Appendix 2 Electronic Supplementary Materials

Appendix 2 – Electronic Supplementary Materials

Table A2-1. AUS accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj. Agg. / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Bare / 287,168 / 156,177 / 125,252 / 122,256 / 122,752 / 154,043 / 61.80 / 58.86 / 59.34 / 96.79
Imp / 164,319 / 73,303 / 70,448 / 66,567 / 65,790 / 103,441 / 93.92 / 86.22 / 84.75 / 50.25
Veg / 142,260 / 84,603 / 121,027 / 119,787 / 119,680 / 102,609 / 22.57 / 24.21 / 24.35 / 52.40
PD / Bare / 1146.85 / 622.45 / 498.54 / 487.12 / 491.83 / 613.68 / 61.77 / 58.97 / 60.12 / 96.71
Imp / 868.75 / 292.15 / 280.13 / 265.15 / 269.11 / 532.41 / 95.91 / 91.05 / 92.31 / 41.18
Veg / 566.60 / 337.19 / 481.89 / 476.90 / 477.19 / 410.49 / 22.64 / 24.30 / 24.20 / 51.57
LSI / Bare / 521.00 / 486.24 / 379.56 / 387.39 / 399.68 / 458.44 / -50.86 / -47.98 / -42.71 / 11.11
Imp / 457.64 / 382.84 / 284.40 / 294.12 / 316.31 / 378.68 / -13.65 / -8.51 / 5.85 / 89.47
Veg / 527.02 / 375.54 / 360.69 / 368.19 / 380.13 / 433.29 / 82.14 / 90.74 / 94.12 / 44.80
TE / Bare / 17,977,000 / 17,477,860 / 16,557,600 / 17,010,700 / 18,067,000 / 23,542,100 / -29.67 / 3.31 / -8.27 / -84.79
Imp / 15,035,600 / 12,948,630 / 14,192,300 / 14,102,600 / 14,024,700 / 15,255,000 / 25.32 / 28.79 / 31.96 / -4.99
Veg / 20,009,300 / 13,404,540 / 17,957,900 / 17,599,100 / 18,159,300 / 19,388,500 / 18.38 / 22.32 / 16.29 / 4.93
ED / Bare / 716.42 / 696.59 / 659.93 / 677.99 / 720.19 / 938.17 / -29.80 / 3.18 / -8.69 / -84.83
Imp / 599.12 / 516.08 / 565.60 / 562.08 / 558.99 / 608.07 / 25.29 / 28.70 / 31.86 / -5.11
Veg / 797.49 / 534.25 / 715.61 / 701.39 / 723.79 / 772.76 / 18.42 / 22.33 / 16.28 / 4.93

Table A2-2. BUF accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj Agg / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Bare / 105,167 / 67,678 / 79,275 / 70,841 / 79,175 / 55,569 / 52.75 / 84.44 / 53.06 / 51.17
Imp / 53,963 / 24,109 / 17,656 / 17,222 / 15,127 / 29,700 / 64.46 / 62.51 / 53.74 / 68.45
Veg / 178,926 / 40,430 / 126,246 / 123,891 / 120,062 / 106,477 / 23.49 / 24.80 / 26.99 / 35.42
PD / Bare / 2,706.56 / 1,740.69 / 2041.59 / 1824.19 / 2038.02 / 1430.65 / 52.49 / 84.09 / 52.92 / 51.40
Imp / 1,386.34 / 620.09 / 453.93 / 443.06 / 389.28 / 761.58 / 64.36 / 62.47 / 53.70 / 68.83
Veg / 4,601.52 / 1,039.87 / 3254.30 / 3190.88 / 3081.64 / 2734.67 / 23.32 / 24.69 / 27.12 / 35.52
LSI / Bare / 360.18 / 260.63 / 292.66 / 277.58 / 306.05 / 229.21 / 51.32 / 70.90 / 37.33 / 52.03
Imp / 229.49 / 177.45 / 118.08 / 123.72 / 123.44 / 140.55 / -6.59 / -1.61 / -1.87 / 17.01
Veg / 483.02 / 217.70 / 277.76 / 308.47 / 251.19 / 377.08 / 63.08 / 49.02 / 77.58 / 24.94
TE / Bare / 4,895,360 / 3,304,920 / 3,896,350 / 3,620,060 / 3,839,450 / 2,447,680 / 45.79 / 66.92 / 49.69 / 29.95
Imp / 4,045,590 / 3,220,070 / 2,809,070 / 2,727,470 / 2,544,070 / 2,022,790 / 33.52 / 25.26 / 9.96 / -18.38
Veg / 7,779,480 / 2,465,570 / 5,503,190 / 5,420,370 / 4,962,250 / 3,889,750 / 27.26 / 28.53 / 36.07 / 57.73
ED / Bare / 1259.17 / 850.0309 / 1002.25 / 930.95 / 987.86 / 629.40 / 45.77 / 66.98 / 49.60 / 29.93
Imp / 1040.47 / 828.2073 / 722.57 / 701.50 / 654.23 / 520.16 / 33.54 / 25.24 / 9.91 / -18.41
Veg / 2000.69 / 634.1487 / 1415.53 / 1393.69 / 1276.63 / 999.93 / 27.24 / 28.55 / 36.04 / 57.77

