1. 1 CLIP - Land Cover and Land Use Group Qi, Lusch, Reid, Maitima, Olson, Palm, Campbell Jiagun Ge, Nate Torbick

2. Jan. 5-9, 2005CLIP Meeting2/20 How do land cover changes impact regional climate and vice versa?

3. Jan. 5-9, 2005CLIP Meeting3/20 APPROACH LULC Biophysical Attributes Regional Climate LCLU Albedo LAI fPAR Ts Precip. etc. LUT LEAF2 Simulation Regional Climate Model (RAMS) RS EOS Data Products GLC2000 Africover IGBP MODIS Simulation

4. Jan. 5-9, 2005CLIP Meeting4/20 Datasets: GLC2000 (Global Land Cover) OGE (Olson Global Ecosystem) LEAF2(RAMS) Africover IGBP (International Geosphere Biosphere Program) Data sources included: AVHRR, MODIS, Landsat ETM+ & TM, SPOT VGT, NDVI Issues : creation dates source data (resolutions) objectives (use vs. cover) formats classification regimes standards scales (level of details) Datasets and Issues

5. Jan. 5-9, 2005CLIP Meeting5/20 Science Questions 1.How to determine the “best ” dataset for CLIP studies? –Part 1. ‘Categorical Assessment’ Qualitative, visual inspection, examine class system Quantitative statistical assessment, compare datasets directly, crosswalk Videography, spectral signatures, MODIS, ASTER, ETM+ –Part 2. ‘Biophysical Assessment’ LAI, Albedo, fPAR Statistical measures 2.What is the “best ” LULC dataset for CLIP studies? Objectives, various datasets, formats, models (RAMS, LTM…) 3.How changes in land cover are going to affect regional climate?

6. Jan. 5-9, 2005CLIP Meeting6/20 Comparing OGE (RAMS), GLC2000, Africover for selected region

7. Jan. 5-9, 2005CLIP Meeting7/20 Answer 3 rd Question: Land  Climate Impacts GLC2000 Africover OGE IGBP Integrate for CLIP hybrid Crosswalk Assessment Comparison LEAF2 characteristics RAMS Answer Questions # 1 and 2: How to determine the best LULC? Which LULC is the best? CLIP – LC Analysis

10. Jan. 5-9, 2005CLIP Meeting10/20 RAMS Inner Domain No Africover dataset for: Mozambique Zambia Central African Republic Malawi Ethiopia Reference with GLC2000

11. Jan. 5-9, 2005CLIP Meeting11/20 Videography - Digital video flown over selected regions for assessment Create FOV coverages Heads Up On-screen digitizing Geo-process flightline FOVs Area calculations, Sampling scheme Capture datapoints Compare datasets KENYA Flight Line Locations

12. Jan. 5-9, 2005CLIP Meeting12/20 Calculate Field Of View Assumptions Flying altitude Tilt & Rotation Zoom, Pan, Camera changes

13. Jan. 5-9, 2005CLIP Meeting13/20 Area comparison Kenya country level vs. Ambo FOV Percentages roughly equivalent Kenya Africover 21Ambo FOV 21 CodeHectares% % AG-11597612.7482.74 85.570.38 AG-27525063.33512.92 3650.39516.19 AG-31151333.2561.98 78.0610.35 AG-4159573.7160.27 18.1560.08 AG-510961.780.02 AG-650019.140.09 BA-11138142.0231.95 BA-2255124.3090.44 180.9310.8 FR-11305470.8452.24 26.8410.12 FR-22086199.8253.58 2865.6912.71 FR-318672989.3332.07 6182.29527.42 FR-449276.7240.08 FR-5415313.7380.71 326.631.45 RL-12998868.9945.15 403.1381.79 RL-216907198.7229.04 6549.33929.05 RL-31449646.3012.49 298.6971.32 RL-41182086.2732.03 1416.246.28 UR44290.6680.08 344.7621.53 WB-11229813.2672.11 120.9990.54 WB-2 102.4430

14. Jan. 5-9, 2005CLIP Meeting14/20 AFRICOVER (LC21) AMBO FOV (15 classes) Confidence 0.95 Precision0.050.1 Option 1 862216 Option 2 711 178 Option 3 Confidence 0.90 Precision0.050.1 Option 1 737185 Option 2 607152 Option 3 Sampling scheme options 1. NO information = 40-50 points/class 2. # Classes & Area information exists 3. Different levels of class importance Distribution Stratified random Confidence level = probability the sample size will be sufficient to perform statistically valid accuracy assessments Precision = acceptable risk when mistaken conclusions occur regarding the accuracy of a map based on the statistical analyses.

