A radiative transfer model-based method for the estimation of grassland aboveground biomass

International Journal of Applied Earth Observation and Geoinformation

Volumn 54, Issue , 2017, Pages 159-168

  Quan, Xingwen ^a   He, Binbin ^a   Yebra, Marta ^b,c   Yin, Changming ^a   Liao, Zhanmang ^a   Zhang, Xueting ^a   Li, Xing ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c BUSHFIRE AND NATURAL HAZARDS COOPERATIVE RESEARCH CENTRE   (Australia)

Author keywords

Grassland aboveground biomass; Ill posed inversion problem; Landsat 8 OLI product; Leaf area index; PROSAILH

Indexed keywords

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; ESTIMATION METHOD; GRASSLAND; INVERSE PROBLEM; LANDSAT; LEAF AREA INDEX; RADIATIVE TRANSFER; REGRESSION ANALYSIS;

CHINA;

EID: 85018630181     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2016.10.002     Document Type: Article

References (103)

1. Anaya, J.A., Chuvieco, E., Palacios-Orueta, A., Aboveground biomass assessment in Colombia: a remote sensing approach. For. Ecol. Manage. 257 (2009), 1237–1246.
2. Anderson, G.L., Hanson, J.D., Haas, R.H., Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sens. Environ. 45 (1993), 165–175.
3. Attema, E.P.W., Ulaby, F.T., Vegetation modeled as a water cloud. Radio Sci. 13 (1978), 357–364.
4. Baghdadi, N., Le Maire, G., Bailly, J.S., Ose, K., Nouvellon, Y., Zribi, M., Lemos, C., Hakamada, R., Evaluation of ALOS/PALSAR L-Band data for the estimation of Eucalyptus plantations aboveground biomass in Brazil. IEEE J. Sel. Topics Earth Obs. Remote Sens. (JSTARS) 8 (2015), 3802–3811.
5. Baret, F., Clevers, J.G.P.W., Steven, M.D., The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches. Remote Sens. Environ. 54 (1995), 141–151.
6. Barrachina, M., Cristóbal, J., Tulla, A.F., Estimating above-ground biomass on mountain meadows and pastures through remote sensing. Int. J. Appl. Earth Obs. 38 (2015), 184–192.
7. Boudreau, J., Nelson, R.F., Margolis, H.A., Beaudoin, A., Guindon, L., Kimes, D.S., Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sens. Environ. 112 (2008), 3876–3890.
8. Cartus, O., Santoro, M., Kellndorfer, J., Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band. Remote Sens. Environ. 124 (2012), 466–478.
9. Chen, Q., Vaglio Laurin, G., Valentini, R., Uncertainty of remotely sensed aboveground biomass over an African tropical forest: propagating errors from trees to plots to pixels. Remote Sens. Environ. 160 (2015), 134–143.
10. Chen, Q., Modeling aboveground tree woody biomass using national-scale allometric methods and airborne LiDAR. ISPRS J. Photogramm. Remote Sens. 106 (2015), 95–106.
11. Cho, M.A., Skidmore, A.K., Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands Majella National Park, Italy. Int. J. Remote Sens. 30 (2009), 499–515.
12. Cho, M.A., Skidmore, A., Corsi, F., van Wieren, S.E., Sobhan, I., Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Obs. 9 (2007), 414–424.
13. Cho, M.A., Mathieu, R., Asner, G.P., Naidoo, L., van Aardt, J., Ramoelo, A., Debba, P., Wessels, K., Main, R., Smit, I.P.J., Erasmus, B., Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125 (2012), 214–226.
14. Clevers, J.G.P.W., van Leeuwen, H.J.C., Combined use of optical and microwave remote sensing data for crop growth monitoring. Remote Sens. Environ. 56 (1996), 42–51.
15. Clevers, J., Van der Heijden, G., Verzakov, S., Schaepman, M., Estimating grassland biomass using SVM band shaving of hyperspectral data. Photogramm. Eng. Remote Sens. 73 (2007), 1141–1148.
16. Cloude, S.R., Papathanassiou, K.P., Polarimetric SAR interferometry. IEEE Trans. Geosci. Remote Sens. 36 (1998), 1551–1565.
17. Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sens. Environ. 112 (2008), 2592–2604.
18. Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsi, F., Cho, M., LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens. 63 (2008), 409–426.
19. Diepen, C.v., Wolf, J., Keulen, H.v., Rappoldt, C., WOFOST: a simulation model of crop production. Soil Use Manage. 5 (1989), 16–24.
20. Doraiswamy, P.C., Sinclair, T.R., Hollinger, S., Akhmedov, B., Stern, A., Prueger, J., Application of MODIS derived parameters for regional crop yield assessment. Remote Sens. Environ. 97 (2005), 192–202.
21. Ehsani, M.R., Upadhyaya, S.K., Slaughter, D., Shafii, S., Pelletier, M., A NIR technique for rapid determination of soil mineral nitrogen. Precis. Agric. 1 (1999), 219–236.
22. Englhart, S., Keuck, V., Siegert, F., Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use. Remote Sens. Environ. 115 (2011), 1260–1271.
