Estimating basal area, trees, and above ground biomass per acre for common tree species across the Uncompahgre Plateau using NAIP CIR imagery

Transcription

1 Estimating basal area, trees, and above ground biomass per acre for common tree species across the Uncompahgre Plateau using NAIP CIR imagery By John Hogland Nathaniel Anderson Greg Jones

2 Obective Develop a flexible and transferable methodology to estimate basal area per acre, trees per acre, and above ground biomass per acre Use consistent and readily available data sources Generate outputs that can be used to model multiple management scenarios Fine grained detail (1m 2 )

3 Proect Area Southwest Colorado 580,000 Acres ²

4 Maor Tree Species Quaking Aspen Ponderosa Pine Lodge Pole Subalpine Fir Corkbark Fir Douglass Fir Engelmann Spruce Blue Spruce Pinyon Pine Rocky Mountain Juniper Utah Juniper Mountain Mahogany Gambel Oak

5 Tree Species Groups Quaking Aspen Ponderosa Pine Lodge Pole Subalpine Fir Corkbark Fir Douglass Fir Engelmann Spruce Blue Spruce Pinyon Pine Rocky Mountain Juniper Utah Juniper Mountain Mahogany Gambel Oak Pinyon/Juniper Shrubs Aspen Pine Spruce/Fir

6 Datasets Imagery NAIP (CIR) DEM? Stratified Random sample Visually interpreted from imagery Field Data Plot data (FIA) Fixed radius

7 2 Stage Approach Probabilistic classification using Polytomous Logistic Regression (grain size 1 m) Identifying tree species groups using spectral bands and texture Multivariate Regression (BAA, TPA, and AGB) Window Circle with radius of 7 meters -> FIA plot size Develop texture metrics that quantify trees vs. open ground/shadow for the probabilistic classification

8 2 Stage Approach Probabilistic classification using Polytomous Logistic Regression (grain size 1 m) Identifying tree species groups using spectral bands and texture Multivariate Regression (BAA, TPA, AGB) Window Circle with radius of 44 meters -> FIA plot size Develop texture metrics that quantify trees vs. open ground/shadow for the probabilistic classification

9 Maximum Likelihood Classification (Discriminant function analysis) MD D ( ) i i 1 x x S x x i 1,...,n 1,..., p i QDS D 1 1 i ln 1 S x x S x x 2 2 i 1 i ln p p,..., n prior for class PP i 1 exp D ( i) 2 1 exp D ( i) 2 i1 where x i and x are the observed vector and mean vector, respectively, and S and S are the pooled sampled covariance matrix and the sample covariances, respectively (Johnson and Wichern, 2002).

10 Polytomous logistic regression Estimated class (response) probabilities {π (x)} are determined by manipulating baseline category logits as follows: given x 1, 1,...,J- 1, x - x. J J 1 1 h h1 ln J x x xβ x 1, x i 1,...p i x J 1 1 h1 exp xβ exp xβ h J x 1 J 1 h1 1 expx β h Where π (x) is the mean probability of group, with class means and variances equal to nπ and nπ (1-π ), respectively (Agresti, 2002).

11 Texture First Order Second Order Grey-Level Co-occurrence Matrix (GLCM) (Haralick, 1979) Summary statistics of all neighboring pixels (N = 9) Summary statistics of all pixel/neighbor pixel relationships in a given direction (Horizontal or Vertical; N = 12) J I P V i, i, N 1 Vi, i, 0

12 Texture GLCM Contrast Dissimilarity Homogeneity Entropy N 1 i, 0 N 1 i, 0 P i 2 i, N 1 i, 0 P i, N 1 i, 0 P i, ln Pi i 1, P i, i 2 Mean N 1 i, 0 Variance Covariance Correlation Max Probability i P i, N 1 i, 0 P i, N 1 i, 0 2 i P N 1 i, 0 i, P i, i i i i i i 2 i max 2 P i, Energy N 1 i, 0 P i, 2 Min Probability min P i, ASM N 1 i, 0 P i, 2 Range max min

