Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987–2011)

Abstract

Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker (VCT) model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks (LTSS). The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM+ scenes with different projections due to distinct data sources (Beijing remote sensing ground station and the USGS EROS Center). The validation results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 (38.3 %) and 1992 (56.2 %) in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis-registration errors of the image pairs. Major forest disturbance types existing in our study area were identified as agricultural expansion (39.8 %), urbanization (24.9 %), forest management practice (19.3 %), and mining (12.8 %). In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China’s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.

Keywords

Landsat time series stack / LEDAPS / Forest disturbance / VCT model

References

[1] Chrysopolitou V, Apostolakis A, Avtzis D, Avtzis NN, Diamandis S, Kemitzoglou D, Papadimos D, Perlerou C, Tsiaoussi V, Dafis S. Studies on forest health and vegetation changes in Greece under the effects of climate changes. Biodiversity Conservation, 2013, 22: 1133-1150.

[2] Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E. Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens, 2004, 25: 1565-1596.

[3] Gao F, Colstoun EBD, Ma RH, Weng QH, Masek JG, Chen J, Pan YZ, Song CH. Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta, China. Int J Remote Sens, 2012, 33(24): 7609-7628.

[4] Goward SN. 2006. Future of land remote sensing: What is needed. In: joint agency commercial imagery evaluation (JACIE) workshop, Patuxent national wildlife research center, Laurel, MD, March 14–16, 2006

[5] Hirsch AI, Little WS, Houghton RA, Scott NA, White JD. The net carbon flux due to deforestation and forest re-growth in the Brazilian Amazon: analysis using a process-based model. Glob Change Biol, 2004, 10: 908-924.

[6] Huang CQ, Townshend JRG, Liang SL, Kalluri SNV, DeFries RS. Impact of sensor’s point spread function on land cover characterization: assessment and deconvolution. Remote Sens Environ, 2002, 80(2): 203-212.

[7] Huang CQ, Goward SN, Masek JG, Gao F, Vermote EF, Thomas N, Schleeweis K, Kennedy RE, Zhu ZL, Eidenshink JC, Townshend JRG. Development of time series stacks of Landsat images for reconstructing forest disturbance history. Int J Digital Earth, 2009, 2(3): 195-218.

[8] Huang CQ, Goward SN, Masek JG, Thomas N, Zhu ZL, Vogelmann JE. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ, 2010, 114: 183-198.

[9] Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP, Morisette JT. An overview of MODIS land data processing and product status. Remote Sens Environ, 2002, 83(1–2): 3-15.

[10] Kennedy RE, Cohen WB, Schroeder TA. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens Environ, 2007, 110: 370-386.

[11] Kennedy RE, Yang ZQ, Cohen WB. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr- temporal segmentation algorithms. Remote Sens Environ, 2010, 114: 2897-2910.

[12] Land Resources Agency of Nanjing City. 2012. The mineral resources and their development and utilization forms in Nanjing City. http://www.mlr.gov.cn/kczyg/kcgh/201211/t20121101_1152798.htm#. Accessed 19 May 2014 (In Chinese)

[13] Land Resources Agency of Zhenjiang City, 2007. The overall planning of mineral resources in Zhenjiang City. Available at: http://www.mlr.gov.cn/printpage.htm Accessed 16 Mar 2014 (In Chinese)

[14] Li MS. Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: a review of research and practice. Sci Total Environ, 2006, 357: 38-53.

[15] Li MS, Huang CQ, Zhu ZL, Shi H, Lu H, Peng SK. Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model. For Ecol Manage, 2009, 257: 1480-1488.

[16] Li MS, Mao LJ, Zhou CG, Vogelmann GE, Zhu ZL. Comparing forest fragmentation and its drivers in China and the USA with Globcover v2.2. J Environ Manage, 2010, 91: 2572-2580.

[17] Li AN, Huang CQ, Sun GQ, Shi H, Toney C, Zhu ZL, Rollins MG, Goward SN, Masek JG. Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data. Remote Sens Environ, 2011, 115: 1837-1849.

