Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forest types, structural attributes and age in sub-tropical coastal region of KwaZulu Natal, South Africa using remote sensing

Applied Geography

Volumn 64, Issue , 2015, Pages 55-65

  Dube, Timothy ^a   Mutanga, Onisimo ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

Carbon allocation; Carbon quantification; Carbon variability; Remotely sensed models; Structural components; Tree age

Indexed keywords

ABOVEGROUND BIOMASS; AGE DETERMINATION; BIOMASS ALLOCATION; CARBON CYCLE; CLIMATE CHANGE; CLIMATE EFFECT; COASTAL ZONE; PLANTATION FORESTRY; REMOTE SENSING; SATELLITE DATA;

KWAZULU-NATAL; SOUTH AFRICA;

EUCALYPTUS; EUCALYPTUS GRANDIS; PINUS TAEDA;

EID: 84942510487     PISSN: 01436228     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apgeog.2015.09.003     Document Type: Article

References (70)

1. Asner G., Flint Hughes R., Varga T., Knapp D., Kennedy-Bowdoin T. Environmental and biotic controls over aboveground biomass throughout a tropical rain forest. Ecosystems 2009, 12(2):261-278.
2. Baishya R., Barik S.K., Upadhaya K. Distribution pattern of aboveground biomass in natural and plantation forests of humid tropics in northeast India. Tropical Ecology 2009, 50(2):295.
3. Bredenkamp B. Volume and mass of logs and standing trees 2000, Vol. 1. Southern African Institute of Forestry, Menlo Park.
4. Carreiras J.M.B., Vasconcelos M.J., Lucas R.M. Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment 2012, 121:426-442.
5. de Castilho C.V., Magnusson W.E., de Araújo R.N.O., Luizao R.C., Luizao F.J., Lima A.P., et al. Variation in aboveground tree live biomass in a central Amazonian forest: effects of soil and topography. Forest Ecology and Management 2006, 234(1):85-96.
6. Cerbu G.A., Swallow B.M., Thompson D.Y. Locating REDD: a global survey and analysis of REDD readiness and demonstration activities. Environmental Science & Policy 2011, 14(2):168-180.
7. Chen Y., Liu Z., Rao X., Wang X., Liang C., Lin Y., et al. Carbon storage and allocation pattern in plant biomass among different forest plantation stands in Guangdong, China. Forests 2015, 6(3):794-808.
8. Chinembiri T.S., Bronsveld M.C., Rossiter D.G., Dube T. The Precision of C Stock estimation in the Ludhikola watershed using model-based and design-based approaches. Natural Resources Research 2013, 1-13.
9. Christie S.I., Scholes R.J. Carbon storage in Eucalyptus and pine plantations in South Africa. Environmental Monitoring and Assessment 1995, 38:231-241.
10. Denman K.L. IPCC climate change 2007. The physical science basis 2007, 499-587. Cambridge University Press. S. S (Ed.).
11. Dixon R.K., Solomon A., Brown S., Houghton R., Trexier M., Wisniewski J. Carbon pools and flux of global forest ecosystems. Science 1994, 263(5144):185-190.
12. Dovey S.B. Estimating biomass and macronutrient content of some commercially important plantation species in South Africa. Southern Forests 2009, 71(3):245-251.
13. Dube T., Mutanga O. 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 2015, 101:36-46.
14. Dube T., Mutanga O. Investigating the robustness of the new Landsat-8 operational land imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. ISPRS Journal of Photogrammetry and Remote Sensing 2015, 108:12-32.
15. Dube T., Mutanga O., Abdel-Rahman E., Ismail R., Slotow R. Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets. International Journal of Remote Sensing 2015, 36(14):3751-3772.
16. Dube T., Mutanga O., Elhadi A., Ismail R. Intra-and-inter species biomass prediction in a plantation forest: testing the utility of high spatial resolution spaceborne multispectral rapideye sensor and advanced machine learning algorithms. Sensors 2014, 14(8):15348-15370.
17. Elith J., Leathwick J.R., Hastie T. A working guide to boosted regression trees. Journal of Animal Ecology 2008, 77(4):802-813.
18. FSA. South African government information: forestry. Government Communication and information system 2010, Vol. 2013. GCIS.
19. Gara T.W., Murwira A., Chivhenge E., Dube T., Bangira T. Estimating wood volume from canopy area in deciduous woodlands of Zimbabwe. Southern Forests: A Journal of Forest Science 2014, 76(4):237-244.
20. Giardina C.P., Ryan M.G. Total belowground carbon allocation in a fast-growing Eucalyptus plantation estimated using a carbon balance approach. Ecosystems 2002, 5(5):487-499.
21. Gibbs H.K., Brown S., Niles J.O., Foley J.A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2007, 2(4):045023.