Table A2-3. TIM accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj. Agg. / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Oak / 9,422 / 50,588 / 24,943 / 18,074 / 11,747 / 7,705 / 23.23 / 11.74 / 2.91 / -2.04
Prairie / 8,217 / 32,651 / 12,598 / 13,439 / 13,882 / 9,589 / 9.85 / 11.97 / 13.11 / 2.89
Red Cedar / 22,452 / 36,659 / 41,207 / 32,473 / 16,706 / 16,467 / 51.50 / 54.48 / -16.82 / -17.40
PD / Oak / 322.37 / 1734.84 / 856.06 / 619.37 / 402.61 / 263.48 / 23.29 / 11.75 / 2.92 / -2.04
Prairie / 281.99 / 1119.72 / 431.48 / 460.94 / 476.52 / 328.49 / 9.80 / 11.96 / 13.14 / 2.85
Red Cedar / 769.90 / 1257.17 / 1413.63 / 1113.66 / 572.61 / 565.18 / 51.39 / 54.50 / -16.84 / -17.36
LSI / Oak / 98.20 / 227.41 / 256.31 / 110.20 / 38.42 / 84.11 / 63.44 / 4.87 / -18.79 / -5.17
Prairie / 93.93 / 150.87 / 101.38 / 104.46 / 108.78 / 140.50 / 7.00 / 10.19 / 14.99 / 69.19
Red Cedar / 135.31 / 239.17 / 200.36 / 47.92 / 86.55 / 91.89 / 45.60 / -29.61 / -19.01 / -17.29
TE / Oak / 778,245 / 1,993,450 / 1,025,980 / 934,653 / 745,831 / 389,123 / 11.35 / 6.88 / -1.32 / -13.80
Prairie / 1,137,820 / 1,918,580 / 1,088,910 / 1,046,090 / 925,505 / 568,911 / -3.04 / -5.55 / -11.97 / -26.70
Red Cedar / 1,862,490 / 3,258,010 / 2,167,280 / 1,849,740 / 1,505,830 / 931,243 / 12.26 / -0.45 / -11.33 / -25.02
ED / Oak / 266.931 / 683.6248 / 351.93 / 320.42 / 255.85 / 133.47 / 11.36 / 6.86 / -1.31 / -13.80
Prairie / 390.265 / 657.9492 / 373.47 / 358.80 / 317.47 / 195.01 / -3.04 / -5.55 / -11.97 / -26.72
Red Cedar / 638.688 / 1117.2874 / 743.25 / 634.30 / 516.42 / 319.38 / 12.26 / -0.46 / -11.33 / -25.01

