Josh Gray

Roman, M. O., Justice, C., Paynter, I., Boucher, P. B., Devadiga, S., Endsley, A., … Wolfe, R. (2024). [Review of Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products]. REMOTE SENSING OF ENVIRONMENT, 302. https://doi.org/10.1016/j.rse.2023.113963

Gray, J., Choi, E., Friedl, M., & Griffiths, P. (2024, March 9). Drought Changes Growing Season Length and Vegetation Productivity. https://doi.org/10.5194/egusphere-egu24-14774

Hinks, I., & Gray, J. (2024, March 9). From Satellites to Soil: Integrating Satellite and Household Survey Data to Assess the Impacts of Adaptations on Smallholder Farmers’ Climate Resilience. https://doi.org/10.5194/egusphere-egu24-14059

Smith, O., Gao, X., & Gray, J. (2024, March 8). Overcoming Big Data Challenges in Satellite Observation: A Variable Resolution Scheme for Modeling Land Surface Phenology. https://doi.org/10.5194/egusphere-egu24-12119

Choi, E., & Gray, J. (2024, March 9). Understanding the role of vegetation responses to drought in regulating autumn senescence. https://doi.org/10.5194/egusphere-egu24-13879

Rasmussen, P., Abrahamson, J., Tang, X., Smith, O., Gray, J., Woodcock, C., & Bosch, M. (2023). Assessment of Performance of Tree-Based Algorithms to Reduce Errors of Omisssion and Commission in Change Detection. IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, pp. 6676–6679. https://doi.org/10.1109/IGARSS52108.2023.10283320

Fidan, E., Gray, J., Doll, B., & Nelson, N. G. (2023). Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes. ENVIRONMENTAL MODELLING & SOFTWARE, 167. https://doi.org/10.1016/j.envsoft.2023.105758

Gao, X., McGregor, I. R. R., Gray, J. M. M., Friedl, M. A. A., & Moon, M. (2023). Observations of Satellite Land Surface Phenology Indicate That Maximum Leaf Greenness Is More Associated With Global Vegetation Productivity Than Growing Season Length. GLOBAL BIOGEOCHEMICAL CYCLES, 37(3). https://doi.org/10.1029/2022GB007462

Wendelberger, L. J., Gray, J. M., Wilson, A. G., Houborg, R., & Reich, B. J. (2022). Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data. Journal of Data Science. https://doi.org/10.6339/22-JDS1072

Sodiya, O. E., Parajuli, R., Abt, R. C., & Gray, J. (2022, December 17). Spatial Analysis of Forest Product Manufacturers in North Carolina. FOREST SCIENCE, Vol. 12. https://doi.org/10.1093/forsci/fxac045

Mei, W., Wang, H., Fouhey, D., Zhou, W., Hinks, I., Gray, J. M., … Jain, M. (2022). Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries. REMOTE SENSING, 14(13). https://doi.org/10.3390/rs14133046

Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. REMOTE SENSING OF ENVIRONMENT, 261. https://doi.org/10.1016/j.rse.2021.112484

Gao, X., Gray, J., Cohrs, C. W., Cook, R., & Albaugh, T. J. (2021). Longer greenup periods associated with greater wood volume growth in managed pine stands. AGRICULTURAL AND FOREST METEOROLOGY, 297. https://doi.org/10.1016/j.agrformet.2020.108237

Johnson, M. C., Reich, B. J., & Gray, J. M. (2021, May 21). Multisensor fusion of remotely sensed vegetation indices using space-time dynamic linear models. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol. 5. https://doi.org/10.1111/rssc.12495

Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., & Friedl, M. A. (2020). Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. REMOTE SENSING OF ENVIRONMENT, 240. https://doi.org/10.1016/j.rse.2020.111685

Singh, K. K., & Gray, J. (2020). Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 86(8), 509–518. https://doi.org/10.14358/PERS.86.8.509

Hannon, D. R., Moorman, C. E., Schultz, A. D., Gray, J. M., & DePerno, C. S. (2020). Predictors of fire-tolerant oak and fire-sensitive hardwood distribution in a fire-maintained longleaf pine ecosystem. Forest Ecology and Management, 477, 118468. https://doi.org/10.1016/j.foreco.2020.118468

