Multi-sensor Remote Sensing for Understanding the Interacting Environmental Disturbances of Forest Fire and Plant Disease

1 online resource (162 pages) : PDF

2019

University of North Carolina at Charlotte

Forests are increasingly affected by a variety of environmental disturbances, including emerging infectious diseases (EIDs) and wildfire, which have caused extensive tree mortality in forest biomass worldwide. Those two types of disturbances may interact and lead to unexpected patterns of tree mortality, posing great challenges to sustainable forest management. Compared to high-cost, labor intensive and time consuming field mensuration, remote sensing offers a low-cost, efficient, and timely solution for monitoring the disturbances over large and complex landscapes. However, the potential of remote sensing in understanding interacting disturbances has yet to be well investigated. To bridge the research gap, this study developed several models integrating multi-sensor remote sensing techniques to investigate the response of forest ecosystem to EID-wildfire interactions. There are three research objectives: (i) developing a remote sensing model to detect the long-term spatial patterns of EID-caused tree mortality in the forest that was simultaneously affected by non-EID disturbances; (ii) developing a remote-sensing model to estimate post-fire burn severity in the presence of forest EID; and (iii) assessing the role of wildfire in determining the spread pattern of EID and exploring the consequence of EID-wildfire interaction on forest recovery at the landscape scale. The study was conducted in the Big Sur ecosystem, where sudden oak death and wildfire coexist and cause a large number of tree deaths. For sudden oak death-caused tree mortality estimation, remote sensing and ecological species distribution modeling were integrated to capture the isolated, patchy distribution patterns of sudden oak death for over a decade. The results revealed an annual disease infection rate of 7% from 2005 to 2016, which was consistent with field observations. For fire severity mapping, the proposed Disturbance Weighting Analysis Model (DWAM) decomposed the burn contribution from diseased and non-diseased trees using remote sensing technology and substantially improved the map accuracy. The outcome from DWAM indicated a 42% improvement of RMSE (root mean square error) as compared with a recently developed, high-performance model. Meanwhile, DWAM’s superior performance was consistent across all three disease infection stages. Based on the derived disease and burn severity estimation, my research further found that wildfire played a significant role in shaping the spread patterns of sudden oak death and the forest landscapes were significantly altered even after eight years of fire occurrence when compared to the pre-fire status. This study demonstrates the feasibility of multi-sensor remote sensing in monitoring forest disturbance interactions at the landscape scale.

doctoral dissertations

Geography

Ph.D.

Geography & Urban Regional Analysis

Chen, Gang

Meentemeyer, Ross
Magi, Brian
Allan, Craig
Hong, Tao

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2019.

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He_uncc_0694D_12213

http://hdl.handle.net/20.500.13093/etd:2179