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Manuscript ID : JEST-1911-4887 (R1) Visit : 111 Page: 17 - 30

10.22034/jest.2020.48928.4887

Article Type: Original Research

Estimating the Spatial Distribution of Above-ground Carbon of Zagros Forests using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 imagery

Subject Areas : GIS

somayeh izadi 1 , Hormoz Sohrabi 2

1 - PhD Graduated, Faculty of Natural Rresources and Marine Sciences, Tarbiat Modares University, Tehran, Iran.
2 - Associate Professor, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran, Iran. *(Correspondence Author)

Received: 2019-11-13 Accepted : 2020-01-11 Published : 2022-05-22

Keywords: Spatial variability, Spatial modeling, Spectral data, geostatistics, Spatial heterogeneity,

Abstract :

Background and Objective: Estimating aboveground carbon (AGC) of forest is a fundamental task for sustainable management of forest ecosystems; therefore, there is a critical need for appropriate approaches for quantifying of AGC. The most commonly used approaches for estimating include global regression models that estimate the target variable over a wide range using cost-effective auxiliary data. Traditional regression models with fixed regression coefficients at all locations do not consider heterogeneity and spatial structure in modeling. The objective of this study is estimating the AGC using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 data and compare methods. Material and Methodology: The study was carried out in part of Zagros Forest, in Kohgiluyeh and Boyer-Ahmad Province. Totally, 184 plots (30×30 meters) surveyed and AGC were calculated by allometric equations. 32 variables were extracted from Landsat 8 as auxiliary data in the modeling process. The assessment of accuracies of methods was evaluated by K-fold cross validation via criteria such as coefficient of variation (R2), root mean square error (RMSE). Findings: The results showed that Geographically Weighted Regression Kriging (R 2 = 0.66, RMSE= 21) had a better performance compared to Regression Kriging. Discussion and Conclusion: Hybrid methods with heterogeneity and spatial correlation can be a good alternative to early regression methods for estimating aboveground carbon (AGC).

References:


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  29. Yang, S.H., Liu, F., Song, X.D., Lu, Y.Y., Li, D.C., Zhao, Y.G., Zhang, G.L., 2019. Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China. Ecological Indicator, Vol. 102, pp. 252–64.
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  30. Kang, D., Dall’erba, S., 2016. Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach, Journal of Geographical Systems, Vol, 18, pp. 125–157.
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_||_

