مدلسازی اندوخته کربن روی‌ زمینی جنگل‌های زاگرس با استفاده از داده‌های زمینی و تصاویر ماهواره لندست 8

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشکده منابع طبیعی و علوم دریایی دانشگاه تربیت مدرس

2 عضو هیات علمی

3 دانشیار دانشگاه گرگان

4 تربیت مدرس

چکیده

سابقه و هدف: اهمیت اطلاع از اندخته کربن روی زمینی جنگل برای مدیریت جنگل در سطح محلی، مدیریت اراضی در سطوح منطقه‌ای و گزارش انتشار کربن در سطوح ملی و بین‌المللی مهم است. به همین سبب یافتن رو‌ش‌های کم‌هزینه و سریع برای برآورد اندوخته کربن در محدوده‌های وسیع به یک ضرورت تبدیل شده است. بر این اساس، در سال‌های اخیر ارزیابی قابلیت داده‌های منابع مختلف سنجش‌ازدوری در برآورد اندوخته کربن روی زمینی جنگل‌ها مورد بررسی قرار گرفته است. در این تحقیق قابلیت تصاویر ماهواره لندست 8 برای برآورد اندوخته کربن توده‌های شاخه‌زاد بلوط جنگل های زاگرس بررسی گردید. همچنین کارایی چهار روش مدل‌سازی ناپارامتری شامل جنگل تصادفی، شبکه‌های عصبی مصنوعی، کوبیست و رگرسیون اسپلاین تطبیقی چندگانه بررسی شد.
مواد و روش‌ها: منطقه مورد مطالعه در بخشی از جنگلهای زاگرس و در استان کرمانشاه در دو منطقه سرفیروزآباد (جنگل شدیداً دست‌خورده) و گهواره (جنگل با حداقل دست‌خوردگی) انجام گرفت. تعداد 124 قطعه نمونه با ابعاد 30×30 متر در یک شبکۀ آماربرداری 200×200 متر در دو منطقه مورد بررسی با استفاده از روش منظم با شروع تصادفی برداشت، و با استفاده از روابط آلومتریک مختص گونه بلوط، مقدار کربن روی زمینی در این نمونه‌ها محاسبه شد. برای مدل‌سازی اندوخته کربن روی زمینی با استفاده از داده-های سنجش‌ازدوری، از متغیرهای مختلف استخراج‌شده از تصاویر لندست 8 مانند مقدارهای‌ باندهای، نسبت‌گیری‌های باندی، شاخص‌های گیاهی، تجزیه مؤلفه‌های اصلی و تبدیل تسلدکپ به‌عنوان متغیر مستقل و مقدارهای محاسبه‌شده اندوخته کربن روی -زمینی در قطعه نمونه‌های برداشت شده به‌عنوان متغیر وابسته استفاده شد. ارزیابی صحت نتایج چهار روش ناپارامتری مدل‌سازی جنگل تصادفی، شبکه‌های عصبی مصنوعی، کوبیست و رگرسیون اسپلاین تطبیقی چندگانه مورد استفاده در این پژوهش با استفاده از اعتبارسنجی متقابل و به روش Leave-one-out صورت گرفته و از معیارهای ارزیابی ضریب تبیین، ریشه میانگین مربعات خطا و اریبی استفاده شد.
یافته‌ها: نتایج نشان داد دقت برآورد اندوخته کربن در منطقه کمتر دستخورده بیشتر از منطقه دست‌خورده است. مقایسه نتایج روش‌های مدل‌سازی مورد استفاده در این مطالعه نشان داد که اختلاف قابل ‌توجهی در بین نتایج روش‌های به‌ کار گرفته شده وجود ندارد و استفاده از روش‌های مختلف تاثیر چندانی در بهبود نتایج نداشت. علاوه‌براین، استفاده از کل نمونه‌های در یک مدل بدون تفکیک نمونه‌ها براساس منطقه مورد مطالعه سبب افزایش صحت برآوردها در منطقه دست‌خورده شد.
نتیجه‌گیری کلی: نتایج برآوردها با ضریب تبیین بیشتر از 7/0 و درصد ریشه میانگین مربعات خطا نزدیک به 32% در مجموع هر دو منطقه مورد بررسی، بیانگر توانایی نسبتا مناسب تصاویر لندست 8 و روش‌های ناپارامتری در کمی کردن اندوخته کربن در جنگل‌های زاگرس با صرفه‌جویی در وقت و هزینه است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling aboveground carbon stock of Zagros forests using field data and Landsat 8 imagery

