Landsacpe complexity & Soil Moisturevariation in south Georgia,USA,for remote sensinapplications<br />Mario A. Girdalo , 2...
We adressed<br />Temporal & spatial <br />Variation <br />Of<br />SM<br />In a heterogenous<br />landscape<br />
0-History ! <br />1-OBJECTIVE<br />&HYPOTHESIS<br />2-METHODOLOGY<br />3-RESULTS<br />4-CONCLUSION<br />IN  THESE  PARTS<b...
SM= soil moistue<br />LRW=little river watershed=study area<br />LULC=LandCover-LandUse<br />ANOVA=analyze of variance<br ...
Western  et al. ,1999<br />SM is not a random phen.<br />Sp. Var. according to<br />Size of Samp. Area & Env. Var.<br />An...
History <br />USDA-NASA 2003  (SMEX03)<br />In situ devices  at LRW produce<br />Relieble inf. Of SM<br />Moran  ,2004<br ...
Investigate <br />Spacial  var.<br />In ground-based <br />SM data collection <br />From small plots (30*30)<br />Similar ...
We want toAssess<br />Suitabolity of<br />Using small plots<br />For  field  validation <br />Of <br />Satellites remote s...
TM,ETM+<br />ASTER & ALI<br />All are expected <br />To better capture <br />Local field conditions <br />Under landscape ...
Dispite<br />Heterogeneity of study area<br />It is possible to find <br />Temporally & Spatiolly<br />Homogenous <br />SM...
Study<br />SM var.<br />Within<br />5 dominate <br />LULC<br />In <br />LRW<br />Landscape.<br />Another Objective<br />
1- increase field knowledge of<br />SM behavior in  Hetero.LS<br />2-lead to better interpretation of the <br />Satellite ...
Methodology<br />
-Study Area<br />-Plot Data collection<br />-Statistical Analysis<br />
Study Area<br />
*Relatively flat topography<br />*Broad flood plains<br />*Poorly defined stream  channels<br />*gently sloping %1-%5<br /...
An in situ Network<br />27 ground stations <br />As a source of ground-based <br />Point Data<br />To<br />1- validate rem...
These 27 stations equipped with Steven-vitel  Hydra-Probe(steven water monitoring sys.)<br />
H-P-s <br />Operated by USDA-ARS<br />And record<br />SM information &Ground tempreture<br />At 3 depths<br />5 , 20 , 30 ...
Plot Data collection<br />
8 of 27 sites <br />Selected <br />Surounding H-P<br />&<br />Associated with the <br />In situ<br />SM<br />Monitoring st...
30 * 03 m area<br />Surrounded H.p<br />And<br />Subsequently sampled for SM<br />SM was measured using<br />Portable thet...
To minimize Errors<br />We use<br />Same  equipment <br />& <br />Same  personnel<br />
3 different Soil Dataduring 2005 - Jan2006<br />
Data collected Randomly<br />Within 8(30*30m) plots<br />1<br />8<br />16<br />26<br />32<br />40<br />50<br />63<br />66<...
testing temporal stability of<br />SM reading over a fairly short time period<br />(here 48 h)<br />On 2 consecutive days<...
Each reading was taken in <br />3 intervals in <br />4 directions <br />From H.P station<br />Total 20 sample per Plot<br />
At some locations <br />Fewer than <br />4 directions <br />Were evaluated <br />Due to the presence of obstacles<br />Suc...
collected from <br />5 field adjacent to plot areas<br />Under the LULC;<br />Grass ,Orchard,Bare land&<br />Agriculture C...
Each LULC 8 – 10 SM readings<br />At 3 intervals along a <br />25 – 30 m transect<br />In 4 dif.times<br />Nov – Dec 2005...
Precipitation from each site<br /><br />Raingages at H.P stations<br />For 12-day period at intervals 5 min<br />For each...
Statistical analysis<br />
1- ANOVA<br />2-  time stability anaysis<br />3-  Tukey & tamhane hoc analysis<br />4-Pearson Correlation Coefficient <br ...
