Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product

References

• Asner, G. P., J. R. Kellner, T. Kennedy-Bowdoin, D. E. Knapp, C. Anderson, and R. E. Martin. 2013. “Forest Canopy gap Distributions in the Southern Peruvian Amazon.” Plos One 8 (4): 10. doi:10.1371/journal.pone.0060875.
   Web of Science ®Google Scholar
• Binder, S., R. G. Haight, S. Polasky, T. Warziniack, M. H. Mockrin, R. L. Deal, and G. Arthaud. 2017. Assessment and Valuation of Forest Ecosystem Services: State of the Science Review. Gen. Tech. Rep. NRS-170. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station.
   Google Scholar
• Chambers, J. Q., R. I. Negron-Juarez, G. C. Hurtt, D. M. Marra, and N. Higuchi. 2009. “Lack of Intermediate-Scale Disturbance Data Prevents Robust Extrapolation of Plot-Level Tree Mortality Rates for old-Growth Tropical Forests.” Ecology Letters 12 (12): E22–E25. doi:10.1111/j.1461-0248.2009.01398.x.
   PubMed Web of Science ®Google Scholar
• Clauset, A., C. R. Shalizi, and M. E. J. Newman. 2009. “Power-law Distributions in Empirical Data.” SIAM Review 51 (4): 661–703. doi:10.1137/070710111.
   Web of Science ®Google Scholar
• Cumming, S. G. 2001. “A Parametric Model of the Fire-Size Distribution.” Canadian Journal of Forest Research 31 (8): 1297–1303. doi:10.1139/x01-032.
   Google Scholar
• Denslow, J. S. 1987. “Tropical Rainforest Gaps and Tree Species Diversity.” In Annual Review of Ecology and Systematics, edited by R. F. Johnston, Vol. 18. Ix+540p. Annual Reviews, Inc.: Palo Alto, California, USA. Illus, 431-452.
   Google Scholar
• Dietze, M. C., and J. S. Clark. 2008. “Changing the Gap Dynamics Paradigm: Vegetative Regeneration Control on Forest Response to Disturbance.” Ecological Monographs 78 (3): 331–347. doi:10.1890/07-0271.1.
   Web of Science ®Google Scholar
• Di Vittorio, A. V., R. I. Negron-Juarez, N. Higuchi, and J. Q. Chambers. 2014. “Tropical Forest Carbon Balance: Effects of Field-and Satellite-Based Mortality Regimes on the Dynamics and the Spatial Structure of Central Amazon Forest Biomass.” Environmental Research Letters 9 (3): 1–10. doi:10.1088/1748-9326/9/3/034010.
   Web of Science ®Google Scholar
• Espirito-Santo, F. D. B., M. Gloor, M. Keller, Y. Malhi, S. Saatchi, B. Nelson, R. C. Oliveira Junior, et al. 2014. “Size and Frequency of Natural Forest Disturbances and the Amazon Forest Carbon Balance.” Nature Communications 5: 3434–3434. doi:10.1038/ncomms4434.
   PubMed Web of Science ®Google Scholar
• Feng, M., J. O. Sexton, C. Huang, A. Anand, S. Channan, X.-P. Song, D.-X. Song, D.-H. Kim, P. Noojipady, and J. R. Townshend. 2016. “Earth Science Data Records of Global Forest Cover and Change: Assessment of Accuracy in 1990, 2000, and 2005 Epochs.” Remote Sensing of Environment 184: 73–85. doi:10.1016/j.rse.2016.06.012.
   Web of Science ®Google Scholar
• Fischer, R., F. Bohn, M. D. de Paula, C. Dislich, J. Groeneveld, A. G. Gutierrez, M. Kazmierczak, et al. 2016. “Lessons Learned From Applying a Forest gap Model to Understand Ecosystem and Carbon Dynamics of Complex Tropical Forests.” Ecological Modelling 326: 124–133. doi:10.1016/j.ecolmodel.2015.11.018.
   Web of Science ®Google Scholar
• Fisher, J. I., G. C. Hurtt, R. Q. Thomas, and J. Q. Chambers. 2008. “Clustered Disturbances Lead to Bias in Large-Scale Estimates Based on Forest Sample Plots.” Ecology Letters 11 (6): 554–563. doi:10.1111/j.1461-0248.2008.01169.x.
   PubMed Web of Science ®Google Scholar
• Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. Stuart Chapin, et al. 2005. “Global Consequences of Land use.” Science 309 (5734): 570–574. doi:10.1126/science.1111772.
   PubMed Web of Science ®Google Scholar
• Frolking, S., M. W. Palace, D. B. Clark, J. Q. Chambers, H. H. Shugart, and G. C. Hurtt. 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-Biogeosciences 114: 1–27. doi:10.1029/2008jg000911.
