SHEN Wenjuan, LI Mingshi, WEI Anshi. Spatio-temporal Variations in Plantation Forests' Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986-2015)[J]. Chinese Geographical Science, 2017, 27(4): 600-613. doi: 10.1007/s11769-017-0880-z
Citation: SHEN Wenjuan, LI Mingshi, WEI Anshi. Spatio-temporal Variations in Plantation Forests' Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986-2015)[J]. Chinese Geographical Science, 2017, 27(4): 600-613. doi: 10.1007/s11769-017-0880-z

Spatio-temporal Variations in Plantation Forests' Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986-2015)

doi: 10.1007/s11769-017-0880-z
Funds:  Under the auspices of the ‘948’ Project sponsored by the State Forestry Administration (SFA) of China (No. 2014-4-25), National Natural Science Foundation of China (No. 31670552, 31270587), Doctorate Fellowship Foundation of Nanjing Forestry University, the PAPD (Priority Academic Program Development) of Jiangsu Provincial Universities, Graduate Research and Innovation Projects in Jiangsu Province (No. KYLX15_0908)
More Information
  • Corresponding author: LI Mingshi.E-mail:nfulms@njfu.edu.cn
  • Received Date: 2016-03-10
  • Rev Recd Date: 2016-07-08
  • Publish Date: 2017-08-27
  • Forest disturbance plays a vital role in modulating carbon storage, biodiversity and climate change. Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study. A Landsat time series stack (LTSS) was fed to the vegetation change tracker model (VCT) to map long-term changes in plantation forests' disturbance and recovery, followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations, magnitudes and rates of plantations' disturbance and recovery. And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%. A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%. Forest disturbance rates' change trend was characterized by a decline first, followed by an increase, then giving way to a decline again. An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001, disturbance rate of 4.51% in 1994 was a notable anomaly, while after 2001 there was a sharp ascending change, forest disturbance rate spiked in 2007 (5.84%). After that, there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015 (2.59%). Two obvious spikes in post-disturbance recovery rates occurred in 1995 (0.26%) and 2008 (0.41%). Overall, forest recovery rates were lower than forest disturbance rates. Moreover, forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery (LandTrendr) algorithms in Fogang County have been conducted, with LandTrendr finding mostly much more disturbance than VCT. Overall, disturbances and recoveries in northern Guangdong were triggered mostly by timber needs, policies and decisions of the local governments. This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.
  • [1] Chen Xingliang, Ju Qian, Lin Kunlun, 2014. Development status' issues and countermeasures of China's Plantation. World Forestry Research, 27(6): 54-59. (in Chinese)
    [2] Cohen W B, Yang Z Q, Stehman S V et al., 2016. Forest disturbance across the conterminous United States from 1985-2012: the emerging dominance of forest decline. Forest Ecology and Management, 360: 242-252. doi:10.10 16/j.foreco.2015.10. 042
    [3] Coppin P R, Jonckheere I, Nackaerts K et al., 2004. Digital change detection methods in ecosystem monitoring: a review. Journal of Remote Sensing, 25(9): 1565-1596. doi: 10.1080/0143116031000101675
    [4] DaleV H, Joyce L A, McNulty S et al., 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. doi:10.1641/0006-3568 (2001)051[0723: CCAFD]2.0.
    [5] Edwards D P, Tobias J A, Sheil D et al., 2014. Maintaining ecosystem function and services in logged tropical forests. Trends in Ecology & Evolution, 29(9): 511-520. doi:10.1016/j.tree. 2014.07.003
    [6] Fang Jingyun, Chen Anping, 2001. Dynamic forest biomass carbon pools in China and their significance. Acta Botanica Sinica, 43(9): 967-973. (in Chinese)
    [7] Frolking S, Palace M W, Clark D B et al., 2009. Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research, 114, G00E02. doi: 10.1029/2008JG000911
    [8] Goward S N, Masek J G, Cohen W et al., 2008. Forest disturbance and North American carbon flux. EOS Transactions, American Geophysical Union, 89: 105-106.
    [9] Huang Congde, Zhang Guoqing, 2009. Impact factors of carbon sequestration in artificial forest carbon stock. World Forestry Research, 22(2): 34-38.
    [10] Huang C Q, Townshend J R G, Liang S L et al., 2002. Impact of sensor's point spread function on land cover characterization: assessment and deconvolution. Remote Sensing of Environment, 80: 203-212. doi: 10.1016/S0034-4257(01)00298-X
    [11] Huang C Q, Goward S N, Schleeweis K et al., 2009. Dynamics of national forests assessed using the Landsat record: case studies in eastern United States. Remote Sensing of Environment, 113: 1430-1442. doi: 10.1016/j.rse.2008.06.016
    [12] Huang C Q, Goward S N, Masek J G et al., 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing Environment, 114: 183-198. doi:10.1016/j.rse. 2009. 08.017
    [13] Kennedy R E, Cohen W B, Schroeder T A, 2007. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing Environment, 110: 370-386. doi: 10.1016/j.rse.2007.03.010
    [14] Kennedy R E, Yang Z Q, Cohen W B, 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1, LandTrendr-Temporal segmentation algorithms. Remote Sensing Environment, 114: 2897-2910. doi: 10.1016/j.rse.2010.07.008
    [15] Kennedy R E, Yang Z Q, Cohen W B et al., 2012. Spatial and temporal patterns of forest disturbance and growth within the area of the Northwest Forest Plan. Remote Sensing Environment, 122: 117-133. doi: 10.1016/j.rse.2011.09.024
    [16] Kennedy R E, Yang Z Q, Braaten J et al., 2015. Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sensing of Environment, 166: 271-285. doi:10.1016/j.rse. 2015.05.005
