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Frontiers of Earth Science

Front. Earth Sci.
Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations (1988–2016)
Fangyan ZHU1,2, Heng WANG1,2, Mingshi LI1,2(), Jiaojiao DIAO1,2, Wenjuan SHEN1,2, Yali ZHANG1,2, Hongji WU1,2
1. College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
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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 regenerating 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     
Corresponding Author(s): Mingshi LI   
Online First Date: 24 July 2020   
 Cite this article:   
Fangyan ZHU,Heng WANG,Mingshi LI, et al. Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations (1988–2016)[J]. Front. Earth Sci., 24 July 2020. [Epub ahead of print] doi: 10.1007/s11707-020-0820-6.
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Fangyan ZHU
Mingshi LI
Jiaojiao DIAO
Wenjuan SHEN
Hongji WU
Image index Acquisition date Satellite Sensor Image quality
1 10/16/1988 Landsat 5 TM High
2 07/15/1989 Landsat 5 TM 10% cloud coverage
3 10/22/1990 Landsat 5 TM 17% cloud coverage
4 09/23/1991 Landsat 5 TM 5% cloud coverage
5 10/09/1991 Landsat 5 TM High
6 10/11/1992 Landsat 5 TM 16% cloud coverage
7 10/01/1994 Landsat 5 TM High
8 09/18/1995 Landsat 5 TM 5% cloud coverage
9 10/20/1995 Landsat 5 TM 14% cloud coverage
10 08/20/1999 Landsat 7 ETM+ 1% cloud coverage
11 10/17/2000 Landsat 5 TM 14% cloud coverage
12 05/13/2001 Landsat 5 TM High
13 08/28/2002 Landsat 7 ETM+ 24% cloud coverage
14 10/07/2002 Landsat 5 TM 41% cloud coverage
15 07/06/2003 Landsat 5 TM 27% cloud coverage
16 07/22/2003 Landsat 5 TM 2% cloud coverage
17 09/26/2004 Landsat 5 TM 1% cloud coverage
18 08/12/2005 Landsat 5 TM 1% cloud coverage
19 10/05/2007 Landsat 5 TM High
20 09/21/2008 Landsat 5 TM 6% cloud coverage
21 10/26/2009 Landsat 5 TM High
22 10/29/2010 Landsat 5 TM High
23 08/10/2010 Landsat 5 TM 23% cloud coverage
24 07/28/2011 Landsat 5 TM 5% cloud coverage
25 09/19/2013 Landsat 8 OLI 4% cloud coverage
26 10/08/2014 Landsat 8 OLI 3% cloud coverage
27 09/27/2016 Landsat 8 OLI High
Tab.1  Landsat TM/ETM+ /OLI scenes used in this analysis (WRS2 path/row= 121/043)
Value Class description in VCT model Aggregated class
0 Background area Abandoned
1 Persisting nonforest Nonforest
2 Persisting forest Forest
4 Persisting water Nonforest
5 Previously disturbed but spectrally restored to forest this year Forest
6 Disturbed in this year Nonforest
7 Post-disturbance nonforest Nonforest
Tab.2  Definition and aggregation of forest disturbance maps developed from VCT
Fig.1  Forest disturbance patterns mapped by the VCT algorithm, 1988–2016. The three black squares with a side length of 3 km are the validation plots that were randomly identified on the disturbance map. The four images on the right show the validation process of the eastern plot: (a) Google Earth image in 2008; (b) Google Earth image in 2009; (c) year of disturbance (showing the disturbance that occurred in 2009); (d) Google Earth image in 2016. Spatial agreement= the area of agreement area between (b) and (c) divided by the area of (b).
Fig.2  Changes in the forest disturbance/recovery area and rates obtained from the VCT algorithm.
Eastern plot Middle plot Western plot
Disturbance year Agreement measure/% Disturbance year Agreement measure/% Disturbance year Agreement measure/%
1989 74.54 1989 92.35 1989 78.15
1990 80.31 1990 91.50 1990 65.84
1991 59.89 1991 80.82 1991 69.31
1992 61.07 1992 71.10 1992 61.01
1995 84.03 1995 70.00 1995 72.54
2000 60.03 2000 60.48 2000 61.44
2001 67.20 2001 75.93 2001 77.89
2004 89.53 2004 60.57 2004 89.63
2005 81.27 2005 86.51 2005 72.32
2006 66.16 2006 74.24 2006 74.92
2007 86.83 2007 79.42 2007 70.08
2008 70.24 2008 73.91 2008 72.25
2009 66.82 2009 70.37 2009 62.31
2010 72.82 2010 73.40 2010 71.30
2011 76.61 2011 73.93 2011 71.65
2013 71.52 2012 78.34 2012 70.81
2014 69.36 2014 72.33 2014 80.12
2016 73.86 2016 74.25 2016 72.33
Tab.3  Spatial agreement measurements of the 9 km2 validation plots
Fig.3  Forest recovery trend based on the Theil-Sen estimation.