Cohrs, C. W., Cook, R. L., Gray, J. M., & Albaugh, T. J. (2020). Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States. Remote Sensing, 12(9), 1406. https://doi.org/10.3390/rs12091406

Bolton, D. K., Melaas, E. K., Gray, J., Moon, M., Eklundh, L., & Friedl, M. A. (2019). A Land Surface Phenology Product for North America from Harmonized Landsat 8 and Sentinel-2 imagery. American Geophysical Union, Fall Meeting, B32E–05.

Stanimirova, R., Cai, Z., Melaas, E. K., Gray, J. M., Eklundh, L., Jonsson, P., & Friedl, M. A. (2019). An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms. REMOTE SENSING, 11(19). https://doi.org/10.3390/rs11192201

Moon, M., Seyednasrollah, B., Richardson, A. D., Gray, J., & Friedl, M. A. (2019). Climate controls on springtime phenology in Eastern Temperate Forests of North America. American Geophysical Union, Fall Meeting, B33K–2629.

Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P., & Friedl, M. A. (2019). Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product. REMOTE SENSING OF ENVIRONMENT, 222, 183–194. https://doi.org/10.1016/j.rse.2018.12.013

Moon, M., Zhang, X., Henebry, G. M., Liu, L., Gray, J. M., Melaas, E. K., & Friedl, M. A. (2019). Long-term continuity in land surface phenology measurements: A comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products. REMOTE SENSING OF ENVIRONMENT, 226, 74–92. https://doi.org/10.1016/j.rse.2019.03.034

Seyednasrollah, B., Young, A. M., Hufkens, K., Milliman, T., Friedl, M. A., Frolking, S., … Zona, D. (2019). PhenoCam Dataset v2. 0: Vegetation Phenology from Digital Camera Imagery, 2000-2018. In ORNL DAAC [Data set]. https://doi.org/10.3334/ORNLDAAC/1674

Kruskamp, N., Singh, K. K., Jones, C. K., Gray, J., & Meentemeyer, R. K. (2019). Web-based Decision Analytics For Mapping Host Species Distributions and Forecasting the Spread of Forest Pests and Pathogens. American Geophysical Union, Fall Meeting, IN52A–03.

Zhang, Z., Martin, K., Gray, J., Stevenson, K., & Yao, Y. (2018). Evaluating machine learning approaches for mapping flood risk. American Geophysical Union, Fall Meeting, H41M–2286.

Zhang, X., Jayavelu, S., Liu, L., Friedl, M. A., Henebry, G. M., Liu, Y., … Gray, J. (2018). Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery. AGRICULTURAL AND FOREST METEOROLOGY, 256, 137–149. https://doi.org/10.1016/j.agrformet.2018.03.003

Singh, K. K., Chen, Y.-H., Smart, L., Gray, J., & Meentemeyer, R. K. (2018). Intra-annual phenology for detecting understory plant invasion in urban forests. ISPRS Journal of Photogrammetry and Remote Sensing, 142, 151–161. https://doi.org/10.1016/J.ISPRSJPRS.2018.05.023

Friedl, M. A., Sulla-menashe, D. J., & Gray, J. (2018). Mapping Annual Land Cover and Phenology from MODIS: Global Data Sets Supporting Modeling and Global Change Science. American Geophysical Union, Fall Meeting, GC14B–08.

Kruskamp, N., Gray, J. M., & Meentemeyer, R. K. (2018). Quantifying emerging infectious disease impacts on above ground biomass. American Geophysical Union, Fall Meeting, B14B–06.

Singh, K. K., Madden, M., Gray, J., & Meentemeyer, R. K. (2018). The managed clearing: An overlooked land-cover type in urbanizing regions? PLOS ONE, 13(2). https://doi.org/10.1371/journal.pone.0192822

Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Chen, M., Gray, J. M., … Frolking, S. (2018, March 13). Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. SCIENTIFIC DATA, Vol. 5. https://doi.org/10.1038/sdata.2018.28

Gray, J. M., Khan, A., & Friedl, M. A. (2018). USA-NPN Observations Reveal the Ecological Relevance of Remotely Sensed Phenology. American Geophysical Union, Fall Meeting, B53C–05.

