Using the bootstrap approach for comparing statistical modeling methods to estimate remotely-sensed aboveground biomass in Zagros forests
Subject Areas : Agriculture, rangeland, watershed and forestry
Amir Safari
1
,
Hormoz Sohrabi
2
1 - PhD of Forestry, Faculty of Natural Resources and Marine Sciences, University of Tarbiat Modares, Thehran, Iran
2 - Associate Professor, Department of Forestry, Faculty of Natural Resources and Marine Sciences, University of Tarbiat Modares, Iran
Keywords: LANDSAT 8, Aboveground biomass, Zagros forest, Bootstrap, Statistical modeling,
Abstract :
Background and ObjectiveConsidering the increasing importance of forest ecosystems in climate change mitigation projects, reliable and cost-effective methods are required to estimate the aboveground biomass (AGB). Common methods used to estimate the aboveground biomass (AGB) include in-situ measurement, the biomass calculation using aalometric equations and using remote sensing techniques. Remote sensing has been widely used to estimate the biomass of forests in recent decades.The used statistical modeling method is one of the most important factors to use remotely-sensed data for estimation of the aboveground biomass. A large number of researches have been carried out about using the modeling methods. However, these studies face the following different challenges: 1) no modeling method has been recommended as the best method 2) the performence of these modeling methods is affected by forest type, the forest structure, and the present disturbance intensity 3) the performance evaluation and the comparion of the results of these methods were done by using goodness-of-fit test and cross-validation methods. The purpose of this study is to considering the role of choosing statistical modeling methods to estimate remotely-sensed aboveground biomass, the current study was conducted to investigate nine statistical modeling methods including linear regression (LR), generalized additive model (GAM), random forest (RF), support vector machine (SVM), boosted regression tree (BRT), k-nearest neighbor (kNN), cubist regression (CR), Gaussian process model (GPR), multivariate adaptive regression spline (MARS) using bootstrap process and 1000-repeated 10-fold cross-validation approach to estimate the aboveground biomass of Zagros forests using Landsat 8 images. Materials and Methods The cuurent study was conducted in Kermanshah forests which is mostly dominated by oak species trees (Quercus spp.) and is located in western Iran on the Zagros Mountains. Zagros forests are generally sparse and open and comprise approximately 20% of Iran’s area and 40% forest regions of Iran. In order to conduct this study, two forest regions with different levels of human disturbances were chosen; SarfiruzAbad region with highly degraded (HD) forests, and Gahvareh forest region with minor degradation (MD). Geographical coordinates of SarfiruzAbad and Gahvareh regions are 33º57′-34º04′N / 47º03′-47º17′E & 34º21′- 34º24′N / 46º16′-46º23′ E respectively. The Leaf area index (LAI) map derived from the Landsat images based on a global model was used to collect field-based sample plots in both regions of the study. Both regions were divided into three low, moderate and high Leaf area index (LAI) strata, and the locations of the sample plots were located by using a systematic inventory at the intersections of a 200m×200 m grid in each stratum. 124 georeferenced square plots of field-based sample plots (63 plots in Gahvareh region and 61 plots in SarfiruzAbad region) with 30m×30m dimensions the same size as a Landsat 8 image’s pixel were collected. Allometric equation developed for oak tree in Zagros forests was used to calculate the amount of the aboveground biomass of each individual tree or sprout-clump. The allometric equation used in this study uses two vertical tree crown diameters to estimate the amount of the biomass of each individual tree or sprout-clump. The sum of the amount of the biomass of each individual tree in sample plot was used to calculate the amount of the biomass plot in sample plot level at a ton per hectare. Our study regions were located in a frame of Landsat 8 images (path/row:167/36). A cloud-free Landsat image relating to 19th Mordad 1394 (10th August 2015) relating to the time when the tree canopies are completely closed and near to the date of land inventory was downloaded from earthexplorer.usgs.gov site. Based on the previous studies, the pre-processing of the used image