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Forests  2013 

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

DOI: 10.3390/f4030632

Keywords: China forests, forest management, distribution and variation, forests composition

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Abstract:

Forests are one of the most important components of the global biosphere and have critical influences on the Earth’s ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data. Based on the differences of histograms among different forests, we obtained the 2001–2011 forests distribution for China at a spatial resolution of 500-m × 500-m. The overall accuracy of validation was 80.4%, an increase of 12.8% relative to that obtained using MODIS LC-1 data. The 2001–2011 forestland pure and mixed pixels of China accounted for an average of 33.72% of all pixels. There is a gradual increase in China’s forestland coverage during 2001–2011; however, the relationship is not statistically significant.

References

[1]  Food and Agriculture Organization (FAO). State of the World’s Forests 2012. Food and Agriculture Organization of the United Nations: Rome, Italy, 2012.
[2]  Bonan, G.B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forest. Science 2008, 320, 1444–1449, doi:10.1126/science.1155121.
[3]  Curran, L.M.; Trigg, S.N.; McDonald, A.K.; Astiani, D.; Hardiono, Y.M.; Siregar, P.; Caniago, I.; Kasischke, E. Lowland forest loss in protected areas of indonesian borneo. Science 2004, 303, 1000–1003, doi:10.1126/science.1091714.
[4]  Gorsevski, V.; Kasischke, E.; Dempewolf, J.; Loboda, T.; Grossmann, F. Analysis of the impacts of armed conflict on the Eastern Afromontane forest region on the South Sudan-Uganda border using multitemporal Landsat imagery. Remote Sens. Environ. 2012, 118, 10–20, doi:10.1016/j.rse.2011.10.023.
[5]  Achard, F.; Eva, H.D.; Stibig, H.-J.; Mayaux, P.; Gallego, J.; Richards, T.; Malingreau, J.-P. Determination of deforestation rates of the World’s humid tropical forests. Science 2002, 297, 999–1002, doi:10.1126/science.1070656.
[6]  Verbesselt, J.; Robinson, A.; Stone, C.; Culvenor, D. Forecasting tree mortality using change metrics derived from MODIS satellite data. For. Ecol. Manag. 2009, 258, 1166–1173, doi:10.1016/j.foreco.2009.06.011.
[7]  Hansen, M.C.; Roy, D.P.; Lindquist, E.; Adusei, B.; Justice, C.O.; Altstatt, A. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sens. Environ. 2008, 112, 2495–2513, doi:10.1016/j.rse.2007.11.012.
[8]  Loveland, T.R.; Reed, B.C.; Brown, J.F.; Ohlen, D.O.; Zhu, Z.; Yang, L.; Merchant, J.W. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 2000, 21, 1303–1330, doi:10.1080/014311600210191.
[9]  Hansen, M.C.; Defries, R.S.; Townshend, J.R.G.; Sohlberg, R. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens. 2000, 21, 1331–1364, doi:10.1080/014311600210209.
[10]  Hansen, M.C.; Reed, B. A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. Int. J. Remote Sens. 2000, 21, 1365–1373, doi:10.1080/014311600210218.
[11]  Bartholomé, E.; Belward, A.S. GLC2000: A new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 2005, 26, 1959–1977, doi:10.1080/01431160412331291297.
[12]  Friedl, M.A.; McIver, D.K.; Hodges, J.C.F.; Zhang, X.Y.; Muchoney, D.; Strahler, A.H.; Woodcock, C.E.; Gopal, S.; Schneider, A.; Cooper, A.; et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ. 2002, 83, 287–302, doi:10.1016/S0034-4257(02)00078-0.
[13]  Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168–182, doi:10.1016/j.rse.2009.08.016.
[14]  Arino, O.; Gross, D.; Ranera, F.; Bourg, L.; Leroy, M.; Bicheron, P.; Latham, J.; di Gregorio, A.; Brockman, C.; Witt, R.; et al. GlobCover: ESA Service for Global Land Cover from MERIS. In Proceedings of IEEE International Geoscience and Remote Sensing SymposiumIGARSS 2007, Barcelona, Spain, 23–28 July 2007; pp. 2412–2415.
