Simulating the post-fire net primary production restoration and its affecting factors by using MTCLIM and 3PGS model in Genhe forest region, Northeast China

doi:10.13287/j.1001-9332.201811.012

Abstract

Abstract: Fire is a major disturbance factor in Daxing’anling region, with important impacts on carbon balance of forest ecosystems. Fire severity and the distinction of microclimates induced by different topography are the primary factors driving the restoration of post-fire net primary productivity (NPP). In this study, we examined the influence of fire severity and topographic factors on the restoration of forest NPP in the Genhe forest region. The spatial and temporal restoration process of post-fire NPP were simulated by combining with MTCLIM and 3PGS model based on multiyear Landsat TM satellite (2008-2012) and climate (1980-2010) data. The results showed that the 3PGS-MTCLIM model could precisely estimate the spatial distribution of NPP at small scales, with a good correlation between simulated and observed values (R²=0.828). The percentage of declined NPP in the year following the fire ranged 43%-80%, and the average NPP recovery period for this region was about 10 years by comparing pre- and post-fire NPP. Fire severity had significant impacts on post-fire recovery. The stronger the fire intensity, the longer the recovery period was needed. The NPP recovered relatively slower after a period of fast-speed recovery. Among the three topographic factors, elevation was the strongest one affecting forest NPP restoration, followed by slope and aspect.

LIN Si-mei, HUANG Hua-guo. Simulating the post-fire net primary production restoration and its affecting factors by using MTCLIM and 3PGS model in Genhe forest region, Northeast China[J]. Chinese Journal of Applied Ecology, 2018, 29(11): 3712-3722.