Table A2-4. GNF accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj Agg / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Bare / 139,891 / 77,321 / 272,765 / 257,539 / 224,625 / 85,920 / -51.50 / -48.46 / -40.37 / 75.83
Shade / 15,562 / 29,390 / 15,103 / 14,882 / 14,721 / 18,881 / -1.63 / -2.40 / -2.95 / 13.63
Snow / 29,591 / 27,795 / 26,490 / 26,886 / 27,547 / 38,610 / 15.84 / 32.78 / 75.70 / -71.52
Veg / 54,154 / 66,922 / 135,605 / 116,942 / 84,149 / 51,563 / -68.65 / -59.33 / -14.87 / -9.21
PD / Bare / 658.27 / 364.23 / 1284.32 / 1212.23 / 1058.01 / 405.25 / -51.56 / -48.51 / -40.47 / 75.52
Shade / 73.36 / 138.45 / 71.33 / 69.99 / 69.47 / 89.33 / -1.54 / -2.53 / -2.90 / 13.99
Snow / 139.30 / 130.93 / 125.04 / 127.08 / 129.47 / 181.32 / 17.33 / 36.98 / 70.23 / -71.51
Veg / 255.50 / 315.25 / 647.62 / 550.97 / 396.61 / 242.66 / -69.53 / -59.56 / -15.32 / -9.70
LSI / Bare / 683.33 / 387.83 / 570.34 / 579.38 / 584.17 / 576.82 / 23.64 / 21.34 / 20.16 / 21.98
Shade / 109.92 / 142.49 / 114.71 / 114.23 / 115.39 / 132.87 / 7.94 / 7.08 / 9.17 / 54.41
Snow / 224.02 / 157.53 / 191.61 / 194.23 / 197.54 / 220.64 / 32.23 / 28.87 / 24.87 / 2.61
Veg / 501.13 / 327.44 / 413.11 / 384.67 / 354.01 / 457.79 / 33.94 / 50.43 / 73.46 / 14.25
TE / Bare / 9,308,490 / 10,535,715 / 24,519,300 / 24,517,500 / 24,946,900 / 24,681,100 / -83.86 / -83.86 / -84.30 / -84.03
Shade / 2,822,840 / 3,611,635 / 3,082,940 / 3,069,640 / 3,100,910 / 3,449,870 / 19.74 / 18.55 / 21.40 / 65.96
Snow / 8,052,030 / 5,712,945 / 7,756,700 / 7,755,600 / 7,780,150 / 8,347,030 / 6.74 / 6.77 / 6.17 / -5.93
Veg / 15,094,700 / 8,536,055 / 16,485,400 / 16,326,800 / 15,864,000 / 16,082,100 / -9.59 / -8.59 / -5.54 / -7.00
ED / Bare / 1,111.56 / 496.30 / 1155.02 / 1154.93 / 1175.16 / 1162.64 / -3.41 / -3.40 / -4.91 / -3.99
Shade / 132.97 / 170.13 / 145.23 / 144.60 / 146.07 / 162.51 / 19.74 / 18.55 / 21.40 / 65.96
Snow / 379.30 / 269.12 / 365.39 / 365.34 / 366.50 / 393.20 / 6.74 / 6.77 / 6.17 / -5.93
Veg / 711.06 / 402.10 / 776.57 / 769.10 / 747.30 / 757.57 / -9.59 / -8.59 / -5.54 / -7.00

Table A2-5. GRO accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj. Agg. / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Brack / 34,809 / 52,514 / 36,457 / 34,348 / 31,385 / 31,448 / 4.88 / -1.28 / -8.82 / -8.67
Fresh / 28,547 / 63,233 / 38,429 / 40,828 / 50,085 / 110,794 / 16.61 / 21.51 / 45.03 / -15.65
Veg / 27,412 / 31,735 / 31,251 / 32,163 / 34,383 / 47,459 / 79.85 / 81.98 / 24.03 / -56.87
Water / 12,969 / 14,388 / 17,450 / 20,253 / 25,862 / 42,916 / -36.68 / -61.05 / -77.99 / -90.53
PD / Brack / 307.33 / 463.09 / 321.38 / 302.89 / 276.47 / 277.80 / 4.72 / -1.40 / -9.01 / -8.66
Fresh / 251.76 / 557.61 / 338.78 / 359.92 / 441.50 / 976.47 / 16.59 / 21.48 / 44.97 / -15.59
Veg / 241.39 / 279.85 / 275.73 / 282.64 / 303.41 / 417.57 / 80.65 / 86.46 / 24.02 / -56.34
Water / 114.08 / 126.88 / 154.10 / 178.88 / 228.43 / 379.49 / -36.02 / -60.49 / -77.61 / -90.35
LSI / Brack / 176.71 / 208.03 / 169.80 / 168.02 / 162.93 / 173.88 / -9.93 / -12.18 / -18.02 / -4.32
Fresh / 158.68 / 213.32 / 158.32 / 163.35 / 176.38 / 256.41 / -0.32 / 4.47 / 19.34 / 11.82
Veg / 165.15 / 164.88 / 156.15 / 158.78 / 167.44 / 221.53 / -94.06 / -91.61 / -81.12 / -99.06
Water / 76.90 / 77.75 / 88.76 / 98.11 / 116.97 / 179.69 / -85.58 / -91.94 / -95.73 / -98.34
TE / Brack / 4,708,600 / 6,008,505 / 4,239,090 / 4,025,500 / 3,561,130 / 2,354,300 / -15.30 / -20.81 / -30.62 / -47.52
Fresh / 2,379,940 / 3,643,970 / 2,000,820 / 1,951,020 / 1,775,220 / 1,189,970 / -13.04 / -14.51 / -19.30 / -32.01
Veg / 2,080,170 / 2,245,800 / 1,585,710 / 1,553,190 / 1,443,810 / 1,040,090 / -59.88 / -61.40 / -65.77 / -75.84
Water / 1,108,580 / 1,473,255 / 851,919 / 848,342 / 806,500 / 554,290 / -26.03 / -26.30 / -29.29 / -43.18
ED / Brack / 415.22 / 529.85 / 373.76 / 354.96 / 314.10 / 207.53 / -15.32 / -20.82 / -30.61 / -47.53
Fresh / 209.91 / 321.34 / 176.40 / 172.02 / 156.48 / 104.95 / -13.07 / -14.53 / -19.34 / -32.02
Veg / 183.45 / 198.04 / 139.83 / 137.02 / 127.23 / 91.64 / -59.92 / -61.41 / -65.84 / -75.89
Water / 97.79 / 129.92 / 75.14 / 74.80 / 71.10 / 48.83 / -26.07 / -26.35 / -29.35 / -43.25