Results <br />
Results of precipitation analysis<br />
No.  Of R.F<br />For 12 day period previos to <br />Sampling date<br />Ranged between<br />3  -  6<br />
Max  average was <br />10 cm <br />In  28  March<br />Min average was less than  2.2<br />In 24 May<br />
March 11<br />March  28<br />
April12<br />May  24<br />
Nov & Dec<br />January <br />2006<br />
During R.F events <br />all sites received simultanous precipitation <br />with small var. among them.<br />SO<br />Water ...
When we compared it with precipitation record:<br />8  greatest cumulative prec. only for 3 Sam.Date.<br />While  63 , 16...
ANOVA of SM<br />
This analys use 2 sets of variation to perform <br />Composition between plots<br />Variation   among  groups<br />Variati...
It shows:<br /><ul><li>High statistical dif. In SM for all Samp.D.
Low SM var. within a given plot which suggest homogenous</li></li></ul><li>Tokey post hoc test<br />
A multiple comparesionamong the plots<br />It one by one comparsionbetween sites<br />
0  = means  weak relationship<br />&<br />More than 0 means have a relationship<br />1  ,  2  ,  3  ,…<br />Bigger  score<...
SO<br />Weakest relationship <br />Were find between <br />8-26  /8-40  /8-66 / 40-50 <br />
SO<br />Strongest <br />Relationship are <br />Between<br />26-32  /  32 – 66 / 50 -63 <br />
SO<br />Site  8<br />Is most unique <br />SM<br />Behaviuor<br />Site  66<br />High level of <br />Similarity <br />With o...
Similarity of 26  &  32 <br />*Precipitation record:<br />similar and <br />Less than 2.5 cm Dif.for all<br />Samp.D.<br /...
Similarity of 16  &  50 <br />*Precipitation:<br />Dif. Cumulative rain<br />10 & even 18 cm<br />*Soil type:<br />16 Tif...
Most dissimilar sites were <br />8-26 / 8-40 /8-66 /40-50/50-32<br />Low assosiation was found between 16 &32<br />In tota...
8 – 26 / 8-40 / 8-66 <br />Dif. Combination of Soil type<br />SO<br />Var. in water infiltration process<br />SO<br />Dif....
Observation suggest that<br />
In LRW<br />LULC <br />Is a greater factor affecting <br />SM conditions<br />Than <br />Cumulative Rainfall<br />
Influnce of LULC<br />In SM<br />Indicated by our results in LRW. LS.<br />That make:<br />Dif.<br />Evatranspiration<br /...
Spatial resolution of R.sensing Sensors<br />That are More appropriate to <br />Capture SM conditions under this LS.<br />...
High levels of fragmetation at LRW<br />With fragments of <br />Small size<br />So<br />Moderate to <br />Fine spatial res...
SM time suitability<br />
Parasmetermean relative diffrence:<br />1-Measure how particular sample <br />compare with <br />Av. SM of plot<br />2-Ind...
26 has highest range on mean Dif.<br />(36%-39%)<br />
16 has<br />Lowest Var.<br />(-4%-+4%)<br />
Highest Av. Of mean relative Dif. Was 2.6% in 66<br />Followed by <br />8 , (2.1%)<br />16 (1.2 %)<br />26 (1.0%)<br />Oth...
Results indicate<br />Samp. Point within the plots are very close to each <br />Plot mean<br />SO<br />Can accurately <br ...
Our results shows<br />High level of in field <br />Homogeniety<br />That’s in contrast with<br />The results of similar r...
in our research<br />By selecting Small LS. fragments.<br />The  in field heterogenitycasued by<br />Topography Diffrence<...
Stability in plots 40  &  50<br />Can be explained by their <br />Relatively Homogenous LC<br />50  part of a larger past...
So<br />HemogeneityVeg.Cover<br />&<br />Homogenietyprecipitation,Slope,Soil physical characters<br /><br />High stabilit...
