Vegetation Modeling With NAIP Color IR Imagery

Vegetation Modeling With NAIP Color IR Imagery 2012 Washington GIS Conference Tacoma WA Chris Behee GISP, GIS Analyst City of Bellingham Planning & Community Development

Outline What is NAIP? How do you get NAIP? Characteristics of 4 Band Imagery Demystifying the NDVI Creating a Vegetation Layer Image Classification Tools & Methods Texture Analysis

What is NAIP?

NAIP is a program to acquire peak growing season leaf on imagery, and deliver this imagery to USDA County Service Centers, in order to maintain the common land unit (CLU) boundaries and assist with farm programs. The goal of NAIP is to collect 1 meter imagery for the entire conterminous United States. The imagery is either natural color or four band imagery, and is delivered in the year of acquisition. NAIP imagery collection began with a pilot program in 2001 and is now collected every 2 years on a state bystate basis.

NAIP imagery for Washington State 2003 2m, Partial Coverage, Natural Color 2004 2m, Partial Coverage, Natural Color 2005 2m, Partial Coverage, Natural Color 2006 1m, Full Coverage, Natural Color 2009 1m, Full Coverage, 4 Band 2011 1m, Full Coverage, 4 Band 2013 2015

Order NAIP Quarter Quad Imagery Online https://coes.apfo.usda.gov/index.html Natural Color countywide SID Mosaics are FREE!!!! 4 Band Imagery by Quarter Quad available for a small charge.

Characteristics of 4 Band Imagery

Spectral Ranges of Typical Aerial Imagery Nm 100 200 300 400 500 600 700 800 900 1,000 1,100 1,200 ULTRAVIOLET VISIBLE LIGHT NEAR INFRARED FAR INFRARED 3 2 1 Panchromatic Film 3 2 1 Natural Color Black & White Color Infrared

Visual Blue

Visual Green Green Band

Visual Red

Near Infrared

RGB Natural Color Composite

RGB Color IR Composite

Vegetation Modeling With NAIP Color IR Imagery RGB Natural Color Composite

Vegetation Modeling With NAIP Color IR Imagery

RGB Color IR Composite

Demystifying the NDVI

Sunlight Visible red is weakly reflected. Infrared is strongly reflected. Leaf

Use this difference Water

How a Vegetation Index Works Red light image. Red light is strongly absorbed by photosynthetic pigments (i.e. chlorophyll a) found in green leaves. Here we have an image of 2 green kale leaves and a 3rd kale leaf that yellowed with just a spot of green remaining. Near-infrared image. IR light either passes through or is reflected by live leaf tissues regardless of their color. Note that the background soil (non-vegetated area) appears similar to the red light image. Vegetation index image. If we subtract the red light image from the near-infrared image, everything that has about the same brightness level in the two wavelengths becomes dark, and everything that is brighter in the near-infrared becomes light. Notice that even the ribs of the leaves disappear since there is no chlorophyll in that part of the leaf. The small green patch is the only part of the yellowed leaf that is still visible.

For Example: Buildings & Pavement Living Vegetation Rocks/Soil/Dead Vegetation Near Infrared Band 200 215 75 Red Band 225 45 65 NIR - Red -25 170 10

Normalized Difference Vegetation Index (NDVI) This is one of the most commonly used indices. The difference in reflectances is divided by the sum of the two reflectances. This compensates for different amounts of incoming light and produces a number between -1 and 1. The typical range of actual values is about 0.1 for bare soils to 0.9 for dense, healthy vegetation. NDVI = (NIR - Red) (NIR + Red)

Vegetation Modeling With NAIP Color IR Imagery NDVI displayed using std. NDVI Shaderamp

Creating A Vegetation Layer

http://video.arcgis.com/watch/392/creation of thevegetation layer for the community map v10

Or you can use the NDVI function in the ArcGIS 10 Mosaic Dataset Properties

Vegetation Modeling With NAIP Color IR Imagery Or you can use the Raster Calculator in Spatial Analyst

The resulting image is the same, regardless of the tool you use.

