Project Funding: 2009 - 2014

NRA: 2008 NASA: Biodiversity   

Funded by NASA

Abstract:
We seek to test the hypothesis that recent advances in remotely sensed technologies can significantly improve our ability to measure the impacts of land-cover and evaluate the role of climate change on biodiversity. We propose to investigate the effects of climate change and landcover change on shifting patterns of biodiversity during two decades of observational data and, through model development we will forecast vegetation, climate and biodiversity into the future. Our measure of biodiversity will be songbirds. Songbird communities offer an excellent taxon for examining changes in biodiversity because they tend to be sensitive to environmental change and can be monitored using regional surveys such as atlases. As such, the New York State Breeding Bird Atlas is a unique survey documenting distribution of ~250 species across the entire state in 1980-1985, and was repeated in 2000-2005. Based on this atlas, we recently found strong evidence of northward range shifts for many species, and that the two likely drivers of these shifts have been regional climatic changes and large-scale afforestation from agricultural abandonment. A principal limitation, however, is the current inability to characterize, at landscape scales, early-successional communities that provide critical habitat to many of the bird species experiencing range shifts. In this research, we propose to acquire LIDAR data to identify these transitional covertypes and other vegetation structure indicators at landscape scales. Climatic change during this period will be evaluated using spatially interpolated historical records (PRISM data). Results from both climatic and land-cover change analyses will be aggregated at the BBA block resolution for statistical modeling. We will model observed range shifts in NYS based on observed changes in climate and modeled changes in vegetation structure from 1980-2005. We will develop a forest succession model based on LIDAR measurements of several chronosequences of old- field communities to derive vegetation structure (e.g., height, canopy architecture), across sites varying in prior land use (e.g., crops, pasture). Our model will hindcast from present day (2010) to the vegetation structure during the first BBA survey (1980) and then forecast to 2030 and beyond. We will use an information-theoretic approach to test competing hypotheses pertaining to the roles of climate and land use change in shifting bird species distributions. The proposed work is relevant to NASA’s priorities and has significant societal value because it seeks to address needs for: (1) rigorous measures of the influence of climate change on biodiversity that can provide leaders in government and business with better information to make policy decisions - our work is a significant step toward assessment of the biodiversity at the species and ecosystems levels; (2) new scientific knowledge through development of models that improve predictive capabilities based on data from NASA satellite missions; (3) proof that LIDAR can complement existing NASA spaceborne sensors to assess biodiversity patterns and processes - our efforts will provide concrete evidence for launching LIDAR technology into orbit; (4) support of the transition between research and operations by providing an assessment of the influence of existing and novel three-dimensional statistics extracted from LIDAR waveform for biodiversity applications allowing development of a common framework for data processing and distribution beyond the LIDAR scientific community - critical as new missions (e.g. DESDynI) are around the corner; (5) showcasing of the scientific value of the unique multi-temporal NY statewide Breeding Bird Survey and provide suggestions for implementations at other sites; (6) reaching out to relevant scientific communities and the general public and inform them of novel data, model and results (e.g. through our own courses and workshops).

Publications:

Beier, C. M., Signell, S. A., Luttman, A., DeGaetano, A. T. 2011. High-resolution climate change mapping with gridded historical climate products. Landscape Ecology. 27(3), 327-342. DOI: 10.1007/s10980-011-9698-8

Bishop, D. A., Beier, C. M. 2013. Assessing Uncertainty in High-Resolution Spatial Climate Data across the US Northeast. PLoS ONE. 8(8), e70260. DOI: 10.1371/journal.pone.0070260

Hong, B., Limburg, K. E., Hall, M. H., Mountrakis, G., Groffman, P. M., Hyde, K., Luo, L., Kelly, V. R., Myers, S. J. 2012. An integrated monitoring/modeling framework for assessing human-nature interactions in urbanizing watersheds: Wappinger and Onondaga Creek watersheds, New York, USA. Environmental Modelling & Software. 32, 1-15. DOI: 10.1016/j.envsoft.2011.08.006