Pearson correlation coefficient<br />
Was compare to evaluate if all<br /> in field locations experience similar <br />SM VAR.s <br />within a <br />short perio...
Correlations were significant in both sets<br />But  Only for 26 , 32 , 63 <br />
P.C.C<br />Is an indicator of <br />Spatial stability<br />Of SM<br />Point data<br />
Low pcc with low significant level<br />Process is unstable in space and for time  lag <br />In which Data collected<br />
Two samples t-test<br />
Computed to evaluate<br />The Variation in<br />Av.SM values <br />From one day to next<br />
Results suggest that<br />Surface SM<br />Will notnecesserily<br />Reflect profile conditions<br />
SO<br />Remote sensing retrieving algorithms<br />Based only in direct or indirect<br />Quantification of<br />Surface con...
These ERRORS<br />Can be<br />Minimized <br />by incorporatig in to Rem.Sen. Analysis data of<br />Precipitation events pr...
Statistical analysis foragriculture LUtransects<br />
Discriptive statistics for<br />SM <br />On 4 dif. Date collection<br />On 25-30 m long transects<br />Related to Dif. LU<...
Results shows<br />Orchard & grass<br />Have the wettest conditions<br />While<br />Cotton& peanut fields<br />Were the dr...
Tilled AG.fiels<br />lowest spatial variability with Av. Values around %1<br />Grass AG.field<br />highest standard  dev...
SM Var. Between LU<br />
ANOVA<br />Between SM valuse for <br />five LULC <br />Showed <br />Significant statistical Dif. <br />For all 4 days <br />
Variation between goups showed by Anova<br />Peanut & Cotton <br /><br />Most similar with<br />No stat.Dif<br />Among th...
Conclusion <br />
AOVA high  Diffrences in SM among  the plots<br />& high Hemogeneity within them<br />Precipitation analysis similar rai...
Temporal stability analysis SM has high stability within the small plots and single point can use to monitor SM Var. of a...
And finally we found stat.Dif. In SM between Dif. LULC  as there adjacent plots<br />
finally<br />
The results confirm that<br />
A remote sensing <br />approach that consider <br />Homogeneous LULC ,LS fragments <br />can be used to identify <br />LS ...
In addition<br />The insitu USDA-ARS network <br />will serve better in remote sensing studies <br />in wich sensors <br /...
Thanx   4Your A 10tion<br />Mari .forootan.1388<br />
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Landsacpe Complexity & Soil Moisture Variation In South Copy

  1. 1. Landsacpe complexity & Soil Moisturevariation in south Georgia,USA,for remote sensinapplications<br />Mario A. Girdalo , 2008 , Uni.GeorgiaUSA<br />Present by : MARI ; FOROOTAN.<br />
  2. 2. We adressed<br />Temporal & spatial <br />Variation <br />Of<br />SM<br />In a heterogenous<br />landscape<br />
  3. 3. 0-History ! <br />1-OBJECTIVE<br />&HYPOTHESIS<br />2-METHODOLOGY<br />3-RESULTS<br />4-CONCLUSION<br />IN THESE PARTS<br />
  4. 4. SM= soil moistue<br />LRW=little river watershed=study area<br />LULC=LandCover-LandUse<br />ANOVA=analyze of variance<br />LS=landscape<br />H-P=hydra-probe<br />Var. =variation<br />P.C.C =pearson correlation coefficient<br />Remember <br />
  5. 5. Western et al. ,1999<br />SM is not a random phen.<br />Sp. Var. according to<br />Size of Samp. Area & Env. Var.<br />Anderson et al. ,2003<br />Combination of Env. Factors<br />(Soil.Veg.c,Topo,Climate)<br />Create spatially distribution of SM<br />Over dif.scales of space & time<br />History <br />
