Ecological Archives A025-135-A1

Aditya Singh, Shawn P. Serbin, Brenden E. McNeil, Clayton C. Kingdon, Philip A. Townsend. 2015. Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications 25:2180–2197. http://dx.doi.org/10.1890/14-2098.1

Appendix A. Comparisons of model accuracies with literature, species-wise estimates of canopy foliar traits,comparisons of models built with different canopy weighing schemes, comparisons of spatial predictions of foliar traits.

Table A1. Review of recent literature on the application of imaging spectroscopy to map foliar chemistry. Note that in order to conform to the analytical methods presented in this study, we do not include studies that employ inversion of radiative transfer models to map canopy chemical properties.

Trait

Vegetation type

Location

Sensor

Plots

Images

Method

R2Cal

RMSECal

R2Val

RMSEVal

Source

C

Mixed needleleaf-broadleaf

Switzerland

HyMap

28

4

B&B

0.31

1.21 (RMSE)

Huber et al. (2008)

Lig

Oak, Pine

Wisconsin

AIS

20

1

MLR

0.85

1.9 (SE)

Wessman et al. (1989)

Lig

Juniper - Coastal rainforest

West-central Oregon

AVIRIS

17

4

MLR

0.93

277.80 (SEC)

Johnson et al. (1994)

Lig

N Hardwood and needleleaf

Harvard Forest, MA

AVIRIS

21

1

MLR

0.70

2.38 (SECV)

0.27

3.87 (SEP)

Martin and Aber  (1997)

Lig

N Hardwood and needleleaf

Blackhawk Island, WI

AVIRIS

20

1

MLR

0.90

0.85 (SECV)

0.01

4.33 (SEP)

Martin and Aber  (1997)

Lig:N

N Hardwood and needleleaf

White Mountain NF, NH

AVIRIS

81

56

PLSR

0.69

0.23 (SEC)

0.23 (SECV)

Ollinger et al. (2002)

Lignin

Juniper - Coastal rainforest

West-central Oregon

AVIRIS

9

1

MLR

0.75

17.90 (SEE)

Matson et al. (1994)

Lignin

Slash Pine

Gainesville, FL

AVIRIS

14

4

MLR

0.98

Curran et al. (1997)

Lignin

Chaparral

Santa Monica, CA

AVIRIS

23

1

MLR

0.81

5.39 (RMSE)

Serrano et al. (2002)

N

N Hardwood and needleleaf

Wisconsin

AIS

20

1

MLR

0.83

0.04 (SE)

Wessman et al. (1989)

N

Juniper - Coastal rainforest

West-central Oregon

AVIRIS

9

1

MLR

0.72

2.40 (SEE)

Matson et al. (1994)

N

Juniper - Coastal rainforest

West-central Oregon

AVIRIS

25

4

MLR

0.90

0.70 (SEC)

Johnson et al.(1994)

N

Slash Pine

Gainesville, FL

AVIRIS

14

4

MLR

0.98

Curran et al. (1997)

N

N Hardwood and needleleaf

Harvard Forest, MA

AVIRIS

21

1

MLR

0.87

0.23 (SECV)

0.83

0.27 (SEP)

Martin and Aber (1997)

N

N Hardwood and needleleaf

Blackhawk Island, WI

AVIRIS

20

1

MLR

0.85

0.15 (SECV)

0.75

1.32 (SEP)

Martin and Aber (1997)

N

Chaparral

Santa Monica, CA

AVIRIS

23

1

MLR

0.75

0.55 (RMSE)

Serrano et al. (2002)

N

N Hardwood and needleleaf

White Mountain NF, NH

AVIRIS

53

36

PLSR

0.82

0.23 (SECV)

Smith et al. (2002)

N

Eucalyptus spp.

Tumbarumba, Australia

Hyperion

14

1

PLSR

0.95

0.11 (SEC)

0.68

0.27 (SECV)

Coops et al. (2003)

N

Eucalyptus spp.

