Low-level Adelges tsugae infestation detection in New England through partition modeling of Landsat data

REMOTE SENSING OF ENVIRONMENT

卷:190

Article

Williams, Justin P.¹  Hanavan, Ryan P.²  Rock, Barrett N.³  Minocha, Subhash C.⁴  Linder, Ernst⁵ 

[1] Univ New Hampshire, Dept Nat Resources & Environm, James Hall, Durham, NH 03824 USA

[2] US Forest Serv, Forest Hlth Protect, 271 Mast Rd, Durham, NH 03824 USA

[3] Univ New Hampshire, Earth Syst Res Ctr, Morse Hall, Durham, NH 03824 USA

[4] Univ New Hampshire, Dept Biol Sci, Rudman Hall, Durham, NH 03824 USA

[5] Univ New Hampshire, Dept Math & Stat, Kingsbury Hall, Durham, NH 03284 USA

关键词: Landsat;    Adelges tsugae;    Invasive species;    NDVI;    Decision tree classification;   

DOI  :  10.1016/j.rse.2016.12.005

来源: Elsevier

【 摘 要 】

The hemlock woolly adelgid (HWA) (Hemiptera: Adelges tsugae Annand) is an invasive insect causing damage to Eastern (Tsuga canadensis (L) Carr.) and Carolina (Tsuga caroliniana Engelm.) hemlock trees in the eastern United States. Maine and New Hampshire are currently the northernmost front of HWA's range. Developing methods to locate newly infested stands is paramount in the effort to monitor HWA range expansion; presently, the most reliable method of detection requires extensive on-the-ground manual surveying. Field surveys for invasive pests like HWA consume multiple resources, limiting the amount of area that can be surveyed, and the results often misrepresent the true extent or patchiness of an invasion. Satellite based remote sensing, using vegetation indices, enables us to detect insect driven changes in forest health at a landscape scale. Our objectives were to classify HWA infested hemlock stands along the infestation front and demonstrate how the classification product could be used to improve HWA survey planning. Our workflow consisted of 1) modeling hemlock habitat suitability using a Maximum Entropy algorithm; 2) developing decision tree rules to classify likely infested stands from a Landsat time series, using the habitat suitability model to mask out non-hemlock areas; and 3) field check the final classification product. The hemlock habitat suitability model attained an overall accuracy of 68.2%. Partitioning of leaf-on multi-year (1995-2013) Landsat 5 and 8 data resulted in seven probability of infestation classes with a training R-2 = 0.782. Agreement between the classification and previously reported HWA infestations was 75.0% in conifer forests, 33.3% in mixed forests and 50.0% in deciduous forests. Agreement between the classification and test-survey locations was 78.6%; verified new infestations were detected as far as 19 km away from previously reported infestations. The methods presented outline how Landsat could be used to detect low-level HWA infestations. Classification products such as this ultimately could be used by federal and state agencies to target specific areas for efficient survey, suppression, and eradication efforts. Published by Elsevier Inc.

【 授权许可】

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