HSV color values for Community Maps vegetation layer.

Vegetation Layer ready for inclusion with Community Maps project.

Image Classification Tools & Methods

Supervised or Unsupervised? Create Signatures Edit Signatures Maximum Likelihood Classification Iso Cluster Maximum Likelihood Classification

Unsupervised Classification is more reliable for 4 band imagery where only one infrared band is present. (DISCLAIMER this is my experience, yours may vary) If using imagery with more than 4 spectral bands, better results might be obtained with supervised classification using well chosen signature training sites. This also takes more time.

Image Bands to Use for Unsupervised Classification Band 1 (Blue) Band 2 (Green) Band 3 (Red) Band 4 (Near IR) NDVI (Integer, 0 255) Iso Cluster Unsupervised Classification

Results in 50 classes of? Need to sort them into meaningful groups.

Classes sorted into: Conifers Dedicuous Grass/Low shrub Bare Soil/Dry grass Urban/Pavement/Rock Water Shadow Unclassified (< 1%)

Conifers are well defined. Grass/Shrub/Deciduous are not. Need a way to separate these classes.

Texture Analysis

Texture Analysis Bibliography Texture Analsyis for Mapping Tamarix Parviflora Using Aerial Photographs Along The Cache Creek, California. Shaokui Ge, Raymond Carruthers, Peng Gong and Angelica Herrera. UC Berkeley & USDA. Texture Integrated Classification of Urban Treed. Areas in High Resolution Color Infrared Imagery. Yun Zhang. University of New Brunswick. The Effectiveness of Texture Analysis for Mapping Forest Land Using the Panchromatic Bands of Landsat 7, SPOT, and IRS Imagery. Michael L. Hoppus, Rachel I. Riemann, Andrew J. Lister, and Mark V. Finco. USDA Forest Service. Urban cover mapping using digital, high spatial resolution aerial imagery. Soojeong Myeong, David J. Nowak, Paul F. Hopkins and Robert H. Brock. SUNY College of Environmental Science and Forestry.

Texture analysis is essentially the idea that the magnitude and pattern of variability within an image can help tell us what we are looking at. Not just color, but also shape.

For Example: A flat grass, or low vegetation raster.

For Example: And a tree crown raster

The range of values for smoother land cover classes is less than that for rougher classes. Range = Neighborhood Max. Neighborhood Min. Neighborhood Range = 5 Neighborhood Range = 48

Use the Focal Statistics tool in the Spatial Analyst Neighborhood toolbox.

7x7 pixel neighborhood selected because in 1 meter resolution imagery a mature tree crown has a radius of about 7 pixels.

Resulting image yields good separation between forested areas with shadowed tree crowns, and evenly illuminated (smooth) low grass/shrub areas.

Create Focal Range images for Band 4 (Near IR), and Band2 (visual green). Then use Raster Calculator to derive the average of the two resulting images.

Now add the new Texture image to the Unsupervised Classification Band 1 (Blue) Band 2 (Green) Band 3 (Red) Band 4 (Near IR) NDVI (Integer, 0 255) Texture Image Iso Cluster Unsupervised Classification

Sort the classes again

Classes sorted into: Conifers Dedicuous Grass/Low shrub Bare Soil/Dry grass Urban/Pavement/Rock Water Shadow Unclassified (< 1%)

Now the Grass/Low shrub class is clearly separated from the Deciduous tree/forest class.

Concluding thoughts Using texture analysis is an effective technique in helping define classes in image processing. Texture analysis does introduce some class ambiguity and edge effects in land cover transition areas (i.e. edge of forest). Other methods include use of LIDAR derived canopy height; if it is available, and acquired within an acceptable timeframe to your imagery.

Thanks! Chris Behee GISP, GIS Analyst City of Bellingham Planning & Community Development cbehee@cob.org 360 778 8346