22. Gil M.V., Blanco D., Carballo M.T., Calvo L.F. Carbon stock estimates for forests in the Castilla y León region, Spain. A GIS based method for evaluating spatial distribution of residual biomass for bio-energy. Biomass and Bioenergy 2011, 35(1):243-252.
23. Gschwantner T., Schadauer K., Vidal C., Lanz A., Tomppo E., di Cosmo L., et al. Common tree definitions for national forest inventories in Europe. Silva Fennica 2009, 43(2):303-321.
24. Güneralp I., Filippi A.M., Randall J. Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling. International Journal of Applied Earth Observation and Geoinformation 2014, 33:119-126.
25. Güneralp I˙., Filippi A.M., Randall J. Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling. International Journal of Applied Earth Observation and Geoinformation 2014, 33(0):119-126.
26. Guo Z., Fang J., Pan Y., Birdsey R. Inventory-based estimates of forest biomass carbon stocks in China: a comparison of three methods. Forest Ecology and Management 2010, 259(7):1225-1231.
27. Henry M., Picard N., Trotta C., Manlay R.J., Valentini R., Bernoux M., et al. Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fennica 2011, 45(3B):477-569.
28. Houghton R. Aboveground forest biomass and the global carbon balance. Global Change Biology 2005, 11(6):945-958.
29. Houghton R.A., B D., B.A.G., K.O.N., S P., S A. Mapping Russian forest biomass with data from satellites and forest inventories. Environmental Research Letters 2007, 2(4).
30. IPCC. Climate change 1995. Impacts, adaptations and mitigation of climate change: scientific-technical analyses. Contribution of working group II to the second assessment report of the intergovernmental panel on climate change 1996, Cambridge University, Cambridge, UK. R.T. Watson, M.C. Zinyowera, R.H. Moss, D.J. Dokken (Eds.).
31. IPCC. Good practice guidance for land use, land-use change and forestry 2003, Institute for Global Environmental Strategies (IGES), Hayama.
32. Jachowski N.R.A., Quak M.S.Y., Friess D.A., Duangnamon D., Webb E.L., Ziegler A.D. Mangrove biomass estimation in Southwest Thailand using machine learning. Applied Geography 2013, 45:311-321.
33. Keller M., Palace M., Hurtt G. Biomass estimation in the Tapajos National Forest, Brazil: examination of sampling and allometric uncertainties. Forest Ecology and Management 2001, 154(3):371-382.
34. Kelsey K.C., Neff J.C. Estimates of aboveground biomass from texture analysis of landsat imagery. Remote Sensing 2014, 6(7):6407-6422.
35. Kirby K.R., Potvin C. Variation in carbon storage among tree species: Implications for the management of a small-scale carbon sink project. Forest Ecology and Management 2007, 246(2-3):208-221.
36. Laurin G.V., Chen Q., Lindsell J.A., Coomes D.A., Del Frate F., Guerriero L., et al. Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 2014, 89:49-58.
37. Loaiza T., Nehren U., Gerold G. REDD+ and incentives: an analysis of income generation in forest-dependent communities of the Yasuní biosphere reserve, Ecuador. Applied Geography 2015, 62:225-236.
38. Luizão R.C., Luizão F.J., Paiva R.Q., Monteiro T.F., Sousa L.S., Kruijt B. Variation of carbon and nitrogen cycling processes along a topographic gradient in a central Amazonian forest. Global Change Biology 2004, 10(5):592-600.
39. Luo Y. Terrestrial carbon-cycle feedback to climate warming. Annual Review of Ecology, Evolution and Systematics 2007, 683-712.
40. MacDicken K.G. A guide to monitoring carbon storage in forestry and agroforestry projects 1997, Winrock International Institute for Agricultural Development USA.
41. Moisen G.G., Freeman E.A., Blackard J.A., Frescino T.S., Zimmermann N.E., Edwards T.C. Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecological Modelling 2006, 199(2):176-187.
42. Muvengwi J., Mbiba M., Nyenda T. Using branch diameter to estimate fresh biomass removal by elephants: comparison of linear and quadratic models. African Journal of Ecology 2015, 53(1):126-129.
43. Nemani R., Pierce L., Running S., Band L. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed leaf area index estimates. International Journal of Remote Sensing 1993, 14(13):2519-2534.
44. Palace M.W., Sullivan F.B., Ducey M.J., Treuhaft R.N., Herrick C., Shimbo J.Z., et al. Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data. Remote Sensing of Environment 2015, 161(0):1-11.
45. Pan Y., Birdsey R.A., Fang J., Houghton R., Kauppi P.E., Kurz W.A., et al. A large and persistent carbon sink in the World's forests. Science 2011, 333(6045):988-993.
