Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations (1988–2016)

Abstract

Climate change, a recognized critical environmental issue, plays an important role in regulating the structure and function of forest ecosystems by altering forest disturbance and recovery regimes. This research focused on exploring the statistical relationships between meteorological and topographic variables and the recovery characteristics following disturbance of plantation forests in southern China. We used long-term Landsat images and the vegetation change tracker algorithm to map forest disturbance and recovery events in the study area from 1988 to 2016. Stepwise multiple linear regression (MLR), random forest (RF) regression, and support vector machine (SVM) regression were used in conjunction with climate variables and topographic factors to model short-term forest recovery using the normalized difference vegetation index (NDVI). The results demonstrated that the regene-rating forests were sensitive to the variation in temperature. The fitted results suggested that the relationship between the NDVI values of the forest areas and the post-disturbance climatic and topographic factors differed in regression algorithms. The RF regression yielded the best performance with an R² value of 0.7348 for the validation accuracy. This indicated that slope and temperature, especially high temperatures, had substantial effects on post-disturbance vegetation recovery in southern China. For other mid-subtropical monsoon regions with intense light and heat and abundant rainfall, the information will also contribute to appropriate decisions for forest managers on forest recovery measures. Additionally, it is essential to explore the relationships between forest recovery and climate change of different vegetation types or species for more accurate and targeted forest recovery strategies.

Keywords

climate change / forest disturbance / forest recovery / vegetation change tracker

References

[1] Bolton D K, Coops N C, Wulder M A (2013). Measuring forest structure along productivity gradients in the Canadian boreal with small-footprint Lidar. Environ Monit Assess, 185(8): 6617–6634

[2] Breiman L (2001). Random forests. Mach Learn, 45(1): 5–32

[3] Carlson T, Perry E, Schmugge T (1990). Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agric Meteorol, 52(1–2): 45–69

[4] Chrysopolitou V, Apostolakis A, Avtzis D, Avtzis N, Diamandis S, Kemitzoglou D, Papadimos D, Perlerou C, Tsiaoussi V, Dafis S (2013). Studies on forest health and vegetation changes in Greece under the effects of climate changes. Biodivers Conserv, 22(5): 1133–1150

[5] Dale V, Joyce L, Mcnulty S, Neilson R P, Ayres M P, Flannigan M D, Hanson P J, Irland L C, Lugo A, Peterson C J, Simberloff D, Swanson F J, Stocks B J, Michael Wotton B (2001). Climate change and forest disturbances climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. Bioscience, 51(9): 723–734

[6] Díaz-Delgado R, Lloret F, Pons X, Terradas J (2002). Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology, 83(8): 2293–2303

[7] Frolking S, Palace M, Clark D, Chambers J, Shugart H, Hurtt G (2015). Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J Geophys Res Biogeosci, 114 (G2)

[8] Goward S, Masek J, Cohen W, Moisen G, Collatz G J, Healey S, Houghton R A, Huang C, Kennedy R, Law B, Powell S, Turner D, Wulder M A (2008). Forest disturbance and North American carbon flux. Eos (Wash DC), 89(11): 105–116

[9] Huang C, Davis L, Townshend J (2002). An assessment of support vector machines for land cover classification. Int J Remote Sens, 23(4): 725–749

[10] Huang C, Goward S, Masek J, Gao F, Vermote E F, Thomas N, Schleeweis K, Kennedy R E, Zhu Z, Eidenshink J C, Townshend J R G (2009). Development of time series stacks of Landsat images for reconstructing forest disturbance history. Int J Digit Earth, 2(3): 195–218

[11] Huang C, Goward S, Masek J, Thomas N, Zhu Z, Vogelmann J (2010). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ, 114(1): 183–198

[12] IPCC (2014). Intergovernmental Panel on Climate Change. Geneva: Fifth Assessment Report

[13] João T, João G, Bruno M, João H (2018). Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series. Ecol Indic, 89: 199–212

[14] Li M, Huang C, Shen W, Ren X, Lv Y, Wang J, Zhu Z (2016). Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987–2011). J For Res, 27(6): 1329–1341