References

[1] Lyu A-F (吕爱锋), Tian H-Q (田汉勤), Liu Y-Q (刘永强). State of the art in quantifying fire distur-bance and ecosystem carbon cycle. Acta Ecologica Sinica (生态学报), 2005, 25(10): 2734-2743 (in Chinese)
[2] Melillo JM, McGuire AD, Kicklighter DW, et al. Glo-bal climate change and terrestrial net primary production. Nature, 1993, 363: 234-2401
[3] Peng C, Apps MJ. Modelling the response of net primary productivity (NPP) of boreal forest ecosystems to changes in climate and fire disturbance regimes. Ecological Modelling, 1999, 122: 175-193
[4] Kasischke ES. Fire, Climate Change, and Carbon Cycling in the Boreal Forest. New York: Springer, 2000
[5] Hicke JA, Asner GP, Kasischke ES, et al. Post-fire response of North American boreal forest net primary productivity analyzed with satellite observations. Global Change Biology, 2003, 9: 1145-1157
[6] Pratolongo P, Vicari R, Kandus P, et al. A new method for evaluating net aboveground primary production (NAPP) of Scirpus giganteus (Kunth). Wetlands, 2005, 25: 228-232
[7] Amiro BD, MacPherson JI, Desjardins RL, et al. Post-fire carbon dioxide fluxes in the western Canadian boreal forest: Evidence from towers, aircraft and remote sen-sing. Agricultural and Forest Meteorology, 2003, 115: 91-107
[8] Veroustraete F, Sabbe H, Eerens H, et al. Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sensing of Environment, 2002, 83: 376-399
[9] Potter CS, Randerson JT, Field CB, et al. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811-841
[10] Running SW, Hunt Jr ER. 8-generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models/ Ehleringer JR, Field CB, eds. Scaling Physiological Processes: Leaf to Globe. San Diego, CA, USA: Academic Press, 1993: 141-158
[11] Liu J, Chen JM, Cihlar J, et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 1997, 62: 158-175
[12] Foley JA, Prentice IC, Ramankutty N, et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles, 1996, 10: 603-628
[13] Coops NC, Waring RH, Landsberg JJ. Assessing forest productivity in Australia and New Zealand using a phy-siologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity. Forest Ecology and Management, 1998, 104: 113-127
[14] Wei Y-X (卫亚星), Wang L-W (王莉雯). Scale effect of vegetation net primary productivity models based on remote sensing. Resources Science (资源科学), 2010, 32(9): 1783-1791 (in Chinese)
[15] Gao K-T (高开通), Liu P-J (刘鹏举), Tang X-M (唐小明). Forecasting forest fire risk grade of forest sub-compartment. Journal of Beijing Forestry University (北京林业大学学报), 2013, 35(4): 61-66 (in Chinese)
[16] Zhao G-S (赵国帅), Wang J-B (王军邦), Fan W-Y (范文义), et al. Vegetation net primary productivity in Northeast China in 2000-2008: Simulation and seasonal change. Chinese Journal of Applied Ecology (应用生态学报), 2011, 22(3): 621-630 (in Chinese)
[17] Wimberly MC, Reilly MJ. Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery. Remote Sensing of Environment, 2007, 108: 189-197
[18] Hungerford RD, Nemani RR, Running SW, et al. MTCLIM: A Mountain Microclimate Simulation Model. New York: USDA Forest Service, 1989
[19] Myneni RB, Williams DL. On the relationship between fAPAR and NDVI. Remote Sensing of Environment, 1994, 49: 200-211
[20] Huemmrich KR, Goward SN. Vegetation canopy PAR absorptance and NDVI: An assessment for ten tree species with the SAIL model. Remote Sensing of Environment, 1997, 61: 254-269
[21] Roderick ML, Noble IR, Cridland SW. Estimating woody and herbaceous vegetation cover from time series satellite observations. Global Ecology & Biogeography, 1999, 8: 501-508
[22] Li H-L (李贺丽), Luo Y (罗毅), Xue X-P (薛晓萍), et al. Assessment of approaches for estimating fraction of photosynthetically active radiation absorbed by winter wheat canopy. Transactions of the Chinese Society of Agricultural Engineering (农业工程学报), 2011, 27(4): 201-206 (in Chinese)
[23] Ruimy A, Kergoat L, Bondeau A. Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency. Global Change Biology, 1999, 5: 56-64
[24] Liu J-F (刘建峰), Xiao W-F (肖文发), Guo M-C (郭明春). Pattern analysis of net primary productivity of China terrestrial vegetation using 3-PGS model. Scientia Silvae Sinicae (林业科学), 2011, 47(5): 16-22 (in Chinese)
[25] Sun L (孙龙), Zhang Y (张瑶), Guo Q-X (国庆喜), et al. Carbon emission and dynamic of NPP post forest fires in 1987 in Daxing’an Mountains. Scientia Silvae Sinicae (林业科学), 2009, 45(12): 100-104 (in Chinese)
[26] Balzter H, Rowland CS, Saich P. Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sensing of Environment, 2007, 108: 224-239
