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{Singh2017,
title = {Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics},
author = {Kunwar K. Singh and Raechel A. Bianchetti and Gang Chen and Ross K. Meentemeyer},
url = {https://doi.org/10.1007/s11252-016-0591-8},
doi = {10.1007/s11252-016-0591-8},
year = {2017},
date = {2017-04-01},
journal = {Urban Ecosystems},
volume = {20},
number = {2},
pages = {265-275},
abstract = {Accurate estimates of biomass in urban forests can help improve strategies for enhancing ecosystem services. Landscape heterogeneity, such as land-cover types and their spatial arrangements, greatly affects biomass growth, and it complicates the estimation of biomass. Application of LiDAR data is a typical approach for mapping forest biomass and carbon stocks across heterogeneous landscapes. However, little is known about how urban land uses and pattern impact biomass and estimates derived from LiDAR analysis. In this study, we examined the relationship between LiDAR-derived biomass and dominant land-cover types using field-measured estimates of aboveground forest biomass in an urbanized region of North Carolina, USA. Three objectives drove this research: 1) we examined the local effects of dominant land cover types on urban forest biomass; 2) we identified the spatial scale at which dominant land cover influences biomass estimates; 3) we investigated whether the fine-scale, spatial heterogeneity of the urban landscape contributed to forest biomass. We used multiple linear regression to relate field-measured biomass to LiDAR metrics and land cover densities derived from Landsat and LiDAR data. The biomass model developed from variables derived from LiDAR first returns produced biomass estimates similar to using all LiDAR returns. Although three land-cover types (impervious surface, managed clearings, and farmland) exhibited a negative relationship with biomass, only impervious surface was statistically significant. The biomass model that used impervious surface densities between 100 m and 175 m radial buffers produced the highest adjusted R 2 with lower RMSE values. Our study suggests that impervious surface impacted forest biomass estimates considerably in urbanizing landscapes with the greatest effect between 100 and 175 m from a forest stand. Managed clearing and farmland types negatively impacted biomass estimation albeit not as strongly as impervious surface. Overall, we found that accounting for impervious surface density and its proximity to forest in biomass models may improve urban forest biomass estimates.},
keywords = {fragmentation, land cover, Landsat, landscape metrics, LiDAR, multiple linear regression, urban forests},
pubstate = {published},
tppubtype = {article}
}

@article{Chen2015,
title = {Mapping burn severity in a disease-impacted forest landscape using Landsat and MASTER imagery},
author = {Gang Chen and Margaret R. Metz and David M. Rizzo and Ross K. Meentemeyer},
url = {http://www.sciencedirect.com/science/article/pii/S0303243415000847},
doi = {dx.doi.org/10.1016/j.jag.2015.04.005},
year = {2015},
date = {2015-04-22},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {40},
pages = {91-99},
abstract = {Global environmental change has increased forest vulnerability to the occurrence of interacting disturbances, including wildfires and invasive diseases. Mapping post-fire burn severity in a disease-affected forest often faces challenges because burned and infested trees may exhibit a high similarity in spectral reflectance. In this study, we combined (pre- and post-fire) Landsat imagery and (post-fire) high-spectral resolution airborne MASTER data [MODIS (moderate resolution imaging spectroradiometer)/ASTER (advanced spaceborne thermal emission and reflection radiometer)] to map burn severity in a California coastal forest environment, where a non-native forest disease sudden oak death (SOD) was causing substantial tree mortality. Results showed that the use of Landsat plus MASTER bundle performed better than using the individual sensors in most of the evaluated forest strata from ground to canopy layers (i.e., substrate, shrubs, intermediate-sized trees, dominant trees and average), with the best model performance achieved at the dominant tree layer. The mid to thermal infrared spectral bands (3.0–12.5 μm) from MASTER were found to augment Landsat’s visible to shortwave infrared bands in burn severity assessment. We also found that infested and uninfested forests similarly experienced moderate to high degrees of burns where CBI (composite burn index) values were higher than 1. However, differences occurred in the regions with low burn severity (CBI values lower than 1), where uninfested stands revealed a much lower burn effect than that in infested stands, possibly due to their higher resilience to small fire disturbances as a result of higher leaf water content.},
keywords = {burn severity, forest fire, interacting disturbances, invasion, Landsat, MASTER, sudden oak death},
pubstate = {published},
tppubtype = {article}
}

@article{Singh2012,
title = {LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy},
author = {Kunwar K. Singh and John B. Vogler and Douglas A. Shoemaker and Ross K. Meentemeyer},
url = {http://dx.doi.org/10.1016/j.isprsjprs.2012.09.009},
doi = {10.1016/j.isprsjprs.2012.09.009},
year = {2012},
date = {2012-10-23},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {74},
pages = {110-121},
abstract = {The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30 m and LiDAR data and LiDAR-TM fusions at 1 m, 5 m, 10 m, 15 m and 30 m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1 m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30 m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1 m, 5 m, and 10 m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5 m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover.},
keywords = {fusion, land change, land cover, Landsat, large-area assessment, LiDAR, managed clearings, mapping accuracy},
pubstate = {published},
tppubtype = {article}
}