23. Fassnacht, F.E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., Koch, B., Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sens. Environ. 154 (2014), 102–114.
24. Fensholt, R., Sandholt, I., Rasmussen, M.S., Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens. Environ. 91 (2004), 490–507.
25. Feret, J.B., Francois, C., Gitelson, A., Asner, G.P., Barry, K.M., Panigada, C., Richardson, A.D., Jacquemoud, S., Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens. Environ. 115 (2011), 2742–2750.
26. Gastellu-Etchegorry, J.P., Martin, E., Gascon, F., DART: a 3D model for simulating satellite images and studying surface radiation budget. Int. J. Remote Sens. 25 (2004), 73–96.
27. Geladi, P., Kowalski, B.R., Partial least-squares regression: a tutorial. Anal. Chim. Acta 185 (1986), 1–17.
28. Godwin, D., Ritchie, J., Singh, U., Hunt, L., A Users Guide to CERES Wheat, Vol. v2 10, 1989.
29. Hajj, M.E., Baghdadi, N., Belaud, G., Zribi, M., Cheviron, B., Courault, D., Hagolle, O., Charron, F., Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data. Remote Sens. 6 (2014), 10002–10032.
30. Hansen, P.M., Schjoerring, J.K., Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86 (2003), 542–553.
31. Hardisky, M., Klemas, V., Smart, R.M., The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm. Eng. Remote Sens. 49 (1983), 77–83.
32. Haykin, S.S., Neural Networks: a Comprehensive Foundation. 2001, Tsinghua University Press.
33. He, B., Quan, X., Xing, M., Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model. Int. J. Appl. Earth Obs. 21 (2013), 78–91.
34. He, B., Li, X., Quan, X., Qiu, S., Estimating the aboveground dry biomass of grass by assimilation of retrieved LAI into a crop growth model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (2015), 550–561.
35. Hill, M.J., Senarath, U., Lee, A., Zeppel, M., Nightingale, J.M., Williams, R.J., McVicar, T.R., Assessment of the MODIS LAI product for Australian ecosystems. Remote Sens. Environ. 101 (2006), 495–518.
36. Hongliang, F., Shunlin, L., Retrieving leaf area index with a neural network method: simulation and validation. IEEE Trans. Geosci. Remote Sens. 41 (2003), 2052–2062.
37. Houborg, R., Soegaard, H., Boegh, E., Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sens. Environ. 106 (2007), 39–58.
38. Houborg, R., Anderson, M., Daughtry, C., Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sens. Environ. 113 (2009), 259–274.
39. Houghton, R.A., Aboveground forest biomass and the global carbon balance. Global Change Biol. 11 (2005), 945–958.
40. Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D., Wu, W., Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agr. For. Meteorol. 204 (2015), 106–121.
41. Huang, J., Ma, H., Su, W., Zhang, X., Huang, Y., Fan, J., Wu, W., Jointly assimilating MODIS LAI and ET products into the SWAP Model for winter wheat yield estimation. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 8 (2015), 4060–4071.
42. Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., Tian, L., Zhang, X., Fan, J., Wu, W., Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agr. For. Meteorol. 216 (2016), 188–202.
43. Ingram, J.C., Dawson, T.P., Whittaker, R.J., Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens. Environ. 94 (2005), 491–507.
44. Jacquemoud, S., Baret, F., PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 34 (1990), 75–91.
45. Jensen, J.R., Qiu, F., Ji, M., Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. Int. J. Remote Sens. 20 (1999), 2805–2822.
46. Jurdao, S., Yebra, M., Guerschman, J.P., Chuvieco, E., Regional estimation of woodland moisture content by inverting Radiative Transfer Models. Remote Sens. Environ. 132 (2013), 59–70.
47. Koch, B., Status and future of laser scanning: synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J. Photogramm. 65 (2010), 581–590.
48. Kooistra, L., Salas, E.A.L., Clevers, J.G.P.W., Wehrens, R., Leuven, R.S.E.W., Nienhuis, P.H., Buydens, L.M.C., Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. Environ. Pollut. 127 (2004), 281–290.
49. Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., Goetz, A.F.H., The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44 (1993), 145–163.
50. Kuusk, A., The hot spot effect in plant canopy reflectance. Myneni, R., Ross, J., (eds.) Photon-Vegetation Interactions, 1991, Springer Berlin Heidelberg, 139–159.
51. Kuusk, A., A two-layer canopy reflectance model. J. Quant. Spectrosc. Radiat. Transfer 71 (2001), 1–9.
