Use of Aerial Videography in Habitat Survey and Computers as Observers Leonard Pearlstine University of Florida

Land Cover Classification Landsat TM Digital Camera TM Band 4 TM Band 2 TM Band 3 Layer 1 Layer 2 Layer 3

Texture The spatial (statistical) distribution of gray tones. -Haralick et al. 1973

Desirable Texture Characteristics Angularly independent Invariant under gray level transformations Simple algorithms

Brazilian Pepper

Measures of Edge DensityMagnitude Rate of Change “Visual discrimination of pattern is based primarily on clusters or lines formed by proximate points of uniform brightness” -Julesz 1962

Edge Signatures

Multivariate Discrimination Logistic Regression selected for Heteroscedastic Variances Dichotomous Classification

Reference Classified Producer’s Accuracy 34% 71% 61% User’s Accuracy 98% 97% 80% Logistic Regression

Commission Error 16% 21% 19% Reference Classified Logistic Regression – No Schinus Images

Omnidirectional Variogram

Compute Homogeneity Index Image Pasture Trees canopy Grass Individual Trees

Edge Textures Application Interface

Birds Detection and Counting Video Still Image showing Birds Colony of approximately 150 birds

Template Matching Identify Bird Template(s) Area Based Matching (e.g. Correlation Matching) 9x9 bird Template

Area Based Matching (correlation Matching) Compute The correlation Coefficient between Template and Reference Image as: R(x,y) = ΣΣ (T’(x’,y’)*I’(x’+x,y’+y)) Where: T ~ (x,y) = T(x,y) – T & I ~ (x+x’,y+y’) = I(x+x’, y+ y’) – I T and I are the mean under the Template and reference windows respectively.

Correlation Image Bright values indicates Template and Reference images match and Birds Existence Correlation Image Reference Image

Threshold and Identify Birds Different Threshold can be used. High Threshold Missing Birds (Increase Omission errors). Low Threshold Add noise and other features as Birds (Increase Commission errors). Threshold = 140 Birds Count = 153 Actual Birds = 150 Missing Birds No Birds

Progressive Scan Video Image

Progressive Scan Video Image with Bird Pattern Matching

Birds Count Application Interface

Conclusions Characterizations of edge can effectively discriminate vegetation classes. Multivariate discrimination using logistic regression substantially improved accuracies. The logit model successfully identified Schinus terebinthifolius and excluded most other vegetation types.

Conclusions Additional work needs to be done to separate Sabal palmetto signatures from Schinus. “Big white birds” can be effectively discriminated in even low quality videography. Larger sample sizes over a greater geographic extent and with additional species will be needed before these procedures can be considered operational.

Conclusions The modeling approach develop in this dissertation provides an effective procedure for rapid and consistent identification of target species from aerial imagery.