spring 2013

for Forestry Spring 2013

Esri News

Better Forest Damage AssessmentArcGIS Saves Time, Improves AccuracyBy Barbara Shields, Esri Writer

Thunderstorms, powerful winds, and deadly tornadoes tore through

39 counties in Alabama in April 2011. The city of Tuscaloosa was flat-

tened. Across the state, people were killed and homes and businesses

were destroyed. Forests suffered loss as well. The Alabama Forestry

Commission (AFC) used ArcGIS to assess forest resource damage.

AFC estimated that 26,733 acres of forestland were impacted by the

April 15 tornadoes, with an assessed value of $37,728,175. The April 27

tornadoes were estimated to have damaged 177,857 acres, with an

Alabama forests were damaged by tornadoes in April 2011.

assessed value of $228,360,576. Impact to the forestland in Alabama

from the April storms totaled 204,590 acres, with an assessed value of

$266,088,751.

“The ability to use GIS technologies to integrate cadastral, remotely

sensed, and observational data greatly reduced the time frame neces-

sary for developing the assessment information,” said Patrick A. Glass,

assistant state forester, AFC. “Geoprocessing models developed in

continued on page 3

Spring 2013

Esri News for Forestry is a publication of the Forestry Group of Esri.To contact the Esri Desktop Order Center, call 1-800-447-9778 within the United States or 909-793-2853, extension 1-1235, outside the United States.

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2 Esri News for Forestry Spring 2013

Contents

The information contained in this work is the exclusive property of Esri or its licensors. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by Esri. All requests should be sent to Attention: Contracts and Legal Services Manager, Esri, 380 New York Street, Redlands, CA 92373-8100 USA.

The information contained in this work is subject to change without notice.

The Geographic Advantage, Esri, the Esri globe logo, 3D Analyst, ArcAtlas, ArcCatalog, ArcData, ArcDoc, ArcEditor, ArcExplorer, ArcGIS, the ArcGIS logo, ArcGlobe, ArcIMS, ARC/INFO, ArcInfo, ArcLogistics, ArcMap, ArcNetwork, ArcNews, ArcObjects, ArcPad, ArcPress, ArcReader, ArcSDE, ArcSurvey, ArcToolbox, ArcTools, ArcUser, ArcView, ArcVoyager, ArcWatch, ArcWeb, ArcWorld, ArcXML, Business Analyst Online, BusinessMAP, CommunityInfo, EDN, Geography Network, GIS Day, MapData, MapObjects, Maplex, MapStudio, ModelBuilder,MOLE, NetEngine, RouteMAP, SDE, Sourcebook•America, StreetMap, Tapestry, @esri.com, esri.com, arcgis.com, geographynetwork.com, gis.com, and gisday.com are trademarks, service marks, or registered marks of Esri in the United States, the European Community, or certain other jurisdictions.

Other companies and products or services mentioned herein may be trademarks, service marks, or registered marks of their respective mark owners.

Copyright © 2013 Esri.All rights reserved. Printed in the United States of America.

1 Better Forest Damage Assessment

4 Clinton Climate Initiative Uses GIS to Preserve and Regrow Forests

5 Quickly and Easily Use Landsat Data with ArcGIS Online

6 Researchers Develop an Effective Approach to Forest Cover Analysis

8 USDA Forest Service FUSION Offers Powerful Lidar Tools

10 Create a Map in Seven Steps

11 Attend a Conference Just for You

11 Esri Invites You to Join the Forestry Group

11 On the Road

response to the April tornadoes allowed for unprece-

dented speed and precision in producing results that are

portable and scalable to any significant geographic area.”

AFC collected GPS Exchange Format (GPX) files

on the tornado paths and converted them to feature

classes for GIS analysis to meet agency objectives. AFC

GIS specialist Abi Dhakal was tasked to create maps for

analyzing and assessing the total forest damage by acre,

by county, and for the entire state. Dhakal created a GIS

model that significantly reduced the time to produce

these geospatial products.

Manually geoprocessing data for 39 counties individu-

ally and generating maps for tornado damage analysis

would have taken weeks, if not months, to complete.

