UMass Boston

2023

Sánchez-Zapero, J., Martínez-Sánchez, E., Camacho, F., Wang, Z., Carrer, D., Schaaf, C., García-Haro, F. J., Nickeson, J., & Cosh, M. (2023). Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4—Application to Three Global Albedo Climate Data Records. Remote Sensing, 15(4), Article 4. https://doi.org/10.3390/rs15041081

2022

Erb, A.M., Li, Z., Sun, Q., Paynter, I., Wang, Z., Schaaf, C. (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. Remote Sensing, 14(21), 5320. https://doi.org/10.3390/rs14215320

Chopping, M., Wang, Z., Schaaf, C., Bull, M. A., & Duchesne, R. R. (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

Wulder, M.A., D.P. Roy, V.C. Radeloff; T.R. Loveland, M.C. Anderson, D.M. Johnson, S. Healey, Z. Zhu, T.A. Scambos, N. Pahlevan, M. Hansen, N. Gorelick, C.J. Crawford, J.G. Masek, T. Hermosilla, J.C. White, A.S. Belward, C. Schaaf, C. Woodcock, J.L. Huntington, L. Lymburner, P. Hostert, F. Gao, A. Lyapustin, J-F. Pekel, P. Strobl, and B.C. Cook. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment. 280: 113195.  https://doi.org/10.1016/j.rse.2022.113195

2021

Kim, J., Kim, Y., Zona, D., Oechel, W., Park, S.-J., Lee, B.-Y., Yi, Y., Erb, A., & Schaaf, C. L. 2021. Carbon response of tundra ecosystems to advancing greenup and snowmelt in Alaska. Nature Communications, 12(1), 6879. https://doi.org/10.1038/s41467-021-26876-7

Liu, Y., McDonough MacKenzie, C., Primack, R. B., Hill, M. J., Zhang, X., Wang, Z., & Schaaf, C. B. (2021). Using remote sensing to monitor the spring phenology of Acadia National Park across elevational gradients. Ecosphere, 12(12), e03888. https://doi.org/10.1002/ecs2.3888

Yan, K.,  Li, H., Song, W., Tong, Y., Hao, D.,  Zeng, Y., Mu, X., Yan, G., Fang, Y., Myneni, R. B., Schaaf, C., 2021. "Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain", in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3064018.

Rouhani, S., Schaaf, C., Huntington, T., Choate, J. 2021 Simulation of Dissolved Organic Carbon Flux in the Penobscot Watershed, Maine. Ecohydrology & Hydrobiology. https://doi.org/10.1016/j.ecohyd.2021.02.005.

Pisek J., Arndt S.K., Erb A., Pendall E., Schaaf C., Wardlaw T.J., Woodgate W., and Knyazikhin Y. 2021 Exploring the Potential of DSCOVR EPIC Data to Retrieve Clumping Index in Australian Terrestrial Ecosystem Research Network Observing Sites. Front. Remote Sens. 2:652436. doi: 10.3389/frsen.2021.652436

Elmes, A.,  Levy, C., Erb, A., Hall, D., Scambos, T., DiGirolamo, N., and Schaaf, C., 2021. Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo. Remote Sensing, 13(2). https://doi.org/10.3390/rs13020227

Pisek, J., Erb, A., Korhonen, L., Biermann, T., Carrara, A., Cremonese, E., Cuntz, M., Fares, S., Gerosa, G., Grunwald, T., Hase, N., Heliasz, M., Ibrom, A., Knohl, A., Kobler, J., Kruijt, B., Lange, H., Leppanen, L., Limousin, J.-M., Serrano, F. R. L., Loustau, D., Lukevs, P., Lundin, L., Marzuoli, R., Molder, M., Montagnani, L., Neirynck, J., Peichl, M., Rebmann, C., Rubio, E., Santos-Reis, M., Schaaf, C., Schmidt, M., Simioni, G., Soudani, K., Vincke, C. 2021. Retrieval, validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests. Biogeosciences, 18(2). 621-635. https://bg.copernicus.org/articles/18/621/2021/

