Environmental evaluation of MODIS-derived land units

A land stratification of the French territory had been previously performed based on time series of vegetation and texture indices. This stratification led to 300 radiometrically homogenous regions that were considered as land units (LUs). In this paper, we present a quantitative analysis of the LUs, with the aim of testing if these LUs are linked to landscape. In this sense, an evaluation of their thematic meaning in terms of environmental variables and land cover was performed. In order to achieve this, we first conducted a statistical analysis at national scale using a set of environmental variables and land cover by means of Moran’s autocorrelation index and Spearman rank correlation index. Second, to analyze the quality of the boundaries between neighboring LUs, we developed a method based on the Spearman rank correlation index calculated on test areas across the boundaries. The first analyses showed that the most explanatory variables of the LUs were land cover, topography and parent material. The boundaries analysis was applied at a regional scale (Pyrenean region), and showed that 89% of the boundaries were well explained by the land cover compositions. The results obtained support the hypothesis that time series of broad resolution remote-sensing images can capture landscape identities and produce LUs maps that have an environmental and land occupation sense.

[1]  D. Munroe.  Effects of Land Use , 2017 .

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

[3]  Caspar A. Mücher,et al.  A new European Landscape Classification (LANMAP): A transparent, flexible and user-oriented methodology to distinguish landscapes , 2010 .

[4]  C. A. Mücher,et al.  European environmental stratifications and typologies: an overview , 2011 .

[5]  D. Stanners,et al.  Europe's environment : the Dobrís assessment , 1995 .

[6]  Stefan W. Maier,et al.  Area-based and location-based validation of classified image objects , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[7]  V. Huvenne,et al.  Objective automated classification technique for marine landscape mapping in submarine canyons , 2015 .

[8]  Julie Betbeder,et al.  Landform and landscape mapping, French Guiana (South America) , 2013 .

[9]  M. Charlton,et al.  Quantitative geography : perspectives on spatial data analysis by , 2001 .

[10]  Mar Bisquert,et al.  Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Land units map of Italy , 2013 .

[12]  Gouldson.  EUROPE'S ENVIRONMENT: THE DOBRIS ASSESSMENT , 1996 .

[13]  Ying Liu,et al.  Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods , 2012 .

[14]  R. Sanderson,et al.  Predicting the distributions of plant species at the regional scale: a hierarchical matrix model , 1995, Landscape Ecology.

[15]  Delin Liu,et al.  Effects of land use classification on landscape metrics based on remote sensing and GIS , 2013, Environmental Earth Sciences.

[16]  Daniel Joly,et al.  Les types de climats en France, une construction spatiale , 2010 .

[17]  C. A. Mücher,et al.  Remote Sensing in Landscape Ecology: Experiences and Perspectives in a European Context , 2005, Landscape Ecology.

[18]  Luke J. Marzen,et al.  Developing a land-cover classification to select indicators of forest ecosystem health in a rapidly urbanizing landscape , 2010 .

[19]  Antoine Masse,et al.  Tools for multitemporal analysis and classification of multisource satellite imagery , 2011, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).

[20]  Roland Pesch,et al.  Application of CART in ecological landscape mapping: Two case studies , 2011 .

[21]  K. Medley,et al.  Landscape Mapping: Gaining “Sense of Place” for Conservation in the Manaslu Conservation Area, Nepal , 2016 .

[22]  Xuezhi Feng,et al.  An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation , 2012, IEEE Geoscience and Remote Sensing Letters.

[23]  Wenzhong Shi,et al.  Quality assessment for geo‐spatial objects derived from remotely sensed data , 2005 .

[24]  M. R. Fernandes,et al.  Riparian vegetation metrics as tools for guiding ecological restoration in riverscapes. , 2011 .

[25]  Clement Atzberger,et al.  Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas , 2012 .

[26]  Brian Johnson,et al.  Unsupervised image segmentation evaluation and refinement using a multi-scale approach , 2011 .

[27]  Kurt H. Riitters,et al.  A Comparison of Vector and Raster GIS Methods for Calculating Landscape Metrics Used in Environmental Assessments , 2003 .