  6. 6. History <br />USDA-NASA 2003 (SMEX03)<br />In situ devices at LRW produce<br />Relieble inf. Of SM<br />Moran ,2004<br />Cashion ,2005<br />Under a complex LS.<br />Remote sensors <br />with coarse spatial resolution<br /> produce inaccurate estimates <br />
  7. 7. Investigate <br />Spacial var.<br />In ground-based <br />SM data collection <br />From small plots (30*30)<br />Similar to pixel size of<br />Small EFOV remote sensors<br />TM , ETM+& ASTER &ALI<br />In a heterogenous landscape <br />OBJECTIVE<br />
  8. 8. We want toAssess<br />Suitabolity of<br />Using small plots<br />For field validation <br />Of <br />Satellites remote sensing <br />Instruments<br />With small EFOV( &lt; 30m )<br />
  9. 9. TM,ETM+<br />ASTER & ALI<br />All are expected <br />To better capture <br />Local field conditions <br />Under landscape environmental <br />Complexity<br />
  10. 10. Dispite<br />Heterogeneity of study area<br />It is possible to find <br />Temporally & Spatiolly<br />Homogenous <br />SM behavior <br />Within small areas of <br />30*30.<br />Our HYPOTHESIS<br />
  11. 11. Study<br />SM var.<br />Within<br />5 dominate <br />LULC<br />In <br />LRW<br />Landscape.<br />Another Objective<br />
  12. 12. 1- increase field knowledge of<br />SM behavior in Hetero.LS<br />2-lead to better interpretation of the <br />Satellite estimates<br />Our Results Will<br />
  13. 13. Methodology<br />
  14. 14. -Study Area<br />-Plot Data collection<br />-Statistical Analysis<br />
  15. 15. Study Area<br />
  16. 16.
  17. 17. *Relatively flat topography<br />*Broad flood plains<br />*Poorly defined stream channels<br />*gently sloping %1-%5<br />*sandyloam soil with sandy surface horizon <br />&heavier subsoil<br /><ul><li>Low W.holding capacity</li></ul>& fast surface drainage<br />*Annual av,RF=120 mm<br />*unevently distribution <br />Short duration in Win.<br />High density in Sum.<br />Sum. are long &hot &humid<br />Win. are short &mild<br />Landscape composed of a diversity of<br />LULC<br />Forest , Cropland , Pasture,<br />Residental area & Wetlands<br />Northen eastern <br />Portion of <br />The 334 km2<br />Little river watershed<br />In South Atlantic coastal plain<br />Of US<br />Near Tifton ,Georgia<br />
  18. 18. An in situ Network<br />27 ground stations <br />As a source of ground-based <br />Point Data<br />To<br />1- validate remote sensing analysis of SM and <br />Soil tempreture& Climate<br />2- long term Hydrological studies<br />In South eastern of US<br />2002 - 2003<br />
  19. 19. These 27 stations equipped with Steven-vitel Hydra-Probe(steven water monitoring sys.)<br />
  20. 20.
  21. 21.
  22. 22.
  23. 23. H-P-s <br />Operated by USDA-ARS<br />And record<br />SM information &Ground tempreture<br />At 3 depths<br />5 , 20 , 30 m<br />Every 30 min<br />Typ. Installed along<br />Agriculture field boundaries,<br />Fence rows<br />& pasture areas &<br />Typ. Surounded by<br />Native grass veg.cover.<br />
  24. 24. Plot Data collection<br />
  25. 25. 8 of 27 sites <br />Selected <br />Surounding H-P<br />&<br />Associated with the <br />In situ<br />SM<br />Monitoring station<br />
  26. 26.