Table A2-6. SUN accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj Agg / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Dry / 69,305 / 59,605 / 50,832 / 51,388 / 51,433 / 53,352 / 5.02 / 8.28 / 8.55 / 21.61
Mang / 126,059 / 50,433 / 19,868 / 22,124 / 30,466 / 63,648 / 42.43 / 45.53 / 58.22 / 70.25
Water / 92,354 / 72,705 / 64,314 / 74,799 / 97,288 / 170,806 / 40.15 / 80.74 / -11.15 / -66.63
Wet / 228,013 / 260,572 / 71,936 / 73,020 / 79,246 / 145,305 / -70.56 / -70.42 / -69.55 / -55.95
PD / Dry / 252.34 / 217.35 / 185.44 / 187.89 / 187.70 / 195.09 / 4.59 / 8.57 / 8.25 / 22.22
Mang / 461.73 / 183.91 / 72.48 / 80.68 / 112.38 / 234.41 / 42.75 / 45.82 / 59.05 / 69.24
Water / 338.46 / 265.12 / 234.09 / 274.17 / 351.11 / 625.72 / 40.54 / 78.04 / -7.94 / -66.20
Wet / 832.78 / 950.18 / 262.99 / 265.59 / 288.66 / 529.32 / -70.82 / -70.72 / -69.85 / -56.38
LSI / Dry / 328.02 / 238.53 / 190.17 / 217.02 / 251.72 / 381.56 / 29.84 / 61.24 / 74.31 / -23.03
Mang / 324.75 / 215.29 / 163.49 / 168.45 / 168.75 / 157.55 / 35.75 / 40.06 / 40.33 / 30.93
Water / 143.18 / 163.44 / 155.44 / 162.99 / 172.74 / 223.54 / 43.35 / 95.64 / 37.09 / -49.57
Wet / 483.09 / 474.86 / 296.15 / 310.89 / 334.35 / 499.03 / -91.20 / -90.45 / -88.94 / -49.21
TE / Dry / 6,429,960 / 4,506,090 / 7,261,200 / 7,475,280 / 7,995,130 / 10,140,700 / -17.77 / -21.36 / -28.92 / -49.09
Mang / 11,468,000 / 7,810,355 / 6,740,890 / 6,408,140 / 5,862,300 / 4,800,870 / 54.75 / 44.57 / 30.50 / 9.72
Water / 5,607,840 / 6,519,695 / 6,612,620 / 6,755,460 / 6,912,700 / 7,891,240 / 81.50 / 58.91 / 39.76 / -20.13
Wet / 14,690,500 / 15,752,940 / 12,691,500 / 13,594,500 / 14,675,200 / 21,880,000 / -48.47 / -34.03 / -0.71 / -70.44
ED / Dry / 234.40 / 164.32 / 264.82 / 272.66 / 291.67 / 369.60 / -17.83 / -21.44 / -29.00 / -49.10
Mang / 418.26 / 284.81 / 245.77 / 233.59 / 213.81 / 175.05 / 54.74 / 44.53 / 30.55 / 9.74
Water / 204.46 / 237.74 / 241.13 / 246.32 / 252.02 / 287.74 / 81.50 / 59.03 / 39.95 / -20.08
Wet / 535.52 / 574.44 / 479.10 / 495.73 / 535.14 / 797.96 / -42.02 / -33.83 / -0.48 / -70.34

Table A2-7. WUI accuracy improvement AI(%) values for the new data aggregation technique compared to majority rules aggregation. Metric prediction results are shown for each threshold [TH(increment)] with associated AI(%) values for each threshold increment. Values that improve over majority aggregation are bolded.

Metric / Class / Maj Agg / True / TH (05) / TH (10) / TH (20) / TH (50) / AI (05) / AI (10) / AI (20) / AI (50)
NP / Rural / 29,905 / 49,129 / 19,540 / 20,053 / 21,518 / 29,661 / -21.24 / -20.40 / -17.91 / -0.63
Urban / 33,671 / 33,069 / 28,944 / 34,320 / 48,562 / 154,343 / -74.53 / -35.05 / -92.52 / -99.01
Wildland / 31,791 / 29,448 / 36,650 / 33,104 / 34,504 / 44,075 / -50.91 / -21.89 / -36.67 / -72.39
PD / Rural / 461.19 / 758.16 / 301.47 / 309.12 / 331.90 / 457.74 / -21.19 / -20.38 / -17.88 / -0.58