SNAMP Spa al Team Workshop...

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Lidar analysis in support of the Sierra Nevada Adap�ve Management Project

SNAMP Spa�al Team Workshop May 16, 17, 2012 Maggi Kelly, Qinghua Guo Sam Blanchard, Jacob Flanagan, Marek Jakubowski, Wenkai Li, Feng Zhao

snamp.cnr.berkeley.edu Sierra Nevada Adap�ve Management Project

SNAMP Spa�al Team Workshop Agenda

9-‐9:10 -‐ Welcome and the day’s overview 9:10-‐10 -‐ PART 1: Introduc�on

–  SNAMP Background –  Lidar refresher: Lidar basics, Types;

Applica�ons –  SNAMP Lidar spec –  Ques�ons

10-‐10:15 -‐ Break 10:15-‐11:00 -‐ PART 2: SNAMP and Lidar

–  SNAMP Lidar products –  SNAMP Lidar research –  Lidar + imagery data sources –  Ques�ons

11:00-‐11:45 -‐ PART 3: Data and So�ware 11:45-‐12:00 – Ques�ons and Evalua�ons 12:00 Adjourn

May 16 2012 – Oakhurst, CA

May 17, 2012 – Foresthill, CA

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Mee�ng Goals and Desired Outcomes

Goals:   To inform stakeholders about SNAMP

spa�al team people and research, with concentra�on on Lidar data.

Desired Outcomes:   Increased knowledge of how spa�al data

are integrated into SNAMP.   Open communica�on between SNAMP

spa�al team and stakeholders.   Par�cipate in the SNAMP adap�ve

management outreach process.


Sierra Nevada Adap�ve Management Project

What is SNAMP?

  A partnership with the goal of learning how to ensure the long-‐term sustainability of the Sierra Nevada forests.

Who is SNAMP?

  A collabora�on among federal and state resource agencies.   An independent “third-‐party” of University researchers.   Public and private stakeholders.

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Fire in the Sierra A century of fire suppression management in the Sierra has resulted in build-‐up of fuel, placing millions of hectares of forest at risk of catastrophic fire.

2004 Sierra Nevada Forest Plan Amendment calls for:

the applica�on of strategic fuel management at landscape levels.

There is great uncertainty about treatments impact on fire behavior, wildlife, forest health and water


Adap�ve Management Common elements to the challenge of stewardship in the era

of global change

1.  Drivers of global change operate on the landscape scale.

2.  The science is not always available to inform management.

3.  Most modern strategies for resource stewardship acknowledges uncertainty.

4.  Most modern strategies invokes “adap�ve management” as part of the solu�on.

Broader implica�ons of SNAMP

  Sierra Nevada Forest Plan Amendment describes a landscape scale management strategy (SPLATs).

  Learning how to do adap�ve management is the explicit goal of SNAMP.

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UC Science Team

The UC Science Team’s role as a neutral third party observer: evaluate the efficacy of treatments across four response variables (water, wildlife, fire,

public par�cipa�on), involve the public, feedback into the AM cycle

Public MOUP

UCST/ AES

County/Local CE

Campus CE


Sierra Nevada Adap�ve Management Project

  SNAMP was formed to develop, implement and test Adap�ve Management processes through tes�ng the efficacy of Strategically Placed Landscape Treatments (SPLATs) across four response variables, including:

  Public par�cipa�on   Wildlife

  Pacific Fisher   California Spo�ed Owl

  Water   Fire/forest health

  Each of these groups has an associated research team, and all are supported by a spa�al team.

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SNAMP Study Areas and Research Teams Two Study Areas:

-‐ Tahoe Na�onal Forest

-‐ Sierra Na�onal Forest


SNAMP Research Design

  Research design   BACI – Before, A�er, Control, Implementa�on

  Spa�al Scale

  Each team will perform their research at a specific scale:   Fire & Forest Health team works at the scale of the

individual tree, to the forest stand, to the fireshed…   Wildlife teams work at the scale of the individual

tree and its environs to the scale of an animal’s home range…

  Water team works at the scale of the watershed…   Public Par�cipa�on works at local and na�onal

scales.   Spa�al models will be used to integrate across

teams, and extrapolate results in a common spa�al framework

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Immediate goal of SNAMP:

Helping to ensure sustainability of Sierra Nevada forests


Spa�al Scaling in the Research

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Op�cal Remote Sensing: the View from Overhead MODIS – Landsat – Orthophotography


Lidar and SNAMP Lidar-‐based forest products as inputs to process-‐based models

Eleva�on, canopy cover, biomass, LAI, all drive fire behavior models and hydrology models…

Forest characteriza�on to understand wildlife habitats

Loca�on and descrip�on of individual trees across a landscape help us understand Pacific fisher or California spo�ed owl behavior…

Novel lidar visualiza�ons

Help describe the poten�al impact of treatments

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SNAMP Spa�al Team

Spa�al Team Goals   Developed to assist in the GIS and

remote sensing technology that all teams require.

