Josh M Gray

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

REMOTE SENSING OF ENVIRONMENT, 308.

Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products

[Review of ]. REMOTE SENSING OF ENVIRONMENT, 302.

Detecting Trajectories of Regime Shifts and Loss of Resilience in Coastal Wetlands using Remote Sensing

Martinez, M., Ardon, M., & Gray, J. (2024, October 31). ECOSYSTEMS, Vol. 10.

Drought Changes Growing Season Length and Vegetation Productivity

Gray, J., Choi, E., Friedl, M., & Griffiths, P. (2024, March 9).

From Satellites to Soil: Integrating Satellite and Household Survey Data to Assess the Impacts of Adaptations on Smallholder Farmers’ Climate Resilience

Hinks, I., & Gray, J. (2024, March 9).

Overcoming Big Data Challenges in Satellite Observation: A Variable Resolution Scheme for Modeling Land Surface Phenology

Smith, O., Gao, X., & Gray, J. (2024, November 27).

The role of rural circular migration in shaping weather risk management for smallholder farmers in India, Nepal, and Bangladesh

GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS, 89.

Thermal Forcing Versus Chilling? Misspecification of Temperature Controls in Spring Phenology Models

Gao, X., Richardson, A. D., Friedl, M. A., Moon, M., & Gray, J. M. (2024, October 28). GLOBAL ECOLOGY AND BIOGEOGRAPHY, Vol. 10.

Understanding the role of vegetation responses to drought in regulating autumn senescence

Choi, E., & Gray, J. (2024, March 9).

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.

Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes

ENVIRONMENTAL MODELLING & SOFTWARE, 167.

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).

Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data

Journal of Data Science.

Spatial Analysis of Forest Product Manufacturers in North Carolina

Forest Science, 69(1), 24–36.

Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries

REMOTE SENSING, 14(13).

Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model

REMOTE SENSING OF ENVIRONMENT, 261.

Multisensor fusion of remotely sensed vegetation indices using space-time dynamic linear models

Johnson, M. C., Reich, B. J., & Gray, J. M. (2021, May 21). JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol. 5.

Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

REMOTE SENSING OF ENVIRONMENT, 240.

Longer greenup periods associated with greater wood volume growth in managed pine stands

AGRICULTURAL AND FOREST METEOROLOGY, 297.

Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 86(8), 509–518.

Predictors of fire-tolerant oak and fire-sensitive hardwood distribution in a fire-maintained longleaf pine ecosystem

Forest Ecology and Management, 477, 118468.

Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

Remote Sensing, 12(9), 1406.

A Land Surface Phenology Product for North America from Harmonized Landsat 8 and Sentinel-2 imagery

American Geophysical Union, Fall Meeting, B32E–05.

An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms

REMOTE SENSING, 11(19).

Climate controls on springtime phenology in Eastern Temperate Forests of North America

American Geophysical Union, Fall Meeting, B33K–2629.

Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product

REMOTE SENSING OF ENVIRONMENT, 222, 183–194.

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.

PhenoCam Dataset v2. 0: Vegetation Phenology from Digital Camera Imagery, 2000-2018

In ORNL DAAC [Data set].

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.

Evaluating machine learning approaches for mapping flood risk

American Geophysical Union, Fall Meeting, H41M–2286.

Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery

AGRICULTURAL AND FOREST METEOROLOGY, 256, 137–149.

Intra-annual phenology for detecting understory plant invasion in urban forests

ISPRS Journal of Photogrammetry and Remote Sensing, 142, 151–161.

Mapping Annual Land Cover and Phenology from MODIS: Global Data Sets Supporting Modeling and Global Change Science

American Geophysical Union, Fall Meeting, GC14B–08.

Quantifying emerging infectious disease impacts on above ground biomass

American Geophysical Union, Fall Meeting, B14B–06.

The managed clearing: An overlooked land-cover type in urbanizing regions?

PLOS ONE, 13(2).

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Chen, M., Gray, J. M., … Frolking, S. (2018, March 13). SCIENTIFIC DATA, Vol. 5.

USA-NPN Observations Reveal the Ecological Relevance of Remotely Sensed Phenology

American Geophysical Union, Fall Meeting, B53C–05.

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

LAND, 6(3).

Exploration of scaling effects on coarse resolution land surface phenology

Remote Sensing of Environment, 190, 318–330.

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.

PhenoCam Dataset v1. 0: Vegetation phenology from digital camera imagery, 2000–2015

In ORNL DAAC [Data set].

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.

Water savings from reduced alfalfa cropping in California’s Upper San Joaquin Valley

American Geophysical Union, Fall Meeting, IN51F–0069.

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.

Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat

Remote Sensing of Environment, 186, 452–464.

Multisource Image Kalman Filtering for Rapid Phenological Monitoring and Forecasting

American Geophysical Union, Fall Meeting Abstracts, B43B–0599. Washington, D.C.: American Geophysical Union.

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.

Incorporating phenology into yield models

American Geophysical Union, Fall Meeting Abstracts, B43A–0540. Washington, D.C.: American Geophysical Union.

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.

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.

Direct human influence on atmospheric CO2 seasonality from increased cropland productivity

Nature, 515(7527), 398–401.

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Biogeosciences, 11(16), 4305–4320.

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.

Mapping Asian Cropping Intensity With MODIS

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3373–3379.

Mapping Crop Cycles in China Using MODIS-EVI Time Series

Remote Sensing, 6(3), 2473–2493.

Net carbon uptake has increased through warming-induced changes in temperate forest phenology

Nature Climate Change, 4(7), 598–604.

Standardizing PhenoCam Image Processing and Data Products

American Geophysical Union, Fall Meeting Abstracts, B41K–019. Washington, D.C.: American Geophysical Union.

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.

Consistent classification of image time series with automatic adaptive signature generalization

Remote Sensing of Environment, 134, 333–341.

Large scale maps of cropping intensity in Asia from MODIS

American Geophysical Union, Fall Meeting Abstracts, B41A–0385. Washington, D.C.: American Geophysical Union.

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.

Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors

Remote Sensing of Environment, 119, 173–183.

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.

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).

Remote Sensing of Vegetation with Landsat Imagery

In Advances in Environmental Remote Sensing (pp. 3–29).

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.