نویسنده [English]

  • Hormoz Sohrabi 2
چکیده [English]

Abstract
Background and objectives: Information on aboveground carbon (AGC) is important for managing forests at local level, land management at regional levels, and carbon emissions reporting at national and international levels; therefore, there is a critical need for low-cost and time-saving approaches for quantifying of AGC. According to this, the estimation of aboveground carbon stock from remotely-sensed data has currently attracted a lot of attention. We assessed the capability of Landsat 8-derived spectral variables for AGC estimates in Zagros coppice oak forests by four non-parametric modeling including: random forest (RF), Cubist, Multivariate adaptive regression spline (MARS) and artificial neural networks (ANNs)
Materials and methods:
The study was carried out in part of Zagros forest, in Kermanshah Province. The values of aboveground carbon (AGC) terrestrial references was determined using field measurement data collected in two sites, Gahvareh (very low degraded (LD) site) and SarfiruzAbad (highly degraded (HD) site). Totally, 124 plots (30×30 meters) surveyed in two studied sites by the systematically-gridded plot design and AGC was calculated by developed species-specific allometric equations for Brant oak trees. For modeling AGC using the remotely-sensed data, we used different Landsat 8 derived variables such as single raw bands, simple band ratios, vegetation indices, principle component analysis and tasseled cap as independent variables and calculated AGC values in plots as dependent variable. The assessment of accuracies of four used non-parametric modeling methods: RF, Cubist, MARS and ANNs and was evaluated by “Leave-one-out” cross validation via criteria such as coefficient of variation (R2), root mean square error (RMSE) and bias.
Results: The results showed the accuracy of AGC estimates in LD site was better than HD site. The comparison of used modeling methods revealed that there are not significant difference in performances and accuracies of used non-parametric approaches. In additional, using the total plots from two test sites in one model caused the increase the results for HD site estimates.
Conclusion: Results showed R2 and relative RMSE values of approximately 0.7 and 32% cross-validated (combined two studied sites) for modeling AGC using Landsat derived variables, which reveals the high potential of Landsat 8 images and non-parametric modeling methods for quantifying AGC in cost and time saving approach for Zagros forests.
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کلیدواژه‌ها [English]