Instead, Dhakal built a GIS model that included batch

processing, which allowed him to quickly and efficiently

render processes on multiple datasets. He easily ac-

cessed ArcGIS tools—such as Select, Intersect, Buffer,

Clip, and Tabulate Area—in the geoprocessing models.

Dhakal designed more than a dozen geoprocesses

and steps to produce results. By slightly tweaking the

models, he could adapt subtle changes in analysis

objectives. Initially, it took him a few days to develop the GIS model,

but upon completion, the model ran data for multiple counties

through geoprocesses and created damage analysis maps in just a few

minutes.

Next, Dhakal and Glass used Forest Inventory & Analysis (FIA) data

outside the model to calculate the volume of timber loss. Finally, they

applied Timber-Mart South standing timber prices to the volume loss

and calculated tornado damage to the forests in US dollar amounts.

The results of the analysis were used by Alabama’s congressional

delegation to document a supplemental funding request for the

USDA Farm Service Agency’s Emergency Forest Restoration Program.

Results were also used by AFC, Alabama Forestry Recovery Task

Force, Alabama Cooperative Extension Service, and other agencies to

manage recovery of the forest resources.

For more information about theforest damage assessment model, contact Abi Dhakal [email protected].

To assess forest types impacted by two tornadoes, a model used tornado path polygons derived from aerial reconnaissance, the National Land Cover Dataset (NLCD), and parcel data. Data from Esri, AFC, NOSS NWS, and the US Geological Survey (USGS) was also used.

The volume of timber damage was calculated for each of the 39 Alabama counties impacted by the tornadoes. Data is from AFC, Esri, NLCD, and USGS.

3Spring 2013 esri.com/forestry

Better Forest Damage Assessment continued from cover

Clinton Climate Initiative UsesGIS to Preserve and Regrow Forests

security, viability of coastal cities, and water

availability around the world.

To reduce emissions, governments and

economies must use less fossil fuels and

increase the use of energy-efficient and

renewable technologies. The CCI Forestry

Program focuses on helping developing

countries reverse deforestation and plant new

trees. By showing that they can monitor and

verify the reduction of their carbon dioxide

emissions, countries become eligible for fund-

ing to manage their forest programs and other

low-carbon economic activities.

CCI uses GIS technology as a centerpiece

of forest carbon measurement, reporting,

and verification (MRV) systems for develop-

ing countries. GIS is one of three legs of the

platform—data, models, and GIS—that allow

CCI used GIS to measure carbon and compute carbon credits that were used to preserve this forest in Guyana.


The Clinton Climate Initiative (CCI) Forestry

Program develops forestry projects and

carbon measurement systems that help

governments and local communities receive

compensation for preserving and regrowing

forests. CCI uses GIS technology to help

countries monitor their carbon levels.

Global warming is caused by increased

carbon dioxide in the atmosphere from burn-

ing fossil fuels, and deforestation accounts

for about 15 percent of total carbon dioxide

emissions in the world. Scientists predict that

if governments and communities don’t take

action to reduce carbon dioxide emissions,

our world will face increasingly drastic con-

sequences ranging from stronger heat waves

to more droughts and floods to increasing

sea level. All these affect agriculture, food

countries to determine how much carbon they

have, how it is changing, and how the drivers

of deforestation and forest degradation can

be monitored and adjusted as required.

With GIS in place for forestry, developing

countries can be eligible for direct payments

through international agreements based on

the effectiveness of their MRV systems. Once

in place, GIS can be used more broadly for

other resource development, land surveys,

and the determination of land tenure.

For example, tree farming projects in Kenya

help make forest conservation and restoration

profitable for local communities. CCI is cur-

rently working on 10 sustainable-forest man-

agement projects, encompassing 644,000

hectares of land, that will benefit more than

353,000 people in forest-dependent commu-

nities around the world.

