Radeloff, Volker: University of Wisconsin-Madison (Project Lead)
Project Funding:
2014 - 2017
NRA: 2013 NASA: The Science of Terra and Aqua
Funded by NASA
Abstract:
Humankind is rapidly transforming the earth's ecosystems, profoundly affecting biodiversity. To predict how species will respond to changing environments, biodiversity science needs better assessments of the current patterns of biodiversity, species distributions, and population densities. Satellite data will be key for such biodiversity assessments, but there are no global indices specifically designed for biodiversity.
We proposed to calculate the Dynamic Habitat Index (DHI) globally from Terra and Aqua MODIS data, evaluate its robustness, and test its utility in models of different types of biodiversity data. The DHI summarizes three key measures of vegetative productivity over the course of the year that are related to biodiversity. The first measure is the

overall, cumulative productivity. A wealth of empirical evidence shows that sites where more energy is available are generally more biodiverse. The second measure is the coefficient of variation in productivity over the course of a year, because sites with less intra-annual variability are generally more biodiverse. The third measure is the minimum productivity throughout the year. Sites that never drop to very low minima are more biodiverse. We have tested the DHI in several research projects, and found it to be highly correlated with biodiversity patterns. However, a thorough assessment of the DHI is lacking so far, and that limits its use for biodiversity studies.
Our proposed assessment will consist of three major parts. The first part is the calculation of the DHI globally, annually for the period for which MODIS data is available, and based on different types of Terra and Aqua MODIS data products, and to correlate DHI values with measures of land cover to better understand the performance and the dynamic range of the DHI.
In the second part of our project, we will test the utility of the DHI to predict (i) alpha- and beta-diversity, (ii) species occurrences, and (iii) species abundances for birds and mammals. First, alpha- and beta-diversity patterns can be derived from species range maps, which are available globally for many taxa. The DHI should be an excellent predictor of alpha-biodiversity, because it captures plant productivity and hence the foundation of ecological food webs. Beta-diversity is the differences in species composition among habitats, and we expect high spatial heterogeneity in DHI values will be associated with the high beta-diversity. Second, when predicting species occurrences (using species distribution modeling), detailed habitat information provides greater site- specific information than broad-scale climate and productivity patterns. We predict that the DHI based on 250-m resolution MODIS NDVI data will give much greater resolution for predictions of species occurrences. Third, we predict that population growth for a given species will be higher where cumulative productivity is higher, and that inter- annual patterns in the DHI will predict population trends.
Our project will rely heavily on NASA assets, especially MODIS data recorded by Terra and Aqua, and will use EOS data products to develop remotely sensed indices tailored for global observing systems for biodiversity, thus advancing the development and implementation of essential biodiversity variables (EBVs). Our proposed project will have broad societal relevance given concerns about biodiversity decline, especially in the context of climate change.
Publications:
Hobi, M. L., Dubinin, M., Graham, C. H., Coops, N. C., Clayton, M. K., Pidgeon, A. M., Radeloff, V. C. 2017. A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness. Remote Sensing of Environment. 195, 142-152. DOI: 10.1016/j.rse.2017.04.018
Radeloff, V. C., Dubinin, M., Coops, N. C., Allen, A. M., Brooks, T. M., Clayton, M. K., Costa, G. C., Graham, C. H., Helmers, D. P., Ives, A. R., Kolesov, D., Pidgeon, A. M., Rapacciuolo, G., Razenkova, E., Suttidate, N., Young, B. E., Zhu, L., Hobi, M. L. 2019. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sensing of Environment. 222, 204-214. DOI: 10.1016/j.rse.2018.12.009
Razenkova, E., Dubinin, M., Pidgeon, A. M., Hobi, M. L., Zhu, L., Bragina, E. V., Allen, A. M., Clayton, M. K., Baskin, L. M., Coops, N. C., Radeloff, V. C. 2023. Abundance patterns of mammals across Russia explained by remotely sensed vegetation productivity and snow indices. Journal of Biogeography. 50(5), 932-946. DOI: 10.1111/jbi.14588
Razenkova, E., Radeloff, V. C., Dubinin, M., Bragina, E. V., Allen, A. M., Clayton, M. K., Pidgeon, A. M., Baskin, L. M., Coops, N. C., Hobi, M. L. 2020. Vegetation productivity summarized by the Dynamic Habitat Indices explains broad-scale patterns of moose abundance across Russia. Scientific Reports. 10(1). DOI: 10.1038/s41598-019-57308-8
Suttidate, N., Hobi, M. L., Pidgeon, A. M., Round, P. D., Coops, N. C., Helmers, D. P., Keuler, N. S., Dubinin, M., Bateman, B. L., Radeloff, V. C. 2019. Tropical bird species richness is strongly associated with patterns of primary productivity captured by the Dynamic Habitat Indices. Remote Sensing of Environment. 232, 111306. DOI: 10.1016/j.rse.2019.111306
Zhu, L., Ives, A. R., Zhang, C., Guo, Y., Radeloff, V. C. 2019. Climate change causes functionally colder winters for snow cover-dependent organisms. Nature Climate Change. 9(11), 886-893. DOI: 10.1038/s41558-019-0588-4
Zhu, L., Radeloff, V. C., Ives, A. R. 2017. Characterizing global patterns of frozen ground with and without snow cover using microwave and MODIS satellite data products. Remote Sensing of Environment. 191, 168-178. DOI: 10.1016/j.rse.2017.01.020
Zhu, L., Radeloff, V. C., Ives, A. R. 2017. Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data. International Journal of Applied Earth Observation and Geoinformation. 58, 1-11. DOI: 10.1016/j.jag.2017.01.012
More details may be found in the following project profile(s):