  • Artificial neural networks
  • Cubist
  • Multivariate Adaptive Regression Splines
  • Random Forest
  • Remote sensing
1. Ahmed, R.U. 2012. Accuracy of Biomass and Structure Estimates from Radar and Lidar.
Ph.D Dissertations in University of Massachusetts Amherst.
2. Amini, J., and Sadeghi, Y. 2013. Optical and radar images in modeling the forests biomass in
north of Iran. Remote sensing and GIS, 4(4): 69-82. (In Persian)
3. Anaya, J.A., Chuvieco, E., and Palacios-Orueta, A. 2009. Aboveground biomass assessment
in Colombia: a remote sensing approach. Forest Ecology and Management. 257: 1237–1246.
4. Asner, G.P., Clark, J.K., Mascaro, J., Vaudry, R., Chadwick, R.D., Vieilledent, G., Rasamoelina,
M., Balaji, A., Kennedy-Bowdoin, T., Maatoug, L., Colgan, M.S., and Knapp, D.E. 2012.
Human and environmental controls over aboveground carbon storage in Madagascar. Carbon
Balance and Management. 7(2): http://www.cbmjournal.com/content/7/1/2.
5. Attarchi, S., and Gloaguen, R. 2014. Improving the Estimation of Above Ground Biomass
Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran).
Remote Sensing. 6: 3693-3715.
6. Avitabile, V., Baccini, A., Friedl, M.A., and Schmullius, C. 2012. Capabilities and limitation
of Landsat and land cover data for aboveground woody biomass estimation of Uganda.
Remote Sensing Environment. 117: 366-380.
7. Boudreau, J., Nelson, R.F., Margolis, H.A., Beaudoin, A., Guindon, L., Kimes, D.S. 2008.
Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec.
Remote Sensing of Environment. 112: 3876–3890.
8. Briceno-Elizondo, E., Garcia-Gonzalo, J., Peltola, H., and Kellomaki, S. 2006. Carbon
stocks and timber yield in two boreal forest ecosystems under current and changing climatic
conditions subjected to varying management regimes. Environmental Science and Policy, 9:
237-252.
9. Calvao, T., and Palmeirim, J.M. 2004. Mapping mediterranean scrub with satellite imagery:
biomass estimation and spectral behaviour. International Journal of Remote Sensing, 25(16):
3113-26.
10. Chen, B., Arain, M.A., Khomik, M., Trofymow, J.A., Grant, R.F., Kruz, W.A., Yeluripati, J.,
and Wang, Z. 2013. Evaluating the impacts of climate variability and disturbance regimes on
the historic carbon budget of a forest landscape. Agricultural and Forest Meteorology. 180:
256-280.
11. Chen, Q., Laurin, G.V., Battles, J.J., and Saah. D. 2012. Integration of airborne lidar and
vegetation types derived from aerial photography for mapping aboveground live biomass.
Remote Sensing of Environment. 121: 108-117.
12. Chen, X., Liu, Sh., Zhu, Zh., Vogelmann, J., Li, Zh., and Ohlen, D. 2011. Estimating
aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus
using data from the Forest Inventory and Analysis Program, Landsat, and LANDFIRE.
Ecological Indicator. 11: 140-148.
13. Cohen, W.B., and Goward, S.N. 2004. Landsat's Role in Ecological Applications of Remote
Sensing. BioScience 54(6): 535-545.
14. Coops, N.C. 2015. Characterizing Forest Growth and Productivity Using Remotely Sensed
Data. Current Forestry Reports, 1(3): 195-205.
15. Dai, L., Jia, J., Yu, D., Lewis, B.J., Zhou, L., Zhou, W., Zhao, W., and 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.
16. Deng, Sh., Shi, Y., Jin, Y., and Wang, L. 2011. A GIS-based approach for quantifying and
mapping carbon sink and stock values of forest ecosystem: A case study. Energy Procedia 5:
1535–1545.
17. Du, H., Cui, R., Zhou, G., Shi, Y., Xu, X., Fan, W., and 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): 257-63.
18. Dube, T., and 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.
19. Eckert, S. 2012. Improved Forest Biomass and Carbon Estimations Using Texture Measures
from WorldView-2 Satellite Data. Remote Sensing. 4: 810-829.
20. Eisfelder, Ch., Kuenzer, C., and Dech, S. 2011. Derivation of biomass information for semiarid
areas using remote-sensing data. International Journal of Remote Sensing., 33(9): 2937-
2984.
21. Fassnacht, F.E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., and 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.
22. Filippi, A.M., Güneralp, I., and Randall, J. 2014. Hyperspectral remote sensing of
aboveground biomass on a river meander bend using multivariate adaptive regression splines
and stochastic gradient boosting, Remote Sensing Letters, 5(5): 432-441.
23. Frazier, R.J., Coops, N.C., Wulder, M.A., and Kennedy, R. 2014. Characterization of
aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation
metrics. ISPRS Journal of Photogrammetry and Remote Sensing, 92: 137-46.
24. Fu, L., Zhao, Y., Xu, Zh., and Wu, B. 2015. Spatial and temporal dynamics of forest