Esri awarded CCI the 2012 Special

Achievement in GIS Award for helping the

country of Guyana become eligible for

$70 million in forest-based payments from the

government of Norway. Guyana is now using

this funding to facilitate specific elements of

a low-carbon development plan envisioned

and put in place by former Guyana president

Bharrat Jagdeo. This project is part of the CCI

Forestry Program and has been supported by

the Rockefeller Foundation and the govern-

ments of Norway and Australia.

Read more about CCI’s forestry projects at clintonfoundation.org.

The Clinton Climate Initiative supports tree farming projects in Kenya. This Kenyan tree farmer is working in a nursery to conserve a local forest.

Quickly and Easily UseLandsat Data with ArcGIS OnlineLandsat imagery is one of several remote sensor systems land analysts use to study vegetation,

land use, soil conditions, and terrain. ArcGIS Online image services provide quick and simple

access to free United States Department of the Interior’s US Geological Survey (USGS) Landsat

data. These image services are based on the Global Land Survey (GLS) datasets created by

USGS and the National Aeronautics and Space Administration (NASA). Landsat data supports

global assessments of land cover, land cover change, and ecosystem dynamics such as

disturbance and vegetation health.

Landsat represents the world’s longest continuously acquired collection of space-based,

moderate-resolution, land remote-sensing, data change research. It is more efficient than any

other technology used to meet decision support requirements.

Esri provides Landsat image services that enable users to explore important imagery

information such as band combination analysis for natural color, infrared, vegetative change,

and Normalized Difference Vegetation Index (NDVI). The user can also compare many years of

imagery to see changes over time.

Two Landsat viewers are available online to help users do just that—LandsatLook Viewer

from USGS and ChangeMatters Viewer from Esri. Both of these viewers use ArcGIS technology

to serve Landsat as web services, accessing many terabytes of imagery. LandsatLook Viewer

provides access to the complete archive of USGS Landsat scenes going back to 1972.

ChangeMatters Viewer provides access to the best cloud-free Landsat scenes from the five

epochs, circa 1975, 1990, 2000, 2005, and 2010. It uses ArcGIS as its underlying technology to

additionally process the Landsat GLS dataset on the fly into multiple data products. These are

served as World Landsat Services on ArcGIS Online. The image service platform is also host to

the Landsat community, a group that shares maps and applications using Landsat imagery.

This map is a mosaicked composite of data from the image service of the Landsat Global Land Survey (GLS) 2010 dataset and can be accessed on ArcGIS Online. This data has been enhanced with radiometric correction and histogram stretching to make it more visually appealing.


Researchers Develop an Effective Approach to Forest Cover AnalysisBy Aaron E. Maxwell, Remote Sensing Analyst, Natural Resource Analysis Center, West Virginia University

Overwatch. The outcome was a statewide

forest cover and forest fragmentation map

created from raster data at a nine-meter cell

size for the state of West Virginia.

The project goal was to capture the

spectral, textural, and land-use variability

with defined classes. WVU researchers relied

on object-based image analysis, which uses

spectral and textural information within an

image to extract thematic data. The pro-

ject’s imagery was from the United States

Department of Agriculture (USDA) Farm

Service Agency. This was four-band, leaf-on,

one-meter pixel size, uncompressed imagery

State resource agencies model wildlife habitat

to support the planning and management of

natural resources. The most widely available

land cover for states to use is the Landsat-

derived National Land Cover Dataset (NLCD).

Because of its coarse resolution (30 meters)

and temporal lag to the current conditions,

using the NLCD is a challenge for wildlife

modeling at a local scale. Researchers from

West Virginia University (WVU) created a more

effective approach for modeling habitat. They

implemented a GIS and remote-sensing meth-

odology for creating statewide forest cover

and forest fragmentation data layers.

Figure 1. This training data was derived from interpreting photo imagery as polygons in ArcGIS. This base imagery is 2011 NAIP orthophotography displayed in true color.

The approach was to use publicly avail-

able orthophotography from the National

Agriculture Imagery Program (NAIP). This pro-

vided better resolution for raster data layers,

increased accuracy of forest cover analysis,

and delineation of fragmented wildlife habi-

tat. NAIP orthophotography has a one-meter

cell size and four-band spectral information

(true color bands and an infrared band). The

photography is captured on a two-year cycle,

so datasets can be regularly updated.

