environmental layers meeting iplant tucson 2012-04-03 roundup benoit parmentier
DESCRIPTION
ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier. What I have been doing working on: Visualization of RMSE fit for Geographically Weighted Regression Writing a code in R to visualize the RMSE using Stations location Kriged error surface from stations - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/1.jpg)
ENVIRONMENTAL LAYERS MEETINGIPLANT TUCSON
2012-04-03
RoundupBenoit Parmentier
![Page 2: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/2.jpg)
What I have been doing working on:
1) Visualization of RMSE fit for Geographically Weighted Regression •Writing a code in R to visualize the RMSE using- Stations location- Kriged error surface from stations
2) Producing LST daily mean Python script (with IDRISI API but with GDAL in mind) to calculate:- Daily mean- Number of valid observation per day.
3) GAM prediction• Some GAM predictions with interaction terms• Including daily mean LST and LC in the GAM regression
![Page 3: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/3.jpg)
1) Visualization of RMSE fit for Geographically Weighted Regression •Writing a code in R to visualize the RMSE using- Stations location- Kriged error surface from stations
1)VISUALIZATION OF RMSE Moving beyond aggregate statistic…
![Page 4: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/4.jpg)
0
5
10
15
20
25
30
35
40
45
RMSE
fit (
deg
C *
10)
Interpolation Date
RMSE FIT USING GWR WITH 30% RETAINED FOR VALIDATION
![Page 5: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/5.jpg)
Run 10-Fit residuals from gwr using 20100902
run dates ns RMSE_gwr110 20100902 120 40.31519
![Page 6: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/6.jpg)
Run 9-Fit residuals from gwr using 20100901
run dates ns RMSE_gwr19 20100901 119 26.01366
![Page 7: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/7.jpg)
Run 8-Fit residuals from gwr using 20100702
run dates ns RMSE_gwr18 20100702 120 27.45119
![Page 8: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/8.jpg)
Run 7-Fit residuals from gwr using 20100701
run dates ns RMSE_gwr17 20100701 123 25.27986
![Page 9: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/9.jpg)
Fit residuals from gwr using 20100701Run 6-Fit residuals from gwr using 20100502
run dates ns RMSE_gwr16 20100502 114 21.33324
Potentially useful to have the 2 sd thresholds…
![Page 10: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/10.jpg)
Run 5-Fit residuals from gwr using 20100501
run dates ns RMSE_gwr15 20100501 113 20.00117
![Page 11: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/11.jpg)
Run 4-Fit residuals from gwr using 20100302
run dates ns RMSE_gwr14 20100302 121 21.83577
![Page 12: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/12.jpg)
Run 3-Fit residuals from gwr using 20100301
NO KRIGED FIT
run dates ns RMSE_gwr13 20100301 120 18.19032
![Page 13: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/13.jpg)
Run 8-Fit residuals from gwr using 20100301Run 2-Fit residuals from gwr using 20100102
run dates ns RMSE_gwr12 20100102 115 23.73444
![Page 14: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/14.jpg)
Run 8-Fit residuals from gwr using 20100301Run 9-Fit residuals from gwr using 20100102
Run 1-Fit residuals from gwr using 20100102
run dates ns RMSE_gwr11 20100101 113 32.1132
![Page 15: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/15.jpg)
•Python script (with IDRISI API but with GDAL in mind) to calculate:- Daily mean- Number of valid observation per day.
LST DAILY MEAM PRODUCTION
![Page 16: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/16.jpg)
MOD11A1hdf
OR83M.rst
MosaicReprojection
QC flagsLevel 1 and 2
Masking Low quality
Daily Mean Daily Valid Obs.
WORKFLOW DAILY MEAN CALCULATION
Part of the process is automated in python with IDRISI API.