comprising the radiometric and topographic corrections was done.using C method. To estimate the aboveground biomass in the study areas by using remote sensing, 38 spectral variables including band values, simple band ratios, vegetation indices and common linear transformations like tasseled cap and principle component analysis were extracted from the used Landsat 8 image. Generally, the efficiency of nine different statistical modeling methods including parametric methods (Linear Regression, LR), semiparametric (Generalized Additive Model, GAM), and nonparametric Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Boosted regression trees (BRT), multivariate additive regression splines, cubist regression (CR), and Gaussian processes regression/model) were compared in order to estimate aboveground biomass. To assess the models, two common quality statistics: (i) determination coefficient and (2) root mean square error via 10 fold cross validation repeated 1000 times approach were calculated. This number of repeats helps to ensure an acceptable assessment of robustness of the results. Results and Discussion The measuredstatistical characteristics of the field sample plots showed that the mean aboveground biomass of SarfiruzAbad and Gahvareh regions were 12.6 ton/ha and 20.5 ton/ha respectively. ANOVA indicated significant differences between modelling methods (treatment effect: p< 0.001) for both R2 and RMSPE calculated in 1000-time repeats using 10-fold cross- validation.The Cubist modeling method with the mean determination coefficient of 0.61 outperformed other methods in SarfiruzAbad region.These resultsfor Gahvareh region showed better efficiency of linear regression (LR), generalized additive model (GAM), and k-nearest neighbor (KNN) with the mean determination coeffieient of 0.87.The multiple comparisons of different models by using Tukey test concerning RMSE showed that in SarfiruzAbad region, cubist method with the mean of RMSE 3.3 ton/ha and kNN and RF methods with the mean of RMSE 5.8 ton/ha had a significant difference in comparison to the other methods. Totally, the results of the research revealed the suitable efficiency of Landsat 8 image for AGB estimation in Zagros forests. The acceptable results are due to the low AGB in our study regions that did not reached the saturation point as one of challenges of using optical images like Landsat. The other results of this research is the assessment of the effiecieny of modeling method in order to increase the accuracy of the estimation of remotely-sensed aboveground biomass.Unlike the results of the previous studies, linear regression yielded better results compared to nonparametric methods that can be due to the presence of the linear relationship between aboveground biomass and spectral variables derived from Landsat images. Among the used various spectral variables, red, near infrared, and shortwave infrared 1 and 2 band ratios were selected as the final variable in most modeling methods. Conclusion In this study, we evaluated the effieincy of different statistical modeling methods to estimate AGB in Zagros forests by using Landsat images. The biomass estimations were compared by using nine parametric, semi-parametric, and non-parametric methods and using 1000-repeated 10-fold cross-validation. The results illustrated the acceptable potentiality of Landsat images for cost-efficient AGB estimating in Zagros oak forests. The accuracy of AGB estimation in Gahvareh region with low-degraded forest stands was higher than SarfiruzAbad region with highly degraded stands.
Aghababaie M, Ebrahimi A, Tahmasebi P. 2018. Comparison vegetation indices and tasseled cap transformation for estimates of soil organic carbon using Landsat-8 OLI images in a semi-steppe rangelands. Journal of RS and GIS for Natural Resources, 9(3): 58-59. (In Persian)
Calvao T, Palmeirim J. 2004. Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. International Journal of Remote Sensing, 25(16): 3113-3126. doi:https://doi.org/10.1080/01431160310001654978.
Castillo JAA, Apan AA, Maraseni TN, Salmo SG. 2017. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 134: 70-85. doi:https://doi.org/10.1016/j.isprsjprs.2017.10.016.
Dai L, Jia J, Yu D, Lewis BJ, Zhou L, Zhou W, Zhao W, Jiang L. 2013. Effects of climate change on biomass carbon sequestration in old-growth forest ecosystems on Changbai Mountain in Northeast China. Forest Ecology and Management, 300: 106-116. doi:https://doi.org/10.1016/j.foreco.2012.06.046.