[15]  Defourny, P.; Vancutsem, C.; Bicheron, P.; Brockmann, C.; Nino, F.; Schouten, L.; Leroy, M. GLOBCOVER: A 300 m Global Land Cover Product for 2005 Using Envisat MERIS Time Series. In Proceedings of the ISPRS Commission VII Mid-Term SymposiumRemote Sensing: From Pixels to Processes, Enschede, The Netherlands, 8–11 May 2006; pp. 8–11.
[16]  Yaqian, H.; Yanchen, B. A Consistency Analysis of MODIS MCD12Q1 and MERIS Globcover Land Cover Datasets over China. In Proceedings of 19th International Conference on Geoinformatics, Shanghai, China, 24–26 June 2011; pp. 1–6.
[17]  Stehman, S.V.; Olofsson, P.; Woodcock, C.E.; Herold, M.; Friedl, M.A. A global land-cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class. Int. J. Remote Sens. 2012, 33, 6975–6993, doi:10.1080/01431161.2012.695092.
[18]  Kaptué Tchuenté, A.T.; Roujean, J.-L.; de Jong, S.M. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scal. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 207–219, doi:10.1016/j.jag.2010.11.005.
[19]  Herold, M.; Mayaux, P.; Woodcock, C.E.; Baccini, A.; Schmullius, C. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ. 2008, 112, 2538–2556, doi:10.1016/j.rse.2007.11.013.
[20]  Zhao, X.; Liang, S.; Liu, S.; Yuan, W.; Xiao, Z.; Liu, Q.; Cheng, J.; Zhang, X.; Tang, H.; Zhang, X.; et al. The Global Land Surface Satellite (GLASS) remote sensing data processing system and products. Remote Sens. 2013, 5, 2436–2450, doi:10.3390/rs5052436.
[21]  Liang, S.; Zhao, X.; Yuan, W.; Liu, S.; Cheng, X. A long-term Global Land Surface Satellite (GLASS) dataset for environmental studies. Int. J. Digit. Earth 2013. in press.
[22]  Kennedy, R.E.; Cohen, W.B.; Schroeder, T.A. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens. Environ. 2007, 110, 370–386, doi:10.1016/j.rse.2007.03.010.
[23]  Kennedy, R.E.; Yang, Z.; Cohen, W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms. Remote Sens. Environ. 2010, 114, 2897–2910, doi:10.1016/j.rse.2010.07.008.
[24]  Cohen, W.B.; Yang, Z.; Kennedy, R. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—Tools for calibration and validation. Remote Sens. Environ. 2010, 114, 2911–2924, doi:10.1016/j.rse.2010.07.010.
[25]  Griffiths, P.; Kuemmerle, T.; Kennedy, R.E.; Abrudan, I.V.; Knorn, J.; Hostert, P. Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania. Remote Sens. Environ. 2012, 118, 199–214, doi:10.1016/j.rse.2011.11.006.
[26]  Kennedy, R.E.; Townsend, P.A.; Gross, J.E.; Cohen, W.B.; Bolstad, P.; Wang, Y.Q.; Adams, P. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens. Environ. 2009, 113, 1382–1396, doi:10.1016/j.rse.2008.07.018.
[27]  Kennedy, R.E.; Yang, Z.; Cohen, W.B.; Pfaff, E.; Braaten, J.; Nelson, P. Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sens. Environ. 2012, 122, 117–133, doi:10.1016/j.rse.2011.09.024.
[28]  Pflugmacher, D.; Cohen, W.B.; Kennedy, E.R. Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens. Environ. 2012, 122, 146–165, doi:10.1016/j.rse.2011.09.025.
[29]  Pflugmacher, D.; Krankina, O.N.; Cohen, W.B.; Friedl, M.A.; Sulla-Menashe, D.; Kennedy, R.E.; Nelson, P.; Loboda, T.V.; Kuemmerle, T.; Dyukarev, E.; et al. Comparison and assessment of coarse resolution land cover maps for Northern Eurasia. Remote Sens. Environ. 2011, 115, 3539–3553, doi:10.1016/j.rse.2011.08.016.
[30]  Meigs, G.W.; Kennedy, R.E.; Cohen, W.B. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens. Environ. 2011, 115, 3707–3718, doi:10.1016/j.rse.2011.09.009.
[31]  Zhu, Z.; Woodcock, C.E.; Olofsson, P. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 2012, 122, 75–91, doi:10.1016/j.rse.2011.10.030.
[32]  Xin, Q.; Olofsson, P.; Zhu, Z.; Tan, B.; Woodcock, C.E. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sens. Environ. 2013, 135, 234–247, doi:10.1016/j.rse.2013.04.002.