  1. Backéus, S., Wikström, P., Lämås, T., 2005. A model for regional analysis of carbon sequestration and timber production, Forest Ecology and Managenment, Vol. 216, pp. 28–40.
    2.
  2. Azizi, Z., Hosseini, A.,  Iranmanesh , Y., 2015. Estimating Biomass of Single Oak Trees Using Terrestrial Photogrammetry, Journal of Environmental Science and Technology, Vol. 19, pp. 82–93.
    3.
  3. Safari, A., Sohrabi, H., Powell, S., Shataee, S., 2017. A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests, International Journal Remote Sensing, Vol. 38, pp. 6407–32.
    4.
  4. Bishop, T.F.A., Mcbratney, A.B., 2006. A comparison of prediction methods for the creation of field-extent soil property maps, Geoderma, Vol. 103, pp. 149-160.
    5.
  5. Lu, D., 2006. The potential and challenge of remote sensing-based biomass estimation, International Journal of  Remote Sensing, Vol. 27, pp. 1297–328.
    6.
  6. 6. Meng, Q., Cieszewski, C., Madden, M., 2009. Large area forest inventory using Landsat ETM+: A geostatistical approach, ISPRS Journal Photogrammetry and Remote Sensing, Vol. 64, pp. 27–36.
    7.
  7. Viana, H., Aranha, J., Lopes, D., Cohen, W.B., 2011. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data , remotely sensed imagery and spatial prediction models, Ecological Modelling, Vol. 226, pp. 22–35.
    8.
  8. Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., Wang, Ch., Muhammad, Sh., 2015. Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling, International Journal of Applied Earth Observation and Geoinformation, Vol. 41, pp. 88–98.
    9.
  9. Sinha, S., Jeganathan, C., Sharma, L.K., Nathawat, M.S., 2015. A review of radar remote sensing for biomass estimation, International Journal of Environmental Science and Technology, Vol. 12, pp. 1779–92.
    10.
  10. Karl, J.W., 2010. Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing. Rangeland Ecology and Managenment, Vol. 63, pp. 335–49.
    11.
  11. 11. Hengl, T., Heuvelink, G.B.M., Rossiter, D.G., 2007. About regression-kriging: From equations to case studies. Computers & Geoscience, Vol. 33, pp. 1301–15.
    12.
  12. Ahadi, Z., Alavi, S.J., Hoseini, S.M., 2017. Beech forest site productivity mapping using ordinary kriging and IDW (Case study: research forest of Tarbiat Modares University), Iranian Journal of Forest and Wood Product, Vol. 70, pp.  93-102. (In Persian)
    13.
  13. Fayad, I., Baghdadi, N., Bailly, J., Barbier, N., Gond, V., Hajj, M. E.l., 2016. Supplementary Materials : Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data : Application on French Guiana, Remote Sensing, Vol. 8, pp. 1–5.
    14.
  14. Wu, C., Shen, H., Shen, A., Deng, J., Gan, M., Zhu, J., Xu, H, Wang, K., 2016. Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery. Journal of Applied Remote Sensing, Vol. 10, pp. 035010-17.
    15.
  15. Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., Li, D., 2018. Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region, Remote Sensing, Vol. 10, pp. 1-22.
    16.
  16. Kumar, S., Lal, R., Liu, D., 2012. A geographically weighted regression kriging approach for mapping soil organic carbon stock, Geoderma, Vol. 189, pp. 627–34.
    17.
  17. Lloyd, C.D., 2010. Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom, Internation Journal of Climatology, Vol. 30, pp. 390–405.
    18.
  18. Propastin, P., 2010. Multiscale analysis of the relationship between topography and aboveground biomass in the tropical rainforests of Sulawesi, Indonesia, International Journal of Geographical Information Science, Vol. 25, pp. 455-472.
    19.
  19. Propastin, P., 2012. Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, Vol. 18, pp. 82–90.
    20.
  20. Van der Laan, C., Verweij, P.A., Quiñones, M.J., Faaij, A.P.C., 2014. Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo, Carbon Balance and Management, Vol. 9, pp. 2-12.
    21.
  21. Chen, L., Ren, C., Zhang, B., Wang, Z., Xi, Y., 2018. Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery, Forests, Vol. 9, pp. 1–20.
    22.
  22. Brunsdon, C., Fotheringham, A.S., Charlton, M.E., 1996. Geographically Weighted Regression, Geographical Analysis, Vol. 28, pp. 281-298.
    23.
  23. Kupfer, J.A., Farris, C.A. 2007. Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models, Landsc Ecology, Vol. 22, pp. 837–52.
    24.
  24. Harris, P., Fotheringham, A.S., Crespo, R., Charlton, M., 2010. The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets, Math Geoscience, Vol, 42, pp. 657–80.
    25.
  25. Wang, K., Zhang, C., Li, W., 2012. Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter, GIScience Remote Sensing, Vol. 49, pp. 915–32.
    26.
  26. Liu, Y., Guo, L., Jiang, Q., Zhang, H., Chen, Y., 2015. Comparing geospatial techniques to predict SOC stocks, Soil & Tillage Research, Vol. 148, pp. 46–58.
    27.
  27. Sohrabi, H., Shirvani, A., 2012. Allometric equations for estimating standing biomass of Atlantic Pistache (Pistacia atlantica var. mutica) in khojir National Park. Iranian Journal of Forest, Vol. 4, pp. 55-64. (In Persian)
    28.
  28. Iranmanesh, Y., Sagheb Talebi, Kh., Sohrabi, H., Jalali, S.GH., Hosseini, S.M., 2014. Biomass and carbon Stocks of Brants oak (Quercus brantii Lindl.) in two vegetation forms in Lordegan, Chaharnahal & Bakhtiari Forests. Iranian Journal of Forest and Research, Vol. 22, pp. 762-749. (In Persian)
    29.
  29. Yang, S.H., Liu, F., Song, X.D., Lu, Y.Y., Li, D.C., Zhao, Y.G., Zhang, G.L., 2019. Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China. Ecological Indicator, Vol. 102, pp. 252–64.
    30.
  30. Kang, D., Dall’erba, S., 2016. Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach, Journal of Geographical Systems, Vol, 18, pp. 125–157.
    31.
  31. Mishra, U., 2010. Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale, Soil & Water management & Conservation, Vol. 74, pp. 906-914.
    32.

 

 

 

 

 

 

 

