Esri News for Forestry - Spring 2013

Esri News for Forestry

Spring 2013

Better Forest Damage Assessment ArcGIS Saves Time, Improves Accuracy

By Barbara Shields, Esri Writer Thunderstorms, powerful winds, and deadly tornadoes tore through 39 counties in Alabama in April 2011. The city of Tuscaloosa was flattened. 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 necessary for developing the assessment information,” said Patrick A. Glass, assistant state forester, AFC. “Geoprocessing models developed in continued on page 3

Contents

Spring 2013

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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

Esri News for Forestry is a publication of the Forestry Group of Esri.

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Esri News for Forestry Spring 2013

Better Forest Damage Assessment

continued from cover

response to the April tornadoes allowed for unprecedented 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 individually 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 accessed 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

 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. 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 the forest damage assessment model, contact Abi Dhakal at abi.dhakal@forestry.alabama.gov.

 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.

Spring 2013 esri.com/forestry

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Clinton Climate Initiative Uses GIS to Preserve and Regrow Forests 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 burning 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 consequences ranging from stronger heat waves to more droughts and floods to increasing sea level. All these affect agriculture, food

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 funding 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 developing countries. GIS is one of three legs of the platform—data, models, and GIS—that allow

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 currently working on 10 sustainable-forest management projects, encompassing 644,000 hectares of land, that will benefit more than 353,000 people in forest-dependent communities 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 governments 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.

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

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Esri News for Forestry Spring 2013

Quickly and Easily Use Landsat Data with ArcGIS Online Landsat 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.

Spring 2013 esri.com/forestry

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Researchers Develop an Effective Approach to Forest Cover Analysis

By Aaron E. Maxwell, Remote Sensing Analyst, Natural Resource Analysis Center, West Virginia University

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 Landsatderived 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 methodology for creating statewide forest cover and forest fragmentation data layers.

The approach was to use publicly available orthophotography from the National Agriculture Imagery Program (NAIP). This provided better resolution for raster data layers, increased accuracy of forest cover analysis, and delineation of fragmented wildlife habitat. 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

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 project’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

 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.

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Esri News for Forestry Spring 2013

 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.

representing forest conditions during the 2011 growing season.

The bulk of the researchers’ time was spent extracting cover from each image and classifying 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 integration 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, researchers merged the resultant raster data to produce a statewide grid at a nine-meter cell

 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.

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 growingseason conditions at a higher resolution than is currently available from existing datasets, such as the NLCD. Figure 2 shows the resultant cover.

To assess the accuracy of the forest cover data, researchers compared the cover extraction to manual photograph interpretation at randomly selected point locations across the state. They streamed the 2011 NAIP orthophotography 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 derivative 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, applying 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 fragmentation 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

Spring 2013 esri.com/forestry

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USDA Forest Service FUSION Offers Powerful Lidar Tools 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 understand, 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 calculates 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 information 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).  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.

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Esri News for Forestry Spring 2013

Before applying the resultant statistical models to the landscape, team members used GIS to ensure the models were applied appropriately 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 sufficient 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.

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 Applied

Learn more about FUSION by contacting Ron Behrendt, managing member, Behron LLC, at behron@centurytel.net.

Over 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.

Spring 2013 esri.com/forestry

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Create a Map in Seven Steps

 The USDA Forest Service published this map of ecological subregions.

 This map shows forest fragmentation risk.

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 subscription at arcgis.com.  By combining two layers, the reader can see forest fragmentation

risk by ecological regions. The result has been saved and published for public use.

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Esri News for Forestry Spring 2013

Attend a Conference Just for You 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 workshop 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.

On the Road Mark Your Calendar Western Forestry Leadership Coalition (WFLC) April 29–May 1, 2013 Denver, Colorado, USA wflccenter.org

Esri Forestry GIS Conference May 14–16, 2013 Redlands, California, USA esri.com/events/forestry

2013 Southern Group of State Foresters (SGSF) Summer Meeting June 3–6, 2013 Savannah, Georgia, USA www.southernforests.org

ElmiaWood

June 5–8, 2013 Jönköping, Sweden elmia.se/en/wood

The Association of Consulting Foresters National Conference June 21–25, 2013 Keystone, Colorado, USA www.acf-foresters.org

Esri International User Conference

Esri Invites You to Join the Forestry Group

July 8–12, 2013 San Diego, California, USA esri.com/uc

Remsoft Modeling Conference and User Group September 9–13, 2013 Fredericton, New Brunswick, Canada remsoft.com

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.

National Association of State Foresters

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

October 23–27, 2013 Charleston, South Carolina, USA safnet.org

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.

September 22–26, 2013 Hot Springs, Virginia, USA stateforesters.org

Society of American Foresters National Convention

Western Forestry Leadership Coalition (WFLC) Fall Meeting October 28–30, 2013 San Diego, California, USA wflccenter.org

COP 19

November 2013 Warsaw, Poland polandcop19.org

Southern Forestry and Natural Resource Management Conference December 8–10, 2013 Athens, Georgia, USA www.soforgis.net/2013

Spring 2013 esri.com/forestry

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