    [17] Levers C, Verkerk P J, Müller D et al., 2014. Drivers of forest harvesting intensity patterns in Europe. Forest Ecology and Management, 315: 160-172. doi:10.1016/j.foreco.2013.12. 030
    [18] Li M S, Huang C Q, Zhu Z L et al., 2009a. Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model. Forest Ecology and Management, 257: 1480-1488. doi:10.1016/j.foreco.2008. 12.023
    [19] Li M S, Huang C Q, Zhu Z L et al., 2009b. Use of remote sensing coupled with a vegetation change tracker model to assess rates of forest change and fragmentation in Mississippi, USA. International Journal of Remote Sensing, 30: 6559-6574. doi: 10.1080/01431160903241999
    [20] Lu D S, Mausel P, Brondízio E et al., 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365-2401. doi: 10.1080/0143116031000139863
    [21] Ma Z Q, Hartmann H, Wang H M et al., 2013. Carbon dynamics and stability between native Masson pine and exotic slash pine plantations in subtropical China. European Journal of Forest Research, 133(2): 307-321. doi: 10.1007/s10342-013-0763-5
    [22] Mas J F, 1999. Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing, 20(1): 139-152. doi: 10.1080/014311699213659
    [23] Masek J G, Goward S N, Kennedy R E et al., 2013. United States forest disturbance trends observed with Landsat time series. Ecosystems, 16: 1087-1104. doi: 10.1007/s10021-013-9669-9
    [24] Neigh C S R, Bolton D K, Williams J J et al., 2014. Evaluating an automated approach for monitoring forest disturbances in the Pacific Northwest from logging, fire and insect outbreaks with Landsat time series data. Forests, 5: 3169-3198. doi: 10.3390/f5123169
    [25] Pei F S, Li X, Liu X P et al., 2015. Exploring the response of net primary productivity variations to urban expansion and climate change: A scenario analysis for Guangdong Province in China. Journal of Environmental Management, 150: 92-102. doi: 10.1016/j.jenvman.2014.11.002
    [26] Pflugmacher D, Cohen W B, Kennedy R E, 2012. Comparison between Landsat derived disturbance history (1972-2010) to predict current forest structure. Remote Sensing of Environment, 122: 146-165. doi: 10.1016/j.rse.2011.09.025
    [27] Piao S L, Fang J Y, Ciais P et al., 2009. The carbon balance of terrestrial ecosystem in China. Nature, 458: 1009-1013. doi: 10.1038/nature07944
    [28] Roy D P, Wulder M A, Loveland T R et al., 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145: 154-172. doi: 10.1016/j.rse.2014.02.001
    [29] Schroeder T A, Moisen G G, Healey S P et al., 2012. Adding value to the FIA inventory: combining FIA data and satellite observations to estimate forest disturbance. In: Morin, Randall S,Liknes, Greg C, comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; [CD-ROM]: 143-148.
    [30] Schroeder T A, Healey S P, Moisen G G et al., 2014. Improving estimates of forest disturbance by combining observations from Landsat time series with US Forest Service Forest Inventory and Analysis data. Remote Sensing of Environment, 154: 61-73. doi: 10.1016/j.rse.2014.08.005
    [31] Shen Wenjuan, Li Mingshi, 2014. Method for Landsat dense time series data format unification and surface reflectance conversion. Remote Sensing for Land & Resources, 26(4): 78-84. doi:10.6046/gtzyyg.2014.04.13 (in Chinese)
    [32] Shen W J, Li M S, Huang C Q et al., 2016. Quantifying live aboveground biomass and forest disturbance of mountainous natural and plantation forests in northern Guangdong, China, based on multi-temporal Landsat, PALSAR and field plot data. Remote sensing, 8(7): 595. doi: 10.3390/rs8070595
    [33] Shen Wenjuan, Li Mingshi, 2016. Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations. Acta Ecologica Sinica, 37(5): 1438-1449. doi:10.5846/stxb201510142074 (in Chinese)
    [34] Stehman S V, Czaplewski R L, 1998. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64: 331-344. doi: 10.1016/S0034-4257(98)00010-8
    [35] Stone R, 2008. Ecologists report huge storm losses in China's forests. Science, 319: 1318-1319. doi:10.1126/science. 319. 5868.1318
    [36] Thomas N E, Huang C Q, Goward S N et al., 2011. Validation of North American forest disturbance dynamics derived from Landsat time series stacks. Remote Sensing of Environment, 115: 19-32. doi: 10.1016/j.rse.2010.07.009
    [37] Townshend J R G, Justice C O, McManus J, 1992. The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing, 30(5): 1054-1060. doi: 10.1109/36.175340
    [38] Turner D P, Ritts W D, Kennedy R E et al., 2015. Effects of harvest, fire, and pest/pathogen disturbances on the West Cascades ecoregion carbon balance. Carbon Balance and Management, 10: 12. doi: 10.1186/s13021-015-0022-9
    [39] Vogelmann J E, Xian G, Homer C et al., 2012. Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment, 122: 92-105. doi:10.1016/j.rse.2011. 06.027
    [40] Woodcock C E, Allen R, Anderson M et al., 2008. Free access to Landsat imagery. Science, 320(5879): 1011. doi: 10.1126/science.320.5879.1011a.
    [41] Wu Zhijun, Su Dongkai, Niu Lijun et al., 2016. Effects of logging intensity on structure and composition of a broadleaf-korean pine mixed forest on Changbai Mountains, Northeast China. Chinese Geographical Science, 26(1): 59-67. doi: 10.1007/s11769-015-0
    [42] Zhao F, Huang C Q, Zhu Z L, 2015. Use of vegetation change tracker and support vector machine to map disturbance types in greater yellowstone ecosystem in a 1984-2010 Landsat time series. IEEE Geoscience and Remote Sensing Letters: 1-5. doi: 10.1109/LGRS.2015.2418159
    [43] Zhou B Z, Gu L H, Ding Y H et al., 2011. The great 2008 Chinese ice storm: its socioeconomic-ecological impact and sustainability lessons learned. Bulletin of the American Meteorological Society, 92(1): 47-60. doi:10.1175/2010 BAMS2857.1
    [44] Zhu Z, Woodcock C E, Olofsson P, 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment, 122: 75-91. doi:10.1016/j.rse. 2011.10.030
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Spatio-temporal Variations in Plantation Forests' Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986-2015)

