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Frontiers of Earth Science

Front. Earth Sci.
RESEARCH ARTICLE
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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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 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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http://journal.hep.com.cn/fesci/EN/10.1007/s11707-020-0820-6
http://journal.hep.com.cn/fesci/EN/Y/V/I/0
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Fangyan ZHU
Heng WANG
Mingshi LI
Jiaojiao DIAO
Wenjuan SHEN
Yali ZHANG
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
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