15. Jan. 5-9, 2005CLIP Meeting15/20 Example Red = Ambo Flight line FOV Green = 2 sample points Africover LU/C database Each point Lat / long coordinate Land use/cover type

16. Jan. 5-9, 2005CLIP Meeting16/20 Class code: AG-1 (Africover dataset) Class Name: Rainfed herbaceous crops (large to medium, continuous fields) XY - Decimal Minutes Convert systems Check reference LU/C

17. Jan. 5-9, 2005CLIP Meeting17/20 LU/C Example Flight Line Points Bare rockUrban Rainfed herbaceousAquatic grasslands

18. Jan. 5-9, 2005CLIP Meeting18/20 December 2000 LAI & fPAR Illustration Source: MODIS Science Team, NASA, BU

19. Jan. 5-9, 2005CLIP Meeting19/20 LAI Curves for selected region 2 South – 4 South

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23. Jan. 5-9, 2005CLIP Meeting23/20 Statistical Measures of Goodness As in RAMS, study area is divided into strips according to their latitude (LAI is estimated using Lat. Information only in RAMS) 1176 931...... 30

24. Jan. 5-9, 2005CLIP Meeting24/20 IGBP 9 LAI of Strip 1

25. Jan. 5-9, 2005CLIP Meeting25/20 Statistical Analysis Maps of IGBP/GLC, AFRI/GLC, OGE/GLC and LEAF/GLC are generated on every level (30, 60, 100, 200, 300, 400, 600, 1200) Histogram are shown for each map. Means of normalized data ( IGBP/GLC, AFRI/GLC, OGE/GLC and LEAF/GLC ) are compared again using Analysis-of-Variance method Some initial conclusions are drawn

26. Jan. 5-9, 2005CLIP Meeting26/20 Statistical Measures of Goodness Develop an approach to measure the spread of histograms on 30km level (strip), and add up to the whole domain. So one final value will indicate the quality of each LC. Error Sum of Squares Where, i ~ strip number; j ~ land cover types in one strip; m ~ month; n ~ LAI pixel number in certain strip, certain land cover type and certain month; N ~ total pixel number in each strip, which is 1176×30 in this case; We get mean error sum of square in each strip by dividing (N – k).

27. Jan. 5-9, 2005CLIP Meeting27/20 Statistics and assumptions In each strip, each land cover, 12 months of LAI values are added up and then averaged. Seasonal LAI information will be included by this part (see histogram). In each strip, the smaller error sum of squares, the more converged histogram By mean error sum of squares, we can be more justified to compare between different LC systems.

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45. Jan. 5-9, 2005CLIP Meeting45/20 Statistical Comparison Results GLC works better than other 4 on every level GLC works much better at mountain area (Kilimanjaro) Because GLC considers altitude factor, such as type 3 (Submontane forest 900 -1500 m) and 4 (Montane forest >1500 m)

46. Jan. 5-9, 2005CLIP Meeting46/20 Statistical Comparison Results With the increase of pixel size, the performance of Africover decreases dramatically, based on the previous mean trend curves. Because Africover is generated based on high resolution data source (30m)

47. Jan. 5-9, 2005CLIP Meeting47/20 Results Among IGBP, AFRI, OGE and LEAF, performance of IGBP increases dramatically with the increase of pixel size, especially from 400 to 1200 ResolutionOrder of Performance 30 oge > leaf > afri > igbp 60 oge > leaf > afri > igbp 100 oge > leaf > afri > igbp 200 oge > leaf > afri > igbp 300 oge > leaf > afri > igbp 400 oge > leaf > afri > igbp 600 oge > igbp > afri > leaf 1200 igbp > oge > leaf > afri

48. Jan. 5-9, 2005CLIP Meeting48/20 Summary Cross-Walk analysis to merge various datasets for RAMS model simulations (paper 1) Videographic data have been extracted for quantitative assessment of LC accuracy (paper 2) A statistical measure (M ) was developed to determine which classification is the “best” and to quantify uncertainty of various land use/cover maps. (paper 3) These results will be used in the next step to determine sensitivity of RAMS model to land cover changes (paper 4)