  27. 27. 30 * 03 m area<br />Surrounded H.p<br />And<br />Subsequently sampled for SM<br />SM was measured using<br />Portable theta capacitance probe<br />(measure dielectric conductivity similar to H.p)<br />
  28. 28. To minimize Errors<br />We use<br />Same equipment <br />& <br />Same personnel<br />
  29. 29. 3 different Soil Dataduring 2005 - Jan2006<br />
  30. 30. Data collected Randomly<br />Within 8(30*30m) plots<br />1<br />8<br />16<br />26<br />32<br />40<br />50<br />63<br />66<br />For each plot<br />10-20 reading<br />On <br />4 dif. Dates <br />March11 <br />March28<br />April12<br />May24<br />2005<br />
  31. 31. testing temporal stability of<br />SM reading over a fairly short time period<br />(here 48 h)<br />On 2 consecutive days<br />*Nov 30 & Dec 1 (2005)<br />*Jan 13 & Jan 14 (2006)<br />Systematic sampling<br />2<br />
  32. 32. Each reading was taken in <br />3 intervals in <br />4 directions <br />From H.P station<br />Total 20 sample per Plot<br />
  33. 33. At some locations <br />Fewer than <br />4 directions <br />Were evaluated <br />Due to the presence of obstacles<br />Such as<br />Road and Channels<br />4 perpendicular <br />direction<br />Sampling <br />location<br />Hdra-probe<br />Station <br />30 M<br />30 M<br />
  34. 34. collected from <br />5 field adjacent to plot areas<br />Under the LULC;<br />Grass ,Orchard,Bare land&<br />Agriculture Cotton & Peanuts<br />Associated with H.P sits:<br />50 , 32 , 66 & 40<br />3<br />
  35. 35. Each LULC 8 – 10 SM readings<br />At 3 intervals along a <br />25 – 30 m transect<br />In 4 dif.times<br />Nov – Dec 2005<br />&<br />Jan 2006<br />
  36. 36. Precipitation from each site<br /><br />Raingages at H.P stations<br />For 12-day period at intervals 5 min<br />For each Sampling Dates<br />
  37. 37. Statistical analysis<br />
  38. 38. 1- ANOVA<br />2- time stability anaysis<br />3- Tukey & tamhane hoc analysis<br />4-Pearson Correlation Coefficient <br />5- t- test <br />(Were used to analyze Data)<br />
  39. 39. Results <br />
  40. 40. Results of precipitation analysis<br />
  41. 41. No. Of R.F<br />For 12 day period previos to <br />Sampling date<br />Ranged between<br />3 - 6<br />
  42. 42. Max average was <br />10 cm <br />In 28 March<br />Min average was less than 2.2<br />In 24 May<br />
  43. 43. March 11<br />March 28<br />
  44. 44. April12<br />May 24<br />
  45. 45. Nov & Dec<br />January <br />2006<br />
  46. 46. During R.F events <br />all sites received simultanous precipitation <br />with small var. among them.<br />SO<br />Water supply <br />was homogeneous <br />for all sites prior to Sample Date<br />SO<br />SM dif. Can be considered <br />As a result of <br />Intrinsic<br />& mostly independet of water supply <br />
  47. 47. When we compared it with precipitation record:<br />8  greatest cumulative prec. only for 3 Sam.Date.<br />While 63 , 16 , 50  greatest prec. inputs for 5 other days<br />Aand<br />For lowest 40 , 32 <br /><br />Prec. Record shows they have above average<br />These observations support <br />the Hypothesis:<br />SM caused by intrinsic <br />Enviromental var.<br />Beyond prec.acting<br />At the local scale<br />Mean volumetric <br />SM & infield<br />Variation<br />Recorded in <br />8 locations <br />Presented <br />8  highest valus of Vol.SM<br />40  lowest valus of Vol.SM<br />
  48. 48. ANOVA of SM<br />
  49. 49. This analys use 2 sets of variation to perform <br />Composition between plots<br />Variation among groups<br />Variation within groups<br />
  50. 50. It shows:<br /><ul><li>High statistical dif. In SM for all Samp.D.
  51. 51. Low SM var. within a given plot which suggest homogenous</li></li></ul><li>Tokey post hoc test<br />
  52. 52. A multiple comparesionamong the plots<br />It one by one comparsionbetween sites<br />
  53. 53. 0 = means weak relationship<br />&<br />More than 0 means have a relationship<br />1 , 2 , 3 ,…<br />Bigger score<br />Stronger relationship<br />
  54. 54. SO<br />Weakest relationship <br />Were find between <br />8-26 /8-40 /8-66 / 40-50 <br />
  55. 55. SO<br />Strongest <br />Relationship are <br />Between<br />26-32 / 32 – 66 / 50 -63 <br />
  56. 56.
