The following 40 publications cited the product NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000.
| Year | Citation |
|---|---|
| 2022 | Chopping, M., Z. Wang, C. Schaaf, M.A. Bull, and R.R. Duchesne. 2022. Forest aboveground biomass in the southwestern United States from a MISR multi-angle index, 2000–2015. Remote Sensing of Environment. 275:112964. https://doi.org/10.1016/j.rse.2022.112964 |
| 2022 | Wang, K. and P. Kumar. 2022. Virtual laboratory for understanding impact of heterogeneity on ecohydrologic processes across scales. Environmental Modelling & Software. 149:105283. https://doi.org/10.1016/j.envsoft.2021.105283 |
| 2022 | Yu, Y., S. Saatchi, G.M. Domke, B. Walters, C. Woodall, S. Ganguly, S. Li, S. Kalia, T. Park, R. Nemani, S.C. Hagen, and L. Melendy. 2022. Making the US national forest inventory spatially contiguous and temporally consistent. Environmental Research Letters. 17(6):65002. https://doi.org/10.1088/1748-9326/ac6b47 |
| 2021 | Ma, L., G. Hurtt, H. Tang, R. Lamb, E. Campbell, R. Dubayah, M. Guy, W. Huang, A. Lister, J. Lu, J. O'Neil-Dunne, A. Rudee, Q. Shen, and C. Silva. 2021. High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA. Environmental Research Letters. 16(4):045014. https://doi.org/10.1088/1748-9326/abe4f4 |
| 2021 | Spafford, L. and A.H. MacDougall. 2021. Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review. Geoscientific Model Development. 14(9):5863-5889. https://doi.org/10.5194/gmd-14-5863-2021 |
| 2021 | Williams, C.A., H. Gu, and T. Jiao. 2021. Climate impacts of U.S. forest loss span net warming to net cooling. Science Advances. 7(7):eaax8859. https://doi.org/10.1126/sciadv.aax8859 |
| 2020 | Liu, J., B.M. Sleeter, Z. Zhu, T.R. Loveland, T. Sohl, S.M. Howard, C.H. Key, T. Hawbaker, S. Liu, B. Reed, M.A. Cochrane, L.S. Heath, H. Jiang, D.T. Price, J.M. Chen, D. Zhou, N.B. Bliss, T. Wilson, J. Sherba, Q. Zhu, Y. Luo, and B. Poulter. 2020. Critical land change information enhances the understanding of carbon balance in the United States. Global Change Biology. 26(7):3920-3929. https://doi.org/10.1111/gcb.15079 |
| 2020 | Zhang, Y. and S. Liang. 2020. Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map. Remote Sensing. 12(16):2559. https://doi.org/10.3390/RS12162559 |
| 2019 | Dong, L., S. Tang, M. Min, F. Veroustraete, and J. Cheng. 2019. Aboveground forest biomass based on OLSR and an ANN model integrating LiDAR and optical data in a mountainous region of China. International Journal of Remote Sensing. 40(15):6059-6083. https://doi.org/10.1080/01431161.2019.1587201 |
| 2019 | Huang, W., K. Dolan, A. Swatantran, K. Johnson, H. Tang, J. O'Neil-Dunne, R. Dubayah, and G. Hurtt. 2019. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters. 14(9):095002. https://doi.org/10.1088/1748-9326/ab2917 |
| 2019 | Spawn, S.A., T.J. Lark, and H.K. Gibbs. 2019. Carbon emissions from cropland expansion in the United States. Environmental Research Letters. 14(4):045009. https://doi.org/10.1088/1748-9326/ab0399 |
| 2018 | Battles, J.J., D.M. Bell, R.E. Kennedy, D.S. Saah, B.M. Collins, R.A. York, J.E. Sanders, and F. Lopez-Ornelas2018. Innovations in Measuring and Managing Forest Carbon Stocks In California. California Natural Resources Agency. |
| 2018 | Collier, N., F.M. Hoffman, D.M. Lawrence, G. Keppel-Aleks, C.D. Koven, W.J. Riley, M. Mu, and J.T. Randerson. 2018. The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation. Journal of Advances in Modeling Earth Systems. 10(11):2731-2754. https://doi.org/10.1029/2018MS001354 |