Domingo D, Lamelas MT, Montealegre AL, García-Martín A, De la Riva J. 2018. Estimation of total biomass in aleppo pine forest stands applying parametric and nonparametric methods to low-density airborne laser scanning data. Forests, 9(4): 158. doi:https://doi.org/10.3390/f9040158.
Du H, Cui R, Zhou G, Shi Y, Xu X, Fan W, Lü Y. 2010. The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI. Acta Ecologica Sinica, 30(5): 257-263. doi:https://doi.org/10.1016/j.chnaes.2010.08.005.
Dube T, Mutanga O. 2015. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 36-46. doi:https://doi.org/10.1016/j.isprsjprs.2014.11.001.
Eisfelder C, Kuenzer C, Dech S. 2012. Derivation of biomass information for semi-arid areas using remote-sensing data. International Journal of Remote Sensing, 33(9): 2937-2984. doi:https://doi.org/10.1080/01431161.2011.620034.
Fassnacht FE, Hartig F, Latifi H, Berger C, Hernández J, Corvalán P, Koch B. 2014. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154: 102-114. doi:https://doi.org/10.1016/j.rse.2014.07.028.
Fernández-Manso O, Fernández-Manso A, Quintano C. 2014. Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images. International Journal of Applied Earth Observation and Geoinformation, 31: 45-56. doi:https://doi.org/10.1016/j.jag.2014.03.005.
Fu L, Zhao Y, Xu Z, Wu B. 2015. Spatial and temporal dynamics of forest aboveground carbon stocks in response to climate and environmental changes. Journal of Soils and Sediments, 15(2): 249-259. doi:10.1007/s11368-014-1050-x.
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, 10(4): 627. doi:https://doi.org/10.3390/rs10040627.
Gasparri NI, Parmuchi MG, Bono J, Karszenbaum H, Montenegro CL. 2010. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments, 74(10): 1262-1270. doi:https://doi.org/10.1016/j.jaridenv.2010.04.007.
Gizachew B, Solberg S, Næsset E, Gobakken T, Bollandsås OM, Breidenbach J, Zahabu E, Mauya EW. 2016. Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data. Carbon balance and management, 11(1): 13. doi:https://doi.org/10.1186/s13021-016-0055-8.
Görgens EB, Montaghi A, Rodriguez LCE. 2015. A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics. Computers and Electronics in Agriculture, 116: 221-227. doi:https://doi.org/10.1016/j.compag.2015.07.004.
Huffman T, Liu J, McGovern M, McConkey B, Martin T. 2015. Carbon stock and change from woody biomass on Canada’s cropland between 1990 and 2000. Agriculture, Ecosystems & Environment, 205: 102-111. doi:https://doi.org/10.1016/j.agee.2014.10.009.
Iranmanesh Y. 2013. Assessment on biomass estimation methods and carbon sequestration of Quercus brantii Lindl. in Chaharmahal & Bakhtiari Forests. PhD Thesis, Tarbiat Modares University, 106 pp, (In Persian)
Karlson M, Ostwald M, Reese H, Sanou J, Tankoano B, Mattsson E. 2015. Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sensing, 7(8): 10017-10041. doi:https://doi.org/10.3390/rs70810017.
Krahwinkler P, Rossman J. 2011. Using decision tree based multiclass support vector machines for forest mapping. In: Lena Halounová EE (ed) IEEE Int. Geoscience and Remote Sensing Symp., Vancouver, Canada. pp 307-318.
Kwak D-A, Lee W-K, Cho H-K, Lee S-H, Son Y, Kafatos M, Kim S-R. 2010. Estimating stem volume and biomass of Pinus koraiensis using LiDAR data. Journal of Plant Research, 123(4): 421-432. doi:10.1007/s10265-010-0310-0.
Latifi H, Fassnacht F, Koch B. 2012. Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sensing of Environment, 121: 10-25. doi:https://doi.org/10.1016/j.rse.2012.01.015.
