Characterization of Ash (Fraxinus spp.) in a Riparian Forest in Southeastern Michigan Using Spectral and Physical Variable Models

Ash;Riparian Forest;Modeling;Landsat;Natural Resources and Environment

Williams, PatrickBrown, Dan ;

University of Michigan

关键词: Ash;    Riparian Forest;    Modeling;    Landsat;    Natural Resources and Environment;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/94537/Williams_Patrick_Thesis_December_2012.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The emerald ash borer (Agrilus planipennis Fairmarie) has killed tens of millionsof ash (Fraxinus spp.) trees in Michigan alone. Riparian and lowland areas typicallycontain large proportions of ash and have been especially affected in SoutheastMichigan. The loss of up to 20-60% of the overstory has significant implications forforest succession and floodplain stability. The goal of this study was to identify theproportion and evaluate the status of ash in a Southeast Michigan riparian forestcommunity and to develop a minimally field-intensive GIS/ remote sensing method foridentifying dominant ash populations employing multiple linear regression (MLR) andbinary logistic regression (LR).I gathered a local sample of nearly 1000 ash trees at 60 locations within theSharonville State Game Area, Washtenaw County, Michigan, and combined this datawith Landsat remotely sensed imagery and physical map-based variables in an effort tomodel ash population distributions. Landsat imagery and derived products wereevaluated for their ability to segregate an ash spectral signature, while the map-basedvariables were evaluated for their ability to represent local hydrologic conditionsinterpreted from the autecology of ash species. An existing, ash containing lowlanddeciduous forest classification for Michigan (IFMAP) was also evaluated for its ability topredict ash presence/ absence.Ash mortality comprised a total of 17% of the sampled deciduous forest withvirtually all trees deceased and symptomatic of emerald ash borer infestation. The MLRand LR predictive models generally out-performed IFMAP in predicting ashiiipresence/absence. A single Landsat scene was generally unable to distinguish an ashrelated spectral signature, though elevation based variables contributed to successfulprediction of ash presence with up to 91.7% accuracy. For the successful prediction ofash percent coverage, hyperspectral remotely sensed imagery would likely benecessary.