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•  Map  and  quan/fy   LCLUC  at   two   spa/al   scales:   (1)   inter-­‐regional   scale  for   the   Greater   Accra,   Central,   and   Ashan/   regions   of   southern   and  central   Ghana,   and   (2)   intra-­‐urban   scale   for   Accra,   Kumasi,   Cape   Coast  and  Obuasi,  the  four  major  ci/es  within  the  study  area.    

•   Inter-­‐regional   iden/fica/on   of   LCLUC   based   on   moderate   spa/al  resolu/on,  mul/-­‐temporal   image   data   from   Landsat   ETM+,   Terra   ASTER  and   SPOT   HRV   op/cal   satellite   systems,   and   ERS-­‐2   synthe/c   aperture  radar  (SAR).    

•   Intra-­‐urban   iden/fica/on   of   LCLUC   based   on   high   spa/al   resolu/on  image  data  from  QuickBird,  WorldView,  IKONOS  and  Geoeye  commercial  satellites.    

•  c.  2000  through  2010  study  period  coincides  with  a  period  of  available  demographic  and  health  survey  data  for  Ghana.    

•  U/lize  quan/ta/ve  spa/al  analysis  techniques  to  examine  rela/onships  between   LCLUC   and   magnitudes   and   changes   of   demographic,  socioeconomic,   and   health   variables   using   generalized   linear   and  mul/-­‐level   regression   models,   mul/nomial   logit   models,   regression   tree  analysis,  and  agent-­‐based  models.  

•   Emphasis   on   the  effects   of   LCLUC  on  quality   of   life   indicators   such   as  child  mortality,  slum  indices,  and  food  security,  within  four  of  the  major  ci/es  of  Ghana.    

Objec4ves  

Study  Area  and  Methodology  

1.   Iden/fy,  map,   and   quan/fy   land   cover   and   land   use   change   (LCLUC)  within  an  extensive  study  area  in  Ghana,  par/cularly  for  the  period  2000  through  2010.  

2.   Understand   the   regional   impacts   of   LCLUC   associated   with   rural-­‐to-­‐urban  migra/on  in  driving  these  changes.  

3.  Assess  LCULC  and  its  effect  on  demographic  and  quality  of   life  factors  for  four  major  urban  centers  during  this  /me  period.  

Research  Approach  

Figure  3.    Landsat  7  ETM+  image  and  ERS-­‐2  “SAR  Precision”  radar  images  from  circa  2000.  SAR  data  are  par/cularly  useful  for  iden/fying  “Built”  and  Agricultural  LCLUC  in  rural  and  peri-­‐urban  areas.  

                                 Moderate  Spa4al  Resolu4on    Op4cal  and  Radar  Satellite  Data  

Benefits  of  Studying  Ghana  •  Abundant  demographic  and  health  data  sets  rela/ve  to  rest  of  Sub-­‐Saharan  

Africa  •  Stable  and  democra/c  government  and  reasonably  safe  environment  

•  Leader  in  science  and  technology  for  Western  Africa  

•  Research  team  has  almost  10  years  of  experience  working  there  

•  Reasonable  imagery  availability  rela/ve  to  other  Western  Africa  countries  

Challenges  Studying  Ghana  

•  Prevalent  cloud  cover  and  winter  Harmafan  wind  and  dust  storms  

•  Limited  high  spa/al  resolu/on  satellite  coverage  for  early  2000s  

•  Limited  Landsat-­‐5  TM  receiving  capability  •  Census  boundary  files  require  georeferencing  and  substan/al  edi/ng  by  SDSU  

team  

Preliminary  Results  (cont.)  

Figure    1.  Map  of  regional  study  area  (tan)  and  study  ci/es  (red)  in  Ghana  

Figure  4.  Preliminary  evalua/on  of  LCLUC  for  greater  Kumasi  area  between  2001  and  2007  based  on  classifica/on  of  Landsat  ETM+  data.  “Built”  land  cover  increased  substan/ally  par/cularly  in  northern  and  eastern  Kumasi,  where  high  spa/al  resolu/on  satellite  image  data  are  available  for  more  detailed  analyses.  

Preliminary  Results  Research  Team  

Intra-­‐urban  Land  Cover/Land  Use  Classifica4on  Scheme  

Inter-­‐regional  Land  Cover/Land  Use  Classifica4on  Scheme  

Table  2  Characteris/cs  of  moderate  spa/al  resolu/on  satellite  (MSRS)  systems  and  data.    

Figure  5.  Vegeta/on  change  between  2002  and  2010  derived  from  classifica/on      of  QuickBird  mul/spectral  data.  A  seven  percent  area-­‐wide  decrease  in  vegeta/on  cover  occurred  in  this  period,  with  greatest  rela/ve  decrease  in  slum  areas.  