  57. 57. SO<br />Site 8<br />Is most unique <br />SM<br />Behaviuor<br />Site 66<br />High level of <br />Similarity <br />With other sites<br />
  58. 58. Similarity of 26 & 32 <br />*Precipitation record:<br />similar and <br />Less than 2.5 cm Dif.for all<br />Samp.D.<br />*Soil type:<br />Both are belong to<br />Tifton soil<br />Series<br />*Veg. cover:<br />32 short grass<br />26edge of agr.field in wich hay <br />is cut for cattle consumption<br />
  59. 59. Similarity of 16 & 50 <br />*Precipitation:<br />Dif. Cumulative rain<br />10 & even 18 cm<br />*Soil type:<br />16 Tifton series<br /> 50Sunsweet series<br />*Veg. cover:<br />Similar homogenous grass<br /> &exposed to transit agri. Equipment<br />Influenc<br />soil physical characteristics<br />
  60. 60. Most dissimilar sites were <br />8-26 / 8-40 /8-66 /40-50/50-32<br />Low assosiation was found between 16 &32<br />In total 6 Sam.D.<br />
  61. 61. 8 – 26 / 8-40 / 8-66 <br />Dif. Combination of Soil type<br />SO<br />Var. in water infiltration process<br />SO<br />Dif. SM content<br />40-50 / 50-32<br />Dif. Cumulative rainfall<br />
  62. 62. Observation suggest that<br />
  63. 63. In LRW<br />LULC <br />Is a greater factor affecting <br />SM conditions<br />Than <br />Cumulative Rainfall<br />
  64. 64. Influnce of LULC<br />In SM<br />Indicated by our results in LRW. LS.<br />That make:<br />Dif.<br />Evatranspiration<br />&<br />Soil water usage<br />
  65. 65. Spatial resolution of R.sensing Sensors<br />That are More appropriate to <br />Capture SM conditions under this LS.<br />Will be defined by <br />*LS. Fragmentation<br />*LULC composition<br />*Sizes of LULC fragments<br />
  66. 66. High levels of fragmetation at LRW<br />With fragments of <br />Small size<br />So<br />Moderate to <br />Fine spatial resolutionsensors<br />(~ 3 0 m)<br />Are expected better suit the countinoussutdy<br />Of SM in LRW<br />
  67. 67. SM time suitability<br />
  68. 68. Parasmetermean relative diffrence:<br />1-Measure how particular sample <br />compare with <br />Av. SM of plot<br />2-Indicator of <br />Infield <br />Variation of surface<br />SM<br />
  69. 69.
  70. 70. 26 has highest range on mean Dif.<br />(36%-39%)<br />
  71. 71. 16 has<br />Lowest Var.<br />(-4%-+4%)<br />
  72. 72. Highest Av. Of mean relative Dif. Was 2.6% in 66<br />Followed by <br />8 , (2.1%)<br />16 (1.2 %)<br />26 (1.0%)<br />Otherwise mean relative Dif. Was 0.1 % below the plot Dif. Approach Zero<br />High level of<br />Homogeneity<br />Within each <br />Sample plot<br />
  73. 73. Results indicate<br />Samp. Point within the plots are very close to each <br />Plot mean<br />SO<br />Can accurately <br />Estimate <br />Surface SM behavior<br />Of <br />Entire 30*30 m plot<br />
  74. 74. Our results shows<br />High level of in field <br />Homogeniety<br />That’s in contrast with<br />The results of similar research performed <br />In large plots<br />
  75. 75. in our research<br />By selecting Small LS. fragments.<br />The in field heterogenitycasued by<br />Topography Diffrence<br />Was <br />Minimize<br />
  76. 76. Stability in plots 40 & 50<br />Can be explained by their <br />Relatively Homogenous LC<br />50  part of a larger pasture and entire plot covered by same grass type<br />40  part of a mechanized AGR.Field & change <br />Veg-cover throughout growing season<br />
  77. 77. So<br />HemogeneityVeg.Cover<br />&<br />Homogenietyprecipitation,Slope,Soil physical characters<br /><br />High stability of<br />SM<br />Recorded at these 2 sites<br />
  78. 78. Pearson correlation coefficient<br />
  79. 79. Was compare to evaluate if all<br /> in field locations experience similar <br />SM VAR.s <br />within a <br />short period<br />(one day in this case)<br />
  80. 80. Correlations were significant in both sets<br />But Only for 26 , 32 , 63 <br />
  81. 81. P.C.C<br />Is an indicator of <br />Spatial stability<br />Of SM<br />Point data<br />
  82. 82. Low pcc with low significant level<br />Process is unstable in space and for time lag <br />In which Data collected<br />
  83. 83. Two samples t-test<br />
  84. 84. Computed to evaluate<br />The Variation in<br />Av.SM values <br />From one day to next<br />
  85. 85.