aboveground carbon stocks in response to climate and environmental changes. Soils
Sediments., 15: 249-259.
25. Gagliasso, D., Hummel, S., and Temesgen, H. 2014. A Comparison of Selected Parametric
and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area.
Forestry., 4(1): 42-48.
26. Gasparri, N.I., Parmuchi, M.G., Bono, J., Karszenbaum, H., and Montenegro, C.L. 2010.
Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in
subtropical dry forests of Argentina Journal of Arid Environments., 74: 1262-1270.
27. Gleason, C.J., and Im, J. 2012. Forest biomass estimation from airborne LiDAR data using
machine learning approaches. Remote Sensing of Environment., 125: 80-91.
28. Gómez, C., White, J.C., Wulder, M.A., and Alejandro, P. 2014. Historical forest biomass
dynamics modelled with Landsat spectral trajectories. ISPRS Journal of Photogrammetry
and Remote Sensing, 93: 14-28.
29. Görgens, E.B., Montaghi, A., and Rodriguez, L.C.E. 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: 221-7.
30. Güneralp, I., Filippi, A.M., and Randall, J. 2014. Estimation of floodplain aboveground
biomass using multispectral remote sensing and nonparametric modeling. International
Journal of Applied Earth Observation and Geoinformation, 33: 119-26.
31. Iranmanesh, Y. 2013. Assessment on biomass estimation methods and carbon sequestration
of quercus brantii Lindl. in chaharmahal and bakhtiari forests, Ph.D. thesis, Faculty of
Natural Resource And Mariane Science, Tarbiat Modares University. (In Persian)
32. Kelsey, K.C., and Neff, J.C. 2014. Estimates of Aboveground Biomass from Texture
Analysis of Landsat Imagery. Remote Sensing., 6: 6407-6422.
33. Kwak, D., Lee, S., Kim, S., Lee, W., Son, Y., Cho, H., and Kafatos, M. 2010. Estimating
stem volume and biomass of Pinus koraiensis using LiDAR data. J. Plant Reasreach. 123:
421–432.
34. Labrecque, S., Fournier, R.A., Luther, J.E., and Piercey, D. 2006. A comparison of four
methods to map biomass from Landsat-TM and inventory data in western Newfoundland.
Forest Ecology and Management, 226: 129–144.
35. Langner, A., Samejima, H., Ong, R.C., Titin, J., and Kitayama, K. 2012. Integration of
carbon conservation into sustainable forest management using high resolution satellite
imagery: A case study in Sabah, Malaysian Borneo. International Journal of Applied Earth
Observation and Geoinformation, 18: 305-12.
36. Latifi, H., Fassnacht, F.E., Hartig, F., Berger, Ch., Hernández, J., Corvalán, P., and Koch, B.
2015. Stratified aboveground forest biomass estimation by remote sensing data. International
Journal of Applied Earth Observation and Geoinformation. 38: 229–241.
37. Lei, Zh, Shaoqiang, W., Georg, K., Guirui, Y., Mei, H., Robert, M., Florian, K., Hao, Sh.,
and Yazhen, G. 2013. Carbon dynamics in woody biomass of forest ecosystem in China with
forest management practices under future climate change and rising CO2 concentration.
Chinese Geographical Science, 23(5): 519-536.
38. Lin, D., Lai, J., Muller-Landau, H.C., Mi, X., and Ma, K. 2012. Topographic Variation in
Aboveground Biomass in a Subtropical Evergreen Broad-Leaved Forest in China. PLoS
ONE 7(10), e48244. doi :10.1371/journal.pone.0048244.
39. Lindner, M., Maroschek-Nethererc, S., Kremer, A., Barbati, A., Garcia-Gonzaloa, J., Seidl,
R., Delzon, S., Corona, P., Kolström, M., Lexer, M.J., and Marchettie, M. 2010. Climate
change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest
Ecology and Management., 259: 698-709.
40. Lu, D., and Batistella, M. 2005. Exploring TM image texture and its relationships with
biomass estimation in Rondônia, Brazilian Amazon. Acta Amazonica. 35(2): 249-257.
http://dx.doi.org/10.1590/S0044-59672005000200015.
41. Lu, D., Mausel, P., Brondizio, E., and Moran, E. 2002. Above-Ground Biomass Estimation
of Successional and Mature Forests Using TM Images in the Amazon Basin. Advances in
Spatial Data Handling: 183-196.
42. Main-Knorn, M., Cohen, W.B., Kennedy, R.E., Grodzki, W., Griffiths, P., Hostert, P.,
Pflugmacher, D. 2013. Monitoring coniferous forest biomass change using a Landsat
trajectory-based approach. Remote Sensing of Environment., 139: 227-290.
43. Mandal, G., and Joshi, S.P. 2015. Biomass accumulation and carbon sequestration potential
of dry deciduous forests. International Journal of Ecology and Development. 30(1): 64-82.
44. Morel, A.C., Fisher, J.B., and Malhi, Y. 2012. Evaluating the potential to monitor
aboveground biomass in forest and oil palm in Sabah, Malaysia, for 2000–2008 with Landsat
ETM+ and ALOS-PALSAR. International Journal of Remote Sensing, 33(11): 3614-3639.
45. Gonzalez, P., Asner, G.A., Battles, J.J., Lefsky, M.A., Waring, K.M., Palace, M. 2012.
Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in
California. Remote Sensing of Environment. 114: 1561-1575.
46. Muukkonen, P., Heiskanen, L. 2007. Biomass estimation over a large area based on
standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify
carbon inventories. Remote Sensing of Environment 107: 617–624.
47. Nelson, R. 2010. Model effects on GLAS-based regional estimates of forest biomass and
carbon. International Journal of Remote Sensing., 31(5): 1359-1372.
48. Nole, A., Law, B.E., 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: 1647–1661.
49. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L.,
Shvidenko, A., Lewis, S.L., and Canadell, J.G. 2011. A large and persistent carbon sink in
the world’s forests. Science, 333 (6045): 988-93.
50. Potter, Ch., Gross, P., Genovese, V., and Smith, M.L. 2007. Net primary productivity of
forest stands in New Hampshire estimated from Landsat and MODIS satellite data. Carbon
Balance and Management. 2:9 doi: 10.1186/1750-0680-2-9.
51. Powell, S.L., Healey, S.P., Cohen, W.B., Kennedy, R.E., Moisen, G.G., Pierce, K.B.,
Ohmann, J.L. 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: 1053-1068.
52. R Core Team, 2016. R: A language and environment for statistical computing. R Foundation
for statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
53. Riaño, D., Chuvieco, E., Salas, J., and Aguado, I. 2003. Assessment of Different
Topographic Corrections in Landsat-TM Data for Mapping Vegetation Types. IEEE
TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 41(5): 1056-1061.
54. Sherestha, R., Wynne, R.H. 2012. Estimating Biophysical Parameters of Individual Trees in
an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar. Remote
Sensing. 4, 484-508; doi:10.3390/rs4020484.
55. Spangler, L., Vierling, L.A. 2011. Quantifying Forest Aboveground Carbon Pools And
Fluxes Using Multi-Temporal Lidar. US Department of Energy Publications. Paper 355.
http://digitalcommons.unl.edu/usdoepub/355.
56. Su, Y., Guo, Q., Xue, B., Hu, T., Alvarez, O., Tao, Sh., and Fang, J. 2016. Spatial
distribution of forest aboveground biomass in China: Estimation through combination of
spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment
173: 187-99.
57. Tan, K., Piao, S., Peng, C., and Fang, J. 2007. Satellite-based estimation of biomass carbon
stocks for northeast China’s forests between 1982 and 1999. Forest Ecology and
Management. 240, 114–121.
58. Torres, A.B., MacMillan, D.C., and Skutsch, M. 2015. ‘Yes-in-my-backyard’: Spatial
differences in the valuation of forest services and local co-benefits for carbon markets in
México. Ecological Economics 109: 130–141.
59. Walton, J. 2008. Subpixel urban land cover estimation: comparing cubist, random forests,
and support vector regression. Photogrammetric Engineering and Remote Sensing, 74(10):
1213–1222.
60. Wang, X., Lewis, B.J., Zhou, L., Dai, L., Shao, G., Qi, G., Chen, H., Yu, D. 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: 612–620.
61. Wani, A.A., Joshi, P.K., and Singh, O. 2015. Estimating biomass and carbon mitigation of
temperate coniferous using spectral modeling and field inventory data. Ecological
Informatics. 25: 63-70.
62. Were, K., Dieu, T.B., Dick, Ø.B., and Singh, B.R. 2015. A comparative assessment of
support vector regression, artificial neural networks, and random forests for predicting and
mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators
52: 394-403.
63. Wijaya, A., Kusnadi, S., Gloaguen, R., and Heilmeier, H. 2010. Improved strategy for
estimating stem volume and forest biomass using moderate resolution remote sensing data
and GIS. Journal of Forestry Research. 21(1): 1−12.
64. Yan, F., Wu, B., and Wang, Y. 2015. Estimating spatiotemporal patterns of aboveground
biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China.
Agricultural and Forest Meteorology. 200: 119-128.
65. Zandler, H., Brenning, A., and Samimi, C. 2015. Quantifying dwarf shrub biomass in an arid
environment: Comparing empirical methods in a high dimensional setting. Remote Sensing
of Environment 158: 140-55.
66. Zhang, Y., and Liang, Sh. 2014. Changes in forest biomass and linkage to climate and forest
disturbances over Northeastern China. Global Change Biology. 20: 2596–2606.
67. Zheng, G., Chen, J.M., Tian, Q.J., Ju, W.M., and Xia, X.Q. 2007. Combining remote sensing
imagery and forest age inventory for biomass mapping. Journal of Environmental
Management. 85: 616–623.
68. Zhu, X., and Liu, D. 2015. Improving forest aboveground biomass estimation using seasonal
Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing. 102:
222-231.