The analyst used Esri’s ArcGIS 10 Desktop

image analysis tools and tools from the

Feature Analyst 5 extension for ArcGIS by


representing forest conditions during the 2011

growing season.

The bulk of the researchers’ time was spent

extracting cover from each image and classify-

ing it as either forested/woody, grasslands/

herbaceous, or barren/nonvegetated cover.

It was necessary to collect a large number

of samples to accurately extract the cover of

interest. Researchers spent from one to three

hours creating training data, which contains

examples of the cover types of interest. They

manually interpreted photographs within the

ArcGIS editor, using its functionality to create

training data as vector and polygon features.

This training data process allowed user input

of vector data and generated examples of the

cover of interest. Figure 1 shows an example

of the training data digitized throughout the

state.

To extract cover, researchers used Feature

Analyst 5 to process each image. The integra-

tion of its extraction tools with ModelBuilder

enabled users to complete mapping tasks in

a timely manner. They visually inspected all

outputs for accuracy and, when necessary,

reprocessed outputs.

Having completed this mapping task, re-

searchers merged the resultant raster data to

produce a statewide grid at a nine-meter cell

size. They did this by using the Mosaic To New

Raster tool in the ArcGIS Data Management

toolbox. The outcome was a statewide forest

cover map representing the 2011 growing-

season conditions at a higher resolution than

is currently available from existing datasets,

such as the NLCD. Figure 2 shows the result-

ant cover.

To assess the accuracy of the forest cover

data, researchers compared the cover extrac-

tion to manual photograph interpretation at

randomly selected point locations across the

state. They streamed the 2011 NAIP ortho-

photography through ArcGIS for Server, which

was hosted by the Aerial Photography Field

Office (APFO) of the USDA. This allowed them

to quickly and easily access photography and

assess the accuracy of the resultant cover.

This approach yielded a forest cover map with

accuracy that was greater than 90 percent.

Forest fragmentation was created as a deriv-

ative of the raster forest cover. First, researchers

smoothed the resultant cover to provide a

more general representation of fragmentation

and to remove small canopy interruptions that

were deemed too small to fragment the forest.

To create the forest fragmentation data, they

employed morphological image analysis, ap-

plying mathematical morphology to analyze the

shape and form of objects.

Running the downloadable Landscape

Fragmentation Tool (LFT) version 2.0 in ArcGIS,

researchers mapped the types of fragmenta-

tion present in specified land cover types,

including patch, edge, perforated, and core,

which is based on a specified edge width.

To complete the processing, researchers

segmented the state into manageable units

and then merged the final results, ultimately

producing a raster grid of statewide forest

fragmentation with a nine-meter cell size.

Figure 3 shows the fragmentation data.

Project Team

Aaron E. Maxwell, Natural Resource Analysis Center, West Virginia University, Morgantown,

West Virginia; Michael P. Strager, Division of Forestry and Natural Resources, West Virginia

University, Morgantown, West Virginia; Elise M. Austin, Natural Resource Analysis Center,

West Virginia University, Morgantown, West Virginia

Figure 2. Researchers created thematic tree cover for West Virginia. They derived this coverage from 2011 NAIP orthophotography by using object-based image analysis tools in the Feature Analyst 5 extension for ArcGIS.

Figure 3: Forest researchers were able to derive this forest fragmentation data for West Virginia by using a script within ArcToolbox to extract forest cover thematic data layers.


Forest managers can now use lidar data in combination with GIS to

help them assess forest inventories and create forest plans. The USDA

Forest Service’s (USFS) FUSION software, combined with Esri’s ArcGIS,

provides lidar analytic tools, a streamlined workflow, and functionality

for storing, organizing, and sharing lidar .las files. Foresters can under-

stand, explore, and analyze lidar data point clouds and interactively

view them in 3D.