[18] Liu GH, Shu HL, Zhang JC, Zhang XC. Research on the model of revegetation in mine spoils of Mufu Mountain in Nanjing. Res Soil Water Conserv, 2005, 12(1): 141-144. (In Chinese)

[19] Liu GH, Shu HL, Zhang JC. Niche of Broussonetia papyrifera population and main associated species naturally revegetated in mine spoils Nanjing Mufu Mountains. Res Soil Water Conserv, 2007, 14(2): 184-188. (In Chinese)

[20] Liu GH, Shu HL, Zhang JC. Research on the dynamics of community and population of revegetation in mine spoils in Mufu Mountains Nanjing. Res Soil Water Conserv, 2008, 15(1): 72-78. (In Chinese)

[21] Long HL, Tang GP, Li XB, Heilig GK. Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China. J Environ Manage, 2007, 83: 351-364.

[22] Long HL, Liu YS, Wu XQ, Dong GH. Spatio-temporal dynamic patterns of farmland and rural settlements in Su-Xi-Chang region: implications for building a new countryside in coastal China. Land Use Policy, 2009, 26: 322-333.

[23] Lu DS, Mausel P, Brondizio E, Moran E. Change detection techniques. Int J Remote Sens, 2004, 25: 2365-2407.

[24] Masek JG, Vermote EF, Saleous NE, Wolfe R, Hall FG, Huemmrich KF, Feng G, Kutler J, Teng KL. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci Remote Sens Lett, 2006, 3(1): 68-72.

[25] Robbins C, Dawson D, Dowell B. Habitat area requirements of breeding forest birds of the middle Atlantic States. Wildlife Monogr, 1989, 103: 3-34.

[26] Seto KC, Woodcock CE, Song C, Huang X, Lu J, Kaufmann RK. Monitoring land-use change in the Pearl River Delta using Landsat TM. Int J Remote Sens, 2002, 23(10): 1985-2004.

[27] Shen WJ, Li MS. The method of Landsat dense time series data format unification and surface reflectance conversion. Remote Sensing for Land and Resources., 2014, 26(4): 78-84. (In Chinese)

[28] Thomas NE, Huang CQ, Goward SN, Powell S, Rishmawi K, Schleeweis K, Hinds A. Validation of North American forest disturbance dynamics derived from Landsat time series stacks. Remote Sens Environ, 2011, 115: 19-32.

[29] Townshend JRG, Justice CO, McManus J. The impact of misregistration on change detection. IEEE Trans Geosci Remote Sens , 1992, 30(5): 1054-1060.

[30] Vogelmann JE, Xian G, Homer C, Tolk B. Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems. Remote Sens Environ, 2012, 122: 92-105.

[31] Woodcock CE, Allen R, Anderson M, Belward A, Bindschadler R, CohenW Gao F, Goward SN, Helder D, Helmer E, Nemani R, Oreopoulos L, Schott J, Thenkabail PS, Vermote EF, Vogelmann J, Wulder MA, Wynne R. Free access to Landsat imagery. Science, 2008, 320 1011

[32] Xin QC, Olofsson P, Zhu Z, Tan B, Woodcock CE. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sens Environ, 2013, 135: 234-247.

[33] Xu HQ. Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogr Eng Remote Sens, 2010, 76(5): 557-565.

[34] Xu C, Liu MS, Yang XJ, Sheng S, Zhang MJ, Huang Z. Detecting the spatial differentiation in settlement change rates during rapid urbanization in the Nanjing metropolitan region, China. Environ Monit Assess, 2010, 171: 457-470.

[35] Zhou BZ, Gu LH, Ding YH, Shao L, Wu ZM, Yang XS, Li CZ, Li ZC, Wang XM, Cao YH, Zeng BS, Yu MK, Wang MY, Wang SK, Sun HG, Duan AG, An YF, Wang X, Kong WJ. The great 2008 Chinese ice storm: its socioeconomic-ecological impact and sustainability lessons learned. Bull Am Meteorol Soc , 2011, 92(1): 47-60.

[36] Zhu Z, Woodcock CE. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ, 2012, 118: 83-94.