61
The deviation of ARD products can be undertaken by yourself if you have the
appropriate background knowledge and software tools. However, the data provider(s)
or other organisations or individuals can provide services to derive these products.
References
Armston, J., Disney, M.I., Lewis, P., Scarth, P., Phinn, S., Lucas, R.M., Bunting, P., Goodwin,
N.: Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sens.
Environ. 134, 24–38 (2013)
Babcock, C., Finley, A.O., Cook, B.D., Weiskittel, A., Woodall, C.W.: Modeling forest biomass and
growth: coupling long-term inventory and LiDAR data. Remote Sens. Environ. 182, 1–12 (2016)
Bater, C., Coops, N.: Evaluating error associated with lidar-derived DEM interpolation. Comput.
Geosci. 35(2), 289–300 (2009)
Bunting, P., Armston, J., Clewley, D., Lucas, R.M.: Sorted pulse data (SPD) library—part II: a
processing framework for LiDAR data from pulsed laser systems in terrestrial environments.
Comput. Geosci. 56, 207–215 (2013)
Chavez Jr., P.S.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24(3), 459–479 (1988)
Claverie, M., Vermote, E.F., Franch, B., Masek, J.G.: Evaluation of the Landsat-5 TM and
Landsat-7 ETM+ surface reflectance products. Remote Sens. Environ. 169, 390–403 (2015)
Coren, F., Sterzai, P.: Radiometric correction in laser scanning. Int. J. Remote Sens. 27(15), 3097–
3104 (2007)
Draper, D.W., Newell, D.A., Wentz, F.J., Krimchansky, S., Skofronick-Jackson, G.M.: The Global
Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument Overview and Early
On-Orbit Performance. Sel. Top. Appl. Earth Obs. Remote Sens. IEEE J. 8(7), 3452–3462
(2015.) http://doi.org/10.1109/JSTARS.2015.2403303
Donoghue, D.N.M., Watt, P.J., Cox, N.J., Wilson, J.: Remote sensing of species mixtures in
conifer plantations using LiDAR height and intensity data. Remote Sens. Environ. 110(4),
509–522 (2007)
Frantz, D., Röder, A., Stellmes, M., Hill, J.: An operational radiometric landsat preprocessing
framework for Large-Area time series applications. IEEE Trans. Geosci. Remote Sens. 1–16
(n.d..) http://doi.org/10.1109/TGRS.2016.2530856
Evans, J.S., Hudak, A.T.: A multiscale curvature algorithm for classifying discrete return LiDAR
in forested environments. IEEE Trans. Geosci. Remote Sens. 45(4), 1029–1038 (2007)
Feng, M., Sexton, J.O., Huang, C., Masek, J.G., Vermote, E.F., Gao, F., et al.: Global surface
reflectance products from Landsat: assessment using coincident MODIS observations. Remote
Sens. Environ. 134, 276–293 (2013)
Gaveau, D.L.A., Balzter, H., Plummer, S.: Forest woody biomass classification with satellite-based
radar coherence over 900 000 km2 in Central Siberia. For. Ecol. Manag. 174(1–3), 65–75 (2003)
Green, M., Kondragunta, S., Ciren, P., Xu, C.: Comparison of GOES and MODIS Aerosol Optical
Depth (AOD) to Aerosol Robotic Network (AERONET) AOD and IMPROVE PM2.5 Mass at
Bondville, Illinois. J. Air Waste Manage. Assoc. 59(9), 1082–1091 (2009)
Higgins, M.A., Asner, G.P., Martin, R.E., Knapp, D.E., Anderson, C., Kennedy-Bowdoin, T.,
Saenz, R., Aguilar, A., Wright, S.J.: Linking imaging spectroscopy and LiDAR with floristic
composition and forest structure in Panama. Remote Sens. Environ. 154, 358–367 (2014)
Ho Tong Minh, D., Le Toan, T., Rocca, F., Tebaldini, S., Villard, L., Réjou-Méchain, M., Phillips,
O.L., Feldpausch, T.R., Dubois-Fernandez, P., Scipal, K., Chave, J.: SAR tomography for the
retrieval of forest biomass and height: cross-validation at two tropical forest sites in French
Guiana. Remote Sens. Environ. 175, 138–147 (2016)
Pre-processing of Remotely Sensed Imagery
The deviation of ARD products can be undertaken by yourself if you have the
appropriate background knowledge and software tools. However, the data provider(s)
or other organisations or individuals can provide services to derive these products.