Foresters use FUSION to quantify vegetation by extracting lidar

point clouds and correlating them with forest inventory plots. It calcu-

lates various canopy metrics, such as height statistics, to describe the

canopy distribution and cover density ratios. FUSION then summarizes

these ratios at the plot level or in a continuous grid cell format. It also

performs a quality routine to assess the appropriateness of lidar point

data for a forestry application.

FUSION provides a robust ability to perform extensive point cloud

analytics of forest inventory variables across large landscapes. The in-

formation it extracts to describe the forest canopy is easily exported as

an ASCII grid and imported into ArcGIS along with other GIS datasets

(figure 1).

USDA Forest Service FUSION Offers Powerful Lidar Tools

Figure 1. The user exported FUSION-derived lidar canopy metrics into ArcGIS to produce this

map showing the first return percent canopy cover grid metric, produced at a 25-meter cell size, across

approximately 85,000 acres in southeast Arizona.

Figure 2. The GIS grid layer (25 m cell size) represents the Basal Area inventory parameter model applied at the landscape level. This GIS layer is one of the end-user products that will be used for future decision making, analysis, and monitoring for the Pinaleño Sky Island study area.


Using ArcGIS Spatial Analyst, users apply conditional logic to the

FUSION-derived canopy structure grids to extract pixels meeting

certain criteria. For example, foresters can locate areas containing tall

trees with relatively low canopy cover.

FUSION AppliedOver the past several years, a workflow incorporating ArcGIS and

FUSION was implemented for a forest restoration effort in the Pinaleño

Mountains of the Coronado National Forest in southeastern Arizona.

To model forest inventory parameters, the team used regression

analysis to determine the correlation between the parameters that

were measured on field plots and the lidar canopy metrics that were

summarized in FUSION and classified as subsets for each plot.

Before applying the resultant statistical models to the landscape,

team members used GIS to ensure the models were applied appropri-

ately and successfully across the landscape. First, they applied a forest/

nonforest mask to ensure the models were applied only in forested

areas. This was accomplished using the Spatial Analyst Conditional

tool in ArcGIS and the canopy height and cover structure grid layers

output from FUSION. Each pixel had to meet a minimum vegetation

height of three meters and 2 percent canopy cover. All pixels that did

not meet the criteria were masked out before models were applied

across the study area.

The final step was to apply the regression models created in the

initial modeling steps to the appropriate ASCII grid layers. This

generated continuous inventory parameter GIS layers covering the

entire study area. Each calculation produced a new grid in which

each 25-meter cell spatially represents the estimated forest inventory

parameter of interest such as biomass, basal area, Lorey’s mean height,

and timber volume (figure 2). The resultant GIS inventory layers were

qualitatively validated by local experts and conformed well to trends

known to occur on the landscape.

The project team estimated that the cost for obtaining data suf-

ficient to implement the Pinaleño Ecosystem Restoration Project using

lidar was half the cost of traditional methods. Furthermore, capturing

the lidar data to measure sample areas was easier than deploying field

crews that could not safely measure trees in extreme terrain. Lidar

made it possible for the team to create continuous coverage of all

forested areas.

FUSION software is currently managed by Robert McGaughey of the

USDA Forest Service—Pacific Northwest Research Station. Download

USFS FUSION software free of charge at http://forsys.cfr.washington

.edu/fusion/fusionlatest.html.

For more information, tutorials, and sample lidar datasets, visit the USFS Remote Sensing Applications Center at www.fs.fed.us/eng/rsac.

Learn more about FUSION by contacting Ron Behrendt, managing member, Behron LLC, at [email protected].


Create a Map in Seven Steps

ArcGIS Online is the mapping platform for your organization. CEOs,

staff, and contractors can access maps and data for their work. See

opportunities and gain insight into your data. Do this quickly with no

data to install.

Create an ArcGIS Online forest map in seven steps at arcgis.com.

Click Sign In and note the ribbon on the top of the page. Click the word

ArcGIS to get to the ArcGIS Online page.

Let’s get started.

1. Open a map: Click Make a Map, and you see a map of the

United States.