[15] Li M, Huang C, Zhu Z, Shi H, Lu H, Peng S (2009). Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model. For Ecol Manage, 257(6): 1480–1488

[16] Li M, Zhu Z, Vogelmann J, Xu D, Wen W, Liu A (2011). Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: a case study from Kecheng district of Zhejiang Province. Appl Geogr, 31(3): 1026–1035

[17] Li W, Wang Q, Shen L (2014). Impact of climate change on forest ecosystems and countermeasures of sustainable forest development. Forest Inventory and Planning, 1: 94–97

[18] Liu X, Wu J, Xu J (2006). Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS. Environ Pollut, 141(2): 257–264

[19] Liu Y, Xu Z, Wen S, Zhang X (2004). Study on forest regional and industrial features and its strategic development in Guangdong Province. J South Chin Agric Univ, 2004(04): 50–57

[20] Liu Z (2016). Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China. Sci Rep, 6(1): 819–822

[21] Luo D, Huang J G, Jiang X, Ma Q, Liang H, Guo X, Zhang S (2017). Effect of climate and competition on radial growth of Pinus massoniana and Schima superba in China’s subtropical monsoon mixed forest. Dendrochronologia, 46: 24–34

[22] Masek J, Vermote E, Saleous N, Wolfe R, Hall F G, Huemmrich K F, Gao F, Kutler J, Lim T K (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci Remote Sens Lett, 3(1): 68–72

[23] Mccauley L, Robles M, Woolley T, Marchall R, Kretchun A, Gori D (2019) Large-scale forest restoration stabilizes carbon under climate change in Southwest United States. Ecological Applications, 29(8): 1–14

[24] Meng R, Dennison P, Huang C, Moritz M A, D’Antonio C (2015). Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California. Remote Sens Environ, 171: 311–325

[25] Meng R, Wu J, Zhao F, Cook B D, Hanavan R P, Serbin S P (2018). Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques. Remote Sens Environ, 210: 282–296

[26] Mildrexler D, Yang Z, Cohen W B, Bell D M (2016). A forest vulnerability index based on drought and high temperatures. Remote Sens Environ, 173: 314–325

[27] Minore D, Laacke R J (1992). Natural Regeneration. Corvallis: Oregon State University Press,258–283

[28] National Forestry and Grassland Administration (2018). China Forestry Yearbook. Beijing: China Forestry Publishing House

[29] O’Halloran T, Law B, Goulden M, Wang Z, Barr J G, Schaaf C, Brown M, Fuentes J D, Göckede M, Black A, Engel V (2012). Radiative forcing of natural forest disturbances. Glob Change Biol, 18(2): 555–565

[30] Pan Y, Birdsey R A, Fang J, Houghton R, Kauppi P E, Kurz W A, Phillips O L, Shvidenko A, Lewis S L, Canadell J G, Ciais P, Jackson R B, Pacala S W, McGuire A D, Piao S, Rautiainen A, Sitch S, Hayes D (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045): 988–993

[31] Pang G, Wang X, Yang M (2017). Using the NDVI to identify variations in, and responses of, vegetation to climate change on the Tibetan Plateau from 1982 to 2012. Quat Int, 444: 87–96

[32] Parmesan C, Yohe G (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918): 37–42

[33] Pringle M, Schmidt M, Muir J (2009). Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS J Photogramm Remote Sens, 64(6): 654–664

[34] Roopsind A, Wortel V, Hanoeman W, Putz F (2017). Quantifying uncertainty about forest recovery 32-years after selective logging in Suriname. For Ecol Manage, 391: 246–255

[35] Savage M (1991). Structural dynamics of a southwestern pine forest under chronic human influence. Ann Assoc Am Geogr, 81(2): 271–289

[36] Shen W, Li M, Huang C, Tao X, Wei A (2018). Annual forest aboveground biomass changes mapped using ICESat/GLAS measure-ments, historical inventory data, and time-series optical and radar imagery for Guangdong province, China. Agric Meteorol, 259: 23–38

[37] Shen W, Li M, Huang C, He T, Tao X, Wei A (2019). Local land surface temperature change induced by afforestation based on satellite observations in Guangdong plantation forests in China. Agric Meteorol, 276–277: 107641