1.  Soil  or  Natural  Vegeta/on  to  Residen/al  –  undeveloped  to  residen/al            dwellings;    2.  Soil  or  Natural  Vegeta/on  to  Non-­‐Residen/al  Built  –  undeveloped  to            nonresiden/al  buildings  or  infrastructure;    

3.  Agriculture  to  Residen/al  –  urban  periphery  or  conversion  of  urban            agriculture;    4.  Agriculture  to  Non-­‐Residen/al  Built  –  urban  periphery  or  conversion  of            urban  agriculture;  and  

5.  Urban  Densifica/on  –  increase  in  density  of  buildings  or  infrastructure.      

1.  Agriculture   to  Built  –  expansion  of  urban  edge,   village  expansion,  new  village  or  dwelling  clusters;  

2.  Family  to  Industrial  Agriculture  –  intensifica/on  of  agricultural   land  use  through  change  in  land  ownership  and  mechaniza/on;    

3.    Natural  Vegeta/on  to  Agriculture  –  smaller  private  plots,  large  agro-­‐            business  fields;    4.    Natural  Vegeta/on  clearing  –  ini/al  stage  of  agricultural  or  urban    

         development;  and  

5.    Natural  Vegeta/on  to  Built  –  forest  to  dwelling  cluster  or  village.  

*ASTER  SWIR  not  func/onal  aler  April  2008    

**  Polariza/on  mode:  VV  ***  Scan  Line  Correc/on  off  (SLC-­‐-­‐-­‐off)  imagery  aler  May  2003  ****  Polariza/on  modes:  VV,  HH,  VV/HH,  HV/HH,  or  VH/VV    

Table  1.  Characteris/cs  of  commercial  high  spa/al  resolu/on  satellite  (CHSRS)  systems  and  data.    

Figure  2.  Processing  flow:  a.  Regional-­‐scale  LCLUC  mapping;  b.  Urban  LCLUC  mapping.  

Douglas  Stow  (PI),  John  Weeks  (Co-­‐PI),  Lloyd  Coulter  (Project  Manager),    Li  An  (Co-­‐Inves/gator),  Magdalena  Benza-­‐Fiocco  (PhD  student),    Sory  Toure  (PhD  Student),  Sean  Taugher  (MA  student),    Milo  Verjaska  (MS  student)  -­‐-­‐  San  Diego  State  University  (lead)  

Ryan  Engstrom  (PI)  -­‐-­‐  The  George  Washington  University  

David  Lopez-­‐Carr  (Co-­‐Inves/gator)  -­‐-­‐  University  of  California  Santa  Barbara  

Samuel  Agyei-­‐Mensah  and  Foster  Mensah  (Collaborators)  -­‐-­‐  University  of  Ghana  Legon.  

The  Urban  Transi4on  in  Ghana  and  Its  Rela4on  to  Land  Cover  and  Land  Use  Change  

Through  Analysis  of  Mul4-­‐scale  and  Mul4-­‐temporal  Satellite  Image  Data  

2002   2010  

Absolute  vegeta/on  change  =  %2010  -­‐  %2002  

Rela/ve  vegeta/on  change  =  (%2010  -­‐  %2002)/%2002  

Connec4ng  QuickBird  Derived  Metrics  With  Socio-­‐demographic  Survey  Data  for  Accra  

Table  4  (below)  Results  from  exhaus/ve  exploratory  regression  analysis  between  changes  in  image-­‐,  census-­‐  and  health  survey-­‐derived  variables.  Metrics  were  computed  from  registered  QuickBird  satellite  mul/spectral  data  captured  in  2002  and  2010  and    Ghanaian  census  and  health  survey  data,  reflec/ve  of  housing  quality,  socioeconomic  status,  and  health  condi/ons.  Significant  correla/ons  were  found  for  31  enumera/on  areas  considered  to  be  located  in  “slum”  neighborhoods  based  on  a  defini/on  established  by    UN-­‐Habitat.    Understanding  the  degree  of  co-­‐variability  between  LCLUC  and  quality  of  life  is  an  integral  step  in  modeling  the  changing  urban  gradient  of  developing  countries  in  Sub-­‐Saharan  Africa.      

Figure  6.    Δ  Mean  NIR/B  values  for  31  slum  enumera/on  areas  overlaid  on  a  2002  Quickbird  NIR/B  image  of  Accra.  

Acknowledgements  

•         NASA  Interdisciplinary  Research  in  Earth  Science  program  grant  G00009708,              Dr.  Garik  Gutman,  program  manager;  August  1,  2102  through  July  31,  2015  

•         High  spa/al  resolu/on  satellite  data  provided  through  Na/onal  Geospa/al-­‐              Intelligence  Agency  NextView  program,  facilitated  by  Jaime  Nickeson                (NASA  GSFC)  and  Giuseppe  Molinario  (UMd)