  Members of the spa�al team have the responsibility for suppor�ng all other teams’ GIS, remote sensing and spa�al analysis needs.

  Conduct applied research in applica�on of spa�al technology to forest science and management

Spa�al Team Ac�vi�es   LIDAR data acquisi�on and analysis   Field campaign   SNAMP team support

Principal Inves�gators:

Qinghua Guo, UC Merced

Maggi Kelly, UC Berkeley

Graduate Students:

Marek Jakubowski, UCB

Wenkai Li, UCM

Jacob Flanagan, UCM

Postdoc:

Feng Zhao, UCB

Staff:

Hong Yu, UCM

Sam Blanchard, UCB

LiDAR = Light Detection And Ranging

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Terrestrial Lidar Lasers for terrestrial applica�ons generally have wavelengths in the range of 900–1064 nanometers, where vegeta�on reflectance is high.

One drawback of working in this range of wavelengths is absorp�on by clouds, which impedes the use of these devices during overcast condi�ons.

terrestrial lidar

Bathymetric lidar

how does it work?

amplitude of etected signal

t

laser pulse detector

LiDAR

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how does it work?


t

LiDAR

how does it work?


t

LiDAR

total time traveled

distance = time x speed

total distance traveled = (total time traveled) x (speed of light)

distance to the ground = (total time traveled) x (speed of light) 2 distance

to the ground

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what about multiple returns? LiDAR


t

what about multiple returns? LiDAR


t

last return 1st return

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what about multiple returns?

LiDAR

GPS IMU

(X, Y, elevation)

(yaw, pitch, roll angles)

roll angle

dist

ance

(X1, Y1, Z1)

(X2, Y2, Z2)

Discrete and Waveform Lidar

Graphics from Bernhard Hoefle, University of Heidelberg

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Waveform targets In addi�on to height, we can analyze three other characteris�cs of the lidar return:

1.  Echo width: distribu�on of returns, rougher objects give larger widths

2.  Amplitude: strength of reflec�on (reflectance and area size of target)

3.  Backsca�er cross-‐sec�on (E x A): electromagne�c energy intercepted and reradiated by objects

Graphics from Bernhard Hoefle, University of Heidelberg

Lidar Applications In 2002, Lefsky wrote: “Developments in lidar remote sensing are occurring so rapidly that it is difficult to predict which applications will be dominant in 5 years.” Currently ecological applications of lidar remote sensing:   ground topography,   3D structure and function of

vegetation canopies,   forest stand structure

attributes,   Carbon, carbon, carbon

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0

500

1000

1500

2000

2500

3000

3500

4000

year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

lidar+topography

lidar+forest

lidar+carbon, biomass

lidar+wildlife

Lidar publications (from Google Scholar) are trending upwards through the past decade (any use of the keywords in the entire article)

lidar+topography lidar+forest

lidar+wildlife lidar+carbon, biomass

Number of articles

Year

Lidar increasingly used for large-scale mapping of topography

DEM of Neuse River basin in North Carolina derived from 390 million lidar 390 million points and grid DEMs containing more than 1.3 billion

cells

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NNaattiioonnaall EElleevvaattiioonn DDaattaasseett ((NNEEDD)) vvss lliiddaarr--ddeerriivveedd DDEEMM

From: jesse amundson, http://www.trishock.com/academic/index.shtml#information

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Mapping Sea Level Rise Poten�al sea-‐level rise impact to downtown Ft. Lauderdale, FL. Blue indicates impacted areas at two feet above mean higher high water, and orange indicates the associated uncertainty area based on the eleva�on data and water surface calcula�on. From: h�p://www.csc.noaa.gov/digitalcoast

Lidar and wetlands Moeslund, J.E., Arge, L., Burcher, P.K., Nygaard, B., Svenning, J.C., 2011. Geographically Comprehensive Assessment of Salt-‐Meadow Vegeta�on-‐Eleva�on Rela�ons Using LiDAR. Wetlands, 1-‐12.

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Mapping carbon Combining field measurements, airborne lidar-‐based observa�ons, and satellite-‐based imagery, we developed a 30-‐meter-‐resolu�on map of aboveground C density spanning 40 vegeta�on types found on the million-‐hectare Island of Hawaii.

Asner, et al. 2011. High-‐resolu�on carbon mapping on the million-‐hectare Island of Hawaii. Fron�ers in Ecology and the Environment 9, 434-‐439.