McRoberts RE, Magnussen S, Tomppo EO, Chirici G. 2011. Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data. Remote Sensing of Environment, 115(12): 3165-3174. doi:https://doi.org/10.1016/j.rse.2011.07.002.
Nolè A, Law B, Magnani F, Matteucci G, Ferrara A, Ripullone F, Borghetti M. 2009. Application of the 3-PGS model to assess carbon accumulation in forest ecosystems at a regional level. Canadian Journal of Forest Research, 39(9): 1647-1661. doi:https://doi.org/10.1139/X09-077.
Noorian N, Shataee S, Mohamadi J. 2019. Evaluation of RapidEye satellite data for estimation some quantitative structure variables in the Caspian forests of Gorgan region. Journal of RS and GIS for Natural Resources, 9(4): 1-16. (In Persian)
Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG. 2011. A large and persistent carbon sink in the world’s forests. Science, 333(6045): 988-993. doi:https://doi.org/10.1126/science.1201609.
Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114(5): 1053-1068. doi:https://doi.org/10.1016/j.rse.2009.12.018.
Rajashekar G, Fararoda R, Reddy RS, Jha CS, Ganeshaiah KN, Singh JS, Dadhwal VK. 2018. Spatial distribution of forest biomass carbon (Above and below ground) in Indian forests. Ecological Indicators, 85: 742-752. doi:https://doi.org/10.1016/j.ecolind.2017.11.024.
Riaño D, Chuvieco E, Salas J, Aguado I. 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003). IEEE Transactions on Geoscience and Remote Sensing, 41(5): 1056-1061. doi:https://doi.org/10.1109/TGRS.2003.811693.
Safari A, Sohrabi H. 2019. The effect of digital preprocessing and modeling method on an estimation of aboveground carbon stock of Zagros forests using Landsat 8 imagery. Journal of RS and GIS for Natural Resources, 9(4): 73-89. (In Persian)
Safari A, Sohrabi H, Powell SL. 2018. Comparison of satellite-based estimates of aboveground biomass in coppice oak forests using parametric, semiparametric, and nonparametric modeling methods. Journal of Applied Remote Sensing, 12(4): 046026. doi:https://doi.org/10.1117/1.JRS.12.046026.
Sagheb-Talebi K, Pourhashemi M, Sajedi T. 2014. Forests of Iran. The Netherlands: Springer Netherlands, 152 pp. https://doi.org/10.1007/978-94-007-7371-4.
Sarker LR, Nichol JE. 2011. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 115(4): 968-977. doi:https://doi.org/10.1016/j.rse.2010.11.010.
Shao Z, Zhang L. 2016. Estimating forest aboveground biomass by combining optical and SAR data: A case study in Genhe, Inner Mongolia, China. Sensors, 16(6): 834. doi:https://doi.org/10.3390/s16060834.
Wang X, Shao G, Chen H, Lewis BJ, Qi G, Yu D, Zhou L, Dai L. 2013. An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China. Environmental Management, 52(3): 612-620. doi:10.1007/s00267-013-0089-6.
Zhao K, Suarez JC, Garcia M, Hu T, Wang C, Londo A. 2018. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, 204: 883-897. doi:https://doi.org/10.1016/j.rse.2017.09.007.
Zhao P, Lu D, Wang G, Wu C, Huang Y, Yu S. 2016. Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sensing, 8(6): 469. doi:https://doi.org/10.3390/rs8060469.
Zhu X, Liu D. 2015. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 222-231. doi:https://doi.org/10.1016/j.isprsjprs.2014.08.014.
Aghababaie M, Ebrahimi A, Tahmasebi P. 2018. Comparison vegetation indices and tasseled cap transformation for estimates of soil organic carbon using Landsat-8 OLI images in a semi-steppe rangelands. Journal of RS and GIS for Natural Resources, 9(3): 58-59. (In Persian)
Calvao T, Palmeirim J. 2004. Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. International Journal of Remote Sensing, 25(16): 3113-3126. doi:https://doi.org/10.1080/01431160310001654978.
