Vegetation Modeling With NAIP Color IR Imagery

Transcription

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

2 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

3 What is NAIP?

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5 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.

6 NAIP imagery for Washington State m, Partial Coverage, Natural Color m, Partial Coverage, Natural Color m, Partial Coverage, Natural Color m, Full Coverage, Natural Color m, Full Coverage, 4 Band m, Full Coverage, 4 Band

7 Order NAIP Quarter Quad Imagery Online Natural Color countywide SID Mosaics are FREE!!!! 4 Band Imagery by Quarter Quad available for a small charge.

8 Characteristics of 4 Band Imagery

9 Spectral Ranges of Typical Aerial Imagery Nm ,000 1,100 1,200 ULTRAVIOLET VISIBLE LIGHT NEAR INFRARED FAR INFRARED Panchromatic Film Natural Color Black & White Color Infrared

10 Visual Blue

11 Visual Green Green Band

12 Visual Red

13 Near Infrared

14 RGB Natural Color Composite

15 RGB Color IR Composite

16 Vegetation Modeling With NAIP Color IR Imagery RGB Natural Color Composite

17 Vegetation Modeling With NAIP Color IR Imagery RGB Natural Color Composite

18 Vegetation Modeling With NAIP Color IR Imagery

19 RGB Color IR Composite

20 Demystifying the NDVI

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

24 Use this difference Water

25 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.

26 For Example: Buildings & Pavement Living Vegetation Rocks/Soil/Dead Vegetation Near Infrared Band Red Band NIR - Red

27 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)

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

29 Creating A Vegetation Layer

30 of thevegetation layer for the community map v10

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36 Vegetation Modeling With NAIP Color IR Imagery

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

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

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

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44 HSV color values for Community Maps vegetation layer.

45 Vegetation Layer ready for inclusion with Community Maps project.

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47 Vegetation Modeling With NAIP Color IR Imagery

48 Image Classification Tools & Methods

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

50 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.

51 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

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

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

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

56 Texture Analysis

57 Vegetation Modeling With NAIP Color IR Imagery

58 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.

59 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.

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

61 For Example: And a tree crown raster

62 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

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

64 7x7 pixel neighborhood selected because in 1 meter resolution imagery a mature tree crown has a radius of about 7 pixels.

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

66 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.

67 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

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69 Sort the classes again

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

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

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73 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.

74 Thanks! Chris Behee GISP, GIS Analyst City of Bellingham Planning & Community Development