Forest Biometrician in Forest Carbon Estimation, Finite Carbon company, 06/2022 - present (US)

Postdoc in Vegetation Dynamics, Los Alamos National Laboratory, 12/2019 - 05/2022 (US)

Postdoc in Forest Advanced Computing and Artificial Intelligence, Purdue University, 03/2019 - 11/2019 (US)

​Postdoc in Forest Carbon Dynamics, University of Vermont, 01/2017 - 02/2019 (US)

PhD in Quantitative Forest Science, West Virginia University, 08/2012 - 12/2016 (US)

MS in Forest Growth Models, Chinese Academy of Forestry, 08/2009- 07/2012 (China)

BS in Forestry, Huazhong Agricultural University, 09/2005 - 07/2009 (China)

Email: wu.ma@finitecarbon.com; wuma@mix.wvu.edu 

All Publications
​
2021
  1. Wu Ma., Zhai, L., Pivovaroff, A., Shuman, J., Buotte, P., Ding, J., Christoffersen, B., Moritz, M., Koven, C.D., Kueppers, L. and Xu, C., 2021. Assessing Climate Change Impacts on Live Fuel Moisture and Wildfire Risk Using a Hydrodynamic Vegetation Model. Biogeosciences, pp.1-35.
  2. Wang, Y., Wu Ma, Farlee, L.D., Jackson, E.A., Shao, G., Ochuodho, T., Liang, J. and Zhou, M., 2021. Assessing the Benefits and Economic Feasibility of Stand Improvement for Central Hardwood Forests. Forest Science.
  3. H He, G Zhu, Wu Ma, F Liu, X Zhang. 2021. Additivity of stand basal area predictions in canopy stratifications for natural oak forests. Forest Ecology and Management, 492, p.119246.
  4. Polly Buotte, Charles Koven, Chonggang Xu, Jacquelyn Shuman, Michael Goulden, Samuel Levis, Jessica Katz, Junyan Ding, Wu Ma, Zachary Robbins, Lara Kueppers. Capturing functional strategies and compositional dynamics in vegetation demographic models. Biogeosciences, pp.1-28.
2020
  1. Wu Ma, G Domke, C Woodall, A D'Amato. 2020. Contemporary Forest Carbon Dynamics in the Northern U.S. Associated with Land Cover Changes. Ecological Indicators. 110: 105901.
  2. Wu Ma, G Lin, J Liang. 2020. Estimating Dynamics of Central Hardwood Forests Using Random Forests. Ecological Modelling. 419: 108947.
2019
  1. Wu Ma, X Zhou, J Liang, M Zhou. 2019. Coastal Alaska Forest under Climate Change: What to Expect? Forest Ecology and Management. 448: 432-444.
  2. Wu Ma, G Domke, C Woodall, A D'Amato. 2019. Land Use Changes, Disturbances, and Their Interactions on Future Forest Aboveground Biomass Dynamics in the Northern U.S. Forests. 10(7):606.
  3. Z Liu, Y Zhu, J Wang, Wu Ma, J Meng. 2019. Species association of the dominant tree species in an old-growth forest and implications for enrichment planting for the restoration of natural degraded forest in subtropical China. Forests.10:957.​
2018
  1. Wu Ma, G Domke, A D'Amato, C Woodall, B Walters, R Deo. 2018. Using matrix models to estimate aboveground forest biomass dynamics in the Eastern USA through various combinations of LiDAR, Landsat, and Forest Inventory Data. Environmental Research Letters.13, no. 12:125004. 
  2. Wu Ma, C Woodall, G Domke, A D'Amato, B Walters. 2018. Stand age versus tree diameter as a driver of forest carbon inventory simulations in the northeast U.S. Canadian Journal of Forest Research. 48(10):1135-1147.
  3. W Liu, Y Yan, D Wang, Wu Ma. 2018. Integrate carbon dynamics models for assessing the impact of land use intervention on carbon sequestration ecosystem service. Ecological Indicators. 91: 268-277.
  4. L Cui, W Li, C Gao, M Zhang, X Zhao, Z Yang, Y Lei, D Huang, Wu Ma. 2018. Identifying the influence factors at multiple scales on river water chemistry in the Tiaoxi Basin, China. Ecological Indicators.
  5. Li Wei, Dou Zhiguo, Wang Yan, Wu Gaojie, Zhang Manyin, Lei Yinru, Ping Yunmei, Wang Jiachen, Cui Lijuan, Wu Ma. 2018. Estimation of above-ground biomass of reed (Phragmites communis) based on in situ hyperspectral data in Beijing Hanshiqiao Wetland, China. Wetlands Ecology and Management.
2017
  1. Wu Ma, M Zhou. 2017. Assessments of harvesting regimes in central hardwood forests under climate and fire uncertainty. Forest Science. 64(1):57-73.
  2. J Meng, Y Bai, W Zeng, Wu Ma. 2017. A management tool for reducing the potential risk of windthrow for coastal casuarina equisetifolia stands on Haihan Island, China. European Journal of Forest Research.136 (3), 543-554.