2. Create the basemap: Click Basemap and select Light Gray Canvas.

3. Add a data layer: Click the Add button on the ribbon; select Search

for layers; and in the Find field, type “USFS Ecological Subregions”

(the In field should say “ArcGIS Online”) and click Go. When the layer

appears, click Add beside the layer title.

4. To add another layer package, go to the Find field and type “USGS

Forest Fragmentation”. Add it to your map.

5. At the bottom of the Search for layers to add section, click the Done

Adding Layers button.

6. See your layers in the table of contents. Point to a layer title and click

the arrow to its right. Play with the zoom, transparency, and visibility

tools and set them to your preference.

7. Click Save to save your map in your account folder.

Explore your map. Click the layer title to see the map’s data layer

contents and select what you want to see. Above the Contents line,

pause your mouse pointer over an icon to see its function. Click Show

Map Legend to open it and click again to close it. Click the data layer

to select subsets on the map. Zoom to see greater detail. Click on the

map to get specific information. Click the Details button on the ribbon

to turn the contents column on and off.

ArcGIS Online is a service that lets you use GIS software and data.

Create maps that tell your story, build applications, and more.

Sign up for a 30-day free trial subscriptionat arcgis.com.

The USDA Forest Service published this map of ecological subregions.

This map shows forest fragmentation risk.

By combining two layers, the reader can see forest fragmentation risk by ecological regions. The result has been saved and published for public use.


Join forestry professionals from around the world at the Esri Forestry

GIS Conference, which is hosted by the Esri Forestry Group. You will

meet with a community of GIS for forestry users, share ideas, hear best

practices, and talk to experts.

This year’s theme is Benefiting from Change: Realizing the Value of

GIS in Forest Management. The conference offers a hands-on work-

shop for you to develop your forestry GIS skills. You can also attend

sessions that explore these topics: • Forest spatial optimization • Integrating data and systems • Automated workflows • A complete desktop, web, and mobile system • Image management, classification, and analysis • Web applications and services for many user types

Esri Forestry GIS Conference

May 14–16, 2013 | Esri Headquarters, Redlands, California

For more information and to register, visit esri.com/events/forestry.

Mark Your Calendar

Western Forestry Leadership Coalition (WFLC)April 29–May 1, 2013Denver, Colorado, USAwflccenter.org

Esri Forestry GIS ConferenceMay 14–16, 2013Redlands, California, USAesri.com/events/forestry

2013 Southern Group of State Foresters (SGSF) Summer MeetingJune 3–6, 2013Savannah, Georgia, USAwww.southernforests.org

ElmiaWoodJune 5–8, 2013Jönköping, Swedenelmia.se/en/wood

The Association of Consulting Foresters National ConferenceJune 21–25, 2013Keystone, Colorado, USAwww.acf-foresters.org

Esri International User ConferenceJuly 8–12, 2013San Diego, California, USAesri.com/uc

Remsoft Modeling Conference and User GroupSeptember 9–13, 2013Fredericton, New Brunswick, Canadaremsoft.com

National Association of State Foresters September 22–26, 2013Hot Springs, Virginia, USAstateforesters.org

Society of American Foresters National ConventionOctober 23–27, 2013Charleston, South Carolina, USAsafnet.org

Western Forestry Leadership Coalition (WFLC) Fall MeetingOctober 28–30, 2013San Diego, California, USAwflccenter.org

COP 19November 2013Warsaw, Poland polandcop19.org

Southern Forestry and Natural Resource Management ConferenceDecember 8–10, 2013Athens, Georgia, USAwww.soforgis.net/2013

On the RoadAttend a Conference Just for You

Esri invites you to join the Esri Forestry Group (EFG). Your participation

in this dynamic group will help you get more from your GIS and your

forest and land management data. Meet like-minded professionals,

share experiences, and exchange knowledge.

Become a Member • Community: Connect with a community of professionals

passionate about GIS. • Information: Stay current on Esri forestry products, events,

webinars, and resources. • Solutions: Learn about forestry GIS tools, applications, and projects.

Join today or renew your membership at esri.com/efg.

Esri Invites You to Join the Forestry Group


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