Castillo JAA, Apan AA, Maraseni TN, Salmo SG. 2017. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 134: 70-85. doi:https://doi.org/10.1016/j.isprsjprs.2017.10.016.
Dai L, Jia J, Yu D, Lewis BJ, Zhou L, Zhou W, Zhao W, Jiang L. 2013. Effects of climate change on biomass carbon sequestration in old-growth forest ecosystems on Changbai Mountain in Northeast China. Forest Ecology and Management, 300: 106-116. doi:https://doi.org/10.1016/j.foreco.2012.06.046.
Domingo D, Lamelas MT, Montealegre AL, García-Martín A, De la Riva J. 2018. Estimation of total biomass in aleppo pine forest stands applying parametric and nonparametric methods to low-density airborne laser scanning data. Forests, 9(4): 158. doi:https://doi.org/10.3390/f9040158.
Du H, Cui R, Zhou G, Shi Y, Xu X, Fan W, Lü Y. 2010. The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI. Acta Ecologica Sinica, 30(5): 257-263. doi:https://doi.org/10.1016/j.chnaes.2010.08.005.
Dube T, Mutanga O. 2015. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 36-46. doi:https://doi.org/10.1016/j.isprsjprs.2014.11.001.
Eisfelder C, Kuenzer C, Dech S. 2012. Derivation of biomass information for semi-arid areas using remote-sensing data. International Journal of Remote Sensing, 33(9): 2937-2984. doi:https://doi.org/10.1080/01431161.2011.620034.
Fassnacht FE, Hartig F, Latifi H, Berger C, Hernández J, Corvalán P, Koch B. 2014. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154: 102-114. doi:https://doi.org/10.1016/j.rse.2014.07.028.
Fernández-Manso O, Fernández-Manso A, Quintano C. 2014. Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images. International Journal of Applied Earth Observation and Geoinformation, 31: 45-56. doi:https://doi.org/10.1016/j.jag.2014.03.005.
Fu L, Zhao Y, Xu Z, Wu B. 2015. Spatial and temporal dynamics of forest aboveground carbon stocks in response to climate and environmental changes. Journal of Soils and Sediments, 15(2): 249-259. doi:10.1007/s11368-014-1050-x.
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, 10(4): 627. doi:https://doi.org/10.3390/rs10040627.
Gasparri NI, Parmuchi MG, Bono J, Karszenbaum H, Montenegro CL. 2010. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments, 74(10): 1262-1270. doi:https://doi.org/10.1016/j.jaridenv.2010.04.007.
Gizachew B, Solberg S, Næsset E, Gobakken T, Bollandsås OM, Breidenbach J, Zahabu E, Mauya EW. 2016. Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data. Carbon balance and management, 11(1): 13. doi:https://doi.org/10.1186/s13021-016-0055-8.
Görgens EB, Montaghi A, Rodriguez LCE. 2015. A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics. Computers and Electronics in Agriculture, 116: 221-227. doi:https://doi.org/10.1016/j.compag.2015.07.004.
Huffman T, Liu J, McGovern M, McConkey B, Martin T. 2015. Carbon stock and change from woody biomass on Canada’s cropland between 1990 and 2000. Agriculture, Ecosystems & Environment, 205: 102-111. doi:https://doi.org/10.1016/j.agee.2014.10.009.
Iranmanesh Y. 2013. Assessment on biomass estimation methods and carbon sequestration of Quercus brantii Lindl. in Chaharmahal & Bakhtiari Forests. PhD Thesis, Tarbiat Modares University, 106 pp, (In Persian)
Karlson M, Ostwald M, Reese H, Sanou J, Tankoano B, Mattsson E. 2015. Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sensing, 7(8): 10017-10041. doi:https://doi.org/10.3390/rs70810017.
Krahwinkler P, Rossman J. 2011. Using decision tree based multiclass support vector machines for forest mapping. In: Lena Halounová EE (ed) IEEE Int. Geoscience and Remote Sensing Symp., Vancouver, Canada. pp 307-318.