52. Lefsky, M.A., Hudak, A.T., Cohen, W.B., Acker, S., Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest. Remote Sens. Environ. 95 (2005), 532–548.
53. Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., Wang, C., Muhammad, S., Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. Int. J. Appl. Earth Obs. 41 (2015), 88–98.
54. Liao, Z., He, B., Quan, X., Modified enhanced vegetation index for reducing topographic effects. APPRES, 9, 2015 096068–096068.
55. Liu, J., Pattey, E., Miller, J.R., McNairn, H., Smith, A., Hu, B., Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens. Environ. 114 (2010), 1167–1177.
56. Liu, Y.Y., van Dijk, A.I.J.M., de Jeu, R.A.M., Canadell, J.G., McCabe, M.F., Evans, J.P., Wang, G., Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5 (2015), 470–474.
57. Lu, D., The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 27 (2006), 1297–1328.
58. Ma, G., Huang, J., Wu, W., Fan, J., Zou, J., Wu, S., Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Math. Comput. Modell. 58 (2013), 634–643.
59. Ma, H., Huang, J., Zhu, D., Liu, J., Su, W., Zhang, C., Fan, J., Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST–ACRM model with Ensemble Kalman Filter. Math. Comput. Modell. 58 (2013), 759–770.
60. Matthew, M.W., Adler-Golden, S.M., Berk, A., Richtsmeier, S.C., Levine, R.Y., Bernstein, L.S., Acharya, P.K., Anderson, G.P., Felde, G.W., Hoke, M.L., Status of atmospheric correction using a MODTRAN4-based algorithm, AeroSense 2000. Int. Soc. Opt. Photonics, 2000, 199–207.
61. Matthews, S., Effect of drying temperature on fuel moisture content measurements. Int. J. Wildland Fire 19 (2010), 800–802.
62. Moreau, S., Le Toan, T., Biomass quantification of Andean wetland forages using ERS satellite SAR data for optimizing livestock management. Remote Sens. Environ. 84 (2003), 477–492.
63. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., Running, S.W., Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83 (2002), 214–231.
64. North, P., Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans. Geosci. Remote Sens. 34 (1996), 946–956.
65. Paul, K.I., Roxburgh, S.H., England, J.R., Ritson, P., Hobbs, T., Brooksbank, K., John Raison, R., Larmour, J.S., Murphy, S., Norris, J., Neumann, C., Lewis, T., Jonson, J., Carter, J.L., McArthur, G., Barton, C., Rose, B., Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings. For. Ecol. Manage. 310 (2013), 483–494.
66. Quan, X., He, B., Wang, Y., Tang, Z., Li, X., An extended fourier approach to improve the retrieved leaf area index (LAI) in a time series from an alpine wetland. Remote Sens. 6 (2014), 1171–1190.
67. Quan, X.W., He, B.B., Li, X., A bayesian network-based method to alleviate the ill-posed inverse problem: a case study on leaf area index and canopy water content retrieval. IEEE Trans. Geosci. Remote Sens. 53 (2015), 6507–6517.
68. Quan, X., He, B., Li, X., Tang, Z., Estimation of grassland live fuel moisture content from ratio of canopy water content and foliage dry biomass. IEEE Geosci. Remote Sens. Lett. 12 (2015), 1903–1907.
69. Quan, X., He, B., Li, X., Liao, Z., Retrieval of grassland live fuel moisture content by parameterizing radiative transfer model with interval estimated LAI. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9 (2016), 910–920.
70. Ramoelo, A., Cho, M.A., Mathieu, R., Madonsela, S., van de Kerchove, R., Kaszta, Z., Wolff, E., Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. Int. J. Appl. Earth Obs. 43 (2015), 43–54.
71. Romero, A., Aguado, I., Yebra, M., Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion. Int. J. Remote Sens. 33 (2012), 396–414.
72. Schlerf, M., Atzberger, C., Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sens. Environ. 100 (2006), 281–294.
73. Schmidtlein, S., Sassin, J., Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sens. Environ. 92 (2004), 126–138.
74. Smith, J.A., LAI inversion using a back-propagation neural network trained with a multiple scattering model. IEEE Trans. Geosci. Remote Sens. 31 (1993), 1102–1106.
75. Solberg, S., Astrup, R., Breidenbach, J., Nilsen, B., Weydahl, D., Monitoring spruce volume and biomass with InSAR data from TanDEM-X. Remote Sens. Environ. 139 (2013), 60–67.
76. Su, Y., Guo, Q., Xue, B., Hu, T., Alvarez, O., Tao, S., Fang, J., Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne LiDAR, optical imagery, and forest inventory data. Remote Sens. Environ. 173 (2016), 187–199.
