1. Assessment of Current Field Plots and LiDAR ‘Virtual’ Plots as Guides to Classification Procedures for Multitemporal Analysis of Historic and Current Landsat Data for Determining Forest Age Classes
Dr. William H. Cooke
Department of Geosciences, GeoResources Institute,
Mississippi State University

2. Objective
The ultimate goal of this study is to map above ground biomass and relate the biomass estimates to the amount of carbon sequestered by even-aged pine stands.

3. Special Thanks to: Robert Wallis (recently employed) Chitra Prabhu (can you just do one more thing)

4. Cooperative Study
Curt Collins presented earlier this morning a multi-temporal analysis of Landsat data to determine forest age classes.
Here, we present a alternative approach for determination of forest age classes using an NDVI threshold to discriminate forest from non-forest pixels followed by an unsupervised classification to further differentiate pines from other species.

5. Introduction
This study is a preliminary data analysis study designed to provide a benchmark for comparisons of information derived from field data and recent Landsat ETM+ data alone with ‘value added’ models developed using data from all sources.
The study was undertaken to test the usefulness of field plots and Landsat TM data alone to estimate volume, which is closely related to above-ground biomass.

6. Introduction
Results of these tests provide valuable information regarding the feasibility for using field plots and satellite data alone for volume estimation, and for improving classifications of historic Landsat imagery.

7. Questions of Interest
How well do NDVI thresholds discriminate forest from non-forest conditions;
What proportion of field plots are necessary to optimize classification accuracies while maintaining a sufficient number of accuracy assessment plots;
Are there optimum Euclidian distances that can be specified for signature generation based on field plots in a supervised classification approach; and
What is the optimum combination of bands and band transformations for supervised classification of volume based on field plots?

8. Choose NDVI threshold of 0.3 to separate forest from non-forest
Methods
TM Scene
Choose NDVI threshold of 0.3 to separate forest from non-forest F NF P PF
Mask (remove NF), then Unsupervised Classification to Separate Pine from Other Forest
Draw Random Plots
Proportional to Age Classes and Grow Signatures 10, 12, 14, 16, 18, 20 Euclidean Distances

9. Methods (Question 1)
Preliminary studies helped determine that an NDVI threshold of 0.3 did a good job discriminating forest from non-forest pixels.
Visual analyses for the results of the NDVI threshold are promising for the ETM+ scene that has been partitioned using this approach.
An unsupervised classification was used to further differentiate pixels that were predominantly pine from other forest pixels.

11. Methods (Question 2)
Once the scene was partitioned into pine pixels, the field plots were randomly drawn in equal proportion by age classes and used as ‘seeds’ for generating volume signatures for a supervised classification process.
Plots were grouped by forest age class and 10% of the plots in each age class were used to generate signatures.
Since the distribution of plots by age class was relatively normally distributed around the year age class, the decision to sample proportionally by age class was predicated on the desire not to under-sample the low and high age classes.

12. Two replications are complete for tests of 34 volume classes.
Methods (Question 3)
Euclidean distances of 10, 12, 14, 16, 18, and 20 were chosen for analysis.
Two replications are complete for tests of 34 volume classes.
Three replications are complete for 7 aggregated volume classes and also for 2 aggregated volume classes.

13. Methods (Question 4)
Three replications are complete for the following bands and combinations:
- Raw bands only
- NDVI only
- 1st and 2nd Principle Components bands only
- Raw bands + NDVI
- Raw bands + 1st and 2nd Principle Components bands
- Raw bands + NDVI + 1st and 2nd Principle Components bands

14. Results Euclidean Distances
For 34 volume classes, for both draws, classification accuracy increases with Euclidean distance and reaches an optimum value at Euclidean distance of 18.

15. Results Euclidean Distances
Classification accuracies for 7 volume classes reach an optimum value at Euclidean distance 18 for draw 1. For draws 2 and 3, Euclidean distance 16 gives the highest accuracy.

16. Results Euclidean Distances
Results for 3 volume classes indicate that classification accuracies reach an optimum value for Euclidean distance 18 for draws 2 and 3, and 16 for draw one.

17. Results Euclidean Distances
For 2 volume classes, classification accuracies reach an optimum value at Euclidean distance Additional replications are needed to determine whether the high classification accuracies for distance 14 are an anamoly.

18. Results Mean by Draw
Results for mean classification accuracies by draw indicate that Euclidean distance 14 gives an optimum value for 2 classes, Euclidean distance 18 for 3 classes, and Euclidean distance 16 for 7 classes. For 7 and 2 aggregated volume classes, Euclidean distance 18 gives the second highest accuracy.

19. Results Band and Transforms
For 7 volume classes there are no improvements in the accuracies that accrue to the addition of transformed bands.

20. Results Mean Band and Transforms
Mean accuracy results for 7 classes indicate that addition of PC and NDVI transforms to the raw bands give a slightly higher accuracy compared to the raw bands alone. High classification accuracies for NDVI ‘alone’ are significant since NDVI provides radiometric normalization advantages.

21. Results Band and Transforms
For 3 volume classes, NDVI yields the highest classification accuracy for draw 2. For draw 1 and draw 3, raw bands yield the highest classification accuracies.

22. Results Raw vs. NDVI 2 6 10 12 14 16 18 20
Tests for additional Euclidean distances were performed to address scaling concerns of transformed bands.

23. Conclusions
A priori designation of Euclidean distances can help remove analyst subjectivity and enable front-end interface development where a text file of x,y coordinates is passed to the signature growing process.
The ability to detect ‘real’ change in biomass over time and over large geographic areas may depend on radiometric normalization.
The results of these studies are too limited in geographic extent and number of replications to make definitive recommendation for automated processes.
The real benefit of these studies is determination of the potential for automating processes in decision support systems that are designed for long-term monitoring of biomass.
NDVI alone yields classification accuracies similar to raw bands and provides image radiometric normalization needed to track biomass changes over time.
Euclidean distances can be chosen that optimize classification accuracies.