  3. F Ge, W Zeng, Wu Ma, J Meng. 2017. Does the slope of the self-thinning line remain a constant value across different site qualities? - An implication for plantation density management. Forests. 8, 355. doi:10.3390/f8100355.​
  4. W Li, L Cui, B Sun, X Zhao, C Gao, Y Zhang, M, Zhang, X Pan, Y Lei, Wu Ma. 2017. Distribution patterns of plant communities and their associations with environmental soil factors on the Eastern Shore of Lake Taihu, China. Ecosystem Health and Sustainability.
2016
  1. Wu Ma, J Liang, J Cumming, E Lee, A Welsh, J Watson, M Zhou. 2016. Fundamental shifts of Central Hardwood forests under climate change. Ecological Modelling, 332, 28-41.
  2. Chen D, Huang X, Sun X, Wu Ma, Zhang S. 2016. A comparison of hierarchical and non-hierarchical Bayesian approaches for fitting allometric larch (Larix. spp.) biomass equations. Forests, 7(1), 18.
  3. Meng J, Li S, Wang W, Liu Q, Xie S, Wu Ma. 2016. Estimation of forest structural diversity using the spectral and textural information derived from SPOT-5 satellite images. Remote Sensing, 8(2), 125.
  4. Liu J, Zhu L, Wang H, Yang Y, Liu J, Qiu D, Wu Ma, Zhang Z, Liu J. 2016. Dry deposition of particulate matter at an urban forest, wetland and lake surface in Beijing. Atmospheric Environment, 125, 178-187.
  5. H Zang, X Lei, Wu Ma, W Zeng. 2016. Spatial heterogeneity of climate change effects on dominant height for larch plantations in northern and northeastern China. Forests. 7(7), 151.
  6. L Zhu, J Liu, L Cong, W Ma, Wu Ma, Z Zhang. Spatiotemporal characteristics of particulate matter and dry deposition flux in the Cuihu wetland of Beijing. Plos One. 11(7): e0158616.
  7. S Fan, F Guan, X Xu, D Forrester, Wu Ma, X Tang. 2016. Ecosystem carbon stock loss after land use change in subtropical forests in China. Forests. 7(7), 142.
  8. J Meng, S Li, W Wang, Q Liu, S Xie, Wu Ma. 2016. Estimation of forest health using the spectral and textural information derived from SPOT-5 satellite images. Remote Sensing. 8(9), 719.
​2015
  1. Wu Ma, X Lei. 2015. Nonlinear simultaneous equations for individual-tree diameter growth and mortality model of natural Mongolian oak forests in Northeast China. Forests, 6 (6), 2261 - 2280.
  2. Wu Ma, X Lei, G Xu, Y Yang, Q Wang. 2015. Growth models for natural Mongolian oak forests:ⅠDiameter growth model. Journal of Northwest A&F University, 43(2), 99-105. (In Chinese with English abstract)
  3. Wu Ma, X Lei, G Xu, Y Yang, Q Wang. 2015. Growth models for natural Mongolian oak forests: II individual-tree height-diameter model. Journal of Northwest A&F University, 43(3):83-90. (In Chinese with English abstract)
  4. Wu Ma, X Lei, G Xu, Y Yang, Q Wang. 2015. Growth models for natural Mongolian oak forests: III Individual-tree mortality model. Journal of Northwest A&F University, 43(4):59-64, 72. (In Chinese with English abstract)
  5. Wu Ma, X Lei, G Xu, Y Yang, Q Wang. 2015. Growth models for natural Mongolian oak forests: IV recruitment model. Journal of Northwest A&F University, 43(5): 58-64. (In Chinese with English abstract)
2014 and older
  1. Wu Ma, X Lei. 2012. Forest growth and management simulation platform CAPSIS and its application. China Forestry Science and Technology, 4, 79-83. (In Chinese with English abstract)
  2. Wu Ma, C Shen, X Lei, S Dufour, F de Coligny. 2011. Developing larch-spruce-fir forest matrix growth model (LSFMGM) based on CAPSIS platform. Journal Northeast Forestry University, 39(9), 112-115. (In Chinese with English abstract)
  3. C Shen, X Lei, F Wang, Wu Ma, J Shen. 2012. Competitive states in natural middle-aged forest of Mongolian oak at Jincang Forest Farm. Forest Research, 25(3): 339-345. (In Chinese with English abstract).
  4. J Diao, X Lei, J Wang, J Lu, H Guo, L Fu, C Shen, Wu Ma, and J Shen. 2014. Quantifying the variability of internode allometry within and between trees for pinus tabulaeformis carr. using a multilevel nonlinear mixed-effect model. Forests, 5(11), 2825-2845.