| 2018 | Fargione, J.E., S. Bassett, T. Boucher, S.D. Bridgham, R.T. Conant, S.C. Cook-Patton, P.W. Ellis, A. Falcucci, J.W. Fourqurean, T. Gopalakrishna, H. Gu, B. Henderson, M.D. Hurteau, K.D. Kroeger, T. Kroeger, T.J. Lark, S.M. Leavitt, G. Lomax, R.I. McDonald, J.P. Megonigal, D.A. Miteva, C.J. Richardson, J. Sanderman, D. Shoch, S.A. Spawn, J.W. Veldman, C.A. Williams, P.B. Woodbury, C. Zganjar, M. Baranski, P. Elias, R.A. Houghton, E. Landis, E. McGlynn, W.H. Schlesinger, J.V. Siikamaki, A.E. Sutton-Grier, and B.W. Griscom. 2018. Natural climate solutions for the United States. Science Advances. 4(11):eaat1869. https://doi.org/10.1126/sciadv.aat1869 |
| 2018 | Hooper, S. and R.E. Kennedy. 2018. A spatial ensemble approach for broad-area mapping of land surface properties. Remote Sensing of Environment. 210:473-489. https://doi.org/10.1016/j.rse.2018.03.032 |
| 2018 | Kennedy, R.E., J. Ohmann, M. Gregory, H. Roberts, Z. Yang, D.M. Bell, V. Kane, M.J. Hughes, W.B. Cohen, S. Powell, N. Neeti, T. Larrue, S. Hooper, J. Kane, D.L. Miller, J. Perkins, J. Braaten, and R. Seidl. 2018. An empirical, integrated forest biomass monitoring system. Environmental Research Letters. 13(2):025004. https://doi.org/10.1088/1748-9326/aa9d9e |
| 2018 | Santoro, M. and O. Cartus. 2018. Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations. Remote Sensing. 10(4):608. https://doi.org/10.3390/rs10040608 |
| 2018 | Turner, S.B., D.P. Turner, A.N. Gray, and W. Fellers. 2018. An approach to estimating forest biomass change over a coniferous forest landscape based on tree-level analysis from repeated lidar surveys. International Journal of Remote Sensing. 1-18. https://doi.org/10.1080/01431161.2018.1528401 |
| 2017 | Bachelet, D., K. Ferschweiler, T. Sheehan, B. Sleeter, and Z. Zhu. 2017. Translating MC2 DGVM Results into Ecosystem Services for Climate Change Mitigation and Adaptation. Climate. 6(1):1. https://doi.org/10.3390/cli6010001 |
| 2017 | Fortier, M.O.P., G.W. Roberts, S.M. Stagg-Williams, and B.S.M. Sturm. 2017. Determination of the life cycle climate change impacts of land use and albedo change in algal biofuel production. Algal Research. 28:270-281. https://doi.org/10.1016/j.algal.2017.06.009 |
| 2017 | Hardiman, B.S., J.A. Wang, L.R. Hutyra, C.K. Gately, J.M. Getson, and M.A. Friedl. 2017. Accounting for urban biogenic fluxes in regional carbon budgets. Science of The Total Environment. 592:366-372. https://doi.org/10.1016/j.scitotenv.2017.03.028 |
| 2017 | Huang, W., A. Swatantran, L. Duncanson, K. Johnson, D. Watkinson, K. Dolan, J. O'Neil-Dunne, G. Hurtt, and R. Dubayah. 2017. County-scale biomass map comparison: a case study for Sonoma, California. Carbon Management. 8(5-6):417-434. https://doi.org/10.1080/17583004.2017.1396840 |
| 2017 | Lin, J.C., D.V. Mallia, D. Wu, and B.B. Stephens. 2017. How can mountaintop CO<sub>2</sub> observations be used to constrain regional carbon fluxes?. Atmospheric Chemistry and Physics. 17(9):5561-5581. https://doi.org/10.5194/acp-17-5561-2017 |
| 2017 | Mcgarigal, K., B. Compton, E. Plunkett, B. Deluca, J. Grand2017. Designing Sustainable Landscapes: Biomass settings variable. North Atlantic Conservation Cooperative, US Fish and Wildlife Service, Northeast Region. |
| 2017 | Mcgarigal, K., B. Compton, E. Plunkett, B. Deluca, J. Grand2017. Designing Sustainable Landscapes: Modeling Forest Succession and Disturbance. North Atlantic Conservation Cooperative, US Fish and Wildlife Service, Northeast Region. |
| 2016 | Deo, R.K., M.B. Russell, G.M. Domke, C.W. Woodall, M.J. Falkowski, and W.B. Cohen. 2016. Using Landsat Time-Series and LiDAR to Inform Aboveground Forest Biomass Baselines in Northern Minnesota, USA. Canadian Journal of Remote Sensing. 43(1):28-47. https://doi.org/10.1080/07038992.2017.1259556 |