R2 MRE RMSE Maximum Minimum Mean
Stepwise LM 0.5475 0.0979 0.0028 0.9020 0.3348 0.6924
Random forest (RF) 0.9464 0.0345 0.0004 0.8430 0.1736 0.6920
SVM 0.9784 0.0196 0.0001 0.8719 0.1577 0.6911
Observed NDVI 0.883 0.1413 0.6924
Tab.4  Fitting accuracy
R2 MRE RMSE Maximum Minimum Mean
Stepwise LM 0.5814 0.1616 0.0044 0.8578 0.3798 0.6773
Random forest (RF) 0.7667 0.0714 0.0017 0.8170 0.2124 0.6783
SVM 0.6870 0.0803 0.0021 0.8200 0.2425 0.6758
Observed NDVI 0.8265 0.1615 0.6762
Tab.5  Validation accuracy
1 D K Bolton, N C Coops, M A Wulder (2013). Measuring forest structure along productivity gradients in the Canadian boreal with small-footprint Lidar. Environ Monit Assess, 185(8): 6617–6634 pmid: 23291915
2 L Breiman (2001). Random forests. Mach Learn, 45(1): 5–32
3 T Carlson, E Perry, T Schmugge (1990). Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agric Meteorol, 52(1–2): 45–69
4 V Chrysopolitou, A Apostolakis, D Avtzis, N Avtzis, S Diamandis, D Kemitzoglou, D Papadimos, C Perlerou, V Tsiaoussi, S Dafis (2013). Studies on forest health and vegetation changes in Greece under the effects of climate changes. Biodivers Conserv, 22(5): 1133–1150
5 V Dale, L Joyce, S Mcnulty, R P Neilson, M P Ayres, M D Flannigan, P J Hanson, L C Irland, A Lugo, C J Peterson, D Simberloff, F J Swanson, B J Stocks, B Michael Wotton (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[0723:CCAFD]2.0.CO;2
6 R Díaz-Delgado, F Lloret, X Pons, J Terradas (2002). Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology, 83(8): 2293–2303[2293:SEODRI]2.0.CO;2
7 S Frolking, M Palace, D Clark, J Chambers, H Shugart, G Hurtt (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 S Goward, J Masek, W Cohen, G Moisen, G J Collatz, S Healey, R A Houghton, C Huang, R Kennedy, B Law, S Powell, D Turner, M A Wulder (2008). Forest disturbance and North American carbon flux. Eos (Wash DC), 89(11): 105–116
9 C Huang, L Davis, J Townshend (2002). An assessment of support vector machines for land cover classification. Int J Remote Sens, 23(4): 725–749
10 C Huang, S Goward, J Masek, F Gao, E F Vermote, N Thomas, K Schleeweis, R E Kennedy, Z Zhu, J C Eidenshink, J R G Townshend (2009). Development of time series stacks of Landsat images for reconstructing forest disturbance history. Int J Digit Earth, 2(3): 195–218
11 C Huang, S Goward, J Masek, N Thomas, Z Zhu, J Vogelmann (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 T João, G João, M Bruno, H João (2018). Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series. Ecol Indic, 89: 199–212
14 M Li, C Huang, W Shen, X Ren, Y Lv, J Wang, Z Zhu (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 M Li, C Huang, Z Zhu, H Shi, H Lu, S Peng (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 M Li, Z Zhu, J Vogelmann, D Xu, W Wen, A Liu (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 W Li, Q Wang, L Shen (2014). Impact of climate change on forest ecosystems and countermeasures of sustainable forest development. Forest Inventory and Planning, 1: 94–97
18 X Liu, J Wu, J Xu (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 pmid: 16271428
19 Y Liu, Z Xu, S Wen, X Zhang (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 Z Liu (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 pmid: 27857204
21 D Luo, J G Huang, X Jiang, Q Ma, H Liang, X Guo, S Zhang (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 J Masek, E Vermote, N Saleous, R Wolfe, F G Hall, K F Huemmrich, F Gao, J Kutler, T K Lim (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci Remote Sens Lett, 3(1): 68–72
23 L Mccauley, M Robles, T Woolley, R Marchall, A Kretchun, D Gori (2019) Large-scale forest restoration stabilizes carbon under climate change in Southwest United States. Ecological Applications, 29(8): 1–14