[33]  Rodriguez-Galiano, V.F.; Chica-Olmo, M.; Abarca-Hernandez, F.; Atkinson, P.M.; Jeganathan, C. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 2012, 121, 93–107, doi:10.1016/j.rse.2011.12.003.
[34]  Schapire, R.E.; Freund, Y.; Bartlett, P.L.; Lee, W.S. Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Stat. 1998, 26, 1651–1686, doi:10.1214/aos/1024691352.
[35]  Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213, doi:10.1016/S0034-4257(02)00096-2.
[36]  Loveland, T.R.; Belward, A.S. The IGBP-DIS global 1 km land cover data set, DISCover: First results. Int. J. Remote Sens. 1997, 18, 3289–3295, doi:10.1080/014311697217099.
[37]  Lotsch, A.; Tian, Y.; Friedl, M.A.; Myneni, R.B. Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors. Int. J. Remote Sens. 2003, 24, 1997–2016, doi:10.1080/01431160210154858.
[38]  Myneni, R.B.; Hoffman, S.; Knyazikhin, Y.; Privette, J.L.; Glassy, J.; Tian, Y.; Wang, Y.; Song, X.; Zhang, Y.; Smith, G.R.; et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 2002, 83, 214–231, doi:10.1016/S0034-4257(02)00074-3.
[39]  Running, S.W.; Loveland, T.R.; Pierce, L.L.; Nemani, R.R.; Hunt, E.R., Jr. A remote sensing based vegetation classification logic for global land cover analysis. Remote Sens. Environ. 1995, 51, 39–48, doi:10.1016/0034-4257(94)00063-S.
[40]  Bonan, G.B.; Levis, S.; Kergoat, L.; Oleson, K.W. Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models. Glob. Biogeochem. Cycles 2002, 16, 1–23, doi:10.1029/2001GB001398.
[41]  Justice, C.O.; Townshend, J.R.G.; Vermote, E.F.; Masuoka, E.; Wolfe, R.E.; Saleous, N.; Roy, D.P.; Morisette, J.T. An overview of MODIS Land data processing and product status. Remote Sens. Environ. 2002, 83, 3–15, doi:10.1016/S0034-4257(02)00084-6.
[42]  Omuto, C.T. A new approach for using time-series remote-sensing images to detect changes in vegetation cover and composition in drylands: A case study of eastern Kenya. Int. J. Remote Sens. 2011, 32, 6025–6045, doi:10.1080/01431161.2010.499384.
[43]  Pan, Y.; Li, L.; Zhang, J.; Liang, S.; Zhu, X.; Sulla-Menashe, D. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sens. Environ. 2012, 119, 232–242, doi:10.1016/j.rse.2011.10.011.
[44]  Sakamoto, T.; Wardlow, B.D.; Gitelson, A.A.; Verma, S.B.; Suyker, A.E.; Arkebauer, T.J. A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens. Environ. 2010, 114, 2146–2159, doi:10.1016/j.rse.2010.04.019.
[45]  Schowengerdt, R.A. Remote Sensing: Models and Methods for Image Processing, 3rd ed. ed.; Academic Press: Burlington, MA, USA, 2007; pp. 144–145.
[46]  ENVI Reference Guide Version 4.7 August, 2009 Edition. ITT Visual Information Solutions. Available online: http://www.exelisvis.com/portals/0/pdfs/envi/Reference_Guide.pdf (accessed on 26 May 2013).
[47]  Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84, 471–475.
[48]  Bontemps, S.; Defourny, P.; Bogaert, E.V.; Arino, O.; Kalogirou, V.; Perez, J.R. GLOBCOVER 2009 Products Description and Validation Report; Technical Report for ESA GlobCover project: UCLouvain & ESA Team; 2011; p. 53.
[49]  Pierre, D.; Leon, S.; Sergey, B.; Sophie, B.; Peter, C.; Allard, D.W.; Carlos, D.B.; Bruno, G.; Chandra, G.; Valerie, G.; et al. Accuracy Assessment of a 300 m Global Land Cover Map: The GlobCover Experience. In Proceedings of the 33rd International Symposium on Remote Sensing of Environment, Stresa, Italy, 4–9 May 2009.
[50]  Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35–46, doi:10.1016/0034-4257(91)90048-B.
[51]  Song, C.; Zhang, Y. Forest Cover in China from 1949 to 2006. In Reforesting Landscapes; Nagendra, H., Southworth, J., Eds.; Springer Netherlands: Dordrecht, The Netherlands, 2010; Volume 10, pp. 341–356.
[52]  Xu, J.C. China’s new forests aren’t as green as they seem. Nature 2011, 477, 371, doi:10.1038/477371a.

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