Brent Oblinger, Zhangfeng (Leo) Liu, Beverly Bulaon & Lisa - - PowerPoint PPT Presentation

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Mar 02, 2023 302 likes •502 views

Brent Oblinger, Zhangfeng (Leo) Liu, Beverly Bulaon & Lisa Fischer USDA Forest Service Pacific Southwest Region Forest Health Protection Objective Provide estimates of tree mortality levels at the local scale using simple GIS tools

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Brent Oblinger, Zhangfeng (Leo) Liu, Beverly Bulaon & Lisa Fischer USDA Forest Service – Pacific Southwest Region Forest Health Protection

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Objective Provide estimates of tree mortality levels at the local scale using simple GIS tools

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High Meadows in 2010

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NAIP Imagery

CA : 2005, 2009, 2010 & NV: 2006, 2010

University of Nevada, Reno Keck Library

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Planned Acquisition of NAIP Imagery

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Mapping Methods

I. Visually rate tree mortality levels

across a grid

II. Digitize tree mortality polygons

using visual interpretation

III. Use remote sensing software to

classify and map tree mortality

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I. Visually rate tree mortality levels

across a grid

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Grid for Visual Interpretation (1 acre cells) 2005 NAIP Image 2010 NAIP Image

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Example: Percent Mortality Throughout the Canopy (included older and recent mortality) : None visible – trace or single crown : 2 tree crowns – 25% of total canopy present appears dead : 26% - 50% of total canopy appears dead : 51% - 75% of total canopy appears dead : 76% - 100% of total canopy appears dead

Develop Severity Rating Scheme for Visual Interpretation

f Grid Cells

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Map % Mortality Throughout the Canopy

___Legend___

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20 40 60 80 100 120 140 160

None Visible- Single Tree Crown >Single Crown

25% of Total

Canopy Cover 26% - 50% of Total Canopy Cover 51% - 75% of Total Canopy Cover 76% - 100% of Total Canopy Cover

Number of cells (of 256 total)

2005 2010

Comparing % Mortality Throughout the Canopy in 2005 to 2010

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II. Digitize tree mortality polygons

using visual interpretation

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20 acres with mortality of 256 acres in project area 79 acres with mortality of 256 acres in project area

Delineation of tree mortality after manually drawing polygons

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III. Use remote sensing software to

classify and map tree mortality

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Image Classification with Remote Sensing Software

 Tools now within

ArcGIS make classification available to more users

 Supervised

classification example

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Image Classification Results

Mapped mortality after image classification ( 29 acres ) Mapped mortality after digitizing ( 20 acres )

vs.

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Simple Less time required Less precise mapping Multiple options for data collection Very Simple More time required More precise mapping Provides presence / absence of mortality data

Grid Approach Digitizing Approach Image Classification Approach

More Advanced Less time mapping but more time correcting errors Moderate precision-to- more precise mapping Provides presence / absence of mortality data

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http://www.fs.usda.gov/main/r5/forest-grasslandhealth