Tumbarumba, Australia

Hyperion

14

1

MLR

0.83

0.10 (SEC)

Coops et al. (2003)

N

N Hardwood and needleleaf

Bartlett Exp. Forest, NH

AVIRIS

49

1

PLSR

0.83

0.17 (SEC)

0.79

0.19 (RMSEP)

Smith et al. (2003)

N

N Hardwood and needleleaf

Bartlett Exp. Forest, NH

Hyperion

49

1

PLSR

0.82

0.17 (SEC)

0.60

0.25 (RMSEP)

Smith et al. (2003)

N

Deciduous Oak

Green Ridge SF, MD

Hyperion

20

1

PLSR

0.97

Townsend et al. (2003)

N

Deciduous Oak

Green Ridge SF, MD

AVIRIS

17

1

PLSR

0.84

Townsend et al. (2003)

N

N Hardwood and needleleaf

Bartlett Exp. Forest, NH

AVIRIS

56

1

PLSR

0.83

0.17(SEC)

0.70

0.19(RMSEP)

Ollinger and Smith (2005)

N

N Hardwood and needleleaf

Bartlett Exp. Forest, NH

Hyperion

56

1

PLSR

0.82

0.17(SEC)

0.25

0.25(RMSEP)

Ollinger and Smith (2005)

N

N Hardwood and boreal

Adirondacks, NY

Hyperion

28

2

PLSR

0.93

0.28(%)

McNeil et al. (2008)

N

Mixed needleleaf-broadleaf

Switzerland

HyMap

28

4

B&B

0.53

0.38 (RMSE)

Huber et al. (2008)

N

Various

USA, Costa Rica, Australia

AVIRIS

42-75

5

PLSR

0.83

0.14 (SEC)

0.19 (SECV)

Martin et al. (2008)

N

Various

USA, Costa Rica, Australia

Hyperion

42-75

6

PLSR

0.82

0.22 (SEC)

0.25 (SECV)

Martin et al. (2008)

N

Picea abies

Gerolstein, Germany

HyMap

13

1

MLR

0.57

0.05 (RMSE)

Schlerf et al. (2010)

N

Needleleaf

Vancouver Island, Canada

AVIRIS

17

1

PLSR

0.77

0.21 (SECV)

Hilker et al. (2012)

N

Needleleaf, Eucalyptus spp.

Tumut, NSW, Australia

HyMap

80

5

PLSR

0.54

0.90 (SEC)

0.11 (SECV)

Youngentob et al. (2012)

N

Needleleaf, Eucalyptus spp.

Tumut, NSW, Australia

HyMap

80

5

MLR

0.60

0.10 (SEC)

0.10 (SECV)

Youngentob et al. (2012)

N

Needleleaf, Eucalyptus spp.

Tumut, NSW, Australia

HyMap

80

5

MLR

0.58

0.10 (SEC)

0.10 (SECV)

Youngentob et al. (2012)

N

Sagebrush

Eastern Idaho

HyMap

35

1

PLSR

0.95

0.56

0.25 (PRESS)

Mitchell et al. (2012)

 

Table A2. Estimates of foliar traits measured in this study stratified by dominant canopy species. Species sorted by leaf habit (needleleaf, deciduous), and by relative frequency (%) in the overall dataset. Estimates of mean traits are presented along with standard deviations.

N%

Marea

C%

ADF%

ADL%

Cellulose%

δ15N‰

Sp. Code

Species

Rel. Freq (%)

