Land Cover and Landscape Diversity Analysis in the West Polesie Biosphere Reserve

Abstract The aim of this research was to present the land cover structure and landscape diversity in the West Polesie Biosphere Reserve. The land cover classification was performed using Object Based Image Analysis in Trimble eCognition Developer 8 software. The retrospective land cover changes analysis in 3 lake catchments (Kleszczów, Moszne, Bia³eW³odawskie Lakes)was performed on the basis of archival aerial photos taken in 1952, 1971, 1984, 1992, 2007 and one satellite scene from 2003 (IKONOS).On the basis of land cover map structure, Shannon diversity index was estimated with the moving window approach enabled in Fragstats software. The conducted research has shown that the land cover structure of the West Polesie Biosphere Reserve is diverse and can be simply described by selected landscape metrics. The highest level of land cover diversity, as showed by Shannon Diversity Index, was identified in the western part of the West Polesie Biosphere Reserve, which is closely related to the agricultural character of land cover structure in those regions. The examples of three regional retrospective land cover analyses demonstrated that the character of land cover structure has changed dramatically over the last 40 years.

[1]  Jordi Linares-Pellicer,et al.  A free and open source programming library for landscape metrics calculations , 2012, Environ. Model. Softw..

[2]  Piotr Tompalski,et al.  LIDAR AND VHRS DATA FOR ASSESSING LIVING QUALITY IN CITIES - AN APPROACH BASED ON 3D SPATIAL INDICES , 2012 .

[3]  Bruce T. Milne,et al.  Effects of changing spatial scale on the analysis of landscape pattern , 1989, Landscape Ecology.

[4]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[5]  C. McAlpine,et al.  Testing landscape metrics as indicators of habitat loss and fragmentation in continuous eucalypt forests (Queensland, Australia) , 2002, Landscape Ecology.

[6]  Christopher Y. Choi,et al.  GROUND-BASED REMOTE SENSING OF WATER AND NITROGEN STRESS , 2003 .

[7]  J. Roosaare,et al.  Landscape Metrics and Indices: An Overview of Their Use in Landscape Research , 2009 .

[8]  H. Asadi,et al.  Long-term effects of land use on soil aggregate stability , 2011 .

[9]  C. Kollas,et al.  Bioenergy potential in Germany - assessing spatial patterns of biomass production with aspen short-rotation coppice , 2009 .

[10]  N. Bagheri,et al.  Soil-line vegetation indices for corn nitrogen content prediction , 2012 .

[11]  R. D. Groot,et al.  Function-analysis and valuation as a tool to assess land use conflicts in planning for sustainable, multi-functional landscapes , 2006 .

[12]  Piotr Tompalski,et al.  Aerial Orthophoto and Airborne Laser Scanning as Monitoring Tools for Land Cover Dynamics: A Case Study from the Milicz Forest District (Poland) , 2014, Pure and Applied Geophysics.

[13]  Eric J. Gustafson,et al.  Quantifying Landscape Spatial Pattern: What Is the State of the Art? , 1998, Ecosystems.

[14]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[15]  E. Uuemaa,et al.  Trends in the use of landscape spatial metrics as landscape indicators: A review , 2013 .

[16]  Alistair Lamb,et al.  Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas , 2009 .

[17]  M. Antrop.  Why landscapes of the past are important for the future , 2005 .

[18]  A. Perera,et al.  Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation , 2004, Landscape Ecology.

[19]  Jan Adamowski,et al.  Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery , 2011 .

[20]  Antonio Di Gregorio,et al.  Land-use data collection using the “land cover classification system”: results from a case study in Kenya , 2003 .

[21]  M. Mahmoodabadi.  Sediment yield estimation using a semi-quantitative model and GIS-remote sensing data , 2011 .

[22]  R. Kok,et al.  APPLICATION OF THE OBJECT BASED IMAGE ANALYSIS OF VHR SATELLITE IMAGES IN LAND-USE CLASSIFICATION ZASTOSOWANIE OBIEKTOWEJ ANALIZY WYSOKOROZDZIELCZYCH OBRAZ"W SATELITARNYCH W KLASYFIKACJI FORM UflYTKOWANIA TERENU , 2006 .

[23]  W. Drzewiecki,et al.  Quantitative and Qualitative Assessment of Soil Erosion Risk in Małopolska (Poland), Supported by an Object-Based Analysis of High-Resolution Satellite Images , 2014, Pure and Applied Geophysics.

[24]  Yan Gao,et al.  Sensitivity of landscape pattern metrics to classification approaches , 2010 .

[25]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[26]  P. Verburg,et al.  From land cover change to land function dynamics: a major challenge to improve land characterization. , 2009, Journal of environmental management.

[27]  Diego de la Rosa,et al.  Analysis of soil capability versus land use change by using CORINE land cover and MicroLeis , 2011 .