Peter B Boucher, Ian Paynter, David A Orwig, Ilan Valencius, Crystal Schaaf, Sampling forests with Terrestrial Laser Scanning, Annals of Botany, 2021;, mcab073, https://doi.org/10.1093/aob/mcab073

Duncanson, L., Armston, J., Disney, M. I. et al. (2021) Aboveground Woody Biomass Product Validation Good Practices Protocol. Version 1.0. In L. Duncanson, M. Disney, J. Armston, J. Nickeson, D. Minor, and F. Camacho (Eds.), Good Practices for Satellite Derived Land Product Validation,(p. 236): Land Product Validation Subgroup (WGCV/CEOS), https://doi.org/10.5067/doc/ceoswgcv/lpv/agb.001

2020

Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Patrick Bentley, L., Chave, J., Danson, F. M., Demol, M., Disney, M., Gaulton, R., Krishna Moorthy, S. M., Levick, S. R., Saarinen, N., Schaaf, C., Stovall, A., Terryn, L., Wilkes, P., Verbeeck, H., 2020. Terrestrial laser scanning in forest ecology: expanding the horizon. Remote Sensing of Environment, 251. https://doi.org/10.1016/j.rse.2020.112102

Boucher, P.B., Hancock, S., Orwig, D.A., Duncanson, L., Armston, J., Tang, H., Krause, K., Cook, B., Paynter, I., Li, Z., Elmes, A., and Schaaf, C., 2020. Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation. Remote Sensing, 12 (8): 1304. https://doi.org/10.3390/rs12081304

2019

Potter, S., Solvik, K., Erb, A., Goetz, S.J., Johnstone, J.F., Mack, M.C., Randerson, J.T., Román, M.O., Schaaf, C.L., Turetsky, M.R., Veraverbeke, S., Walker, X.J., Wang, Z., Massey, R., and Rogers, B.M., 2019. Climate change decreases the cooling effect from post‐fire albedo in boreal North America. Global change biology. https://doi.org/10.1111/gcb.14888.

Bruegge, C., Coburn, C., Elmes, A., Helmlinger, M. C., Kataoka, F., Kuester, M., Kuze, A., Ochoa, T., Schaaf, C., Shiomi, K., and Schwander, F. M., 2019. Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa. Remote Sensing, 11 (12). http://doi:10.3390/rs11222601

Wei, S., Fang, H., Schaaf, C. B., He, L., Chen, J. M., 2019. Global 500 m clumping index product derived from MODIS BRDF data (2001–2017). Remote Sensing of Environ., 232. https://doi.org/10.1016/j.rse.2019.111296.

Wulder, M.A., Loveland, T.R., Roy, D.P., Crawford, C.J., Masek, J.G., Woodcock, C.E., Allen, R.G., Anderson, M.C., Belward, A.S., Cohen, W.B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., Huntington, J., Johnson, D., M., Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorckel, J., Pahlevan, N., Scambos, T. A., Schaaf, C., Schott, J. R., Sheng, Y., Storey, J., Vermote, E., Vogelmann, J., White, J. C., Wynne, R. H., and Zhu, Z., 2019. Current Status of Landsat Program, Science, and Applications. Remote Sensing of Environ., 225: 127–47. https://doi.org/10.1016/j.rse.2019.02.015.

Wang, Z., Schaaf, C., Lattanzio, A., Carrer, D., Grant, I., Román, M., Camacho, F., Yu, Y., Sánchez-Zapero, J. & Nickeson, J. 2019. Global Surface Albedo Product Validation Best Practices Protocol. Version 1.0. In Z. Wang, J. Nickeson & M. Román (Eds.), Best Practice for Satellite Derived Land Product Validation (p. 45): Land Product Validation Subgroup (WGCV/CEOS). doi:10.5067/DOC/CEOSWGCV/LPV/ALBEDO.001.

