Vegetation Mapping using MSN Analysis in INFORMS

Slides:



Advertisements
Similar presentations
NRIS FSVEG Data Translation
Advertisements

A move from E-Governance to G- Governance ( G.P. Singh Technical Director National Informatics Centre, Govt. of India Kendriya Bhawan.
H EURISTIC S OLVER  Builds and tests alternative fuel treatment schedules (solutions) at each iteration  In each iteration:  Evaluates the effects of.
FVS, State - Transition Model Assumptions, and Yield tables – an Application in National Forest Planning Eric Henderson Analyst, Hiawatha National Forest,
Selected results of FoodSat research … Food: what’s where and how much is there? 2 Topics: Exploring a New Approach to Prepare Small-Scale Land Use Maps.
Figure 3. Outlines of the study with links between different components used. The figure presents the main inputs and outputs from the model used (Glob3PG)
VEGETATION MAPPING FOR LANDFIRE National Implementation.
Introducing a Fire Danger Rating System for South Africa
Blue Mountains Forest Plan Revision Developing Long-term Sustainable Yield and ASQ Estimates December
The Effects of Site and Soil on Fertilizer Response of Coastal Douglas-fir K.M. Littke, R.B. Harrison, and D.G. Briggs University of Washington Coast Fertilization.
Western Wildlands Environmental Threat Assessment Center Wildfire Risk Analysis and Fuel Treatment Planning Alan Ager, Western Wildlands Environmental.
Using the Landscape Management System (LMS) on the Colville Reservation Colville Confederated Tribes Natural Resources Department McAllister Helicopter.
Attribution of Haze Phase 2 and Technical Support System Project Update AoH Meeting – San Francisco, CA September 14/15, 2005 Joe Adlhoch - Air Resource.
Forest Surveys and GIS Applications on the Nez Perce Reservation GIS on Fire! Worley - Plummer, Idaho May 11, 2005 Rich Botto, Nez Perce Tribe
Raster Based GIS Analysis
Geographic Information Systems Applications in Natural Resource Management Chapter 13 Raster GIS Database Analysis Michael G. Wing & Pete Bettinger.
Duncan Lutes Systems for Environmental Management Bob Keane – USFS – Research Ecologist, P.I. Carl Key – USGS – Geographer John Caratti – SEM – Systems.
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
Draft LiDAR Strategy Pacific Northwest Region. Regional Strategy Team Pete Heinzen, DRM Brian Wing, Pacific Southwest Station Tom DeMeo, NR Leah Rathbun,
Raster Data. The Raster Data Model The Raster Data Model is used to model spatial phenomena that vary continuously over a surface and that do not have.
INTEGRATION OF MEASURED, MODELLED & REMOTELY SENSED AIR QUALITY DATA & IMPACTS ON THE SOUTH AFRICAN HIGHVELD Kubeshnie Bhugwandin - October 2007.
TREMA Tree Management and Mapping software Raintop Computing - Oxford.
The Coeur d'Alene Tribe is learning the remote sensing methodology developed by LANDFIRE, and will be attempting to apply the methods to higher resolution.
The Rural Technology Initiative –“Better technology in rural areas for managing forests for increased product and environmental values in support of local.
Utilization of the SWAT Model and Remote Sensing to Demonstrate the Effects of Shrub Encroachment on a Small Watershed Jason Afinowicz Department of Biological.
Intro. To GIS Lecture 6 Spatial Analysis April 8th, 2013
DROUGHT MONITORING THROUGH THE USE OF MODIS SATELLITE Amy Anderson, Curt Johnson, Dave Prevedel, & Russ Reading.
Oct-03FOFEM 5 Overview An Overview of FOFEM 5 Missoula Fire Sciences Laboratory Systems for Environmental Management.
ESRM 250 & CFR 520: Introduction to GIS © Phil Hurvitz, KEEP THIS TEXT BOX this slide includes some ESRI fonts. when you save this presentation,
Bringing stand level fire risk to the landscape level: Fire risk assessment using FFE-FVS with the Landscape Management System. Kevin Ceder And James McCarter.
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.
What is Sure BDCs? BDC stands for Batch Data Communication and is also known as Batch Input. It is a technique for mass input of data into SAP by simulating.
Mapping Forest Canopy Height with MISR We previously demonstrated a capability to obtain physically meaningful canopy structural parameters using data.