  86. 86. Results suggest that<br />Surface SM<br />Will notnecesserily<br />Reflect profile conditions<br />
  87. 87. SO<br />Remote sensing retrieving algorithms<br />Based only in direct or indirect<br />Quantification of<br />Surface conditions<br />As<br />Temp. , SM <br />May produce Errors<br />When used in this LS.<br />
  88. 88. These ERRORS<br />Can be<br />Minimized <br />by incorporatig in to Rem.Sen. Analysis data of<br />Precipitation events prior to Samp.D<br />&<br />Infiltration analysis for soil type dominated<br />of study area<br />
  89. 89. Statistical analysis foragriculture LUtransects<br />
  90. 90. Discriptive statistics for<br />SM <br />On 4 dif. Date collection<br />On 25-30 m long transects<br />Related to Dif. LU<br />Adajent to 5 plots<br />
  91. 91.
  92. 92. Results shows<br />Orchard & grass<br />Have the wettest conditions<br />While<br />Cotton& peanut fields<br />Were the driest<br />
  93. 93. Tilled AG.fiels<br />lowest spatial variability with Av. Values around %1<br />Grass AG.field<br />highest standard deviation with values btween<br />%1.8 - %2.6 <br />Bare land<br /> Intermediate behavior<br />
  94. 94. SM Var. Between LU<br />
  95. 95. ANOVA<br />Between SM valuse for <br />five LULC <br />Showed <br />Significant statistical Dif. <br />For all 4 days <br />
  96. 96. Variation between goups showed by Anova<br />Peanut & Cotton <br /><br />Most similar with<br />No stat.Dif<br />Among them<br /> for any 4 days<br />Greatest Dif. With orchard, grass & bareland<br />Showing Stat.Dif on all<br />Samp.Dates<br />
  97. 97. Conclusion <br />
  98. 98. AOVA high Diffrences in SM among the plots<br />& high Hemogeneity within them<br />Precipitation analysis similar rainfall conditions<br />So SM.Var. explained by in situ local conditions<br />
  99. 99. Temporal stability analysis SM has high stability within the small plots and single point can use to monitor SM Var. of all plot<br />T-statistical analysisSMstatesin upper soil layer changes within 24H<br />
  100. 100. And finally we found stat.Dif. In SM between Dif. LULC as there adjacent plots<br />
  101. 101. finally<br />
  102. 102. The results confirm that<br />
  103. 103. A remote sensing <br />approach that consider <br />Homogeneous LULC ,LS fragments <br />can be used to identify <br />LS units of similar SM behavior <br />under Heterogeneous landscapes<br />
  104. 104. In addition<br />The insitu USDA-ARS network <br />will serve better in remote sensing studies <br />in wich sensors <br />with fine spatial resolution <br />are evaluated<br />
  105. 105. Thanx 4Your A 10tion<br />Mari .forootan.1388<br />
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