Lidar + carbon Example LiDAR-based maps of aboveground carbon stocks highlighting: (A) effects of deforestation on humid, low-elevation forests in the northern region, and (B) a natural gradient in elevation and plant available water, from dry forests with low canopy height in the lowlands to humid forests in the uplands. Asner et al. High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon Biogeosciences Discuss., 9, 2445-2479, 2012 doi:10.1186/1750-0680-7-2

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Lidar + hyperspectral imagery can map biomass and stress Swatantran, et al. 2011. Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sensing of Environment. 115(11): 2917-‐2930

Lidar Applications: Vegetation Canopies From Vierling et al. 2008. Lidar: shedding new light on habitat characterization and modeling. Frontiers in Ecology and Environment. Lidar can provide fine-grained information about the 3-D structure of ecosystems across broad spatial extents. This data can be used to

investigate:   Animal-habitat

relationships;   Fire behavior;   Carbon and energy

balance;   Infiltration, canopy

capture and sublimation.

Lidar-‐derived ver�cal distribu�on plots showing the percentage of laser pulse hits that occurred within a par�cular height classifica�on. This kind of analysis helps us more fully understand the 3-‐D structure of vegeta�on.

High: 100

Low: 0

Percent

Canopy

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Lidar and Habitat LiDAR-derived predictors described between 4.7 and 21.6% of the variability in overall forest songbird species richness and that of several guilds. Lesak, et al. Modeling forest songbird species richness using LiDAR-derived measures of forest structure. Remote Sensing of Environment 115(11): 2823-2835

Lidar and archaeology Chase et al. Airborne LiDAR, archaeology, and the ancient Maya landscape at Caracol, Belize. Journal of Archaeological Science 38(2): 387–398

Photograph and LiDAR imagery of “Caana,” Caracol’s main architectural complex; varying colors in the LiDAR data represent different eleva�ons.

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Ground-based Lidar Ground-‐based lidar systems are hemispherical scanning laser range finders that fire millions of laser pulses and records detailed structural informa�on at a range of up to 200 m. Data can be used to derive:

  canopy height   basal area and stem density   ver�cal foliage distribu�on   leaf area index

Yao et al. Measuring forest structure and biomass in New England forest stands using Echidna ground-‐based lidar. Remote Sensing of Environment, 115(11): 2965–2974.

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Satellite Lidar: the promise of DESDynI Special Issue: Remote Sensing of Environment. Volume 115, Issue 11, Pages 2751-2974 (15 November 2011) DESDynI VEG-3D Special Issue From Scott Goetz: DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) was to provide fundamental information on multiple components of the earth system using a combination of radar and, uniquely, lidar technology. Sadly, on 15 February 2011 the equivalent of a cease and desist order was placed on DESDynI — the very same week that President Obama's science advisor stated, on NPR's Science Friday, that earth observation was the single area he would place most emphasis on were budget priorities not limiting.

Dis

cret

e Li

dar y

ield

s m

ultip

le re

turn

s

first returns

second returns

third returns

last returns

all returns

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Lidar pulse density depending on flying height, discrete return lidar can yield pulse densities of 1-12 pulses/m2. How much is enough?

Lidar pulse density is important but flying height, flight paths, sensor design can increase costs

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SNAMP Lidar Specifica�ons   Sugar Pine Area (117km2):

–  Acquisi�on specs:   Collected Sept 13-‐15 2007   Al�tude: ~700m AGL   4 range measurements per pulse   Scan angle: +/-‐ 24 degrees   Scan frequency: 40 Hz   Pulse rate frequency: 100 KHz   Wavelength: 1064 nm

–  Lidar data specs:   5-‐10 cm eleva�on accuracy   6-‐12 points per m2   Footprint size: ~0.18m   Swath width: 510 m

  Last Chance Area (107km2): –  Acquisi�on specs:

  Collected Sept 18-‐22 2008   Al�tude: ~900m AGL   4 range measurements per pulse   Scan angle: +/-‐ 20 degrees   Scan frequency: 40 Hz   Pulse rate frequency: 70 KHz   Wavelength: 1064 nm

–  Lidar data specs:   5-‐10 cm eleva�on accuracy   9-‐20 points per m2   Footprint size: ~0.225m   Swath width: 580 m

Contracted with National Center for Airborne Laser Mapping (NCALM;) Both surveys used an Optech GEMINI Airborne Laser Terrain Mapper (ALTM) mounted in a twin-‐engine Cessna Skymaster.

SNAMP Lidar information

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Our Lidar Data

Acquired pre-‐treatment. Discrete return (4 returns), contracted with NCALM, 6-‐9 pts per m2

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