doi: 10.1007/s11769-017-0880-z
Funds:  Under the auspices of the ‘948’ Project sponsored by the State Forestry Administration (SFA) of China (No. 2014-4-25), National Natural Science Foundation of China (No. 31670552, 31270587), Doctorate Fellowship Foundation of Nanjing Forestry University, the PAPD (Priority Academic Program Development) of Jiangsu Provincial Universities, Graduate Research and Innovation Projects in Jiangsu Province (No. KYLX15_0908)
    Corresponding author: LI Mingshi.E-mail:nfulms@njfu.edu.cn

Abstract: Forest disturbance plays a vital role in modulating carbon storage, biodiversity and climate change. Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study. A Landsat time series stack (LTSS) was fed to the vegetation change tracker model (VCT) to map long-term changes in plantation forests' disturbance and recovery, followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations, magnitudes and rates of plantations' disturbance and recovery. And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%. A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%. Forest disturbance rates' change trend was characterized by a decline first, followed by an increase, then giving way to a decline again. An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001, disturbance rate of 4.51% in 1994 was a notable anomaly, while after 2001 there was a sharp ascending change, forest disturbance rate spiked in 2007 (5.84%). After that, there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015 (2.59%). Two obvious spikes in post-disturbance recovery rates occurred in 1995 (0.26%) and 2008 (0.41%). Overall, forest recovery rates were lower than forest disturbance rates. Moreover, forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery (LandTrendr) algorithms in Fogang County have been conducted, with LandTrendr finding mostly much more disturbance than VCT. Overall, disturbances and recoveries in northern Guangdong were triggered mostly by timber needs, policies and decisions of the local governments. This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.

SHEN Wenjuan, LI Mingshi, WEI Anshi. Spatio-temporal Variations in Plantation Forests' Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986-2015)[J]. Chinese Geographical Science, 2017, 27(4): 600-613. doi: 10.1007/s11769-017-0880-z
Citation: SHEN Wenjuan, LI Mingshi, WEI Anshi. Spatio-temporal Variations in Plantation Forests' Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986-2015)[J]. Chinese Geographical Science, 2017, 27(4): 600-613. doi: 10.1007/s11769-017-0880-z
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