Two Publications on How to Best use Remote Sensing Data at a 1-km scale

Two Publications on How to Best use Remote Sensing Data at a 1-km scale

 

NCEAS
The NCEAS Environment and Organisms Working Group set about to assemble a state-of-the-art set of environmental layers that incorporate well-known but rarely used measures that have direct links to physiological processes (e.g. water stress, growing season, soil properties, etc.). The team has combined various land-cover variables into a unified, global, high-resolution (1 kilometer) dataset that is available to the public. In addition, they have identified best practices for generating fine resolution maximum daily air temperature datasets. The results of this Working Group have recently appeared in two peer-reviewed publications.

Land-Cover: The most recent manuscript addresses the shortcomings of existing remote sensing-derived land-cover products by generating a global 1-km resolution consensus land-cover product that provides scale-integrated, accuracy-weighted information on a continuous scale. The consensus dataset broadly surpasses any base product the utility for modeling species distributions. The results of their work were recently published in Global Ecology and Biogeography.

1. Tuanmu, M.-N. and W. Jetz (2014). A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Global Ecology and Biogeography. doi: 10.1111/geb.12182

http://onlinelibrary.wiley.com/doi/10.1111/geb.12182/abstract

Climatic Variables: The second manuscript addresses lack of accurate and spatially contiguous climatic variables at fine temporal and spatial grains. The authors evaluate best practices for generating gridded, one-kilometer resolution, daily maximum air temperature surfaces in a regional context, the state of Oregon, USA. These results were recently published inRemote Sensing.

2. Parmentier, B., B. McGill, A. Wilson, J. Regetz, W. Jetz, R. Guralnick, M.-N. Tuanmu, N. Robinson and M. Schildhauer (2014). An Assessment of Methods and Remote-Sensing Derived Covariates for Regional Predictions of 1 km Daily Maximum Air Temperature. Remote Sensing. doi:10.3390/rs6098639

http://www.mdpi.com/2072-4292/6/9/8639

 

For more information on the project, participants and other products of the Environment and Organisms Working Group (full project name “Choosing (and making available) the right environmental layers for modeling how the environment controls the distribution and abundance of organisms”)

This work was supported by the National Center for Ecological Analysis and Synthesis, National Science Foundation (Grant #EF-0553768), University of California, Santa Barbara, and the State of California.

 

 

Source: NCEAS

Categories: Research Papers

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