  1. Backéus, S., Wikström, P., Lämås, T., 2005. A model for regional analysis of carbon sequestration and timber production, Forest Ecology and Managenment, Vol. 216, pp. 28–40.
    2.
  2. Azizi, Z., Hosseini, A.,  Iranmanesh , Y., 2015. Estimating Biomass of Single Oak Trees Using Terrestrial Photogrammetry, Journal of Environmental Science and Technology, Vol. 19, pp. 82–93.
    3.
  3. Safari, A., Sohrabi, H., Powell, S., Shataee, S., 2017. A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests, International Journal Remote Sensing, Vol. 38, pp. 6407–32.
    4.
  4. Bishop, T.F.A., Mcbratney, A.B., 2006. A comparison of prediction methods for the creation of field-extent soil property maps, Geoderma, Vol. 103, pp. 149-160.
    5.
  5. Lu, D., 2006. The potential and challenge of remote sensing-based biomass estimation, International Journal of  Remote Sensing, Vol. 27, pp. 1297–328.
    6.
  6. 6. Meng, Q., Cieszewski, C., Madden, M., 2009. Large area forest inventory using Landsat ETM+: A geostatistical approach, ISPRS Journal Photogrammetry and Remote Sensing, Vol. 64, pp. 27–36.
    7.
  7. Viana, H., Aranha, J., Lopes, D., Cohen, W.B., 2011. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data , remotely sensed imagery and spatial prediction models, Ecological Modelling, Vol. 226, pp. 22–35.
    8.
  8. Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., Wang, Ch., Muhammad, Sh., 2015. Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling, International Journal of Applied Earth Observation and Geoinformation, Vol. 41, pp. 88–98.
    9.
  9. Sinha, S., Jeganathan, C., Sharma, L.K., Nathawat, M.S., 2015. A review of radar remote sensing for biomass estimation, International Journal of Environmental Science and Technology, Vol. 12, pp. 1779–92.
    10.
  10. Karl, J.W., 2010. Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing. Rangeland Ecology and Managenment, Vol. 63, pp. 335–49.
    11.
  11. 11. Hengl, T., Heuvelink, G.B.M., Rossiter, D.G., 2007. About regression-kriging: From equations to case studies. Computers & Geoscience, Vol. 33, pp. 1301–15.
    12.
  12. Ahadi, Z., Alavi, S.J., Hoseini, S.M., 2017. Beech forest site productivity mapping using ordinary kriging and IDW (Case study: research forest of Tarbiat Modares University), Iranian Journal of Forest and Wood Product, Vol. 70, pp.  93-102. (In Persian)
    13.
  13. Fayad, I., Baghdadi, N., Bailly, J., Barbier, N., Gond, V., Hajj, M. E.l., 2016. Supplementary Materials : Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data : Application on French Guiana, Remote Sensing, Vol. 8, pp. 1–5.
    14.
  14. Wu, C., Shen, H., Shen, A., Deng, J., Gan, M., Zhu, J., Xu, H, Wang, K., 2016. Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery. Journal of Applied Remote Sensing, Vol. 10, pp. 035010-17.
    15.
  15. Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., Li, D., 2018. Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region, Remote Sensing, Vol. 10, pp. 1-22.
    16.
  16. Kumar, S., Lal, R., Liu, D., 2012. A geographically weighted regression kriging approach for mapping soil organic carbon stock, Geoderma, Vol. 189, pp. 627–34.
    17.
  17. Lloyd, C.D., 2010. Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom, Internation Journal of Climatology, Vol. 30, pp. 390–405.
    18.
  18. Propastin, P., 2010. Multiscale analysis of the relationship between topography and aboveground biomass in the tropical rainforests of Sulawesi, Indonesia, International Journal of Geographical Information Science, Vol. 25, pp. 455-472.
    19.
  19. Propastin, P., 2012. Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, Vol. 18, pp. 82–90.
    20.
  20. Van der Laan, C., Verweij, P.A., Quiñones, M.J., Faaij, A.P.C., 2014. Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo, Carbon Balance and Management, Vol. 9, pp. 2-12.
    21.
  21. Chen, L., Ren, C., Zhang, B., Wang, Z., Xi, Y., 2018. Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery, Forests, Vol. 9, pp. 1–20.
    22.
  22. Brunsdon, C., Fotheringham, A.S., Charlton, M.E., 1996. Geographically Weighted Regression, Geographical Analysis, Vol. 28, pp. 281-298.
    23.
  23. Kupfer, J.A., Farris, C.A. 2007. Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models, Landsc Ecology, Vol. 22, pp. 837–52.
    24.
  24. Harris, P., Fotheringham, A.S., Crespo, R., Charlton, M., 2010. The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets, Math Geoscience, Vol, 42, pp. 657–80.
    25.
  25. Wang, K., Zhang, C., Li, W., 2012. Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter, GIScience Remote Sensing, Vol. 49, pp. 915–32.
    26.
  26. Liu, Y., Guo, L., Jiang, Q., Zhang, H., Chen, Y., 2015. Comparing geospatial techniques to predict SOC stocks, Soil & Tillage Research, Vol. 148, pp. 46–58.
    27.
  27. Sohrabi, H., Shirvani, A., 2012. Allometric equations for estimating standing biomass of Atlantic Pistache (Pistacia atlantica var. mutica) in khojir National Park. Iranian Journal of Forest, Vol. 4, pp. 55-64. (In Persian)
    28.
  28. Iranmanesh, Y., Sagheb Talebi, Kh., Sohrabi, H., Jalali, S.GH., Hosseini, S.M., 2014. Biomass and carbon Stocks of Brants oak (Quercus brantii Lindl.) in two vegetation forms in Lordegan, Chaharnahal & Bakhtiari Forests. Iranian Journal of Forest and Research, Vol. 22, pp. 762-749. (In Persian)
    29.
  29. Yang, S.H., Liu, F., Song, X.D., Lu, Y.Y., Li, D.C., Zhao, Y.G., Zhang, G.L., 2019. Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China. Ecological Indicator, Vol. 102, pp. 252–64.
    30.
  30. Kang, D., Dall’erba, S., 2016. Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach, Journal of Geographical Systems, Vol, 18, pp. 125–157.
    31.
  31. Mishra, U., 2010. Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale, Soil & Water management & Conservation, Vol. 74, pp. 906-914.
    32.

 

 

 

 

 

 

 

 

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