Steve Reutebuch Hans-Erik Andersen Bob McGaughey Demetrios Gatziolis

RMA Vegetation Monitoring and Remote Sensing TeamUSDA Forest Service PNW Research Station

Use of airborne laser scanning (LIDAR) as a tool for forest measurement and monitoring:

use and potential

Steve Reutebuch Hans-Erik Andersen Bob McGaughey Demetrios Gatziolis

Resource Monitoring & Assessment ProgramVegetation Monitoring & Remote Sensing Team

USDA Forest ServicePNW Research Station


LIDAR—what is it?

Light detection and ranging (LIDAR) Uses laser light to measure distance

Different detection approaches Time of flight Phase difference

Hundreds of applications

In natural resources, 3 LIDAR types are widely available


Widely available LIDAR

Terrestrial laser scanning (TLS)

Primarily used in engineering Some use in forestry research

scanning plots or individual trees and logs



NASA IceSAT satellite LIDAR Global- and continental-scale

forest canopy height and biomass estimates

70 m diameter footprint 175 meters spacing

Difficult to remove topographic effects on canopy heights

Operational 2003-2009 IceSAT-2 launch 2016 ???



Airborne laser scanning (ALS)

Routinely flown commercially over large areas

Large vendor pool Mature mission specs &

deliverables Mature software to process data Many state and federal partners


ALS LIDAR data uses

Topographic mapping of bare earth surface—primary use

Engineering Flood risk mapping Hydrologic modeling Geologic mapping Landslide mapping

Infrastructure mapping—still developing Vegetation measurement and mapping—

still developing, with operational uses


National review of ALS LIDAR data needs

USGS National Digital Elevation Program:

Enhanced Elevation Data Requirements Study Funded: USGS, FEMA, NRCS, NGA (DOD) FY10-12: Conduct study FY13: Initiate enhanced elevation data collection Primary use: update bare earth surface models USGS study recognizes many other uses

130,000 sq miles of data with ARRA funds


USGS recognized uses of LIDAR


2010 State LIDAR efforts

8 states have statewide LIDAR programs North Carolina, Louisiana, New Jersey, Maryland,

Delaware, Pennsylvania, Ohio, Iowa 8 states have program initiatives

Florida, Texas, New York, Oregon, Washington, Minnesota, South Carolina, Mississippi

Many more projects areas have been flown ~25% of the conterminous US already has LIDAR

collected Unknown amount of private forest coverage


2010 Oregon LIDAR Consortium


2010 Puget Sound LIDAR Consortium


Not all LIDAR data are the same

Things that affect LIDAR data for forest measurements:

Mission specs (pulse rate, scan pattern, flying height, airspeed, pulse diameter, etc.)

Time of year (leaf-off, leaf-on, snow free, etc.)

LIDAR sensor and data processing

Experience of LIDAR vendor


Not all LIDAR data are the same

Therefore, don’t expect to get same results when models from one LIDAR dataset are applied to other datasets, even in the same forest type!!!


LIDAR used in forest measurement

When only partial LIDAR coverage of an area is possible:

Sampling within a multi-stage framework

Statistical framework has been developed and tested by several researchers

PNW LIDAR trials in Alaska: Hans Andersen, PI

Kenai Peninsula Interior Alaska


Remote sensing

Field plots

Wall-to-wall low- resolution coverage w/ LANDSAT TM, SPOT, etc.

Subsampling with high res. LIDAR, aerial photos

Measurements of trees, shrubs, moss, soils, down wood.

Example: Multi-level sampling to support forest inventory in remote northern regions

Subsampling high res. satellite imagery


PNW-RMA (Anchorage) is carrying out a project to test a multi-level approach for biomass estimation in the Tok

(1,911 sq km)

Multi-level approach will use:

Satellite imagery (Landsat, SPOT, PALSAR, Quickbird)

27 High-density LIDAR strip samples

Field plot data (80 plots)



When “wall-to-wall” LIDAR coverage is available 2 types of measurements can be made:

1. Forest layers computed solely from the LIDAR

2. Inventory layers predicted from regression models or imputation methods using LIDAR and well measured ground plots


1– Layers computed solely from the LIDAR point cloud—obvious ones

Canopy surface model Bare earth model


3-ft bare earth model3-ft canopy surface model1:12,000 aerial photo


Layers computed solely from the LIDAR point cloud—obvious ones

Bare earth model

Canopy surface model

Canopy height model(Canopy surface minus ground surface)