Pickard, B., Gray, J., & Meentemeyer, R. (2017). Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models. LAND, 6(3). https://doi.org/10.3390/land6030052

Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C., Friedl, M., … Henebry, G. M. (2017). Exploration of scaling effects on coarse resolution land surface phenology. Remote Sensing of Environment, 190, 318–330. https://doi.org/10.1016/J.RSE.2017.01.001

Riveros-Iregui, D. A., Moser, H. A., Christenson, E. C., Gray, J., Hedgespeth, M. L., Jass, T. L., … Emanuel, R. E. (2017). Impacts of Extreme Flooding on Hydrologic Connectivity and Water Quality in the Atlantic Coastal Plain and Implications for Vulnerable Populations. American Geophysical Union, Fall Meeting. Washington, D.C.: American Geophysical Union.

Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Chen, M., Gray, J. M., … Thom, J. E. (2017). PhenoCam Dataset v1. 0: Vegetation phenology from digital camera imagery, 2000–2015. In ORNL DAAC [Data set]. https://doi.org/10.3334/ORNLDAAC/1511

Gray, J. M., Sills, E. O., & Amanatides, M. M. (2017). Using Remote Sensing and Synthetic Controls to Understand Deforestation Drivers and their Moderation by Forest Use in Kalimantan, Indonesia. American Geophysical Union, Fall Meeting, GC52C–07.

Singh, K. K., & Gray, J. (2017). Water savings from reduced alfalfa cropping in California’s Upper San Joaquin Valley. American Geophysical Union, Fall Meeting, IN51F–0069.

Chen, M., Melaas, E. K., Gray, J. M., Friedl, M. A., & Richardson, A. D. (2016). A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios. Global Change Biology, 22(11), 3675–3688. https://doi.org/10.1111/gcb.13326

Melaas, E. K., Sulla-Menashe, D., Gray, J. M., Black, T. A., Morin, T. H., Richardson, A. D., & Friedl, M. A. (2016). Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat. Remote Sensing of Environment, 186, 452–464. https://doi.org/10.1016/J.RSE.2016.09.014

Gray, J. M., Friedl, M. A., & Singh, K. K. (2016). Multisource Image Kalman Filtering for Rapid Phenological Monitoring and Forecasting. American Geophysical Union, Fall Meeting Abstracts, B43B–0599. Washington, D.C.: American Geophysical Union.

Zhang, X., Jayavelu, S., Wang, J., Henebry, G. M., Gray, J. M., Friedl, M. A., … Shuai, A. (2016). Validation of VIIRS Land Surface Phenology using Field Observations, PhenoCam Imagery, and Landsat data. American Geophysical Union, Fall Meeting Abstracts, B33J–06. Washington, D.C.: American Geophysical Union.

Gray, J. M., & Friedl, M. A. (2015). Incorporating phenology into yield models. American Geophysical Union, Fall Meeting Abstracts, B43A–0540. Washington, D.C.: American Geophysical Union.

Melaas, E. K., Sulla-menashe, D. J., Gray, J. M., & Friedl, M. A. (2015). Using three decades of Landsat data to characterize changes and vulnerability of temperate and boreal forest phenology to climate change. American Geophysical Union, Fall Meeting Abstracts, B21G–0548. Washington, D.C.: American Geophysical Union.