Jarzyna, M. A., Finley, A. O., Porter, W. F., Maurer, B. A., Beier, C. M., Zuckerberg, B. 2014. Accounting for the space-varying nature of the relationships between temporal community turnover and the environment. Ecography. DOI: 10.1111/ecog.00747

Jarzyna, M. A., Porter, W. F., Maurer, B. A., Zuckerberg, B., Finley, A. O. 2015. Landscape fragmentation affects responses of avian communities to climate change. Global Change Biology. 21(8), 2942-2953. DOI: 10.1111/gcb.12885

Jarzyna, M. A., Zuckerberg, B., Finley, A. O., Porter, W. F. 2016. Synergistic effects of climate and land cover: grassland birds are more vulnerable to climate change. Landscape Ecology. 31(10), 2275-2290. DOI: 10.1007/s10980-016-0399-1

Jarzyna, M. A., Zuckerberg, B., Porter, W. F., Finley, A. O., Maurer, B. A. 2015. Spatial scaling of temporal changes in avian communities. Global Ecology and Biogeography. 24(11), 1236-1248. DOI: 10.1111/geb.12361

Jin, H., Mountrakis, G. 2022. Fusion of optical, radar and waveform LiDAR observations for land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing. 187, 171-190. DOI: 10.1016/j.isprsjprs.2022.03.010

Jin, H., Mountrakis, G., Li, P. 2012. A super-resolution mapping method using local indicator variograms. International Journal of Remote Sensing. 33(24), 7747-7773. DOI: 10.1080/01431161.2012.702234

Jin, H., Mountrakis, G., Stehman, S. V. 2014. Assessing integration of intensity, polarimetric scattering, interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing. 98, 70-84. DOI: 10.1016/j.isprsjprs.2014.09.017

Luo, L., Mountrakis, G. 2010. Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example. Remote Sensing of Environment. 114(6), 1220-1229. DOI: 10.1016/j.rse.2010.01.008

Luo, L., Mountrakis, G. 2011. A multiprocess model of adaptable complexity for impervious surface detection. International Journal of Remote Sensing. 33(2), 365-381. DOI: 10.1080/01431161.2010.532177

Luo, L., Mountrakis, G. 2011. Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification. ISPRS Journal of Photogrammetry and Remote Sensing. 66(5), 579-587. DOI: 10.1016/j.isprsjprs.2011.03.002

Mountrakis, G., Im, J., Ogole, C. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 66(3), 247-259. DOI: 10.1016/j.isprsjprs.2010.11.001

Mountrakis, G., Li, Y. 2017. A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing. ISPRS Journal of Photogrammetry and Remote Sensing. 129, 200-211. DOI: 10.1016/j.isprsjprs.2017.05.009

Mountrakis, G., Luo, L. 2011. Enhancing and replacing spectral information with intermediate structural inputs: A case study on impervious surface detection. Remote Sensing of Environment. 115(5), 1162-1170. DOI: 10.1016/j.rse.2010.12.018

Mountrakis, G., Zhuang, W. 2012. Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data. PLoS ONE. 7(8), e40093. DOI: 10.1371/journal.pone.0040093

Shi, Y., Song, Q., Jin, T., Zhou, Z. 2011. The AdaBoost Classification of Land-mine Target with Adaptive Feature Selection. Journal of Electronics & Information Technology. 33(8), 1798-1802. DOI: 10.3724/sp.j.1146.2010.01423

Zhuang, W., Mountrakis, G. 2014. An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing. 95, 81-92. DOI: 10.1016/j.isprsjprs.2014.06.004

Zhuang, W., Mountrakis, G. 2014. Ground peak identification in dense shrub areas using large footprint waveform LiDAR and Landsat images. International Journal of Digital Earth. 8(10), 805-824. DOI: 10.1080/17538947.2014.942716

Zhuang, W., Mountrakis, G., Wiley, J. J., Beier, C. M. 2015. Estimation of above-ground forest biomass using metrics based on Gaussian decomposition of waveform lidar data. International Journal of Remote Sensing. 36(7), 1871-1889. DOI: 10.1080/01431161.2015.1029095

More details may be found in the following project profile(s):

• NASA Biological Diversity & Ecological Conservation (BDEC) Project Profile