Kwak D-A, Lee W-K, Cho H-K, Lee S-H, Son Y, Kafatos M, Kim S-R. 2010. Estimating stem volume and biomass of Pinus koraiensis using LiDAR data. Journal of Plant Research, 123(4): 421-432. doi:10.1007/s10265-010-0310-0.
Latifi H, Fassnacht F, Koch B. 2012. Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sensing of Environment, 121: 10-25. doi:https://doi.org/10.1016/j.rse.2012.01.015.
McRoberts RE, Magnussen S, Tomppo EO, Chirici G. 2011. Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data. Remote Sensing of Environment, 115(12): 3165-3174. doi:https://doi.org/10.1016/j.rse.2011.07.002.
Nolè A, Law B, Magnani F, Matteucci G, Ferrara A, Ripullone F, Borghetti M. 2009. Application of the 3-PGS model to assess carbon accumulation in forest ecosystems at a regional level. Canadian Journal of Forest Research, 39(9): 1647-1661. doi:https://doi.org/10.1139/X09-077.
Noorian N, Shataee S, Mohamadi J. 2019. Evaluation of RapidEye satellite data for estimation some quantitative structure variables in the Caspian forests of Gorgan region. Journal of RS and GIS for Natural Resources, 9(4): 1-16. (In Persian)
Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG. 2011. A large and persistent carbon sink in the world’s forests. Science, 333(6045): 988-993. doi:https://doi.org/10.1126/science.1201609.
Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114(5): 1053-1068. doi:https://doi.org/10.1016/j.rse.2009.12.018.
Rajashekar G, Fararoda R, Reddy RS, Jha CS, Ganeshaiah KN, Singh JS, Dadhwal VK. 2018. Spatial distribution of forest biomass carbon (Above and below ground) in Indian forests. Ecological Indicators, 85: 742-752. doi:https://doi.org/10.1016/j.ecolind.2017.11.024.
Riaño D, Chuvieco E, Salas J, Aguado I. 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003). IEEE Transactions on Geoscience and Remote Sensing, 41(5): 1056-1061. doi:https://doi.org/10.1109/TGRS.2003.811693.
Safari A, Sohrabi H. 2019. The effect of digital preprocessing and modeling method on an estimation of aboveground carbon stock of Zagros forests using Landsat 8 imagery. Journal of RS and GIS for Natural Resources, 9(4): 73-89. (In Persian)
Safari A, Sohrabi H, Powell SL. 2018. Comparison of satellite-based estimates of aboveground biomass in coppice oak forests using parametric, semiparametric, and nonparametric modeling methods. Journal of Applied Remote Sensing, 12(4): 046026. doi:https://doi.org/10.1117/1.JRS.12.046026.
Sagheb-Talebi K, Pourhashemi M, Sajedi T. 2014. Forests of Iran. The Netherlands: Springer Netherlands, 152 pp. https://doi.org/10.1007/978-94-007-7371-4.
Sarker LR, Nichol JE. 2011. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 115(4): 968-977. doi:https://doi.org/10.1016/j.rse.2010.11.010.
Shao Z, Zhang L. 2016. Estimating forest aboveground biomass by combining optical and SAR data: A case study in Genhe, Inner Mongolia, China. Sensors, 16(6): 834. doi:https://doi.org/10.3390/s16060834.
Wang X, Shao G, Chen H, Lewis BJ, Qi G, Yu D, Zhou L, Dai L. 2013. An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China. Environmental Management, 52(3): 612-620. doi:10.1007/s00267-013-0089-6.
Zhao K, Suarez JC, Garcia M, Hu T, Wang C, Londo A. 2018. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, 204: 883-897. doi:https://doi.org/10.1016/j.rse.2017.09.007.
Zhao P, Lu D, Wang G, Wu C, Huang Y, Yu S. 2016. Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sensing, 8(6): 469. doi:https://doi.org/10.3390/rs8060469.
Zhu X, Liu D. 2015. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 222-231. doi:https://doi.org/10.1016/j.isprsjprs.2014.08.014.
