Dr. Evan Brooks

I specialize in landscape change, monitoring, and projection. In particular, I like to leverage “big data” to monitor and model temporal trajectories of land cover and land use at regional, national, or continental scales. My dissertation work focused on multitemporal Landsat-based change detection analysis, but I am also interested in landscape ecology, and the spatial patterns and impacts from interactions between human and natural systems.

Resource Planning Act 2020 Assessment (https://www.fs.fed.us/research/rpa/; contributed from 2016-present). The RPA 2020 assessment is focused on providing projections for the land base of the conterminous USA from 2020-2070. My particular roles include generating fine-grain spatial realizations of county-level land use change projections, as well as supporting the plot-level forest dynamics effort with inventory-level fire modeling.

Pine Integrated Network: Education, Mitigation, and Adaptation Project (http://pinemap.org/; contributed from 2013-2016). PINEMAP was a USDA Conservation Activity Plan (CAP) project to forecast the impacts of climate change on loblolly pine (Pinus taeda) in the southeastern USA. My particular role was compiling, generating, and harmonizing soil and climate data for use in both the empirical growth and yield modeling and the process-based Physiological Principles Predicting Growth (3PG) modeling efforts.

• Thomas, V.A.; Wynne, R.H.; Kauffman, J.; McCurdy, W.; Brooks, E.B.; Thomas, R.Q.; Rakestraw, J. Mapping thins to identify active forest management in southern pine plantations using Landsat time series stacks. Remote Sensing of Environment 2021, 252(112127), DOI: https://doi.org/10.1016/j.rse.2020.112127
• Brooks, E.B.; Coulston, J.W.; Riitters, K.H.; Wear, D.N. Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. PLOS ONE 2020, 15(10): p. e0240097, DOI: https://doi.org/10.1371/journal.pone.0240097
• Thomas, R.Q.; Jersild, A.L.; Brooks, E.B.; Thomas, V.A.; Wynne, R.H. A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity. Ecological Applications 2018, 28(6): p. 1503-1519, DOI: https://doi.org/10.1002/eap.1761
  
• Saxena, R.; Watson, L.T.; Wynne, R.H.; Brooks, E.B.; Thomas, V.A.; Zhiqiang, Y.; Kennedy, R.E. Towards a polyalgorithm for land use change detection. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 144(217-234, DOI: https://doi.org/10.1016/j.isprsjprs.2018.07.002
• Healey, S.P.; Cohen, W.B.; Yang, Z.; Kenneth Brewer, C.; Brooks, E.B.; Gorelick, N.; Hernandez, A.J.; Huang, C.; Joseph Hughes, M.; Kennedy, R.E.; Loveland, T.R.; Moisen, G.G.; Schroeder, T.A.; Stehman, S.V.; Vogelmann, J.E.; Woodcock, C.E.; Yang, L.; Zhu, Z. Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment 2018, 204(717-728), DOI: https://doi.org/10.1016/j.rse.2017.09.029
• Burkhart, H.E.; Brooks, E.B.; Dinon-Aldridge, H.; Sabatia, C.O.; Gyawali, N.; Wynne, R.H.; Thomas, V.A. Regional Simulations of Loblolly Pine Productivity with CO2 Enrichment and Changing Climate Scenarios. Forest Science 2018, fxy008-fxy008, DOI: https://doi.org/10.1093/forsci/fxy008
  
• Brooks, E.B.; Wynne, R.H.; Thomas, V.A. Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data. Remote Sens. 2018, 10, 1502. DOI: https://doi.org/10.3390/rs10101502
• Thomas, R.Q.; Brooks, E.B.; Jersild, A.; Ward, E.; Wynne, R.H.; Albaugh, T.J.; Dinon Aldridge, H.; Burkhart, H.E.; Domec, J.-C.; Fox, T.R.; Gonzalez-Benecke, C.A.; Martin, T.A.; Noormets, A.; Sampson, D.A.; Teskey, R.O. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments. Biogeosciences 2017, 14(14): p. 3525–3547, DOI: https://doi.org/10.5194/bg-14-3525-2017
• Cohen, W.; Healey, S.; Yang, Z.; Stehman, S.; Brewer, C.; Brooks, E.; Gorelick, N.; Huang, C.; Hughes, M.; Kennedy, R.; Loveland, T.; Moisen, G.; Schroeder, T.; Vogelmann, J.; Woodcock, C.; Yang, L.; Zhu, Z. How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms? Forests 2017, 8(4): p. 98, DOI: https://doi.org/10.3390/f8040098
• Brooks, E.B.; Yang, Z.; Thomas, V.A.; Wynne, R.H. Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts. Forests 2017, 8(304): p. 18, DOI: https://doi.org/10.3390/f8090304
  
• Brooks, E.B.; Coulston, J.W.; Wynne, R.H.; Thomas, V.A. Improving the precision of dynamic forest parameter estimates using Landsat. Remote Sensing of Environment 2016, 179(162-169), DOI: https://doi.org/10.1016/j.rse.2016.03.017
  
• Brooks, E.B.; Wynne, R.H.; Thomas, V.A.; Blinn, C.E.; Coulston, J.W. On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data. IEEE T Geosci Remote 2014, 52(6): p. 3316-3332, DOI: https://doi.org/10.1109/tgrs.2013.2272545
• Brooks, E.B.; Thomas, V.A.; Wynne, R.H.; Coulston, J.W. Fitting the Multitemporal Curve: A Fourier Series Approach to the Missing Data Problem in Remote Sensing Analysis. IEEE T Geosci Remote 2012, 50(9): p. 3340-3353, DOI: https://doi.org/10.1109/tgrs.2012.2183137