Publications

@article{Chen2017,
title = {Assessing the impact of emerging forest disease on wildfire using Landsat and KOMPSAT-2 data},
author = {Gang Chen and Yinan He and Angela De Santis and Guosheng Li and Richard Cobb and Ross K. Meentemeyer},
url = {https://doi.org/10.1016/j.rse.2017.04.005},
doi = {10.1016/j.rse.2017.04.005},
year = {2017},
date = {2017-06-15},
journal = {Remote Sensing of Environment},
volume = {195},
pages = {218-229},
abstract = {Environmental disturbance regimes are more frequently being altered by historically novel events and disturbance interactions, which may trigger reorganizations of new ecosystem states and processes. Here we examine synergies between emerging forest disease and wildfire to determine whether disease outbreak changes environmental drivers of burn severity using sudden oak death and the basin complex fire in California as a case study of novel disturbance interaction. We mapped the spatial distribution of sudden oak death tree mortality using a new object-based filter with 1.0 m resolution KOMPSAT-2 images. We integrated these data with a physical simulation model of burn severity informed by post-fire Landsat data. Model performance varied across stages of disease establishment (early, middle and late) with stronger relationships occurring during later stages of disease progression. Multiscale statistical analysis of environmental drivers of burn severity in diseased compared to healthy forests showed that sudden oak death tree mortality altered relationships between burn severity and the biophysical environment. Specifically, compared to the healthy forests, those affected by disease exhibited higher landscape heterogeneity at smaller spatial scales (e.g., 25 and 50 m), which has been associated with decreased burn severity in the literature. Our results showed the opposite pattern. That is, a disease-affected landscape comprising less connected patches and higher patch shape complexity was more likely to experience greater burn severity. This suggests that disease-caused increases in surface fuels may have reduced the landscape's resistance to fire and in turn increased burn severity in forest patches neighboring disease-impacted forests.},
keywords = {burn severity, forest disease, interacting disturbances, Landsat, object-based},
pubstate = {published},
tppubtype = {article}
}

@article{Tonini2017,
title = {Tangible geospatial modeling for collaborative solutions to invasive species management},
author = {Francesco Tonini and Douglas Shoemaker and Anna Petrasova and Brendan Harmon and Vaclav Petras and Richard C. Cobb and Helena Mitasova and Ross K. Meentemeyer},
url = {https://doi.org/10.1016/j.envsoft.2017.02.020},
doi = {10.1016/j.envsoft.2017.02.020},
year = {2017},
date = {2017-06-01},
journal = {Environmental Modelling and Software},
volume = {92},
pages = {176-188},
abstract = {Managing landscape-scale environmental problems, such as biological invasions, can be facilitated by integrating realistic geospatial models with user-friendly interfaces that stakeholders can use to make critical management decisions. However, gaps between scientific theory and application have typically limited opportunities for model-based knowledge to reach the stakeholders responsible for problem-solving. To address this challenge, we introduce Tangible Landscape, an open-source participatory modeling tool providing an interactive, shared arena for consensus-building and development of collaborative solutions for landscape-scale problems. Using Tangible Landscape, stakeholders gather around a geographically realistic 3D visualization and explore management scenarios with instant feedback; users direct model simulations with intuitive tangible gestures and compare alternative strategies with an output dashboard. We applied Tangible Landscape to the complex problem of managing the emerging infectious disease, sudden oak death, in California and explored its potential to generate co-learning and collaborative management strategies among actors representing stakeholders with competing management aims.},
keywords = {forest disease, geospatial modeling, landscape epidemiology, participatory research, stakeholder engagement, tangible user interface},
pubstate = {published},
tppubtype = {article}
}