77. Supit, I., Hooijer, A., Van Diepen, C., System Description of the Wofost 6.0 Crop Simulation Model Implemented in CGMS. 1994, Joint research centre, European commission.
78. Svoray, T., Shoshany, M., SAR-based estimation of areal aboveground biomass (AAB) of herbaceous vegetation in the semi-arid zone: a modification of the water-cloud model. Int. J. Remote Sens. 23 (2002), 4089–4100.
79. Talagrand, O., Courtier, P., Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Q. J. R. Meteorolog. Soc. 113 (1987), 1311–1328.
80. Tian, Y., Woodcock, C.E., Wang, Y., Privette, J.L., Shabanov, N.V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Häme, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., Myneni, R.B., Multiscale analysis and validation of the MODIS LAI product: i Uncertainty assessment. Remote Sens. Environ. 83 (2002), 414–430.
81. Tian, X., Su, Z., Chen, E., Li, Z., van der Tol, C., Guo, J., He, Q., Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area. Int. J. Appl. Earth Obs. 14 (2012), 160–168.
82. Tsui, O.W., Coops, N.C., Wulder, M.A., Marshall, P.L., McCardle, A., Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest. ISPRS J. Photogramm. 69 (2012), 121–133.
83. Van Deventer, A., Ward, A., Gowda, P., Lyon, J., Using Thematic Mapper data to identify contrasting soil plains and tillage practices. Photogramm. Eng. Remote Sens. 63 (1997), 87–93.
84. Verhoef, W., Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ. 16 (1984), 125–141.
85. Wang, Y., Woodcock, C.E., Buermann, W., Stenberg, P., Voipio, P., Smolander, H., Häme, T., Tian, Y., Hu, J., Knyazikhin, Y., Myneni, R.B., Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote Sens. Environ. 91 (2004), 114–127.
86. Wang, L., Hunt, J.E.R., Qu, J.J., Hao, X., Daughtry, C.S.T., Towards estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sens. Environ. 115 (2011), 836–840.
87. Wang, L.L., Qu, J.J., Hao, X.J., Hunt, E.R., Estimating dry matter content from spectral reflectance for green leaves of different species. Int. J. Remote Sens. 32 (2011), 7097–7109.
88. Wang, L.L., Hunt, E.R., Qu, J.J., Hao, X.J., Daughtry, C.S.T., Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sens. Environ. 129 (2013), 103–110.
89. Wold, S., Sjöström, M., Eriksson, L., PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58 (2001), 109–130.
90. Xie, Y., Sha, Z., Yu, M., Bai, Y., Zhang, L., A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecol. Modell. 220 (2009), 1810–1818.
91. Xing, M., Quan, X., Li, X., He, B., An extended approach for biomass estimation in a mixed vegetation area using ASAR and TM data. Photogramm. Eng. Remote Sens. 80 (2014), 429–438.
92. Yan, F., Wu, B., Wang, Y., Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land. China Agr. For. Meteorol. 200 (2015), 119–128.
93. Yebra, M., Chuvieco, E., Generation of a species-specific look-up table for fuel moisture content assessment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2 (2009), 21–26.
94. Yebra, M., Chuvieco, E., Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: solving the ill-posed inverse problem. Remote Sens. Environ. 113 (2009), 2403–2411.
95. Yebra, M., Chuvieco, E., Riano, D., Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agr. For. Meteorol. 148 (2008), 523–536.
96. Yebra, M., Dennison, P.E., Chuvieco, E., Riano, D., Zylstra, P., Hunt, E.R., Danson, F.M., Qi, Y., Jurdao, S., A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products. Remote Sens. Environ. 136 (2013), 455–468.
97. Yin, C., He, B., Quan, X., Liao, Z., Chlorophyll content estimation in arid grasslands from Landsat-8 OLI data. Int. J. Remote Sens. 37 (2016), 615–632.
98. Zarco-Tejada, P.J., Rueda, C.A., Ustin, S.L., Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens. Environ. 85 (2003), 109–124.
99. Zhang, G., Ganguly, S., Nemani, R.R., White, M.A., Milesi, C., Hashimoto, H., Wang, W., Saatchi, S., Yu, Y., Myneni, R.B., Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sens. Environ. 151 (2014), 44–56.
100. Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., Ryu, S.-R., Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens. Environ. 93 (2004), 402–411.
101. Zheng, G., Chen, J.M., Tian, Q.J., Ju, W.M., Xia, X.Q., Combining remote sensing imagery and forest age inventory for biomass mapping. J. Environ. Manage. 85:3 (2007), 616–623.
102. Zhu, Z., Woodcock, C.E., Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118 (2012), 83–94.
103. Zolkos, S., Goetz, S., Dubayah, R., A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens. Environ. 128 (2013), 289–298.