   Web of Science ®Google Scholar
• GLCF, & GSFC. 2014. Global Land Cover Facility (GLCF) and Goddard Space Flight Center (GSFC): GLCF Forest Cover Change 2000 2005.
   Google Scholar
• Haddad, N. M., L. A. Brudvig, J. Clobert, K. F. Davies, A. Gonzalez, R. D. Holt, T. E. Lovejoy, et al. 2015. “Habitat Fragmentation and its Lasting Impact on Earth’s Ecosystems.” Science Advances 1 (2): e1500052. doi:10.1126/sciadv.1500052.
   PubMed Web of Science ®Google Scholar
• Hahn, G. J., and S. S. Shapiro. 1994. Statistical Models in Engineering. New York, NY: Wiley.
   Google Scholar
• Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–853. doi:10.1126/science.1244693.
   PubMed Web of Science ®Google Scholar
• Huang, C., S. N. Goward, J. G. Masek, N. Thomas, Z. Zhu, and J. E. Vogelmann. 2010. “An Automated Approach for Reconstructing Recent Forest Disturbance History Using Dense Landsat Time Series Stacks.” Remote Sensing of Environment 114 (1): 183–198. doi:10.1016/j.rse.2009.08.017.
   Web of Science ®Google Scholar
• Hurtt, G. C., R. Q. Thomas, J. P. Fisk, R. O. Dubayah, and S. L. Sheldon. 2016. “The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux.” Plos One 11 (4): e0152883. doi:10.1371/journal.pone.0152883.
   PubMed Web of Science ®Google Scholar
• Kennedy, R. E., Z. Yang, and W. B. Cohen. 2010. “Detecting Trends in Forest Disturbance and Recovery Using Yearly Landsat Time Series: 1. LandTrendr - Temporal Segmentation Algorithms.” Remote Sensing of Environment 114 (12): 2897–2910. doi:10.1016/j.rse.2010.07.008.
   Web of Science ®Google Scholar
• Kim, D.-H., J. O. Sexton, P. Noojipady, C. Huang, A. Anand, S. Channan, M. Feng, and J. R. Townshend. 2014. “Global, Landsat-Based Forest-Cover Change From 1990 to 2000.” Remote Sensing of Environment 155: 178–193. doi:10.1016/j.rse.2014.08.017.
   Web of Science ®Google Scholar
• Kuhn, M., and K. Johnson. 2013. Applied Predictive Modeling. New York, NY: Springer.
   Google Scholar
• Li, Y., D. Sulla-Menashe, S. Motesharrei, X.-P. Song, E. Kalnay, Q. Ying, and Z. Ma. 2017. “Inconsistent Estimates of Forest Cover Change in China Between 2000 and 2013 From Multiple Datasets: Differences in Parameters, Spatial Resolution, and Definitions.” Scientific Reports 7. Article number 8748. doi:10.1038/s41598-017-07732-5.
   Web of Science ®Google Scholar
• Lloyd, J., E. U. Gloor, and S. L. Lewis. 2009. “Are the Dynamics of Tropical Forests Dominated by Large and Rare Disturbance Events?” Ecology Letters 12 (12): E19–E21. doi:10.1111/j.1461-0248.2009.01326.x.
   PubMed Web of Science ®Google Scholar
• Lu, A. F., H. Q. Tian, M. L. Liu, J. Y. Liu, and J. M. Melillo. 2006. “Spatial and Temporal Patterns of Carbon Emissions From Forest Fires in China From 1950 to 2000.” Journal of Geophysical Research-Atmospheres 111 (D5). Article number D05313. doi:10.1029/2005jd006198.
   Web of Science ®Google Scholar
• Malamud, B. D., J. D. A. Millington, and G. L. W. Perry. 2005. “Characterizing Wildfire Regimes in the United States.” Proceedings of the National Academy of Sciences of the United States of America 102 (13): 4694–4699. doi:10.1073/pnas.0500880102.
   PubMed Web of Science ®Google Scholar
• Masek, J. G., S. N. Goward, R. E. Kennedy, W. B. Cohen, G. G. Moisen, K. Schleeweis, and C. Huang. 2013. “United States Forest Disturbance Trends Observed Using Landsat Time Series.” Ecosystems 16 (6): 1087–1104. doi:10.1007/s10021-013-9669-9.
   Web of Science ®Google Scholar
• Milojevic, S. 2010. “Power law Distributions in Information Science: Making the Case for Logarithmic Binning.” Journal of the American Society for Information Science and Technology 61 (12): 2417–2425. doi:10.1002/asi.21426.
   Web of Science ®Google Scholar
• Moorcroft, P. R., G. C. Hurtt, and S. W. Pacala. 2001. “A Method for Scaling Vegetation Dynamics: The Ecosystem Demography Model (ED).” Ecological Monographs 71 (4): 557–585. doi:10.1890/0012-9615(2001)071[0557:amfsvd]2.0.co;2.
