Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Axinn says data show incidents of sexual assault start at 'very young age'

Miech on 'generational forgetting' about drug-use dangers

Impacts of H-1B visas: Lower prices and higher production - or lower wages and higher profits?

More News


Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

More Highlights

Next Brown Bag

Mon, Feb 13, 2017, noon:
Daniel Almirall, "Getting SMART about adaptive interventions"

Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns

Publication Abstract

Laurent, E.J., H.J. Shi, D. Gatziolis, J.P. LeBouton, M.B. Walters, and Jianguo Liu. 2005. "Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns." Remote Sensing of Environment, 97(2): 249-262.

We investigated the potential of using unclassified spectral data for predicting the distribution of three bird species over a similar to 400,000 ha region of Michigan's Upper Peninsula using Landsat ETM+ imagery and 433 locations sampled for birds through point count surveys. These species, Black-throated Green Warbler, Nashville Warbler, and Ovenbird, were known to be associated with forest understory features during breeding. We examined the influences of varying two spatially explicit classification parameters on prediction accuracy: 1) the window size used to average spectral values in signature creation and 2) the threshold distance required for bird detections to be counted as present. Two accuracy measurements, proportion correctly classified (PCC) and Kappa, of maps predicting species' occurrences were calculated with ground data not used during classification. Maps were validated for all three species with Kappa values >03 and PCC >0.6. However, PCC provided little information other than a summary of sample plot frequencies used to classify species' presence and absence. Comparisons with rule-based maps created using the approach of Gap Analysis showed that spectral information predicted the occurrence of these species that use forest subcanopy components better than could be done using known land cover associations (Kappa values 0.1 to 0.3 higher than Gap Analysis maps). Accuracy statistics for each species were affected in different ways by the detection distance of point count surveys used to stratify plots into presence and absence classes. Moderate-to-large detection distances (100 in and 180 in) best classified maps of Black-throated Green Warbler and Nashville Warbler occurrences, while moderate detection distances (50 in and 100 in), which ignored remote observations, provided the best source of information for classification of Ovenbird occurrence. Window sizes used in signature creation also influenced accuracy statistics but to a lesser extent. Highest Kappa values of majority maps were typically obtained using moderate window sizes of 9 to 13 pixels (0.8 to 1.2 ha), which are representative of the study species territory sizes. The accuracy of wildlife occurrence maps classified from spectral data will therefore differ given the species of interest, the spatial precision of occurrence records used as ground references and the number of pixels included in spectral signatures. For these reasons, a quantitative examination is warranted to determine how subjective decisions made during image classifications affect prediction accuracies.

DOI:10.1016/j.rse.2005.04.015 (Full Text)

Country of focus: United States of America.

Browse | Search : All Pubs | Next