[38] Sun G, Zeng X, Liu X (2007). Effects of moderate high-temperature stress on photosynthesis in three saplings of the constructive tree species of subtropical forest. Acta Ecol Sin, 27(4): 1283–1290

[39] Sun Y, Cao F, Wei X, Welham C, Chen L, Pelz D, Yang Q, Liu H (2017). An ecologically based system for sustainable agroforestry in sub-tropical and tropical forests. Forests, 8(4): 1–18

[40] Sun Y, Wu J, Shao Y, Zhou L, Mai B, Lin Y, Fu S (2011). Responses of soil microbial communities to prescribed burning in two paired vegetation sites in southern China. Ecol Res, 26(3): 669–677

[41] Theil H (1992). A rank-invariant method of linear and polynomial regression analysis. Nederl akad wetensch proc, 12(2): 345–381

[42] Upgupta S, Sharma J, Jayaraman M, Kumar V, Ravindranath N (2015). Climate change impact and vulnerability assessment of forests in the Indian Western Himalayan region: a case study of Himachal Pradesh, India. Clim Risk Manage, 10(2): 63–76

[43] van Leeuwen W, Casady G, Neary D, Bautista S, Alloza J A, Carmel Y, Wittenberg L, Malkinson D, Orr B J (2010). Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. Int J Wildland Fire, 19(1): 75–93

[44] Vermote E, Justice C, Claverie M, Franch B (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ, 185(2): 46–56

[45] Wang H (2018). Assessing forest disturbance, post-fire forest recovery and its coupling mechanism for climate change. Dissertation for the Master’s Degree. Nanjing: Nanjing Forestry University (in Chinese)

[46] Wang W, He H, Thompson F III, Fraser J, Dijak W (2016). Landscape- and regional-scale shifts in forest composition under climate change in the Central Hardwood Region of the United States. Landsc Ecol, 31(1): 149–163

[47] Wu L (2014). Forest disturbance detection by remote sensing: a case study of Jiangxi Province. Dissertation for the Master’s Degree. Nanjing: Nanjing University of Information Science & Technology

[48] Xin Q, Olofsson P, Zhu Z, Tan B, Woodcock C (2013). Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sens Environ, 135: 234–247

[49] Xu Z (1999). A discussion of forestry pollcy in Guangdong Province. Journal of Southwest Forestry College, 02: 105–108

[50] Zhan X, Liang X, Xu G, Zhou L (2013). Influence of plant root morphology and tissue composition on phenanthrene uptake: stepwise multiple linear regression analysis. Environ Pollut, 179: 294–300

[51] Zhao F, Huang C, Zhu Z (2015). Use of vegetation change tracker and support vector machine to map disturbance types in Greater Yellowstone ecosystems in a 1984–2010 landsat time series. IEEE Geosci Remote Sens Lett, 12(8): 1650–1654

[52] Zhao Z, Wang H, Du J, Bai X, Geng S, Wan F (2016). Spatial distribution of forest carbon based on GIS and geostatistical theory in a small Earth-Rocky Mountainous Area of North China. J Biobased Mater Bioenergy, 10(2): 90–99

[53] Zhen Y, Sun P, Liu S (2011). Response of normalized difference vegetation index in main vegetation types to climate change and their variations in different time scales along a North-South Transect of Eastern China. Acta Phytoecol Sin, 35(11): 1117–1126

[54] Zhou B, Gu L, Ding Y, Shao L, Wu Z, Yang X, Li C, Li Z, Wang X, Cao Y, Zeng B, Yu M, Wang M, Wang S, Sun H, Duan A, An Y, Wang X, Kong W (2011). The great 2008 Chinese ice storm: its socioeconomic-ecological impact and sustainability lessons learned. Bull Am Meteorol Soc, 92(1): 47–60

Acknowledgments

We express our gratitude to the USGS EROS Center for providing the standardized Landsat images to support the analysis. This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 31971577 and 31670552), the Biodiversity Investigation, Observation and Assessment Program sponsored by the Ministry of Ecology and Environment of China (2019-2023), the China Postdoctoral Science Foundation (No. 2019M651842), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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