Overall accuracies for forest cover type classification, 722 subplots

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

1 2 3 4 5 6 7 8 9 10

Number of nearest neighbors used

Ove

rall

accu

racy

Upland, March-April-May-Julyimages

Lowland, March-April-May-Julyimages

Upland, March-April-May images

Lowland, March-April-May images

Upland, own cluster excluded, 4dates

Lowland, own cluster excluded, 4dates

Upland, own cluster excluded, 3dates

Lowland, own cluster excluded, 3dates

Cross-validation RMSE's for volume, 696 subplots

50

55

60

65

70

75

80

85

90

95

1 2 3 4 5 6 7 8 9 10

Number of nearest neighbors used

RM

SE

, m

3/h

a

Standard deviation of data

Mean of data

RMSE, all subplots used

RMSE, own cluster excluded

RMSE, subplots closer than 40meters not used

RMSE, simple locationoptimization used

These early results were obtained using one year’s set of FIA field data,

DISTANCE: EUCLIDEANWEIGHTING FUNCTION: NO WEIGHTS.NUMBER OF PLOTS: 696NUMBER OF BANDS: 34NUMBER OF NEIGHBORS: 1VOLUME CROSS-VALIDATION RMSE (ALL) = 81.1354 m3 ,126.495 %VOLUME ALL MIN AND MAX =0 and 384.71VOLUME ALL MEAN = 64.1413VOLUME STANDARD DEVIATION (ALL) = 70.0947VOLUME BIAS (ALL) = -1.40832

Class\Ref 0-40 41-80 8 1-120 121-160 >160 User’s acc 0-40 217 54 35 12 23 0.6363 41-80 47 35 17 16 10 0.28 81-120 40 14 24 12 15 0.2285121-160 15 20 13 7 8 0.1111 >160 17 10 9 11 15 0.2419Prod.acc 0.645833 0.263158 0.244898 0.12069 0.211268-------------------------------------------------------------------- VOLUME CROSS-VALIDATION RMSE (LOWLAND) = 66.624 m3 ,154.966 %NUMBER OF PLOTS (LOWLAND) = 164VOLUME LOWLAND MIN AND MAX = 0 and 254.07VOLUME LOWLAND MEAN = 42.9926VOLUME STANDARD DEVIATION (LOWLAND) = 53.712VOLUME BIAS (LOWLAND) = -2.87561

Class\Ref 0-40 41-80 8 1-120 121-160 >160 User’s acc0-40 82 8 12 3 5 0.745441-80 7 7 3 0 1 0.388881-120 9 4 6 3 1 0.2608121-160 4 0 2 0 0 0>160 4 1 1 0 1 0.1666Prod.acc 0.773585 0.350 0.250 0.000 0.125--------------------------------------------------------------------VOLUME CROSS-VALIDATION RMSE (UPLAND) = 86.8592 m3 ,122.924 %NUMBER OF PLOTS (UPLAND) = 532VOLUME UPLAND MIN AND MAX = 0 and 384.71VOLUME UPLAND MEAN = 70.6608VOLUME STANDARD DEVIATION (UPLAND) = 73.2422VOLUME BIAS (UPLAND) = -0.0196241

Class\Ref 0-40 41-80 8 1-120 121-160 >160 User’s acc0-40 134 42 23 9 20 0.587741-80 42 29 13 16 7 0.27181-120 27 14 20 9 13 0.2409121-160 12 18 11 7 9 0.1228>160 15 10 7 11 14  0.2456Prod.acc 0.582609 0.256637 0.27027 0.134615 0.222222

Wall-to-wall extension of Forest Inventory and Analysis:Wall-to-wall extension of Forest Inventory and Analysis:K-nearest neighbor estimation and classification

3. Early results concerning accuracy, imagery and data

Reija Haapanen, Alan Ek, Marvin Bauer, Kali Sawaya -- Department of Forest Resources, University of Minnesota

• The new FIA 4-subplot data has some special features due to the close proximity of subplots; in forest cover type classification, the nearest neighbors tend to be in the same cluster. When use of neighboring subplots is prohibited, the accuracies fall to 30%

• Of the satellite imagery tested (March, April, May and July combined), the first three dates tend to carry enough information for cover type classification• Upland-lowland stratification improves forest cover type classification

Example of program outputs showing volume accuracies

only 696 subplots for our image. The volume accuracies are quite low partly due to the small number of plots. However, by using simple optimization of the field plot locations, the errors drop considerably.

Field measurements

Large-area statistics &

error estimations

Preprocessed image & field data combined

Preprocessed field data

Statistics for small area

(e.g., county)

Thematicforest maps

Image analysis:estimation & classification

Preprocessed image data

Original satellite image

Preprocessing

New FIA 4-subplot field cluster design

Spectral band 1 of Landsat 7 ETM+satellite image

A georeferenced Landsat 7 ETM+ image, cloudsexcluded. The black dots represent FIA field plot clusters

The Minnesota land use/land cover classification

Cover type classification

Post-processing

‘‘kNN’kNN’

2. Flow of the kNN process and materials used

• The kNN estimation and classification method is a simple, but very powerful way to extend a wide range of field data to landscapes

• The method assigns a pixel the field data of the most similar pixel, for which actual field plot data exists. The similarity is defined in terms of the feature space (e.g., Euclidean distance between satellite image pixels)

• kNN retains the full range of variability inherent in the sample

Forest/ non-forest

stratification

Research funding provided by:

The USDA Forest Service Forest Inventory and Analysis program (FIA) has been conducting state-by-state and ultimately nationwide forest inventories for decades.  Yet these field plot based inventories have not been able to produce precise county and local estimates and useful operational maps.  Additionally, traditional satellite-based forest classifications have been unable to match detailed forest type identification with ground based survey definitions to provide for interpolation and extrapolation of FIA data.  Precise classification has been limited to general or aggregate classes of little use for improving inventory precision and providing truly useful operational forest maps.  

The k-nearest neighbors approach (kNN), adapted from the Finnish Forest Research Institute, offers a means of applying satellite and GIS data so as to impute forest cover type, timber volume, and other FIA data from field plots surrounding large or small areas on the basis of the spectral characterization of neighboring pixels.  To the extent that such post-stratification can be successfully applied, the method offers agencies and industry a) greater precision at survey unit to local levels of estimation, and b) detailed inventory attributes within type polygons over large areas.   In fact, our researchers can map virtually any FIA plot attribute.  The method produces estimates and maps according to the actual inventory classifications and definitions rather than an abstract set that must later be reinterpreted.

1. Rationale

Photo courtesy of Dave Hansen