References
Armston, J., Disney, M.I., Lewis, P., Scarth, P., Phinn, S., Lucas, R.M., Bunting, P., Goodwin,
N.: Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sens.
Environ. 134, 24–38 (2013)
Babcock, C., Finley, A.O., Cook, B.D., Weiskittel, A., Woodall, C.W.: Modeling forest biomass and
growth: coupling long-term inventory and LiDAR data. Remote Sens. Environ. 182, 1–12 (2016)
Bater, C., Coops, N.: Evaluating error associated with lidar-derived DEM interpolation. Comput.
Geosci. 35(2), 289–300 (2009)
Bunting, P., Armston, J., Clewley, D., Lucas, R.M.: Sorted pulse data (SPD) library—part II: a
processing framework for LiDAR data from pulsed laser systems in terrestrial environments.
Comput. Geosci. 56, 207–215 (2013)
Chavez Jr., P.S.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24(3), 459–479 (1988)
Claverie, M., Vermote, E.F., Franch, B., Masek, J.G.: Evaluation of the Landsat-5 TM and
Landsat-7 ETM+ surface reflectance products. Remote Sens. Environ. 169, 390–403 (2015)
Coren, F., Sterzai, P.: Radiometric correction in laser scanning. Int. J. Remote Sens. 27(15), 3097–
3104 (2007)
Draper, D.W., Newell, D.A., Wentz, F.J., Krimchansky, S., Skofronick-Jackson, G.M.: The Global
Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument Overview and Early
On-Orbit Performance. Sel. Top. Appl. Earth Obs. Remote Sens. IEEE J. 8(7), 3452–3462
(2015.) http://doi.org/10.1109/JSTARS.2015.2403303
Donoghue, D.N.M., Watt, P.J., Cox, N.J., Wilson, J.: Remote sensing of species mixtures in
conifer plantations using LiDAR height and intensity data. Remote Sens. Environ. 110(4),
509–522 (2007)
Frantz, D., Röder, A., Stellmes, M., Hill, J.: An operational radiometric landsat preprocessing
framework for Large-Area time series applications. IEEE Trans. Geosci. Remote Sens. 1–16
(n.d..) http://doi.org/10.1109/TGRS.2016.2530856
Evans, J.S., Hudak, A.T.: A multiscale curvature algorithm for classifying discrete return LiDAR
in forested environments. IEEE Trans. Geosci. Remote Sens. 45(4), 1029–1038 (2007)
Feng, M., Sexton, J.O., Huang, C., Masek, J.G., Vermote, E.F., Gao, F., et al.: Global surface
reflectance products from Landsat: assessment using coincident MODIS observations. Remote
Sens. Environ. 134, 276–293 (2013)
Gaveau, D.L.A., Balzter, H., Plummer, S.: Forest woody biomass classification with satellite-based
radar coherence over 900 000 km2 in Central Siberia. For. Ecol. Manag. 174(1–3), 65–75 (2003)
Green, M., Kondragunta, S., Ciren, P., Xu, C.: Comparison of GOES and MODIS Aerosol Optical
Depth (AOD) to Aerosol Robotic Network (AERONET) AOD and IMPROVE PM2.5 Mass at
Bondville, Illinois. J. Air Waste Manage. Assoc. 59(9), 1082–1091 (2009)
Higgins, M.A., Asner, G.P., Martin, R.E., Knapp, D.E., Anderson, C., Kennedy-Bowdoin, T.,
Saenz, R., Aguilar, A., Wright, S.J.: Linking imaging spectroscopy and LiDAR with floristic
composition and forest structure in Panama. Remote Sens. Environ. 154, 358–367 (2014)
Ho Tong Minh, D., Le Toan, T., Rocca, F., Tebaldini, S., Villard, L., Réjou-Méchain, M., Phillips,
O.L., Feldpausch, T.R., Dubois-Fernandez, P., Scipal, K., Chave, J.: SAR tomography for the
retrieval of forest biomass and height: cross-validation at two tropical forest sites in French
Guiana. Remote Sens. Environ. 175, 138–147 (2016)
Pre-processing of Remotely Sensed Imagery