# Presentation: 318482

## Characterizing Forest Structure for Assessments of Carbon Cyclingand Biodiversity

**Ralph Dubayah**
**Geography Department**
**University of Maryland, College Park**

## Co-Investigators

**Tropical Biodiversity**
**David Clark, La Selva Biological Station**
**Temperate Biodiversity**
**Scott Goetz, Woods Hole Research Center**
**Robert Waide, University of New Mexico**
**Carbon Modeling**
**George Hurtt, University of New Hampshire**
**Remote Sensing Science**
**Michelle Hofton, University of Maryland**
**Bryan Blair, NASA/GSFC**

## Forest Structure, Carbon Cycling & Biodiversity

** ****Natural and anthropogenic forest structure and dynamics affect carbon cycling and biotic diversity**
**Biodiversity**
**Need to develop and quantify forest metrics related to species richness**
**Canopy cover,biomass, foliar profile, spatial granularity, vertical diversity**
**Carbon Cycling**
**Major source of error in predicting fluxes from inability to correctly prescribe initial forest conditions**
**Forest structure -> biomass, successional status**
**Biomass dynamics -> biogeochemcial fluxes, productivity**
**Linkages**
**Both require information on forest structure and dynamics**
**Production/Structure -> biodiversity**

## Potential of Lidar Remote Sensing

**Major obstacle: inability to measure forest structure over large areas**
**Difficulty characterizing critical vertical structure **
**Prevents testing of hypotheses relating structure, productivity to biotic diversity and habitat characterization across scales**
**Lidar has unique ability to measure vertical and spatial heterogeneity across multiple scales**
**Canopy height, cover, canopy profiles, biomass, light profile, and others derivable**
**Vertical & spatial structure critical for biodiversity studies**
**Granularity, foliar diversity**
**Potential to greatly improve carbon modeling **
**Model initialization & dynamics**

## IDS Objectives

***How can lidar remote sensing be used to characterize dynamic forest spatial and vertical structure relevant to species distribution and richness?***
**Components**
**(1) Production of time series of forest structure from lidar**
**(2) Modeling of carbon stocks, fluxes, productivity using an ecosystem model initialized with lidar data**
**(3) Application of derived and modeled products for assessments of biodiversity**

## Outline

**Research Approach**
**Lidar Remote Sensing**
**Research Sites and Data**
**Carbon Modeling**
**Preliminary Results**
**California Spotted Owl (CASPO) habitat characterization**
**Avian species richness at Patuxent Wildlife National Refuge**
**Future Work and Prospects**

## Overview

- Forest Structure
- &
- Biodiversity

- Productivity
- &
- Biodiversity

- _Forest Structure_
- height, canopy cover,
- biomass, basal area,
- vertical profile, granularity

- _Biodiversity Data_
- species richness,
- demography, LTER,
- US sites, La Selva

- _Productivity_
- Measures

- Direct & Modeled
- Retrievals

- Ecosystem
- Demography
- Model

- LIDAR

## Small Footprint Lidar

## Laser Vegetation Imaging Sensor (LVIS)

**Laser Vegetation Imaging Sensor (LVIS)**

**            *****Instrument Specifications:***
- Footprint Diameter	25 m (1 - 70 m)
- Rep-rate		300 - 500 Hz
- Swath width	up to 1 km
- Instrument PI	J. Bryan Blair

- Sub-canopy topography of the Patuxent
- Watershed, MD, mapped using LVIS

- 3 km

- 30 km

***Unique Technology***
-      - wide swath
-      - large footprint
-      - imaging
-      - full digitization

## Waveform Lidar

## Lidar Forest Structure

**Notes:**
Figure on left gives a lidar waveform. Last large pulse is the ground,
Waveform clearly shows top of canopy, and secondary canopy. Figure on right is actually distribution of trees within the lidar footprint.

Bottom shows forest structure mapped over the Sierra Nevada in California Spotted owl habitat: height, cover and biomass.
Swath is about 5 miles across. Shows how we can get forest structure across large areas to assess hypotheses that relate structure and productivity, heterogeneity, to species richness and diversity. These maps used to asses Owl habitat on next page.