Ground-Truthing the Habitat Inventory for the Fraser River: Status Report and Lessons Learned March 2007 Fraser River Estuary Management Program.
STRATIFICATION PLOT PLACEMENT CONTROLS Strategy for Monitoring Post-fire Rehabilitation Treatments Troy Wirth and David Pyke USGS – Biological Resources.
Inventory and Monitoring Terrestrial Fauna Inventory and Monitoring Terrestrial Fauna Linking Field Activities to Budget Processes.
Northeastern Research Station Southern Research Station The University of Georgia Artificial Intelligence Center An Intelligent Information System for.
Vegetation Mapping An Interagency Approach The California Department of Forestry and Fire Protection and the USDA Forest Service Mark Rosenberg: Research.
Role of Spatial Database in Biodiversity Conservation Planning Sham Davande, GIS Expert Arid Communities Technologies, Bhuj 11 September, 2015.
Modeling the effects of forest succession on fire behavior potential in southeastern British Columbia S.W. Taylor, G.J. Baxter and B.C. Hawkes Natural.
Integrated Ecological Assessment February 28, 2006 Long-Term Plan Annual Update Carl Fitz Recovery Model Development and.
Wetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis. Nathan Torbick Spring 2003 Update on current.
©2007 Austin Troy Lecture 7: Introduction to GIS 1.Queries and table operations for a single layer in Arc GIS 2.Intro to queries in Access Lecture by Austin.
Vegetation Module Seth Bigelow, Michael Papaik, Malcolm North USFS Pacific Southwest Research Station.
Evaluation of Landscape Vegetation Inventory 2014 Forest Analysis & Inventory Branch (FAIB)
The Value of Your Urban Forest:
What is GIS ? A method to visualize, manipulate, analyze, and display spatial data “Smart Maps” linking a database to the map.
LANDSCAPE SCALE MONITORING OF FOREST TREATMENTS AND DISTURBANCE S.E. Sesnie 1,2, A.D. Olsson 1, B.G. Dickson 1, A. Leonard 3, V. Stein Foster 3 and C.
Applying Pixel Values to Digital Images
USING THE FOREST VEGETATION SIMULATOR TO MODEL STAND DYNAMICS UNDER THE ASSUMPTION OF CHANGING CLIMATE Climate-FVS Version 0.1 Developed by : Nicholas.
A Cyberinfrastructure for Drought Risk Assessment An Application of Geo-Spatial Decision Support to Agriculture Risk Management.
1 januari 2008 RIBASIM input data by Wil N.M. van der Krogt.
U.S. Department of the Interior U.S. Geological Survey October 22, 2015 EROS Fire Science Understanding a Changing Earth.
Corn Yield Comparison Between EPIC-View Simulated Yield And Observed Yield Monitor Data by Chad M. Boshart Oklahoma State University.
Developing the Vegetation Drought Response Index (VegDRI): Monitoring Vegetation Stress from a Local to National Scale Brian Wardlow National Drought Mitigation.
Introduction to FFI: Why and how FFI was developed Introduction to FFI: Why and how FFI was developed 04/02/2013.
FSVeg Spatial Data Analyzer Imputation, Climate, and More Collaborative Restoration Workshop Denver, CO - April 2016.
(Srm) model application: SRM was developed by Martinec (1975) in small European basins. With the progress of satellite remote sensing of snow cover, SRM.
Forest Management Service Center Providing Biometric Services to the National Forest System Program Emphasis: We provide products and technical support.
Module 4 Data Management-Applications Coastal Applications of ArcGIS.
NASA BAER Project: Improving Post-Fire Remediation Through Hydrological Modeling NASA Applied Science Program Applied Sciences Program - Wildfires.
Western Mensurationists Meeting 2016
ECOSYSTEM MODEL EVALUATION TEMPLATE
VegDRI History, Current Status, and Related Activities
An Introduction to VegDRI
Incorporating Ancillary Data for Classification
NADSS Overview An Application of Geo-Spatial Decision Support to Agriculture Risk Management.
Figure 1. Spatial distribution of pinyon-juniper and ponderosa pine forests is shown for the southwestern United States. Red dots indicate location of.
ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz
Precision Ag Precision agriculture (PA) refers to using information, computing and sensing technologies for production agriculture. PA application enables.
Presentation transcript:

Vegetation Mapping using MSN Analysis in INFORMS INtegrated FORest Management System MSN Most Similar Neighbor Analysis

Overview Use MSN to create a current wall to wall vegetation layer utilizing NRIS FSVeg, DEM and Landsat data. Utilize the Forest Vegetation Simulator (FVS) to grow stand data to current and future year conditions. Create alternatives and model vegetation treatments (i.e. thinning) for NEPA analysis and impacts evaluation.

Definitions NRIS INFORMS A project-level landscape analysis framework. Most Similar Neighbor (MSN) A powerful application used to impute available ground-based inventory data to non-inventoried units. NRIS FSVeg Forest Service Field-Sampled Vegetation database. Forest Vegetation Simulator (FVS) An individual-tree, distance-independent growth and yield model. Websites: FVS – MSN -

What is Most Similar Neighbor (MSN)? The MSN application is a powerful tool used to impute available ground-based inventory data to non- inventoried units. The MSN method uses available data from the ground- based sample units and globally available data measured on all sample units to guide the imputation. Examples of global information for all sample units include topographic data and satellite imagery. Landscape of vegetation data is available for analysis based on imputations from the MSN process.

MSN Calibration Most Similar Neighbor analysis command files are prepared and tested for each FVS variant. Calibration (selection of variables) is the most critical part of Most Similar Neighbor analysis. Variables contained in the command files are carefully selected in cooperation with the researchers who developed the Most Similar Neighbor application and methods. Once calibrated, there is a standardized methodology for each FVS variant. Notes for bullet points: MSN command files are specific files identifying the correct attributes for MSN use in creating imputations. We change them to be specific to each FVS variant (hence calibration to each FVS variant). Selection of attributes is the most critical part of Most Similar Neighbor Analysis. There are multiple FVS variants for many regions, some only having one. The best attributes are continuous variables.

MSN Calibration Variables Calibration is the process of finding the correct combination of global and sampled data for each FVS variant. Global Data = Data that is available for all polygons (i.e. slope, aspect, Landsat, etc.). Sampled Data = Data that is available for sampled polygons (i.e. stand exams) or other vegetation sampling (i.e. range data, fuels plots). Examples include Basal Area, Trees per Acre, QMD, Volumes, etc.

Where MSN is calibrated by FVS Variant This shows the order Fuels and Fire Extension by variant will be available to the Forest Vegetation Simulator (FVS). This critical component need for this fuels reduction analysis. FMSC Slide. Completed Not Done Yet

Forests Mapped with MSN

MSN Mapping Status by National Forest Forests Completed Forests In Progress Region 1 Idaho Panhandle Region 3 Lincoln Carson Gila Coconino Kaibab Apache- Sitgreaves 2 districts Region 6 Malheur Umatilla Wallowa- Whitman 2 districts Deschutes 2 districts Siuslaw 1 district Region 1 Nez-Perce Region 2 Shoshone Region 3 Santa-Fe Cibola Corrinodo Tonto Updated: 12/2006

Data Requirements Populated NRIS FSVeg database. Non-forested survey data (i.e. rangeland data). Local Vegetation coverage which is related to the FSVeg data. DEM derived grids for Slope (in radians), Slope Catchment Area, Insulation and Duration. Landsat grids for reflectance values for bands 1, 2, 3, 4, 5 and 7. These data requirements are for using MSN within INFORMS.

Vegetation Grouping Most Similar Neighbor analysis is run separately on Forested and Non-Forested polygons. Vegetation polygons must be divided into three groups by the local GIS shop by adding an attribute into the local stands layer: Forested Vegetation (FV) Non-Forested Vegetation (NF) Non-Vegetated (NV)

Preparing Global and Sampled Data in INFORMS Global Data Preparation Tool: Summarizes data from the DEM and Landsat Scene into an input format for Most Similar Neighbor analysis. Sampled Data Preparation Tool for Forested Polygons: Grows all stand data forward to the year of the Landsat scene to calibrate stand data to the current condition using FVS. Sampled Data Preparation Tool for Non-Forested Polygons: Data is currently used to impute Fuel Models and other light fuel vegetative data (described in the next series of slides). This is done via ‘tools’ in the INFORMS application.

Non-Forest Data Preparation Fuels data is being loaded into the FSVeg database. Summary cover by lifeform (grass, forbs, shrubs, trees). Non-Forest fuels transects as defined by Texas A&M process (described in the next few slides). (Texas A&M BRASS-G website also utilizes this data.) This data is modeled using the Phygrow growth simulator.

BRASS-G Website http://brass.tamu.edu/ BRASS-G: Burning Risk Advisory Supporting System for Grazinglands. BRASS-G is maintained by Texas A&M University. BRASS-G presents an interactive map interface to non- forested vegetation polygons. Polygons are populated using the Most Similar Neighbor process. Burning conditions are updated daily. Every imputed stand has its own unique vegetation modeled using localized weather using Phygrow.

Navigation Map for BRASS-G (Lincoln NF – New Mexico) Polygon shading represents maximum 30-minute burn area predicted for the next week. By double-clicking on a polygon, current burning condition graphs will be displayed.

BRASS-G: Low 30-minute burn spread These graphs represent: 30-minute burn area Spread rate Flame length Fuel Moistures Weather variables The prediction points are graphed in 3-hour increments. The weather is based on 2.5km grids from NOAA.