Friedl, M. A., Gray, J. M., Melaas, E. K., Richardson, A. D., Hufkens, K., Keenan, T. F., … O’Keefe, J. (2014). A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research Letters, 9(5), 054006. https://doi.org/10.1088/1748-9326/9/5/054006

Gray, J. M., Frolking, S., Kort, E. A., Ray, D. K., Kucharik, C. J., Ramankutty, N., & Friedl, M. A. (2014). Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature, 515(7527), 398–401. https://doi.org/10.1038/NATURE13957

Klosterman, S. T., Hufkens, K., Gray, J. M., Melaas, E., Sonnentag, O., Lavine, I., … Richardson, A. D. (2014). Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences, 11(16), 4305–4320. https://doi.org/10.5194/bg-11-4305-2014

Keenan, T., Bohrer, G., Friedl, M., Gray, J., Hollinger, D., Munger, J. W., … Yang, B. (2014). Increased carbon uptake in the eastern US due to warming induced changes in phenology. European Geosciences Union General Assembly Abstracts, 16. Munchen: European Geosciences Union.

Gray, J., Friedl, M., Frolking, S., Ramankutty, N., Nelson, A., & Gumma, M. K. (2014). Mapping Asian Cropping Intensity With MODIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3373–3379. https://doi.org/10.1109/jstars.2014.2344630

Li, L., Friedl, M., Xin, Q., Gray, J., Pan, Y., & Frolking, S. (2014). Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sensing, 6(3), 2473–2493. https://doi.org/10.3390/rs6032473

Keenan, T. F., Gray, J., Friedl, M. A., Toomey, M., Bohrer, G., Hollinger, D. Y., … Richardson, A. D. (2014). Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change, 4(7), 598–604. https://doi.org/10.1038/NCLIMATE2253

Milliman, T. E., Richardson, A. D., Klosterman, S., Gray, J. M., Hufkens, K., Aubrecht, D., … Friedl, M. A. (2014). Standardizing PhenoCam Image Processing and Data Products. American Geophysical Union, Fall Meeting Abstracts, B41K–019. Washington, D.C.: American Geophysical Union.

Friedl, M. A., Melaas, E. K., Sulla-menashe, D. J., & Gray, J. M. (2014). Using Time Series of Landsat Data to Improve Understanding of Short-and Long-Term Changes to Vegetation Phenology in Response to Climate Change. American Geophysical Union, Fall Meeting Abstracts. Washington, D.C.: American Geophysical Union.

Gray, J., & Song, C. (2013). Consistent classification of image time series with automatic adaptive signature generalization. Remote Sensing of Environment, 134, 333–341. https://doi.org/10.1016/J.RSE.2013.03.022

Gray, J. M., Friedl, M. A., Frolking, S., Ramankutty, N., & Nelson, A. (2013). Large scale maps of cropping intensity in Asia from MODIS. American Geophysical Union, Fall Meeting Abstracts, B41A–0385. Washington, D.C.: American Geophysical Union.

Frick, E. A., Friedl, M. A., Melaas, E. K., & Gray, J. M. (2012). A comparison of phenophase transition dates calculated from MODIS EVI and NBAR-EVI. American Geophysical Union, Fall Meeting Abstracts, B11C–0438. Washington, D.C.: American Geophysical Union.

Gray, J., & Song, C. (2012). Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sensing of Environment, 119, 173–183. https://doi.org/10.1016/j.rse.2011.12.016

Friedl, M. A., Richardson, A. D., Pless, R., Frolking, S., Milliman, T. E., Klosterman, S., … Gray, J. M. (2012). PhenoCam: A Continental Observatory in Support of Monitoring, Modeling, and Forecasting Phenological Responses to Climate Change. American Geophysical Union, Fall Meeting Abstracts, GC54A–06. Washington, D.C.: American Geophysical Union.

Gray, J. M. (2012). Understanding regional water resource dynamics due to land-cover/land-use and climate changes in the North Carolina Piedmont (Doctoral Dissertation, University of North Carolina-Chapel Hill). https://doi.org/10.17615/gfa6-ec52

Song, C., Gray, J., & Gao, F. (2011). Remote Sensing of Vegetation with Landsat Imagery. In Advances in Environmental Remote Sensing (pp. 3–29). https://doi.org/10.1201/b10599-3

Song, C., Gray, J. M., & Zhang, S. (2008). Retrieving LAI from Remotely Sensed Images: Spectral Indices vs Spatial Texture. American Geophysical Union, Fall Meeting Abstracts, B33D–03. Washington, D.C.: American Geophysical Union.