Publications

@article{Chen2017b,
title = {Uncertainties in Mapping Forest Carbon in Urban Ecosystems},
author = {Gang Chen and Emre Ozelkan and Kunwar K. Singh and Jun Zhou and Marilyn R. Brown and Ross K. Meentemeyer},
url = {https://doi.org/10.1016/j.jenvman.2016.11.062},
doi = {10.1016/j.jenvman.2016.11.062},
year = {2017},
date = {2017-02-01},
journal = {Journal of Environmental Management},
volume = {187},
pages = {229-238},
abstract = {Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density – 5.8, 4.6, 2.3, and 1.2 pt s/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery – 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7–11.0% of variation) than changing NAIP image resolution (causing 6.2–8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.},
keywords = {carbon mapping, neighborhood pattern, remote sensing, uncertainty analysis, urban forests},
pubstate = {published},
tppubtype = {article}
}

@article{Cord2013,
title = {Modelling species distributions with remote sensing data: bridging disciplinary perspectives},
author = {Anna F. Cord and Ross K. Meentemeyer and Pedro J. Leitao and Tomas Vaclavik},
url = {http://onlinelibrary.wiley.com/doi/10.1111/jbi.12199/full},
doi = {10.1111/jbi.12199},
year = {2013},
date = {2013-12-01},
journal = {Journal of Biogeography},
volume = {40},
number = {12},
pages = {2226-2227},
abstract = {Over the last few decades, correlative species distribution models (SDMs) have been adopted as the most widely used approach for describing and predicting spatial patterns of relationships between species occurrence and environmental conditions (Elith & Leathwick, 2009). Over this same period, the discipline of remote sensing (RS) has produced a breadth of novel geospatial datasets and analytical algorithms for mapping biogeographical heterogeneity. It is not surprising that RS data are now commonly used in SDMs: space- and airborne RS data provide a low cost means to map environmental changes across multiple spatio-temporal scales and are attractive for their ability to measure spatial factors often impossible to quantify otherwise (e.g. landscape connectivity). Looking into this trend further, our literature search – with keywords ‘remote sensing’ and ‘species distribution’ or ‘habitat suitability’ – returned 210 articles where remote sensing was integrally used in SDMs, 60% of which were published just in the past 5 years (ISI Web of Science, 2 May 2013). This development should be a good thing, right? However, several scientists have critically pointed out that the content and spatial scale of RS predictors do not often match species' life-history strategies (e.g. Bradley et al., 2012; Lechner et al., 2012). Our objective here is not to disagree with their recently proposed methodological guidelines, but rather to reflect on the various facets of using RS in SDMs in an effort to bridge disciplinary perspectives.},
keywords = {remote sensing, species distribution model},
pubstate = {published},
tppubtype = {article}
}