46. Patenaude G., Hill R.A., Milne R., Gaveau D.L.A., Briggs B.B.J., Dawson T.P. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment 2004, 93(3):368-380.
47. Penman J., Gytarsky M., Hiraishi T., Krug T., Kruger D., Pipatti R., et al. Good practice guidance for land use, land-use change and forestry. Intergovernmental Panel on Climate Change 2003, Institute for Global Environmental Strategies, Kanagawa, Japan, Available at:. http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/GPG_LULUCF_FULL.pdf.
48. Pregitzer K.S., Euskirchen E.S. Carbon cycling and storage in world forests: biome patterns related to forest age. Global Change Biology 2004, 10(12):2052-2077.
49. Raich J.W., Clark D.A., Schwendenmann L., Wood T.E. Aboveground tree growth varies with belowground carbon allocation in a tropical rainforest environment. PLoS One 2014, 9(6):e100275.
50. Roberts J.W., Tesfamichael S., Gebreslasie M., van Aardt J., Ahmed F.B. Forest structural assessment using remote sensing technologies: an overview of the current state of the art. Southern Hemisphere Forestry Journal 2007, 69(3):183-203.
51. Rock B., Vogelmann J., Williams D., Vogelmann A., Hoshizaki T. Remote detection of forest damage. BioScience 1986, 439-445.
52. Sandbrook C., Nelson F., Adams W.M., Agrawal A. Carbon, forests and the REDD paradox. Oryx 2010, 44(03):330-334.
53. Schönau A., Boden D. Preliminary biomass studies in young Eucalypts. South African Forestry Journal 1982, 120.
54. Sharma C., Gairola S., Baduni N., Ghildiyal S., Suyal S. Variation in carbon stocks on different slope aspects in seven major forest types of temperate region of Garhwal Himalaya, India. Journal of Biosciences 2011, 36(4):701-708.
55. Singh S.P., Adhikari B.S., Zobel D.B. Biomass, productivity, leaf longevity, and forest structure in the central Himalaya. Ecological Monographs 1994, 401-421.
56. Souza A.F., Cortez L.S.R., Longhi S.J. Native forest management in subtropical South America: long-term effects of logging and multiple-use on forest structure and diversity. Biodiversity and Conservation 2012, 21(8):1953-1969.
57. Tiwari A.K., Singh J.S. Mapping forest biomass in India through aerial photographs and nondestructive field sampling. Applied Geography 1984, 4(2):151-165.
58. UNFCCC. Kyoto protocol to the United Nations framework convention on climate change 1998, UNFCCC, Bonn.
59. UNFCCC. United Nations framework convention on climate change. UNFCCC. (2001-2011). The Marrakesh accords and the Marrakesh declaration. In Seventh Conference of the Parties (COP 7) 1998, UNFCCC, Marrakesh, Morocco.
60. Wasige J.E., Groen T.A., Rwamukwaya B.M., Tumwesigye W., Smaling E.M.A., Jetten V. Contemporary land use/land cover types determine soil organic carbon stocks in south-west Rwanda. Nutrient Cycling in Agroecosystems 2014, 1-15.
61. Wei Y., Li M., Chen H., Lewis B.J., Yu D., Zhou L., et al. Variation in carbon storage and its distribution by stand age and forest type in boreal and temperate forests in Northeastern China. PLoS One 2013, 8(8):e72201.
62. van der Werf G.R., Randerson J.T., Giglio L., Collatz G.J., Kasibhatla P.S., Arellano A.F. Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics 2006, 6(11):3423-3441.
63. Wessels N.O., Kassier H.W. A computerised system for forest management and silvicultural planning and control in even-aged plantation forestry. South African Forestry Journal 1985, 132(1):62-64.
64. Wolf A., Ciais P., Bellassen V., Delbart N., Field C.B., Berry J.A. Forest biomass allometry in global land surface models. Global Biogeochemical Cycles 2011, 25(3).
65. Yang K., Guan D. Changes in forest biomass carbon stock in the pearl river delta between 1989 and 2003. Journal of Environmental Sciences 2008, 20(12):1439-1444.
66. Yang C., Huang H., Wang S. Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna, China. International Journal of Remote Sensing 2011, 32(20):5741-5756.
67. Zhang G., Ganguly S., Nemani R.R., White M.A., Milesi C., Hashimoto H., et al. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sensing of Environment 2014, 151(0):44-56.
68. Zhang H., Guan D., Song M. Biomass and carbon storage of Eucalyptus and Acacia plantations in the Pearl River Delta, South China. Forest Ecology and Management 2012, 277(0):90-97.
69. Zhang X., Kondragunta S. Estimating forest biomass in the USA using generalized allometric models and MODIS land products. Geophysical Research Letters 2006, 33(9):L09402.
70. Zhao K., Popescu S., Nelson R. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment 2009, 113(1):182-196.