Updated Cover Type Map of Cloquet Forestry Center For Continuous Forest Inventory

Introduction  Cloquet Forestry Center founded in 1909  Carlton County  3,506 acres of land  Continuous Forest Inventory (CFI) established in 1959  Conducted ever 5-10 years  402 permanent plots  Tree-level measurements and observations

Location of Cloquet Forestry Center

Introduction (Contd..) Goals of Inventory:  Demonstrate systematic random sampling with random starts  Provide up-to-date inventory statistics for management purposes  Examine techniques for surveying mortality and related forest characteristics  Contrast estimates obtained from fixed and variable radius plots

Introduction (Contd..) Why Continuous Forest Inventory?  Assess management schemes over time  Adapt management based on inventory findings  Identify trends throughout time  Create models to predict future changes in forests

Objective  Develop an updated cover type map of Cloquet Forestry Center using eCognition  Enhance understanding of continuous forest inventory  Aid in field measurement acquisition

Data Sources Detailed satellite imagery of Cloquet Forestry Center were obtained from:  Light Detection and Ranging (LiDAR) 2011 Minnesota Geospatial Information Office  National Agricultural Imagery Program (NAIP) 2013 National Association of Regional Councils’ Geospatial Data Gateway 1 meter resolution 4 band; Red,Green, Blue, Near Infrared (USDA, 2013)

Data Sources (Contd..)  Color InfraRed (CIR) MnGEO NAIP Imagery WebService  CFC Stand boundary layer 2005 vegetation mapping effort at CFC  Roads layer and xy-coordinate of CFI plots

Methods  Object Based Image Analysis was done to create an updated map of Cloquet Forestry Center  NAIP 2013 imagery was used to derive the Normalized Difference Vegetation Index (NDVI)  NDVI was calculated using the equation (NIR-Red)/(NIR+Red) in eCognition  LiDAR 2011 was used to create Digital Elevation Model (DEM), Digital Surface Model (DSM) and Normalized Digital Surface Model (nDSM) from LiDAR data

Figure shows the nDSM image of CFC created using ArcMAP Methods (Contd..)

Description of Rule Set  The forest was first classified into Tall, Short, Vegetation, Low/Non-Vegetation, Grass, Buildings, Roads/Parking lots, Bare Soil, Water/Shadows and Evergreen and Deciduous forest  Multi-Resolution segmentation was used to do the object based image analysis  nDSM was used to distinguish between tall and short objects

Description of Rule Set (Contd..)  In class ‘tall’, NDVI was used to differentiate vegetation from non-vegetation  NIR from NAIP imagery was used to differentiate evergreen vs. deciduous  In class ‘short’, NDVI was used to separate non-vegetation from low vegetation  Water bodies were identified using the brightness level and blue color

Description of Rule Set (Contd..)  Within low vegetation, nDSM was used to differentiate the grasses from shrubs  From class non-vegetation, bare soil and roads were separated  Roads and parking lots were differentiated from bare soil using NIR and brightness  All the values were picked by randomly clicking the cursor over whole image

Figure shows the classification map produced using eCognition Results

Cover type stand map (with stand ID) of CFC Results (Contd..)

Discussion  In theory, eCognition quite simple to use  Practice makes perfect  Problems:  Thresholds  Roads  Shadows (buildings and forest edge)  Updated cover type map good tool for field crews and managers  Accuracy assessment with more time

Acknowledgement  Jo Knight  Don Kilberg  Alan R. Ek  Keith Pelletier

Thank you Questions and Suggestions