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

Needleleaf

PIST

Pinus stroba

13.41

1.70

0.220

156.12

33.128

50.84

0.600

43.31

3.943

26.17

2.941

17.12

1.525

-1.11

1.172

PIRE

Pinus resinosa

7.66

1.43

0.211

190.70

15.563

50.87

0.581

46.50

2.129

26.68

1.316

18.81

1.343

-1.59

0.859

TSCA

Tsuga canadensis

4.21

1.75

0.272

116.14

16.517

49.95

0.308

30.23

4.271

15.95

2.572

13.79

1.910

-4.97

0.832

PIBA

Pinus banksiana

4.21

1.61

0.241

177.29

12.403

50.76

0.510

50.33

2.469

28.92

1.423

21.02

1.072

-4.48

0.350

ABBA

Abies balsamifera

2.68

1.64

0.148

149.74

23.107

51.79

1.113

40.02

3.409

23.42

2.140

14.87

1.568

-3.65

1.396

PIRU

Pinus rubens

1.92

1.40

0.262

150.87

6.423

50.71

0.742

44.28

5.162

24.68

1.637

17.80

4.043

-6.05

1.184

THOC

Thuja occidentalis

1.92

1.44

0.364

168.69

20.953

49.86

0.610

39.56

2.622

24.48

2.587

15.47

1.181

-3.91

1.277

PIMA

Pinus mariana

1.15

1.02

0.047

201.82

22.306

50.68

0.169

46.88

4.283

25.18

2.393

21.28

1.332

-6.03

0.215

JUVI

Juniperous virginiana

0.77

2.16

0.029

205.90

5.781

48.73

0.042

40.51

0.618

26.86

0.480

17.01

0.151

-1.91

0.146

LALA

Larix larcinia

0.77

1.50

0.103

168.59

7.197

50.16

0.031

50.17

0.650

30.58

0.075

19.50

0.713

-6.01

0.289

PISY

Pinus sylvatica

0.77

2.14

0.108

161.53

5.096

50.42

0.162

42.62

0.685

23.81

0.620

18.42

0.022

-1.86

0.056

PIAB

Picea abies

0.38

2.48

-

134.14

-

49.48

-

35.53

-

21.51

-

14.98

-

-3.00

-

PIVI

Pnius virginiana

0.38

1.71

-

136.64

-

50.54

-

39.34

-

22.06

-

18.31

-

-3.43

-

Broadleaf

ACSM

Acer saccharum

17.62

2.48

0.321

77.60

12.999

49.03

0.840

32.10

5.034

17.58

3.457

14.70

2.079

-3.72

1.141

QUAL

Quercus alba

6.90

2.86

0.212

92.66

10.772

49.44

0.478

33.40

1.951

19.29

1.319

15.33

0.521

-3.48

0.469

QURU

Quercus rubrum

6.51

2.75

0.184

97.14

9.028

49.78

0.835

36.47

3.005

22.51

2.541

15.70

1.393

-3.16

0.782

FAGR

Fagus grandifolia

4.21

2.20

0.332

82.02

12.634

50.21

0.567

43.01

2.940

24.93

1.973

17.91

1.929

-5.79

0.743

ACRU

Acer rubrum

3.45

2.28

0.264

82.25

12.101

49.81

0.877

34.84

5.373

19.72

3.178

15.40

2.698

-4.39

1.202

POTR

Populus tremuloides

3.07

2.56

0.243

79.84

13.786

50.35

0.804

35.60

3.292

25.51

3.653

17.62

1.491

-2.45

0.534

ACSN

Acer saccharinium

2.68

2.52

0.110

85.61

4.615

49.68

0.648

24.71

4.436

15.07

2.182

10.83

2.063

-2.17

0.838

QUPR

Quercus prinoides

1.92

2.30

0.321

92.94

16.650

49.70

0.578

36.74

2.525

21.74

1.882

16.09

0.935

-2.99

0.832

LITU

Liriodendron tulipifera

1.53

2.60

0.132

91.43

15.222

48.69

0.648

32.95

1.183

19.55

0.825

13.77

0.903

-1.88

0.766

PODE

Populus deltoides

1.53

2.64

0.147

82.53

1.982

48.98

0.251

30.81

1.718

19.42

0.920

12.20

0.838

-1.05

0.399

CAOV

Carya ovata

1.15

2.86

0.328

84.70

29.652

47.65

0.881

36.88

0.506

20.10

2.372

16.99

1.327

-2.80

0.975

FRNI

Fraxinus nigra

1.15

2.80

0.050

85.87

7.002

47.85

0.345

28.21

4.989

16.93

1.729

11.71

2.805

-1.29

0.807

QUMA

Quercues macrocarpa

1.15

2.82

0.198

103.91

7.179

48.91

0.465

35.61

0.766

20.87

0.793

16.09

0.144

-3.16

0.729

ROPS

Robinia pseudoacacia

1.15

3.21

0.167

64.32

6.954

48.26

0.651

35.86

1.992

23.15

1.306

16.81

0.392

-1.11

0.439

TIAM

Tilia americana

1.15

2.82

0.194

88.82

2.871

48.49

0.762

35.18

1.665

21.36

0.832

15.70

0.827

-2.50

0.417

CEOC

Celtis occidentalis

0.77

2.98