[27] Yang Q-P (杨清培), Li M-G (李鸣光), Wang B-S (王伯荪), et al. Dynamics of biomass and net primary productivity in succession of south subtropical forests in Southwest Guangdong. Chinese Journal of Applied Ecology (应用生态学报), 2003, 14(12): 2136-2140 (in Chinese)
[28] Xiao S-L (肖生苓), Yang J-L (杨嘉龙). Individual tree aboveground biomass of Larix gmelinii natural forest in the northern greater Khingan mountains. Scientia Silvae Sinicae (林业科学), 2014, 50(8): 22-29 (in Chinese)
[29] Meng S, Liu Q, Zhou G, et al. Aboveground tree additive biomass equations for two dominant deciduous tree species in Daxing’anling, northernmost China. Journal of Forest Research, 2017, 22: 1-8
[30] Zhao X-Y (赵晓炎), Wang C-K (王传宽), Huo H (霍宏), et al. Variations in photosynthetic capacity and associated factors for Larix gmelinii from diverse origins. Acta Ecologica Sincica (生态学报), 2008, 28(8): 3798-3807 (in Chinese)
[31] Shi L (石磊), Sheng H-C (盛后财), Man X-L (满秀玲), et al. Rainfall redistribution and the spatial hete-rogeneity of throughfall in Larix gmelinii forest, northeast China. Journal of Nanjing Forestry University (南京林业大学学报), 2017, 41(2): 91-96 (in Chinese)
[32] Wang X-W (王秀伟), Mao Z-J (毛子军). Temporal and spatial variation in gas exchange in canopy of Larix gmelinii plantation. Scientia Silvae Sinicae (林业科学), 2007, 43(11): 43-49 (in Chinese)
[33] Feng X, Liu G, Chen JM, et al. Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing. Journal of Environmental Management, 2007, 85: 563-573
[34] White JD, Ryan KC, Key CC, et al. Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 1996, 6: 125-136
[35] Lozano FJ, Suárez-Seoane S, Luis ED, et al. Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sensing of Environment, 2007, 107: 533-544
[36] Li M-Z (李明泽), Kang X-R (康祥瑞),Fan W-Y (范文义), et al. Burned area extraction in Huzhong forests based on remote sensing and the spatial analysis of the burned severity. Scientia Silvae Sinicae (林业科学), 2017, 53(3): 163-174 (in Chinese)
[37] Liu W-G (刘卫国), Lü M-L (吕鸣伦). The method of mountain environment gradient analysis based on geographic information system and remote sensing technology. Geographical Research (地理研究), 1997, 16(3): 63-69 (in Chinese)
[38] Hu H-Q (胡海清), Su Z-J (苏志杰), Wei S-J (魏书精), et al. Estimation of net primary productivity of northern great Xing’an Mountain after fire disturbance using multi-temporal remote sensing images. Forest Engineering (森林工程), 2013, 29(2): 1-7 (in Chinese)
[39] Zhou GS, Wang YH, Jiang YL, et al. Estimating biomass and net primary production from forest inventory data: A case study of China’s Larix forests. Forest Ecology and Management, 2002, 169: 149-157
[40] Piao S-L (朴世荣), Fang J-Y (方精云), Guo Q-H (郭庆华). Application of CASA model to the estimation of Chinese terrestrial net primary productivity. Acta Phytoecologica Sinica (植物生态学报), 2001, 25(5): 603-608 (in Chinese)
[41] Li M-Z (李明泽), Wang B (王斌), Fan W-Y (范文义), et al. Simulation of forest net primary production and the effects of fire disturbance in northeast China. Chinese Journal of Plant Ecology (植物生态学报), 2015, 39(4): 322-332 (in Chinese)
[42] Guo Z-X (国志兴), Wang Z-M (王宗明), Zhang B (张柏), et al. Analysis of temporal-spatial characteri-stics and factors influencing vegetation NPP in northeast China from 2000 to 2006. Resources Science (资源科学), 2008, 30(8): 1226-1235 (in Chinese)
[43] Ni J, Zhang XS, Scurlock MO. Synthesis and analysis of biomass and net primary productivity in Chinese forests. Annals of Forest Science, 2001, 58: 351-384
[44] Miller JD, Thode AE. Quantifying burn severity in a hete-rogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 2007, 109: 66-80
[45] Han F-L (韩风林), Bu R-C (布仁仓), Chang Y (常禹), et al. Dynamics of recovery process of understory vegetation of Betula platyphylla-Larix gmelinii forest in Daxing’an Mountains after fire disturbance. Chinese Journal of Ecology (生态学杂志), 2015, 34(2): 312-318 (in Chinese)
[46] Shen Z-H (沈泽昊), Zhang X-S (张新时), Jin Y-X (金义兴). Gradient analysis of the influence of mountain topography on vegetation pattern. Acta Phytoecologica Sinica (植物生态学报), 2000, 24(4): 430-435 (in Chinese)
[47] Xie F-J (解伏菊), Xiao D-N (肖笃宁), Li X-Z (李秀珍), et al. Forest landscape restoration and its affec-ting factors in burned area of northern Great Xing’an Mountains: Taking forest coverage as an example. Chinese Journal of Applied Ecology (应用生态学报), 2005, 16(9): 1711-1718 (in Chinese)