Spatio-temporal Variations in Plantation Forests’ Disturbance and Re-covery of Northern Guangdong Province Using Yearly Landsat TimeSeries Observations (1986–2015)
SHEN Wenjuan1, 2, LI Mingshi1, 2, WEI Anshi3
(1. College of Forestry, Nanjing Forestry University, Nanjing 210037, China;
2. Co-Innovation Center for Sustainable Forestry inSouthern China, Nanjing Forestry University, Nanjing 210037, China;
3. Guangdong Provincial Center for Forest ResourcesMonitoring, Guangzhou 510173, China)
Chin. Geogra. Sci. 2017 Vol. 27 No. 4 pp. 600–613
Abstract:
Forest disturbance plays a vital role in modulating carbon storage, biodiversity and climate change. Yearly Landsat imageryfrom 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study. ALandsat 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, magni-tudes and rates of plantations’ disturbance and recovery. And the validation results of the disturbance year maps derived from five ran-domly identified sample plots with 25 km2 located at the four corners and the center of the scene showed the majority of the spatialagreement measures ranged from 60% to 83%. A confusion matrix summary of the accuracy measures for all four validation sites inFogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%. Forest disturbance rates’ changetrend was characterized by a decline first, followed by an increase, then giving way to a decline again. An undulated and gentle decreas-ing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001, disturbancerate 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 2011to 2015 (2.59%). Two obvious spikes in post-disturbance recovery rates occurred in 1995 (0.26%) and 2008 (0.41%). Overall, forest recov-ery rates were lower than forest disturbance rates. Moreover, forest disturbance and recovery detection based on VCT and the Landsat-baseddetections of trends in disturbance and recovery (LandTrendr) algorithms in Fogang County have been conducted, with LandTrendr findingmostly much more disturbance than VCT. Overall, disturbances and recoveries in northern Guangdong were triggered mostly by timberneeds, policies and decisions of the local governments. This study highlights that a better understanding about plantations’ changes wouldprovide a critical foundation for local forest management decisions in the southern China.
Keywords: plantation; Landsat dense time series; remote sensing; forest disturbance and recovery; driving forces; northern Guangdong
4 Conclusions
This work has developed a forest disturbance and re-covery history using a vegetation change tracker (VCT)model within the plantation in northern Guangdongbased on an assembled LTSS consisting of annualLandsat time series observations spanning from 1986 to2015. It has demonstrated that VCT has a stable map-ping abrupt disturbance although implementing VCTconfronts more difficulties outside of the USA. AndVCT and LandTrendr algorithms utilized in FogangCounty have been conducted, with LandTrendr findingmostly much more disturbance than VCT. The mappedforest change patterns in northern Guangdong provinceare not only highly related to Chinese economic, demo-graphic, environmental and political policies, but also tosome extreme catastrophic events under the specialgeographical conditions. It is our belief that populariz-ing the automated algorithms is beneficial to create aclear sight of forest disturbance and recovery historiesand provide a critical component for assessing forestmanagement, forest carbon sequestration and planningbiodiversity conservation under climate change.
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