0.033

66.71

2.060

46.59

0.286

28.84

1.282

17.46

0.423

13.78

0.816

-0.74

0.208

FRPE

Fraxinus pennsylvanica

0.77

2.68

0.269

70.37

13.070

47.23

0.278

31.64

0.688

18.56

0.075

14.82

0.940

-2.99

1.068

NYSY

Nyssa sylvatica

0.77

2.40

0.065

81.28

0.948

49.62

0.358

32.22

0.217

18.00

0.034

14.22

0.224

-3.67

0.032

QUEL

Quercues ellipsoides

0.77

2.96

0.187

94.99

14.752

49.84

0.253

33.95

0.157

20.52

1.474

15.25

0.259

-3.19

0.918

CASP

Carya spp.

0.38

2.27

-

129.13

-

49.47

-

37.12

-

23.16

-

15.26

-

-1.86

-

JUNI

Juglans nigra

0.38

2.55

-

82.34

-

47.96

-

18.02

-

8.27

-

9.75

-

-1.72

-

PRSE

Prunus serotina

0.38

3.04

-

99.55

-

47.98

-

32.70

-

20.44

-

13.86

-

-1.53

-

RHCA

Rhamnus cathartica

0.38

3.22

-

84.95

-

48.11

-

33.43

-

21.59

-

14.20

-

-1.19

-

 

Table A3. Leaf to canopy trait aggregation schemes used in model building. Note that results from aggregation scheme A (Top of canopy) are reported in detail in the manuscript.

 

 

 

Canopy weighting

Scheme

Weight

 

Top

Mid

Bottom

A

Top of canopy

 

90%

9%

1%

B

Mid-weighted canopy

 

64.5%

32.3%

3.2%

C

Whole canopy

 

40%

40%

20%

D

Only top canopy

 

100%

0%

0%

 

FigA1

Fig. A1. Comparisons of PLSR coefficients obtained by fitting models to foliar traits aggregated according to different schemes described in Table A3; A (green), B (magenta), C (red), D (blue). Overlapping lines (and error estimates) indicate close agreement between all models regardless of aggregation scheme.


 

FigA2

Fig. A2. Model fits obtained by using aggregations scheme A.


 

FigA3

Fig. A3. Model fits obtained by using aggregations scheme B.


 

FigA4

Fig. A4. Model fits obtained by using aggregations scheme C.


 

FigA5

Fig. A5. Model fits obtained by using aggregations scheme D.


 

FigA6

Fig. A6. False color composites of foliar traits (left panels; R/G/B = %ADL/Marea/%N) compared with NLCD 2006 landcover maps (right panels). Foliar trait association maps provide richer information on foliar traits across forest ecotones than discrete classes. Subplot locations are 1: Ottawa NF, MI, 2: Green Ridge SF, MD; 3: Porcupine Mountains SF, MI (boxes B, F and A in Fig. 1, Landcover classification legend is Figure A9).


 

FigA7

Fig. A7. Foliar trait association maps (subplot 1: false color composite R/G/B = %ADL/ Marea/%Nitrogen) provide richer detail than NLCD 2006 landcover classifications (subplot 6), or from fall aerial imagery (subplot 2; 11/08/2010 GoogleEarth™). While leaf-off aerial imagery (subplot D; 4/14/2005 GoogleEarth™) clearly identifies needleleaf forest stands (also see high lignin+Marea [yellow] areas in subplot 1), color enhancement of fall aerial imagery (subplot 3) shows phenological differences (subplot 4) between dominant deciduous species (Quercus rubrum, Acer saccharum) corresponding to spatial patterns of foliar traits in subplot A. Subplot 5 indicates high confidence in mapping traits (%N, S.D. shown) across deciduous forest landcover. High prediction uncertainties are only observed in edges or non-forest areas.


 

FigA8

Fig. A8. Recently disturbed regions show up as sharp boundaries in trait association maps (subplot 1: false color composite R/G/B = %ADL/Marea/%Nitrogen, compare with subplot 2: landcover from NLCD 2006). Changes in foliar nutrient content (%N subplot 3) and elevated uncertainties (subplot 4) capture logging activities (pre-cut subplot 5: 8/24/2007, post-logging subplot 6 10/19/2009, images from GoogleEarth™) near the Fernow Experimental Forest, WV.