## Direct Retrievals from Return Waveforms

**Direct Retrievals from Return Waveforms**

## Forest Structure Lidar Derives Well (really)

| Canopy Height | Direct |
| --- | --- |
| Energy Metrics/Entropy | Direct |
| Canopy Cover | Direct (almost) |
| Canopy Height Profile | Direct (almost) |
| Light Transmittance Profile | Direct (almost) |
| Foliar Profile | Modeled |
| Stem Density & Basal Area | Modeled |
| Biomass | Modeled |
| Canopy Base Height | Modeled |

## La Selva Canopy Heights

**La Selva Canopy Heights**

## La Selva Vertical Forest Structure

## Vertical Structure and Successional State

- Lefsky, M.A., W.B. Cohen, G.G. Parker and D.J. Harding. 2002. Lidar remote sensing for ecosystem studies. Bioscience 52(1) 19-30

**Vertical Structure and Successional State**

## Canopy Base Height

## La Selva Biomass

## IDS Research Sites

**IDS Research Sites**

| Site | Date Acquired |
| --- | --- |
| La Selva, Costa Rica | 1998, 2005 |
| Harvard Forest LTER | 1999, 2003 |
| Hubbard Brook & Bartlett LTER | 1999, 2003 |
| Patuxent Forest, MD | 1998,1999, 2003 |
| Sierra National Forest | 1999, 2006 |
| Coweeta LTER | 1999, 2006 |
| HJ Andrews LTER | 1996, 2006 |
| Sevilleta LTER | 1999, 2006 |

## La Selva Structure Data (TOWERS)

**La Selva Structure Data (TOWERS)**

- Unbiased direct sampling of LAI over a meso-scale landscape
- Concurrent assessment of leaf, branch and bole physiological performance
- Comparison of direct and indirect LAI measurements
- Combine structure and functional data into landscape-scale C-uptake model and compare to real data
- Combine results with remotely-sensed data for scaling to larger landscapes

## Ecosystem Demography Model Linkage

**Young Secondary Forest**

**Older Secondary Forest**

**Pasture**

**More Biomass**

**Height**

**Carbon Sink**

**Lidar measures vertical structure linked to successional state**
**Ideally suited for height-structured ecosystem model**

**Primary ****Forest**

**Ecosystem**
**Demography**
**Model (ED)**

## Carbon Stocks and Fluxes

**Carbon Stocks and Fluxes**

## ED Carbon Flux 

**ED Carbon Flux **

- 0.00 – 0.04
- 0.04 – 0.09
- 0.09 – 0.15
- 0.15 – 0.26
- 0.26 – 0.38
- Boundary

**Primary**
**Secondary**
**Abandoned**

## CASPO Habitat Characterization

**CASPO Habitat Characterization**

**Nest sites (36)**
**Radio collar telemetry**
**Minimum convex polygon**
**50 % home range estimate**
**70 % home range estimate**
**90 % home range estimate**
**Owl analysis areas: **
**areas likely used by owls**
**72 ha**
**168 ha**
**430 ha**
**nest locations (+)**
**175 lidar validation sites**
**0.07/1 ha nested sampling**
**Complete stem mapping**

**Notes:**
The habitat in and around 36 California spotted owl nest sites was examined.  The USFS has determined the home ranges of nesting pairs through radio collar tracking.  Minimum convex polygons were fit to the radio collar telemetry to establish the 60, 70, 90% probability areas in which owls are likely to be found; these “owl analysis areas” correspond to 72, 168, and 430 ha concentric circles around nest sites.

## Sierra Nevada

## Lidar-Derived Habitat Metrics in “Owl Analysis Areas”

## CASPO Potential Habitat

## Patuxent National Wildlife Center LVIS Acquisition & Bird Survey Grid

**Patuxent National Wildlife Center ****LVIS Acquisition & Bird Survey Grid**

## LVIS Canopy Height & Bird Species Richness

**LVIS Canopy Height & Bird Species Richness**

## LVIS Vertical Distribution Ratio (VDR) and Species Richness 

**LVIS Vertical Distribution Ratio (VDR) and ****Species Richness **

## LVIS Canopy Height Spatial Variability and Total Species Abundance and Richness

**LVIS Canopy Height *****Spatial Variability***** and ****Total Species Abundance and Richness**

## Observing Forest Dynamics

## Change Detection

## Messages and Issues

**Lidar provides desired forest structure**
**IDS will provide time series of structure for wide range of sites**
**Develop new metrics with ecologists**
**Work on fusion**
**Efficacy of model-based productivity for biodiversity **
**IDS  will produce contemporaneous productivity **
**Availability of biodiversity data**
**LTER experience not a good one thus far**
**Developing linkages within NASA**

## One Last Plug

**Tell NASA we need a lidar in space**
**Send donations to Hank Shugart...**