Disney, M. I., Burt, A., Calders, K., Schaaf, C. and Stovall, A. 2019. Innovations in ground and airborne technologies as reference and for training and validation: Terrestrial Laser Scanning (TLS), Surveys in Geophysics vol. 71: Forest Biomass and Structure from Space, eds. K. Scipal. R. Dubyah, T. Le Toan, S. Quegan, A. Cazenave and T. Lopez. https://doi.org/10.1007/s10712-019-09527-x.

Paynter, I., Schaaf, C., Bowen, J. L., Deegan, L., Peri, F., & Cook, B. 2019. Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar. Remote Sensing, 11(509). https://doi.org/10.3390/rs11050509

Tang, H., Song, X., Zhao, F. A., Strahler, A. H., Schaaf, C. L., Goetz, S., Huang, C., Hansen, M. C., Dubayah, R. 2019. Definition and measurement of tree cover: A comparative analysis of field-, lidar- and landsat-based tree cover estimations in the Sierra national forests, USA. Agricultural and Forest Meteorology, 268: 258-268. https://doi.org/10.1016/j.agrformet.2019.01.024

Zhu, Z., Wulder, M.A., Roy, D.P., Woodcock, C.E., Hansen, M.C., Radeloff, V.C., Healey, S.P., Schaaf, C., Hostert, P., Strobl, P., Pekel, JF., Lymburner, L., Pahlevan, N., and Scambos, T.A. 2019. Benefits of the free and open Landsat data policy. Remote Sensing of Environ, 224, 382-385. https://doi.org/10.1016/j.rse.2019.02.016

Jiao, Z., Ding, A., Kokhanovsky, A., Schaaf, C., Bréon, F. M., Dong, Y., Wang, Z., Liu, Y., Zhang, X., Yin, S., Cui, L., Mei, L., and Chang, Y. 2019. Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework. Remote Sensing of Environ, 221(19), 198–209. https://doi.org/10.1016/j.rse.2018.11.001

2018

Kim, Y., Kimball, J.S., Du, J., Schaaf, C.L.B., & Kirchner, P.B. 2018. Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange. Environ. Res. Lett, 13, https://doi.org/10.1088/1748-9326/aacf72

Liu, Q., Yan G., Jiao, Z., Xiao, Q., Wen, J., Liang, S., Wang, J., Schaaf, C., and Strahler, A. 2018. From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science -- in Memory of Academician Xiaowen Li. Remote Sensing, 10(11), 1764. https://doi.org/10.3390/rs10111764.

Duchesne, R. R., Chopping, M. J., Tape, K. D., Wang, Z., & Schaaf, C. L. B. 2018. Changes in tall shrub abundance on the North Slope of Alaska, 2000–2010. Remote Sens of Environ, 219, 221–232. https://doi.org/10.1016/j.rse.2018.10.009.

Li, Z., Erb, A., Sun, Q., Liu, Y., Shuai, Y., Wang, Z., Boucher, P., Schaaf, C., 2018. Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures. Remote Sens of Environ, 217, 352–365. https://doi.org/10.1016/j.rse.2018.08.025

Paynter, I., Genest, D., Saenz, E., Peri, F., Li, Z., Strahler, A., Schaaf, C., 2018. Quality Assessment of Terrestrial Laser Scanner Ecosystem Observations Using Pulse Trajectories. IEEE Trans. Geosci Remote Sens, 56(11), 6324-6333. https://doi.org/10.1109/TGRS.2018.2836947

Jiao, Z., Zhang, X., Bréon, F.M., Dong, Y., Schaaf, C.B., Román, M., Wang, Z., Cui, L., Yin, S., Ding, A., & Wang, J. 2018. The influence of spatial resolution on the angular variation patterns of optical reflectance as retrieved from MODIS and POLDER measurements. Remote Sens. Environ. 215, 371–385. doi:10.1016/j.rse.2018.06.025