@article{Chen2015b,
title = {Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery},
author = {Gang Chen and Margaret R. Metz and David M. Rizzo and Whalen W. Dillon and Ross K. Meentemeyer},
url = {http://www.sciencedirect.com/science/article/pii/S0924271615000210},
doi = {doi:10.1016/j.isprsjprs.2015.01.004},
year = {2015},
date = {2015-02-06},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {102},
pages = {38-47},
abstract = {Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively similar performance, where both spectral responses and texture contributed to burn assessments. However, the application of PCA and MNF introduced much greater variation among models across the three stages. For the early-stage disease progression, neither band reduction algorithms improved or retained the accuracy of burn severity modeling (except for the use of 10 MNF components). Compared to the no-band-reduction scenario, band reduction led to a greater level of overestimation of low-degree burns and underestimation of medium-degree burns, suggesting that the spectral variation removed by PCA and MNF was vital for distinguishing between the spectral reflectance from disease-induced dried crowns (still retaining high structural complexity) and fire ash. For the middle-stage, both algorithms improved the model R2 values by 2–37%, while the late-stage models had comparable or better performance to those using the original 50 spectral bands. This could be explained by the loss of tree crowns enabling better signal penetration, thus leading to reduced spectral variation from canopies. Hence, spectral bands containing a high degree of random noise were correctly removed by the band reduction algorithms. Compared to the middle-stage, the late-stage forest stands were covered by large piles of fallen trees and branches, resulting in higher variability of MASTER imagery. The ability of band reduction to improve the model performance for these late-stage forest stands was reduced, because the valuable spectral variation representing the actual late-stage forest status was partially removed by both algorithms as noise. Our results indicate that PCA and MNF are promising for balancing computation efficiency and the performance of burn severity models in forest stands subject to the middle and late stages of sudden oak death disease progression. Compared to PCA, MNF dramatically reduced image spectral variation, generating larger image objects with less complexity of object shapes. Whereas, PCA-based models delivered superior performance in most evaluated cases suggesting that some key spectral variability contributing to the accuracy of burn severity models in diseased forests may have been removed together with true spectral noise through MNF transformations.},
keywords = {band reduction, burn severity, forest disease, GEOBIA, interacting disturbances, MNF, PCA},
pubstate = {published},
tppubtype = {article}
}

@article{Dillon2013,
title = {Rangewide risks to a foundation tree species from disturbance interactions},
author = {Whalen W. Dillon and John B. Vogler and Richard C. Cobb and David M. Rizzo and Ross K. Meentemeyer},
url = {http://dx.doi.org/10.3120/0024-9637-60.2.139},
doi = {10.3120/0024-9637-60.2.139},
year = {2013},
date = {2013-04-01},
journal = {Madrono},
volume = {60},
number = {2},
pages = {139-150},
abstract = {The geographic range of tanoak, Notholithocarpus densiflorus (Hook. & Arn.) Manos, Cannon & S. H. Oh (Fagaceae), encompasses tremendous physiographic variability, diverse plant communities, and complex disturbance regimes (e.g., development, timber harvest, and wildfire) that now also include serious threats posed by the invasive forest pathogen Phytophthora ramorum S. Werres, A.W.A.M. de Cock. Knowing where these disturbance factors interact is critical for developing comprehensive strategies for conserving the abundance, structure, and function of at-risk tanoak communities. In this study, we present a rule-based spatial model of the range-wide threat to tanoak populations from four disturbance factors that were parameterized to encode their additive effects and two-way interactions. Within a GIS, we mapped threats posed by silvicultural activities; disease caused by P. ramorum; human development; and altered fire regimes across the geographic range of tanoak, and we integrated spatially coinciding disturbances to quantify and map the additive and interacting threats to tanoak. We classified the majority of tanoak's range at low risk (3.7 million ha) from disturbance interactions, with smaller areas at intermediate (222,795 ha), and high (10,905 ha) risk. Elevated risk levels resulted from the interaction of disease and silviculture factors over small extents in northern California and southwest Oregon that included parts of Hoopa and Yurok tribal lands. Our results illustrate tanoak populations at risk from these interacting disturbances based on one set of hypothesized relationships. The model can be extended to other species affected by these factors, used as a guide for future research, and is a point of departure for developing a comprehensive understanding of threats to tanoak populations. Identifying the geographic location of disturbance interactions and risks to foundation species such as tanoak is critical for prioritizing and targeting conservation treatments with limited resources.},
keywords = {decision support system, forest disease, forest ecosystems, forest fire, foundation species, landscape epidemiology, Notholithocarpus densiflorus, sudden oak death},
pubstate = {published},
tppubtype = {article}
}