Processing Multitemporal TM Imagery to Extract Forest Cover Change Features in Cleveland National Forest, Southern California

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1 Processing Multitemporal TM Imagery to Extract Forest Cover Change Features in Cleveland National Forest, Southern California John Rogan and Janet Franklin San Diego State University Thanks to Lisa Levien: USDA Forest Service- Sacramento Dar Roberts: UC Santa Barbara

2 Introduction and Background * Forest cover change mapping * Research Question: Can Multitemporal Spectral Mixture Analysis techniques be effectively used to accurately map forest cover changes in southern California? * Specifically: - What categories of forest cover change can be mapped using MSMA techniques?

3 Vegetation Disturbance

4 Conceptual Schema of Spectral Mixture Model SOIL SHADE GV NPV

5 Study Site

6 Descanso: June 1990 Descanso: June 1996 Disturbance

7 Random Sampling Design Field Sampling Protocol Stratified Adaptive Cluster Sampling Design Stratification Stratification Change Sample Plot No Change Change 1 Change 2 Sample Plot Change 3

8 Methods Data Preprocessing Radiometric normalization and Absolute atmospheric correction Spectral Mixture Analysis Endmember selection: Image Vs Reference Spectral Unmixing: Shade, Soil, GV, NPV and RMS Image-differencing Classification: Maximum Likelihood Vs Decision Tree Accuracy Assessment: Contingency Matrix

9 1996 TM Endmember Evaluation Classification Change Map Accuracy Assessment 1990 TM Image Endmember Selection Reflectance Retrieval Classification Reference Spectra Endmember Evaluation Reference Endmember Selection SMA Soil T1 Shade T1 GV T1 NPV T1 Soil T2 Shade T2 GV T2 NPV T2 Derive Change Fractions Soil Shade GV NPV Fraction Analysis

10 FOREST CHANGE CLASS NO CHANGE VEGETATION INCREASE VEGETATION DECREASE CHANGE IN NON- VEGETATED AREAS FRACTION CHARACTERISTICS AND DYNAMICS Consistent shade, soil, NPV and GV fractions over time Increase in GV and possible increase in shade in vegetation due to plant-architectural shade Decrease in Soil and NPV over time due to increased ground cover by vegetation Decrease in GV and plant-architectural Shade Increase Soil and NPV due to decreased vegetation ground cover over time Decrease in GV due to land clearing and construction Increase in Shade, Soil and, possibly NPV

11 Endmembers used to Model Scenes 250 Soil 200 Rescaled Reflectance GV Soil Endmember Selection NPV TM Band Shade

12 Shade 1990 Fractions GV NPV Soil RMS RGB: Soil, GV, NPV

13 Shade 1996 Fractions GV NPV Soil RMS RGB: Soil, GV, NPV

14 FRACTION ANALYSIS Regrowth and Deforestation Regeneration No Change GV 1990 Deforestation GV 1996 Red = GV 1990 Blue, Green = GV 1996

15 Results Contingency Matrix: Maximum Likelihood Classification REF CLASS CORRECT PRODUCER S USER S KAPPA TOTALS TOTALS ACC. ACC. Water % 86.67% 0.82 Recharge No Change % 66.67% 0.52 Vegetation % 56.67% 0.48 Increase Vegetation % 70% 0.63 Decrease Change in % 46.67% 0.38 Nonvegetated areas Totals %

16 Results Contingency Matrix: Decision Tree Classification REF CLASS CORRECT PRODUCER S USER S KAPPA TOTALS TOTALS ACC. ACC. Water % 86.87% 0.82 Recharge No Change % 76.67% 0.66 Vegetation % 66% 0.59 Increase Vegetation % 76.67% 0.72 Decrease Change in % 53% 0.45 Nonvegetated areas Totals % 0.65

17 Results and Discussion Sources of Error--- Spectral confusion- (Topography) Reservoirs: Very Accurate Urban Change: Difficult to adequately characterize using MSMA Veg Decrease: Very Accurate (Fire Scars) Veg Increase: Moderately Accurate- Phenological/ Precipitation differences

18 Conclusions and Future Work Multitemporal Spectral Mixture Analysis Highly accurate in depicting changes in forest cover: Soil, NPV and GV are sensitive to interdate change in natural cover Decision Tree Classifier outperformed a Conventional Maximum Likelihood Classifier by 10% overall and by an average of 9% for each cover change class Inclusion of ancillary data sets to reduce spectral confusion and texture images to enhance accuracy