3-ft resolution canopy height model

Buildings



Bare earth model


Canopy height model

Canopy cover model


% Canopy Cover (0.1 acre pixels)



Bare earth model


Canopy height model

Canopy cover model

Intensity image from 1st returns


1.5-ft resolution intensity image


Layers computed solely from the LIDAR point cloud—not so obvious

Variance, standard deviation, skewness, kurtosis, etc. of the canopy

Mean, min, max, percentile heights of the canopy

Density of the canopy

Forest / non-forest mask


Standard Deviation of Canopy Height



When “wall-to-wall” coverage is available 2 types of measurements can be made:

1. Forest layers computed solely from the LIDAR

2. Inventory layers predicted from regression models or imputation methods using LIDAR and well measured ground plots


WARNINGS !!!

Can’t get species information from the LIDAR data

In some cases, can get: Deciduous vs non-deciduous Live crowns vs dead crowns

Can’t get understory, down wood, etc. Not all LIDAR is the same:

Changes in LIDAR sensors, sensor settings, and flight parameters can change results


MORE WARNINGS !!!!!

Most difficult part of a LIDAR project is:Getting good ground plot data:

1. Matched with regards to geographic position to an accuracy ~ equal to the LIDAR horizontal accuracy (~+/- 1m)

2. Matched with regard to the primary element being measured—large enough to minimize plot edge effect, but small enough to characterize tree size differences within plots (~0.1 – 0.2 ac circular plot)


MORE WARNINGS !!!!! (cont.)

Most difficult part of a LIDAR project is:Getting good ground plot data:

3. Matched in time of measurement--generally within 1-2 yrs of LIDAR acquisition

4. Matched in what’s measured by the LIDAR and on the plot—all stems that make up a significant portion of the above ground canopy—generally down to a 7-10 cm DBH lower limit, including all species


Examples of layers predicted from regression models

Sherman Pass Scenic Byway Colville National Forest 100,000 acres flown in 2008

74 1/10th acre plots used to develop LIDAR inventory regressions measured in 2008


Sherman Pass LIDAR Project

Forest cover minimum: 10ft ht & 2% cover in 66ft pixel

Ground Plots


Regression modelsLorey’s BA-weighted Height ft

[LHT_ft] = 21.4980 + [ElevP90] * 0.7242


Regression modelsLorey’s BA-weighted Height ft


Regression modelsLive Basal Area sqft/ac

[LBA_sqftac] = sqr ( -5.0579 + [ElevSD] * -0.4280 + [ElevP95] * 0.2307 + [PC1stRtsCC] * 0.1039) + 2.809


Regression modelsLive Basal Area sqft/ac


Red areas have LIDAR predictor values >+/-10% beyond the range of the ground plots

Greater than +/- 10% beyond ground plot LIDAR Metrics


Example ArcGIS Calculations

Any of the LIDAR layers can be used in GIS to calculate combinations of forest structure variables


Live Basal Area > 200 sqft/ac


Canopy Cover 80%+ and Height 100ft+


Current limitations on using existing LIDAR data

No coordination within natural resource organizations at any level for:

1. LIDAR specifications necessary for forest measurements

2. Ground plot measurements when large, multi-agency LIDAR acquisitions occur

Missed opportunity to leverage existing LIDAR


Possible problems with use of FIA plots for LIDAR projects

Plots not georeferenced well enough

Not enough plots measured in area within 1-2 years of LIDAR acquisition

Plot layout not well designed for use with high-resolution remote sensing data


Future for LIDAR in forest measurement?

Faster, cheaper, better LIDAR data, but doesn’t solve ground plot problems

Multi-temporal LIDAR datasets for change analysis

Multispectral LIDAR for species classification New satellite-based systems for sampling Beyond LIDAR—other 3D sensors (IFSAR,etc.)


LIDAR software DEMO Thurs

2009 Savannah River DOE Site LIDAR Project

Documents

Steve Reutebuch Hans-Erik Andersen Bob McGaughey Demetrios Gatziolis