The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters

NDVI (Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes since the early eighties. On the other hand, little use has been made of land surface temperatures (LST), due to their sensitivity to the orbital drift which affects the NOAA (National Oceanic and Atmospheric Administration) platforms flying AVHRR sensor. This study presents a new method for monitoring vegetation by using NDVI and LST data, based on an orbital drift corrected dataset derived from data provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group. This method, named Yearly Land Cover Dynamics (YLCD), characterizes NDVI and LST behavior on a yearly basis, through the retrieval of 3 parameters obtained by linear regression between NDVI and normalized LST data. These 3 parameters are the angle between regression line and abscissa axis, the extent of the data projected on the regression line, and the regression coefficient. Such parameters characterize respectively the vegetation type, the annual vegetation cycle length and the difference between real vegetation and ideal cases. Worldwide repartition of these three parameters is shown, and a map integrating these 3 parameters is presented. This map differentiates vegetation in function of climatic constraints, and shows that the presented method has good potential for vegetation monitoring, under the condition of a good filtering of the outliers in the data.

[1]  S. Running,et al.  Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data , 1989 .

[2]  José A. Sobrino,et al.  Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco , 2000 .

[3]  D. A. Stow,et al.  Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems , 2005 .

[4]  Eric F. Lambin,et al.  Land-cover changes in sub-saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale , 1997 .

[5]  Eric F. Lambin,et al.  The surface temperature-vegetation index space for land cover and land-cover change analysis , 1996 .

[6]  A. Strahler,et al.  The use of temporal metrics for land cover change detection at coarse spatial scales , 2000 .

[7]  A. Huete,et al.  Amazon rainforests green‐up with sunlight in dry season , 2006 .

[8]  José A. Sobrino,et al.  Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999 , 2006 .

[9]  Ramakrishna R. Nemani,et al.  LAND COVER CHARACTERIZATION USING MULTITEMPORAL RED, NEAR‐IR, AND THERMAL‐IR DATA FROM NOAA/AVHRR , 1997 .

[10]  Samuel N. Goward,et al.  Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model , 2002 .

[11]  P. A. Schultz,et al.  Global analysis of the relationships among a vegetation index, precipitation and land surface temperature , 1995 .

[12]  Zhanqing Li,et al.  Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter , 2006 .

[13]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .

[14]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[15]  T. Clarke.  An Empirical Approach for Detecting Crop Water Stress Using Multispectral Airborne Sensors , 1997 .

[16]  C. Tucker,et al.  Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.

[17]  José A. Sobrino,et al.  NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[18]  S. Goetz.  Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site , 1997 .

[19]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[20]  E. Vermote,et al.  Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views , 1995 .

[21]  Eric F. Lambin,et al.  Broad scale land-cover classification and interannual climatic variability , 1996 .

[22]  W. Yue,et al.  The relationship between land surface temperature and NDVI with remote sensing : application to Shanghai Landsat 7 ETM + data , 2009 .

[23]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[24]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[25]  S. Running,et al.  Developing Satellite-derived Estimates of Surface Moisture Status , 1993 .

[26]  C. Tucker,et al.  A comparative study of NOAA–AVHRR derived drought indices using change vector analysis , 2006 .