Estimating pinyon and juniper cover across Utah using NAIP imagery

Darrell B. Roundy; April Hulet; Bruce A. Roundy; Ryan R. Jensen; Jordan B. Hinkle; Leann Crook; Steven L. Petersen; Darrell B. Roundy; April Hulet; Bruce A. Roundy; Ryan R. Jensen; Jordan B. Hinkle; Leann Crook; Steven L. Petersen

doi:10.3934/environsci.2016.4.765

AIMS Environmental Science

2016, Volume 3, Issue 4: 765-777. doi: 10.3934/environsci.2016.4.765

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Research article Special Issues

Estimating pinyon and juniper cover across Utah using NAIP imagery

1.
Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA
2.
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID, 83844-1133, USA
3.
Department of Geography, Brigham Young University, Provo, UT, 84602, USA
4.
Research Assistant, Chandler, AZ, 85244, USA
5.
Research Assistant, Madison, WI, 53713, USA

Received: 15 August 2016 Accepted: 09 November 2016 Published: 17 November 2016

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature Analyst^TMfor ArcMap 10.1^®, ENVI Feature Extraction^®, and Trimble eCognition Developer 8.2^®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst^TM, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature Analyst^TM, and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel’s oak), which had similar spectral values as P-J.
- Object-based image analysis,
- canopy cover,
- National Agriculture Imagery Program,
- eCognition,
- Feature Analyst^TM,
- ENVI Feature Extraction
Citation: Darrell B. Roundy, April Hulet, Bruce A. Roundy, Ryan R. Jensen, Jordan B. Hinkle, Leann Crook, Steven L. Petersen. Estimating pinyon and juniper cover across Utah using NAIP imagery[J]. AIMS Environmental Science, 2016, 3(4): 765-777. doi: 10.3934/environsci.2016.4.765

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Abstract

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature Analyst^TMfor ArcMap 10.1^®, ENVI Feature Extraction^®, and Trimble eCognition Developer 8.2^®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst^TM, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature Analyst^TM, and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel’s oak), which had similar spectral values as P-J.

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