| 2016 | Dilling, L., K.C. Kelsey, D.P. Fernandez, Y.D. Huang, J.B. Milford, and J.C. Neff. 2016. Managing Carbon on Federal Public Lands: Opportunities and Challenges in Southwestern Colorado. Environmental Management. 58(2):283-296. https://doi.org/10.1007/s00267-016-0714-2 |
| 2016 | Dowie, N.J., L.C. Grubisha, S.M. Trowbridge, M.R. Klooster, and S.L. Miller. 2016. Variability of ecological and autotrophic host specificity in a mycoheterotrophic system: Pterospora andromedea and associated fungal and conifer hosts. Fungal Ecology. 20:97-107. https://doi.org/10.1016/j.funeco.2015.11.005 |
| 2016 | Gu, H. and P.A. Townsend. 2016. Mapping forest structure and uncertainty in an urban area using leaf-off lidar data. Urban Ecosystems. 20(2):497-509. https://doi.org/10.1007/s11252-016-0610-9 |
| 2016 | Qi, W. and R.O. Dubayah. 2016. Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping. Remote Sensing of Environment. 187:253-266. https://doi.org/10.1016/j.rse.2016.10.018 |
| 2016 | Su, Y., Q. Ma, and Q. Guo. 2016. Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery. International Journal of Digital Earth. 10(3):307-323. https://doi.org/10.1080/17538947.2016.1227380 |
| 2015 | Huang, W., A. Swatantran, K. Johnson, L. Duncanson, H. Tang, J. O'Neil Dunne, G. Hurtt, and R. Dubayah. 2015. Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA. Carbon Balance and Management. 10(1): https://doi.org/10.1186/s13021-015-0030-9 |
| 2015 | M. Sleeter, B., J. Liu, C. Daniel, L. Frid, and Z. Zhu. 2015. An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California. AIMS Environmental Science. 2(3):577-606. https://doi.org/10.3934/environsci.2015.3.577 |
| 2014 | Chopping, M., R. Duchesne, and M. North. 2014. Assessing remotely-sensed aboveground biomass estimates in the Sierra National Forest. 1041-1044. https://doi.org/10.1109/IGARSS.2014.6946606 |
| 2014 | Krankina, O.N., D.A. DellaSala, J. Leonard, and M. Yatskov. 2014. High-Biomass Forests of the Pacific Northwest: Who Manages Them and How Much is Protected?. Environmental Management. 54(1):112-121. https://doi.org/10.1007/s00267-014-0283-1 |
| 2014 | Pond, N.C., R.E. Froese, R.K. Deo, and M.J. Falkowski. 2014. Multiscale Validation of an Operational Model of Forest Inventory Attributes Developed with Constrained Remote Sensing Data. Canadian Journal of Remote Sensing. 40(1):43-59. https://doi.org/10.1080/07038992.2014.917581 |
| 2014 | Raciti, S.M., L.R. Hutyra, and J.D. Newell. 2014. Mapping carbon storage in urban trees with multi-source remote sensing data: Relationships between biomass, land use, and demographics in Boston neighborhoods. Science of The Total Environment. 500-501:72-83. https://doi.org/10.1016/j.scitotenv.2014.08.070 |
| 2014 | Thurner, M., C. Beer, M. Santoro, N. Carvalhais, T. Wutzler, D. Schepaschenko, A. Shvidenko, E. Kompter, B. Ahrens, S.R. Levick, and C. Schmullius. 2014. Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography. 23(3):297-310. https://doi.org/10.1111/geb.12125 |
| 2013 | Montgomery A. (2013) Geospatial Analysis of Select Ecosystem Services provided by the Protected Lands of The Land Trust for Central North Carolina. Department: School of the Environment, Duke University. |
| 2013 | Parks, D.H., T. Mankowski, S. Zangooei, M.S. Porter, D.G. Armanini, D.J. Baird, M.G.I. Langille, and R.G. Beiko. 2013. GenGIS 2: Geospatial Analysis of Traditional and Genetic Biodiversity, with New Gradient Algorithms and an Extensible Plugin Framework. PLoS ONE. 8(7):e69885. https://doi.org/10.1371/journal.pone.0069885 |