13 Texture

14 Stage 1 Probabilistic Classification

15 Methodology Create stratified random sample ISO cluster Randomly allocate sample locations Visually interpret locations Create first order Standard Deviation of NAIP Ordinate NAIP and Standard Deviation Create GLCM Relate visually interpreted locations to ordination values and texture metrics Model species group probabilities

16 Stratified Random sample locations of 20 ISO spectral classes Note sample clustering along the northeastern and southwestern edges of the UP boundary. These spectral groups where relatively scarce across the UP but were represented in the model through the stratification process. ²

17 Area Subset 0 1,320 2,640 5,280 Feet ²

18 NAIP Color

19 NAIP Color Infrared (CIR)

20 Rescaled Standard Deviation (SD) of NAIP Color Infrared

21 Principle Components Analysis (PCA) Problem NAIP CIR highly correlated among bands SDs highly correlated with each other Variables are not independent Obective Reduce dimensionality Create independent variables for modeling

22 PCA Results Eigen Values Component Eigen Value Proportion Cumulative % 64.03% % 87.94% % 98.52% % 99.29% % 99.83% % Eigen Vectors Parameter PC1 PC2 PC3 PC4 PC5 PC6 NAIP Band NAIP Band NAIP Band SD Band SD Band SD Band

23 3 band color composite of Principle components 1, 2, and 3 for area subset

24 Quantified GLCM Texture Contrast Dissimilarity Homogeneity Entropy Energy ASM Mean Variance Covariance Correlation Max Probability Min Probability Range

25 3 band color composite of Horizontal Homogeneity, Horizontal Correlation, and Vertical Correlation GLCM output from Brightness component (3*3 moving window)

26 PLR Model Selection Rank Parameters DF -2 Log Likelihood AIC ΔAIC 1 PC1-3, H_Ho, V_Corr, H_Corr, _H_Ho PC1-3, H_Ho, V_Corr, H_Corr PC1-3, H_Ho, V_Corr, H_Corr, _V_Var PC1-3, V_Corr, H_Corr PC1-3, H_Ho, V_Corr, H_Corr, _V_Corr PC1, PC3, V_Ho, V_Corr, H_Corr, H_Con PC1, PC3, V_Ho, H_Ho, V_Corr, H_Corr, V_Var PC3, H_MEAN, V_Ho, V_Corr, H_Corr, H_Con NA

27 Outputs

28 Pine

29

30 Stage 2 BAA, TPA, & AGB

31 Methodology Create predictive variables Probability texture metrics from stage 1 classification Summarized GLCM texture metrics Summarized PCA values 2 1 Summarize FIA plot data (subplot) 4 3 Relate FIA plot data to Texture metrics Multivariate regression to estimate BAA, TPA, and AGB by species group

32 Probabilistic Texture

33 Summarized FIA Data AGB FIA Plot Summary by Species

34 Summarized FIA Data QMD, BAA, and TPA FIA Plot Summary by Species

35 Sample Metrics

36 Multivariate Regression BAAi TPAi AGBi i i i i i i QMD i BAAi TPA i where i = 1 number of species groups and = 1 number of predictor variables

37 Modeled BAA, TPA, and AGB Aspen

38 Modeling Management Activity Existing Condition Pine BAA Residuals & Removal Pine BAA BAA Pine TPA Pine QMD Thresholds Max Diameter Cut Min TPA Min BAA Min AGB Pine TPA TPA Pine QMD QMD Pine AGB Pine AGB AGB

39 Summarizing Activities Residuals & Removal BAA TPA Pine BAA Pine TPA Unit Totals Treatment units BAA Pine QMD QMD Treatment units TPA AGB Pine AGB Zonal Statistics Treatment units QMD Treatment Units Treatment units AGB

40 RMRS Raster Utility Toolbar Key functionality Simplified data access from web services Raster Sampling Improved raster processing (Function Modeling) SAS integration

41 Contact Information John Hogland, Biological Scientist Rocky Mountain Research Station PO Box East Broadway Missoula, MT Phone: (406)