Detecting drought induced environmental changes in a Mediterranean wetland by remote sensing.

Abstract Water is a vital resource for supporting agriculture and wetlands in semiarid environments and can play an important role in minimizing greenhouse gas emissions in wetlands. When droughts occur, serious social conflicts become apparent in relation to water management practices, i.e. whether to use water for irrigation of croplands or for wetland conservation. Mediterranean wetlands in south-eastern Spain are extremely valuable due to the biodiversity and their role as water reservoirs. In this study, land-cover changes in an artificial wetland are analysed for a drought-affected hydrologic year (2004–2005) in comparison to an average hydrologic year (2000–2001) by means of remote sensing techniques. Land-cover components (vegetation, soil and water) obtained from linear spectral unmixing (LSU) were used to detect temporal changes within and outside the “ El Hondo ” Natural Park wetland. During the drought period significant differences in vegetation, soil and water components were observed within the protected area with respect to outside. This suggests different water management practices within and outside the Park during the drought. The land-cover maps of 2001 and 2005 that were derived from the LSU components highlight these significant land-cover changes, especially within the protected area.

[1]  J. R. Jensen,et al.  Subpixel classification of Bald Cypress and Tupelo Gum trees in thematic mapper imagery , 1997 .

[2]  Gyanesh Chander,et al.  Revised Landsat-5 Thematic Mapper Radiometric Calibration , 2007, IEEE Geoscience and Remote Sensing Letters.

[3]  D. Ojima,et al.  High resolution modeling of the regional impacts of climate change on irrigation water demand , 2007 .

[4]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

[5]  M. A. Rodrigo,et al.  Environmental variables and planktonic communities in two ponds of El Hondo Wetland (SE Spain) , 2001 .

[6]  Stacy L. Ozesmi,et al.  Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.

[7]  Magaly Koch,et al.  Geological controls of land degradation as detected by remote sensing: A case study in Los Monegros, north-east Spain , 2000 .

[8]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[9]  Stefan Hajkowicz,et al.  A Review of Multiple Criteria Analysis for Water Resource Planning and Management , 2007 .

[10]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[11]  Paul M. Mather,et al.  Computer Processing of Remotely-Sensed Images: An Introduction , 1988 .

[12]  Ghassem R. Asrar,et al.  Theory and applications of optical remote sensing. , 1989 .

[13]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[14]  Stephen J. Walsh,et al.  Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA , 2001, Plant Ecology.

[15]  C. Small,et al.  Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis , 2006 .

[16]  A. Rogers,et al.  Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices , 2004 .

[17]  E. Salinero.  Teledetección ambiental: la observación de la Tierra desde el espacio , 2002 .

[18]  N. A. Quarmby,et al.  Linear mixture modelling applied to AVHRR data for crop area estimation , 1992 .

[19]  Y. E. Shimabukuro,et al.  Identification and mapping of the Amazon habitats using a mixing model , 1997 .

[20]  P. Chavez.  Radiometric calibration of Landsat Thematic Mapper multispectral images , 1989 .

[21]  C. Richardson,et al.  Effects of agriculture and wetland restoration on hydrology, soils, and water quality of a Carolina bay complex , 2003, Wetlands Ecology and Management.

[22]  John R. Jensen.  Introductory Digital Image Processing , 2004 .

[23]  A. Hastings,et al.  Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data , 2005 .

[24]  Christopher Small,et al.  The Landsat ETM+ spectral mixing space , 2004 .

[25]  Thomas Schmid,et al.  Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain) , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[26]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[27]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[28]  John Fox,et al.  GETTING STARTED WITH THE R COMMANDER: A BASIC-STATISTICS GRAPHICAL USER INTERFACE TO R , 2005 .

[29]  John M. Melack,et al.  Global wetland distribution and functional characterizaton: Trace gases and the hydrologic cycle. , 1998 .

[30]  M. S. Moran,et al.  Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output , 1992 .

[31]  S. Sanjeevi,et al.  A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India , 2006 .

[32]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[33]  Matthew P. McCartney,et al.  Dynamics of Usangu plains wetlands: Use of remote sensing and GIS as management decision tools , 2006 .

[34]  K. Sreenivas,et al.  The vegetation and waterlogging dynamics as derived from spaceborne multispectral and multitemporal data , 2002 .