# Presentation: 946734

## Estimating Forest Height & Biomass using the Geoscience Laser Altimeter System (GLAS)

*Dirk Pflugmacher – Oregon State University*
*Warren Cohen – U.S. Forest Service*
*Michael Lefsky – Colorado State University*
*Robert Kennedy – U.S. Forest Service*

- June, 13th 2007
- Landsat Science Team Meeting

## Investigate LIDAR waveforms from the GLAS sensor to provide 
estimates of forest height and biomass 
in two pilot study areas in the Western and Eastern U.S.

- Investigate LIDAR waveforms from the GLAS sensor to provide
- estimates of _forest height_ and _biomass_
- in two pilot study areas in the Western and Eastern U.S.

- 2| Objective

**Image credit: NASA**

**Notes:**
onboard ICESat since 2/2003 3 laser diodes waveform sampling Lidar 1064 nm wavelength footprint ~ 65 m diameter spacing 175 m over 900 million waveforms collected

## 1. Height estimation
Development of algorithms for vegetation heights using GLAS waveforms with coincident field data (CSU)
Evaluate region of applicability of height algorithms with regional distributions of Forest Inventory samples (PNW-OSU) 

2. Biomass estimation
Develop regression models to predict aboveground biomass from stand height using FIA data (PNW-OSU)
Compare population estimates from GLAS and FIA (PNW-OSU)

- Development of algorithms for vegetation heights using GLAS waveforms with coincident field data (CSU)
- Evaluate region of applicability of height algorithms with regional distributions of Forest Inventory samples (PNW-OSU)
- 2. Biomass estimation
- Develop regression models to predict aboveground biomass from stand height using FIA data (PNW-OSU)
- Compare population estimates from GLAS and FIA (PNW-OSU)

- 3| Approach

## 4| Forest Inventory Data

**Notes:**
Coniferous tree dominated study site in oregon and broadleaf tree dominated study site in the appalachians

## 5| GLAS data

**Notes:**
Here is a spatial distribution of glas shots within the two study areas. The GLAS sample size is much higher, but are less dispersed.

## 6| Height estimation

**Image courtesy M. Lefsky**

**Lefsky et al. (forthcoming)**

**Performance on height algorithm at**
**coincident field plots**

**GLAS waveform schematic**

**Notes:**
Vertical extent of each waveform increases as a function of terrain slope, footprint size and the spatial pattern of ground surfaces
Over sloped terrain returns from canopy and ground surfaces are convoluted (occur at the same elevation)
Given forest stands with uniform height, and information on terrain slope, separation of waveforms into ground and canopy returns is straight forward.
Leading edge is a function of the spatial organization of the canopy, the decrease of laser energy with depth into the canopy as the pulse intercepted by plant surfaces 

## 7| GLAS & FIA heights of dominant\co-dominant trees

** ****Simple random sampling**
** ****Similar results when post-**
**  ****stratified by forest type group**
**  ****and ecological substrata**

## 8| Height – biomass allometry using FIA data

**Cascades**

**Appalachians**

**Notes:**
Model fit improved only insignificantly when forest type group and ecological subregion classification were taken into account.
Prediction accuracy dropped by less than 1% of the oberservd biomass range.

## 
1 Height estimation
complicated in steep terrain
allgorithm works best with mean height of dominant-codominanat trees
regional ‘bias’ not related to forest type and ecological substrata 

2 Height-biomass allometry
Forest types and ecological subregions have little effect on the prediction accuracy of the regression models.
Horizontal stand structure might be more important, and can be described with multi-spectral data.

3 Biomass estimation
GLAS biomass estimates are lower then estimates from FIA in both study areas

4 Sampling
Explore sampling strategies to improve estimate population totals and their variances.

5 Error propagation

- 1 Height estimation
- complicated in steep terrain
- allgorithm works best with mean height of dominant-codominanat trees
- regional ‘bias’ not related to forest type and ecological substrata
- 2 Height-biomass allometry
- Forest types and ecological subregions have little effect on the prediction accuracy of the regression models.
- Horizontal stand structure might be more important, and can be described with multi-spectral data.
- 3 Biomass estimation
- GLAS biomass estimates are lower then estimates from FIA in both study areas
- 4 Sampling
- Explore sampling strategies to improve estimate population totals and their variances.
- 5 Error propagation

- | Conclusions & Outlook

**Notes:**
This has also been found by others studies (Lefsky et al. 2002, 2005; Mette et al. 2003)
Mette et al. suggest that height-biomass relationships do not vary as much between species but more with horizontal stand structure, e.g. stand density or canopy cover.
This is information that can be obtained with multi-spectral sensors. We are currently looking at this.

## Thank you!

- Thank you!

- June, 13th 2007
- Landsat Science Team Meeting

## 9| Mean and Total Biomass Estimates

**Cascades**

**Appalachians**

**Appalachians**

**Cascades**

**FIA**

**GLAS**