Publications
2017 |
Chen, Gang; He, Yinan; Santis, Angela De; Li, Guosheng; Cobb, Richard; Meentemeyer, Ross K Assessing the impact of emerging forest disease on wildfire using Landsat and KOMPSAT-2 data Journal Article Remote Sensing of Environment, 195 , pp. 218-229, 2017. Abstract | Links | BibTeX | Tags: burn severity, forest disease, interacting disturbances, Landsat, object-based @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} } 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. |
2013 |
Meentemeyer, Ross K; Tang, Wenwu; Dorning, Monica A; Vogler, John B; Cunniffe, Nik J; Shoemaker, Douglas A FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm Journal Article Annals of the Association of American Geographers, 103 (4), pp. 785-807, 2013, ISSN: 0004-5608. Abstract | Links | BibTeX | Tags: fragmentation, land change, nonstationarity, object-based, region growing algorithm @article{Meentemeyer2013, title = {FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm}, author = {Ross K. Meentemeyer and Wenwu Tang and Monica A. Dorning and John B. Vogler and Nik J. Cunniffe and Douglas A. Shoemaker}, url = {http://www.tandfonline.com/doi/abs/10.1080/00045608.2012.707591}, doi = {10.1080/00045608.2012.707591}, issn = {0004-5608}, year = {2013}, date = {2013-08-01}, journal = {Annals of the Association of American Geographers}, volume = {103}, number = {4}, pages = {785-807}, abstract = {We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.}, keywords = {fragmentation, land change, nonstationarity, object-based, region growing algorithm}, pubstate = {published}, tppubtype = {article} } We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes. |
| 1. | Chen, Gang; He, Yinan; Santis, Angela De; Li, Guosheng; Cobb, Richard; Meentemeyer, Ross K: Assessing the impact of emerging forest disease on wildfire using Landsat and KOMPSAT-2 data. In: Remote Sensing of Environment, 195 , pp. 218-229, 2017. (Type: Journal Article | Abstract | Links | BibTeX) @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
| 2. | Meentemeyer, Ross K; Tang, Wenwu; Dorning, Monica A; Vogler, John B; Cunniffe, Nik J; Shoemaker, Douglas A: FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. In: Annals of the Association of American Geographers, 103 (4), pp. 785-807, 2013, ISSN: 0004-5608. (Type: Journal Article | Abstract | Links | BibTeX) @article{Meentemeyer2013, title = {FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm}, author = {Ross K. Meentemeyer and Wenwu Tang and Monica A. Dorning and John B. Vogler and Nik J. Cunniffe and Douglas A. Shoemaker}, url = {http://www.tandfonline.com/doi/abs/10.1080/00045608.2012.707591}, doi = {10.1080/00045608.2012.707591}, issn = {0004-5608}, year = {2013}, date = {2013-08-01}, journal = {Annals of the Association of American Geographers}, volume = {103}, number = {4}, pages = {785-807}, abstract = {We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes. |