DownloadingMissing Data Assessment
![Page 17: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/17.jpg)
OREGON- DAILY MEAN FOR DOY 001
mean_day001_rescaled.rst
![Page 18: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/18.jpg)
OREGON-NUMBER OF VALID OBSERVATION FOR DOY 001
mean_day_valid_obs_001_Sum.rst
![Page 19: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/19.jpg)
OREGON- DAILY MEAN FOR DOY 182
mean_day182_rescaled.rst
![Page 20: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/20.jpg)
OREGON-NUMBER OF VALID OBSERVATION FOR DOY 182
mean_day_valid_obs_182_Sum.rst
![Page 21: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/21.jpg)
SUMMARY INFORMATION OF THE DAILY MEAN CALCULATION
A full assessment of the temporal and spatial distribution of mean would be necessary:- Most dates have 10 images (on average 9.88 images).- The number of valid values seems to be lower in Winter (more check needed).- Average per month may be quite helpful.
Missing data:
The average was done over the 2001-2010 time period and there were 45 missing images (out of a total of 3652).
Missing DOY 78 to 88: 2002-03-19 to 2002-03-28Missing DOY 166 to 181: 2001-06-15 to 2001-07-02 (with July 01 missing 2)Missing DOY 301 to 305Missing DOY 351 to 357: 2003-12-17 to 2003-12-23 (355 to 357 missing 2)
![Page 22: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/22.jpg)
3)GAM MODELING USING LST AND LC
GAM regressions:• Some GAM predictions with interaction terms• Including daily mean LST and LC in the GAM regression
![Page 23: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/23.jpg)
AggregatedClassification class
Class No.
GLC20001 UMD MODIS GlobCover2
Forest 1 1,2,3,4,5,6,7,8
1,2,3,4,5,6
1,2,3,4,5,8
40,50,60,70,90,100,160,170
Shrub 2 9,10,11,12,14 7,8,9 6,7,9 110,120,130,150Grass 3 13 10 10 140Crop 4 16 11 12 11,14Mosaic3 5 17,18 14 20,30Urban 6 22 13 13 190Barren 7 19 12 16 200Snow 8 21 15 220Wetland 9 15 11 180Water body 10 20 0 17 210
Table 5. Legend for the 10 aggregated land cover classes and the corresponding classes from the six individual global land cover legends. Modified from (Nakaegawa 2011).1I added class 3 to ‘forest’ since it was missing in original table. The class 2 entry under ‘shrub’ is probably an error and so is removed.2GlobCover class assignment needs to be finalized.3Mosaic is composed of cropland and natural vegetation.
LAND COVER CONSENSUS CATEGORIES
![Page 24: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/24.jpg)
GAM MODELS USED FOR THIS ANALYSIS
mod1<- tmax~ s(lat) + s (lon) + s (ELEV_SRTM) mod2<- tmax~ s(lat,lon,ELEV_SRTM) mod3<- tmax~ s(lat) + s (lon) + s (ELEV_SRTM) + s (Northness)+ s (Eastness) + s(DISTOC) mod4<- tmax~ s(lat) + s (lon) + s(ELEV_SRTM) + s(Northness) + s (Eastness) + s(DISTOC) + s(LST) mod5<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)
mod6<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC1) mod7<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC3)
![Page 25: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/25.jpg)
RMSE FOR DIFFERENT DATES AND MODELS
![Page 26: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/26.jpg)
RMSE FOR ALL DATES AND MODELS
![Page 27: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/27.jpg)
PROBLEM WITH MISSING DATA
If screening is used such as LST> 258 & LST<313)… the number of observations can drop to 48 and 20 for training and testing compared to 120 and 50 stations.
![Page 28: ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-04-03 Roundup Benoit Parmentier](https://reader033.vdocuments.us/reader033/viewer/2022051419/56815aa3550346895dc82f59/html5/thumbnails/28.jpg)
What's next..?
1) Continue the Visualization of RMSE for GAM and GWR
2) Influence of sampling on results• GWR • Prediction
3) Producing LST monthly
4) GAM using LST and Land Cover
5) Use Kriging and co-kriging to predict tmax
6) Documentation of the analysis