Riihelä, A., Manninen, T., Key, J., Sun, Q., Sütterlin, M., Lattanzio, A., & Schaaf, C. 2018. A Multisensor Approach to Global Retrievals of Land Surface Albedo. doi:10.3390/rs10060848

Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G. M., Friedl, M. A., Schaaf, C. B. 2018. Generation and evaluation of the VIIRS land surface phenology product. Remote Sens. Environ., 216, 212–229. https://doi.org/10.1016/j.rse.2018.06.047

Zhang, X., Jayavelu, S., Liu, L., Friedl, M.A., Henebry, G.M., Liu, Y., Schaaf, C.B., Richardson, A.D., & Gray, J. 2018. Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery. Agric. For. Meteorol. 256–257, 137–149. https://doi.org/10.1016/j.agrformet.2018.03.003

Jiao, Z., Dong, Y., Schaaf, C.B., Chen, J.M., Román, M., Wang, Z., Zhang, H., Ding, A., Erb, A., Hill, M.J., Zhang, X., & Strahler, A. 2018. An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model. Remote Sens. Environ. 209, 594–611. https://doi.org/10.1016/j.rse.2018.02.041

Kim, J. H., Hwang, T., Yang, Y., Schaaf, C. L., Boose, E., & Munger, J. W. 2018. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. Journal of Geophysical Research: Biogeosciences, 123(6), 1960–1975. https://doi.org/10.1029/2018JG004438

Kim, J., Hwang, T., Schaaf, C.,  Kljun, N., & Munger, J. W, 2018. Seasonal variation of source contributions to eddy-covariance CO2 measurements in a mixed hardwood-conifer forest. Agricultural and Forest Meteorology, 253-254, 71–83. https://doi.org/10.1016/j.agrformet.2018.02.004

Manninen, T., Riihelä, A., Heidinger, A., Schaaf, C., Lattanzio A., & Key, J. 2018. Intercalibration of Polar-Orbiting Spectral Radiometers Without Simultaneous Observations. IEEE Trans. Geosci. Remote Sens., 56(3), 1507-1519. https://doi.org/10.1109/TGRS.2017.2764627

Paynter, I., Genest, D., Peri, F., & Schaaf, C. 2018. Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems. Interface Focus 8(2). https://doi.org/10.1098/rsfs.2017.0043

Orwig, D.A., Boucher, P., Paynter, I., Saenz, E., Li, Z., & Schaaf, C. 2018. The potential to characterize ecological data with terrestrial laser scanning in Harvard Forest, MA. Interface Focus 8(2). https://doi.org/10.1098/rsfs.2017.0044

Li, Z., Schaefer, M., Strahler, A., Schaaf, C., & Jupp, D. 2018. On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements. Interface Focus 8(2). https://doi.org/10.1098/rsfs.2017.0039

Media Coverage: 

Royal Society Blogs: The Terrestrial Laser Scanning Revolution in Forest Ecology

Interface Focus Issue on TLS in Forest Ecology

Paynter, I., Genest, D., Saenz, E., Peri, F., Boucher, P., Li, Z., Strahler, A., & Schaaf, C. 2018. Classifying Ecosystems with Metaproperties from Terrestrial Laser Scanner Data. Methods in Ecology and Evolution, 9(2), 210–222.

Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y., & Román, M. O. 2018. Capturing Rapid Land Surface Dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) Products. Remote Sensing of Environment, 207(February), 50–64.

Klosterman, S., Melaas, E., Wang, J., Martinez, A., Frederick, S., O’Keefe, J., Orwig, D.A., Wang, Z., Sun, Q., Schaaf, C., Friedl, M.A., & Richardson, A.D. 2018. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agric. For. Meteorol. 248, 397–407. 

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