BRASS-G: High 30-minute burn spread This is based on the actual vegetation grown to this point in time. This is based on weather and soil inputs. 2-3 years of previous daily weather variables are used to grow the plants to their current condition. This is done primarily using soil water budgets.

BRASS-G: Imputed area photos This is a photo taken during sampling. Imputed stands have a photo from the reference stand associated with the record. Click the photo link to see an sample photo of the area.

BRASS-G: Imputed area photos This is a photo taken during sampling. Imputed stands have a photo from the reference stand associated with the record.

Running MSN MSN is run as a tool in INFORMS once all of the data is prepared. ‘Go/no go’ statistics are presented when MSN is run. This advises the user whether the MSN run should be used for further analysis. Statistics are also produced for specific vegetation attributes resulting from the MSN run. (e.g. basal area, stand height, etc.) Bullet point notes: MSN can be run without INFORMS. In this process, MSN is run inside INFORMS.

MSN Results The NRIS FSVeg database contains a table, NRV_MSN_FOR_USE, that stores the MSN results. This table contains a list of links of un-sampled stand polygons pointing them to their ‘most similar neighbor’ with sampled data. This process allows INFORMS tools to use imputed data without loading hypothetical data into the corporate FSVeg stand and tree tables. Bullet Point 3: Data is not loaded….

MSN Report for Forested Vegetation Produced with each MSN run is the MSN Report. It is a text file summarizing the key elements of the MSN run. These are the attributes in the vegetation map.

MSN Forest Vegetation Quality This is presented so the user can understand the quality of each individual stand imputation according to the statistics within MSN. Gray = Reference (sampled) stands Green = OK Quality (Imputed) Red = Poor Quality (Imputed) Yellow = Non-Veg (rocks, lakes, etc.) Brown = Non-Forest (grass, shrubs, etc.)

Reference Stands NRV_MSN_FOR_USE Reference Stands Stands with sampled data. Note: FOR_GIS_LINK and USE1_GIS_LINK are the same.

Imputed Stands NRV_MSN_FOR_USE Imputed Stands Stands that have not been sampled. Note: FOR_GIS_LINK and USE1_GIS_LINK are different. USE1_GIS_LINK is the best match.

Imputed MSN Stands Red = MSN Imputed Stands Blue = FSVeg Stand Exams Yellow = No Data

How does MSN help you? Provides a method to easily maintain and annually update a current vegetation layer. Provides a current wall-to-wall vegetation layer containing base-scale attributes. Allows a site to grow the current vegetation layer forward into the future using FVS for analysis of future conditions. Some examples are: Current and Future Fire Regime Condition Class (FRCC) Current and Future Vegetative Structural Stage (VSS) Current and Future individual stand burning conditions Allows for modeling of treatments to the vegetation layer for NEPA analysis and impacts evaluation.

Vegetation Layer The results from FVS and MSN are used to generate current and future vegetation layers for each decade in the simulation. Fuel Model – Same Year Without MSN With MSN If the tool is run in INFORMS, these layers are created.

Base FVS Vegetation Layer A wall-to-wall base vegetation layer is built for each decade in the simulation. This layer contains information such as basal area, stand height, qmd, canopy cover and more.

MSN Accuracy Assessment An accuracy assessment methodology exists for MSN as used in INFORMS. Cooperators were: Natural Resource Information System (NRIS) Rocky Mountain Research Station (RMRS) Remote Sensing Applications Center (RSAC) Plans are to complete an accuracy assessment on the following forests: Carson National Forest (Region 3) Deschutes National Forest (Region 6) After several assessments are complete, this should provide a standard by which to evaluate and improve base vegetation layer maps.

Alternative Building in INFORMS Vegetative future conditions are created by defining alternatives and applying vegetative treatment prescriptions. There are three methods for applying prescriptions to a stand or a portion of a stand. A tool is available to split a stand. If MSN analysis is used, prescriptions can be applied to imputed stands (stands which do not have a stand exam in FSVeg). Treatments are applied through FVS keyword files. This changes future condition vegetative values.

Prescription Assignment You can point and click on a stand in the ArcView map.

Prescription Assignment: View Results You can overlay a map with codes

Basal Area Before and After Treatment – Same Year Fuels Reduction Thinning Treatment No Treatment

Summary INFORMS and MSN currently provide a methodology for vegetative treatments and fuels analysis. INFORMS provides the ability to produce multiple alternatives for various treatment scenarios. MSN provides the ability to do landscape-level fuels analysis (i.e. fire spread). Several MSN accuracy assessments are currently being completed to provide ID teams with more defensible results.

For More Information Steve Williams/Eric Twombly – Project Leads Lynne Bridgford – Developer Jonathan Marston – Developer Web: fsweb.nris.fs.fed.us/products/INFORMS