Verification of the analysis method for extracting the spatial continuity of the vegetation distribution on a regional scale

Abstract Vegetation plays a key role in not only improving urban environments, but also conserving ecosystems. The spatial continuity of vegetation distributions can be expected to make green corridors for landscape management, wind paths against heat island phenomena. In this paper, we develop a spatial analysis method of vegetation distributions using remotely sensed data on a regional scale. The method consists of a spatial autocorrelation analysis, an overlay analysis, and a hydrological analysis with the Normalized Difference Vegetation Index (NDVI) adopted as the proxy of vegetation abundance. Application of the method leads to the extraction of the lines between the core areas and sparse areas of vegetation. The purpose of this study is to verify our method through applying a vegetation map digitized from aerial photographs. The map contained three vegetation types of land cover: grasslands, agricultural fields, and tree-covered areas. We use remotely sensed data collected at four different time periods at the regional scale, along with information on the seasonal fluctuations of the vegetation. As a result, the exclusion of seasonal land-cover changes, as in the reaping of agricultural fields, in the process of applying the proposed method produces an effect. The analysis reveals steady areas unaffected by the seasonal fluctuation of vegetation along the lines extracted by applying the proposed method.

[1]  S. Franklin,et al.  Caribou habitat mapping and fragmentation analysis using Landsat MSS, TM, and GIS data in the North Columbia Mountains, British Columbia, Canada , 2001 .

[2]  Florian Siegert,et al.  Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT VEGETATION imagery , 2006 .

[3]  Marc Antrop,et al.  Reflecting upon 25 years of landscape ecology , 2007, Landscape Ecology.

[4]  Clayton C. Kingdon,et al.  Spatial pattern analysis for monitoring protected areas , 2009 .

[5]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[6]  M. Duggin,et al.  A temporal analysis of urban forest carbon storage using remote sensing , 2006 .

[7]  K. Kumagai ANALYSIS OF VEGETATION DISTRIBUTION IN URBAN AREAS : SPATIAL ANALYSIS APPROACH ON A REGIONAL SCALE , 2008 .

[8]  Carlos Carroll,et al.  Extinction Debt of Protected Areas in Developing Landscapes , 2004 .

[9]  Scott J. Goetz,et al.  Connectivity of core habitat in the Northeastern United States: Parks and protected areas in a landscape context , 2009 .

[10]  Ángel M. Felicísimo,et al.  Modeling the Potential Distribution of Forests with a GIS , 2002 .

[11]  Jean Paul Metzger,et al.  Landscape ecology: perspectives based on the 2007 IALE world congress , 2008, Landscape Ecology.

[12]  A. Getis The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[13]  Kiichiro KUMAGAI ANALYSIS OF THE SPATIAL CONTINUITY OF VEGETATION-COVERED AREAS ON A REGIONAL SCALE , 2006 .

[14]  Mark G. Anderson,et al.  Selecting and conserving lands for biodiversity: The role of remote sensing , 2009 .

[15]  Emilio F. Moran,et al.  Settlement Design, Forest Fragmentation, and Landscape Change in Rondonia, Amazonia , 2003 .

[16]  Wei-Ning Xiang,et al.  Planning for multi-purpose greenways in Concord, North Carolina , 2004 .

[17]  Jiunn-Der Duh,et al.  Estimating Error in an Analysis of Forest Fragmentation Change Using North American Landscape Characterization (NALC) Data , 2000 .

[18]  R. Jongman Nature conservation planning in Europe: developing ecological networks , 1995 .

[19]  M. Nowak,et al.  Habitat destruction and the extinction debt , 1994, Nature.

[20]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[21]  P. Mumby,et al.  A review of remote sensing for the assessment and management of tropical coastal resources , 1996 .

[22]  D. Roberts,et al.  A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery , 2002 .

[23]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[24]  Eric F. Lambin,et al.  Change Detection at Multiple Temporal Scales: Seasonal and Annual Variations in Landscape Variables , 1996 .

[25]  Jeffrey S. Wilson,et al.  Evaluating environmental influences of zoning in urban ecosystems with remote sensing , 2003 .

[26]  R. Jongman,et al.  European ecological networks and greenways , 2004 .

[27]  J. Ord,et al.  Local Spatial Autocorrelation Statistics: Distributional Issues and an Application , 2010 .

[28]  M. A. Gilabert,et al.  Vegetation cover seasonal changes assessment from TM imagery in a semi-arid landscape , 2004 .

[29]  Larry D. Harris,et al.  Nodes, networks, and MUMs: Preserving diversity at all scales , 1986 .

[30]  Edward T. McMahon,et al.  Green Infrastructure: Linking Landscapes and Communities , 2006 .

[31]  Peter Vogt,et al.  A National Assessment of Green Infrastructure and Change for the Conterminous United States Using Morphological Image Processing , 2010 .

[32]  Ian Olthof,et al.  Treeline vegetation composition and change in Canada's western Subarctic from AVHRR and canopy reflectance modeling , 2010 .

[33]  Yeqiao Wang,et al.  Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects , 2009 .

[34]  Alex M. Lechner,et al.  Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping , 2009 .

[35]  J. G. Fábos Greenway planning in the United States: its origins and recent case studies , 2004 .

[36]  Greg Baxter,et al.  Corridor Ecology: The Science and Practice of Linking Landscapes for Biodiversity Conservation , 2007 .