   Web of Science ®Google Scholar
• Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’amico, et al. 2001. “Terrestrial Ecoregions of the Worlds: A New Map of Life on Earth.” Bioscience 51 (11): 933–938. doi:10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2.
   Web of Science ®Google Scholar
• Sexton, J. O., P. Noojipady, A. Anand, X.-P. Song, S. McMahon, C. Huang, M. Feng, S. Channan, and J. R. Townshend. 2015. “A Model for the Propagation of Uncertainty From Continuous Estimates of Tree Cover to Categorical Forest Cover and Change.” Remote Sensing of Environment 156: 418–425. doi:10.1016/j.rse.2014.08.038.
   Web of Science ®Google Scholar
• Sexton, J. O., X.-P. Song, M. Feng, P. Noojipady, A. Anand, C. Huang, D.-H. Kim, et al. 2013. “Global, 30-m Resolution Continuous Fields of Tree Cover: Landsat-Based Rescaling of MODIS Vegetation Continuous Fields with Lidar-Based Estimates of Error.” International Journal of Digital Earth 6 (5): 427–448. doi:10.1080/17538947.2013.786146.
   Web of Science ®Google Scholar
• SFA. 2005. State Forestry Administration (SFA): The Sixth National Forest Resource Inventory Report (1999-2003). Beijing, China.
   Google Scholar
• Shure, D. J., D. L. Phillips, and P. E. Bostick. 2006. “Gap Size and Succession in Cutover Southern Appalachian Forests: an 18 Year Study of Vegetation Dynamics.” Plant Ecology 185 (2): 299–318. doi:10.1007/s11258-006-9105-8.
   Web of Science ®Google Scholar
• Song, D.-X., C. Huang, P. Noojipady, S. Channan, and J. R. Townshend. 2014. “Comparison of Remote Sensing Based Forest Area and Change Estimation with National Forestry Inventory Between 2000 and 2005 in China.” Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, 13–18 July 2014.
   Google Scholar
• Tong, X., M. Brandt, Y. Yue, P. Ciais, M. R. Jepsen, J. Penuelas, and R. Fensholt. 2020. “Forest Management in Southern China Generates Short Term Extensive Carbon Sequestration.” Nature Communications 11 (1). Article number 129. doi:10.1038/s41467-019-13798-8.
   Web of Science ®Google Scholar
• Townshend, J. R., J. G. Masek, C. Huang, E. F. Vermote, F. Gao, S. Channan, and R. E. Wolfe. 2012. “Global Characterization and Monitoring of Forest Cover Using Landsat Data: Opportunities and Challenges.” International Journal of Digital Earth 5 (5): 373–397. doi:10.1080/17538947.2012.713190.
   Web of Science ®Google Scholar
• Turner, M. G., W. L. Baker, C. J. Peterson, and R. K. Peet. 1998. “Factors Influencing Succession: Lessons From Large, Infrequent Natural Disturbances.” Ecosystems 1 (6): 511–523. doi:10.1007/s100219900047.
   Web of Science ®Google Scholar
• Virkar, Y., and A. Clauset. 2014. “Power-law Distributions in Binned Empirical Data.” Annals of Applied Statistics 8 (1): 89–119. doi:10.1214/13-aoas710.
   Web of Science ®Google Scholar
• Wang, J., J. Wang, H. Zhou, and Z. Xiao. 2017. “Detecting Forest Disturbance in Northeast China From GLASS LAI Time Series Data Using a Dynamic Model.” Remote Sensing 9 (12). Article number 1293. doi:10.3390/rs9121293.
   Web of Science ®Google Scholar
• Wu, J., and H. Li. 2006. “Perspectives and Methods of Scaling.” In Scaling and Uncertainty Analysis in Ecology, edited by J. Wu, K. B. Jones, H. Li, and O. L. Loucks, 17–44. Dordrecht: Springer Netherlands.
   Google Scholar
• Wu, H., Z. Li, K. C. Clarke, W. Shi, L. Fang, A. Lin, and J. Zhou. 2019. “Examining the Sensitivity of Spatial Scale in Cellular Automata Markov Chain Simulation of Land use Change.” International Journal of Geographical Information Science 33 (5): 1040–1061. doi:10.1080/13658816.2019.1568441.
   Web of Science ®Google Scholar
• Xu, J. T., R. S. Yin, Z. Li, and C. Liu. 2006. “China's Ecological Rehabilitation: Unprecedented Efforts, Dramatic Impacts, and Requisite Policies.” Ecological Economics 57 (4): 595–607. doi:10.1016/j.ecolecon.2005.05.008.
   Web of Science ®Google Scholar