 

FigA9

Fig. A9. Legend from the National Land Cover Database 2006 (www.mrlc.gov/nlcd06_leg.php).


 

 

Literature cited

Coops, N. C., M. L. Smith, M. E. Martin, and S. V. Ollinger. 2003. Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data. Ieee Transactions on Geoscience and Remote Sensing 41:1338-1346.

Curran, P. J., J. A. Kupiec, and G. M. Smith. 1997. Remote sensing the biochemical composition of a slash pine canopy. Ieee Transactions on Geoscience and Remote Sensing 35:415-420.

Hilker, T., L. Lepine, N. C. Coops, R. S. Jassal, T. A. Black, M. A. Wulder, S. Ollinger, O. Tsui, and M. Day. 2012. Assessing the impact of N-fertilization on biochemical composition and biomass of a Douglas-fir canopy-A remote sensing approach. Agricultural and Forest Meteorology 153:124-133.

Huber, S., M. Kneubuhler, A. Psomas, K. Itten, and N. E. Zimmermann. 2008. Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. Forest Ecology and Management 256:491-501.

Johnson, L. F., C. A. Hlavka, and D. L. Peterson. 1994. Multivariate-analysis of aviris data for canopy biochemical estimation along the oregon transect. Remote Sensing of Environment 47:216-230.

Martin, M. E., and J. D. Aber. 1997. High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications 7:431-443.

Martin, M. E., L. C. Plourde, S. V. Ollinger, M. L. Smith, and B. E. McNeil. 2008. A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment 112:3511-3519.

Matson, P., L. Johnson, C. Billow, J. Miller, and R. L. Pu. 1994. Seasonal patterns and remote spectral estimation of canopy chemistry across the Oregon transect. Ecological Applications 4:280-298.

McNeil, B. E., J. M. Read, T. J. Sullivan, T. C. McDonnell, I. J. Fernandez, and C. T. Driscoll. 2008. The spatial pattern of nitrogen cycling in the Adirondack Park, New York. Ecological Applications 18:438-452.

Mitchell, J. J., N. F. Glenn, T. T. Sankey, D. R. Derryberry, and M. J. Germino. 2012. Remote sensing of sagebrush canopy nitrogen. Remote Sensing of Environment 124:217-223.

Ollinger, S. V., and M. L. Smith. 2005. Net primary production and canopy nitrogen in a temperate forest landscape: An analysis using imaging spectroscopy, modeling and field data. Ecosystems 8:760-778.

Ollinger, S. V., M. L. Smith, M. E. Martin, R. A. Hallett, C. L. Goodale, and J. D. Aber. 2002. Regional variation in foliar chemistry and N cycling among forests of diverse history and composition. Ecology 83:339-355.

Schlerf, M., C. Atzberger, J. Hill, H. Buddenbaum, W. Werner, and G. Schuler. 2010. Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation 12:17-26.

Serrano, L., J. Penuelas, and S. L. Ustin. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment 81:355-364.

Smith, M. L., M. E. Martin, L. Plourde, and S. V. Ollinger. 2003. Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor. Ieee Transactions on Geoscience and Remote Sensing 41:1332-1337.

Smith, M. L., S. V. Ollinger, M. E. Martin, J. D. Aber, R. A. Hallett, and C. L. Goodale. 2002. Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecological Applications 12:1286-1302.

Townsend, P. A., J. R. Foster, R. A. Chastain, and W. S. Currie. 2003. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. Ieee Transactions on Geoscience and Remote Sensing 41:1347-1354.

Wessman, C. A., J. D. Aber, and D. L. Peterson. 1989. An evaluation of imaging spectrometry for estimating forest canopy chemistry. International Journal of Remote Sensing 10:1293-1316.

Youngentob, K. N., L. J. Renzullo, A. A. Held, X. P. Jia, D. B. Lindenmayer, and W. J. Foley. 2012. Using imaging spectroscopy to estimate integrated measures of foliage nutritional quality. Methods in Ecology and Evolution 3:416-426.


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