24 R Meng, P Dennison, C Huang, M A Moritz, C D’Antonio (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 R Meng, J Wu, F Zhao, B D Cook, R P Hanavan, S P Serbin (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 D Mildrexler, Z Yang, W B Cohen, D M Bell (2016). A forest vulnerability index based on drought and high temperatures. Remote Sens Environ, 173: 314–325
27 D Minore, R J Laacke (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 T O’Halloran, B Law, M Goulden, Z Wang, J G Barr, C Schaaf, M Brown, J D Fuentes, M Göckede, A Black, V Engel (2012). Radiative forcing of natural forest disturbances. Glob Change Biol, 18(2): 555–565
30 Y Pan, R A Birdsey, J Fang, R Houghton, P E Kauppi, W A Kurz, O L Phillips, A Shvidenko, S L Lewis, J G Canadell, P Ciais, R B Jackson, S W Pacala, A D McGuire, S Piao, A Rautiainen, S Sitch, D Hayes (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045): 988–993 pmid: 21764754
31 G Pang, X Wang, M Yang (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 C Parmesan, G Yohe (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918): 37–42 pmid: 12511946
33 M Pringle, M Schmidt, J Muir (2009). Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS J Photogramm Remote Sens, 64(6): 654–664
34 A Roopsind, V Wortel, W Hanoeman, F Putz (2017). Quantifying uncertainty about forest recovery 32-years after selective logging in Suriname. For Ecol Manage, 391: 246–255
35 M Savage (1991). Structural dynamics of a southwestern pine forest under chronic human influence. Ann Assoc Am Geogr, 81(2): 271–289
36 W Shen, M Li, C Huang, X Tao, A Wei (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 W Shen, M Li, C Huang, T He, X Tao, A Wei (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 G Sun, X Zeng, X Liu (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 Y Sun, F Cao, X Wei, C Welham, L Chen, D Pelz, Q Yang, H Liu (2017). An ecologically based system for sustainable agroforestry in sub-tropical and tropical forests. Forests, 8(4): 1–18
40 Y Sun, J Wu, Y Shao, L Zhou, B Mai, Y Lin, S Fu (2011). Responses of soil microbial communities to prescribed burning in two paired vegetation sites in southern China. Ecol Res, 26(3): 669–677
41 H Theil (1992). A rank-invariant method of linear and polynomial regression analysis. Nederl akad wetensch proc, 12(2): 345–381
42 S Upgupta, J Sharma, M Jayaraman, V Kumar, N Ravindranath (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 W van Leeuwen, G Casady, D Neary, S Bautista, J A Alloza, Y Carmel, L Wittenberg, D Malkinson, B J Orr (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 E Vermote, C Justice, M Claverie, B Franch (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ, 185(2): 46–56 pmid: 32020955
45 H Wang (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 W Wang, H He, F III Thompson, J Fraser, W Dijak (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 L Wu (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 Q Xin, P Olofsson, Z Zhu, B Tan, C Woodcock (2013). Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sens Environ, 135: 234–247
49 Z Xu (1999). A discussion of forestry pollcy in Guangdong Province. Journal of Southwest Forestry College, 02: 105–108
50 X Zhan, X Liang, G Xu, L Zhou (2013). Influence of plant root morphology and tissue composition on phenanthrene uptake: stepwise multiple linear regression analysis. Environ Pollut, 179: 294–300 pmid: 23708267
51 F Zhao, C Huang, Z Zhu (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 Z Zhao, H Wang, J Du, X Bai, S Geng, F Wan (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 Y Zhen, P Sun, S Liu (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 B Zhou, L Gu, Y Ding, L Shao, Z Wu, X Yang, C Li, Z Li, X Wang, Y Cao, B Zeng, M Yu, M Wang, S Wang, H Sun, A Duan, Y An, X Wang, W Kong (2011). The great 2008 Chinese ice storm: its socioeconomic-ecological impact and sustainability lessons learned. Bull Am Meteorol Soc, 92(1): 47–60
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