

MODELING ABOVE GROUND BIOMASS IN CALABRIAN PINE FORESTS OF DÜZLERÇAMI (ANTALYA)

Year 2017, Volume: 26 Issue: 2, 151 - 161, 26.12.2017

Doğukan Doğu Yavaşlı M. Kirami Ölgen

Abstract

Karbon stoklarındaki değişimlerin
izlenmesi ile birlikte çeşitli diğer amaçlar için orman biyokütlesinin
belirlenmesine ihtiyaç duyulmaktadır. Bu çalışma Düzlerçamı kızılçam ormanında
(Antalya) toprak üstü orman biyokütlesinin Landsat ve ICESat/GLAS verileri
kullanarak belirlenebilmesini test etmektedir. 2017 yılında arazi çalışmaları
ile örneklem alanlarından toplanan veriler ile allometrik eşitlikler
kullanılarak gerçek biyokütle verileri hesaplanmıştır. Toprak üstü orman
biyokütlesinin modellenmesinde çok değişkenli regresyon analizi ile GLAS
parametreleri ve çeşitli Landsat vejetasyon indekslerinden yararlanılmıştır.
Birinci modelde (ModelA) GLAS verisinden medyan enerjinin yüksekliği (HOME) ve
HOME’un maksimum yüksekliğe oranı parametrelerinin araziden toplanan biyokütle
verileri ile olan ilişkisinde determinasyon katsayısı (R2) 0.87 olarak tespit
edilmiştir. ModelA’dan elde edilen toprak üstü orman biyokütlesi ile çeşitli
Landsat indekslerinin kullanıldığı ikinci modelde (ModelB) 0.52 bulunan R2
değeri GLAS verisinin çalışma alanında Landsat veriler ile zayıf bir
korelasyonu bulunduğunu göstermiştir. Toprak üstü orman biyokütlesini açıklamak
için Landsat indeks değerlerinin bağımsız değişken olarak kullanıldığı ModelC’de
ise 0.91 R2 ile istatistiksel olarak daha anlamlı bir istatistiksel ilişki
belirlenmiştir. Sonuçlar toprak üstü orman biyokütlesinin belirlenmesinde hava
lidar verilerinin bulunmadığı durumlarda optik sensörlerin ve uydu tabanlı
lidar verilerin güncel potansiyelini göstermektedir.

Keywords

Toprak üstü biyokütle orman ICESat/GLAS Landsat

References

  • Baccini A., Friedl M.A., Woodcock C.E., Warbington R., 2004. Forest biomass estimation over regional scales using multisource data. Geophysical Research Letters, 31, L10501, doi: 10.1029.
  • Baccini A., Laporte N., Goetz S. J., Sun M., Dong H., 2008. A First Map of Tropical Africa’s Above-ground Biomass Derived from Satellite Imagery, Environmental Research Letters, 3(4).
  • Birth, G. S., & McVey, G. R., 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640-643.
  • Brown L., Chen J. M., Leblanc S. G., Cihlar J., 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis. Remote sensing of environment, 71(1), 16-25.
  • Brown, S., 1997. Estimating biomass and biomass change of tropical forests: a primer (Vol. 134). Food & Agriculture Org..
  • Chambers, J. Q., Higuchi N., Teixeira L. M., Santos J. D., Laurance S. G., Trumbore S. E., 2004. Response of tree biomass and wood litter to disturbance in a Central Amazon forest, Oecologia, 141, 596 – 614.
  • Chave, J., Réjou‐Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B., ... & Henry, M. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global change biology, 20(10), 3177-3190.
  • Crist E. P., Cicone R,. 1984. Application of the Tasseled Cap Concept to Simulated Thematic Mapper Data, Photogrammetric Engineering and Remote Sensing,50, 343-352.
  • Dong J., Kaufmann R.K., Myneni R.B., Tucker C.J., Kauppi P.E., Liski J., Buermann W., Alexeyev V., Hughes M.K., 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84, 393–410.
  • Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Prince, S., 2002. Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment. 79 (2), 305–319.
  • Foody G. M., Boyd D. S. Cutler M. E. J., 2003. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85(4), 463-474.
  • Huete A. R., Liu H., Batchily K., van Leeuwen W., 1997. A Comparison of Vegetation Indices Over a Global Set of TM Images for EOS-MODIS. Remote Sensing of Environment, 59 (3), 440-451.
  • Huete A.R, 1998., A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25 (3), 295-309.
  • Kaufman Y. J., Tanre D., 1996. Strategy for Direct and Indirect Methods for Correcting the Aerosol Effect on Remote Sensing: from AVHRR to EOSMODIS. Remote Sensing of Environment. 55, 65-79.
  • Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Espirito-Santo, D.B., F, Hunter, M.O., de Oliveira, R., Jr., 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32.
  • Lu D., Mausel P., Brondizio E., Moran E., 2002. Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin. Symposium on Geospatial Theory, Processing and Applications. Lu D., Mausel P., Brondizio E., Moran E., 2004. Relationships Between Forest Stand Parameters and Landsat TM Spectral Responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198 (1-3), 149–167.
  • Pinty B., Verstraete M. M., 1992. GEMI: a Non-linear Index to Monitor Global Vegetation from Satellites, Plant Ecology, 101 (1), 15-20.
  • Popescu, S. C., 2007. Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655.
  • Qi J., Chehbouni A., Huete A. R., Kerr Y. H., Sorooshian S., 1994a. A Modified Soil Adjusted Vegetation Index, Remote Sensing of Environment, 48 (2), 119-126.
  • Qi J., Kerr Y., Chehbouni A., 1994b. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 723-730.
  • Rosette, J.A.B., North, P.R.J., Suarez, J.C., 2008. Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. International Journal of Remote Sensing. 29 (5), 1475–1493.
  • Saatchi S. S., Houghton R. A., Dos Santos Alvalá R. C., Soares J. V., Yu Y., 2007. Distribution of Aboveground Live Biomass in the Amazon Basin, Global Change Biology, 13 (4), 816–37.
  • Saatchi S. S., Moghaddam M., 1995. Biomass of Boreal Forest Using Polarimetric SAR Imagery. Geoscience and Remote Sensing, IEEE Transactions, 38(2), 697-709.
  • Steininger M. K., 2000. Satellite estimation of tropical secondary forest aboveground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing, 21, 1139–1157.
  • Sun O., Uğurlu S., Özer E., 1980. Kızılçam (Pinus brutia Ten.) türüne ait biyolojik kütlenin saptanması. Ormancılık Araştırma Enstitüsü Teknik Bülteni, Teknik Bülten Serisi No: 104.
  • Tucker C. J., 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8, 127-150.
  • Yavaşlı, D. D., 2016. Estimation of above ground forest biomass at Muğla using ICESat/GLAS and Landsat data. Remote Sensing Applications: Society and Environment, 4, 211-218.
  • Zolkos, S.G., Goetz, S.J., Dubayah, R., 2013. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment. 128, 289–298.
There are 28 citations in total.

Details

Journal Section Articles
Authors

Doğukan Doğu Yavaşlı

M. Kirami Ölgen

Publication Date December 26, 2017
Submission Date November 1, 2017
Acceptance Date December 20, 2017
Published in Issue Year 2017 Volume: 26 Issue: 2

Cite

APA Yavaşlı, D. D., & Ölgen, M. K. (2017). MODELING ABOVE GROUND BIOMASS IN CALABRIAN PINE FORESTS OF DÜZLERÇAMI (ANTALYA). Ege Coğrafya Dergisi, 26(2), 151-161.
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