The mixed grassland in Canada is characterized by low to medium green vegetation cover, with a large amount of canopy background, such as non-photosynthetic vegetation residuals (litter), bare soil, and ground level biological crust. It is a challenge to extract the canopy information from satellite images because of the influence of canopy background. Therefore, this study aims to extract a soil line, a representation of bare soil with litter and soil crust in the surface, from Landsat images to reduce the background effect. Field work was conducted in the West Block of Grasslands National Park (GNP) in Canada, which represents the northern mixed grassland from late June to early July 2005. Six TM images with either no or only a small amount of cloud content were collected in 2005. In this study, soil lines were extracted directly from images by quantile regression and the (R, NIR min) method. The results show that, (1) both cloud and cloud shadow have obvious influence on simulating soil line automatically from images; (2) green up and late senescence seasons are relatively better for soil line simulation; (3) the (R, NIR min) method is better for soil line simulation than quantile regression to extract green biomass or green cover information.
References
[1]
Zhang, C.; Guo, X. Measuring biological heterogeneity in the northern mixed prairie: A remote sensing approach. Can. Geogr 2007, 51, 462–474.
[2]
Milton, S.J.; Dean, W.R.J.; Ellis, R.P. Rangeland health assessment: A practical guide for ranchers in arid Karoo shrublands* 1. J. Arid Environ 1998, 39, 253–265.
[3]
Eisfelder, C.; Kuenzer, C.; Dech, S. A Review on derivation of biomass information in semi-arid regions based on remote sensing data. Proc. SPIE 2010, 7831, doi:10.1117/12.868505.
[4]
Frank, A.B.; Karn, J.F. Vegetation indices, CO2 flux, and biomass for Northern Plains Grasslands. J. Range Management 2003, 56, 382–387.
[5]
Feng, X.M.; Liu, Y.; Zhao, Y.S. Remote Sensing Linked Modeling of the Aboveground Biomass of Semiarid Grassland in Inner Mongolia. Proceedings of 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’05, Seoul, South Korea, 25–29 July 2005; 5, pp. 3047–3050.
[6]
Bao, A.M.; Cao, X.M.; Chen, X.; Xia, Y. Study on models for monitoring of aboveground biomass about Bayinbuluke grassland assisted by remote sensing. Proc. SPIE 2008, 7083, doi:10.1117/12.791724.
[7]
Shen, M.; Tang, Y.; Klein, J.; Zhang, P.; Gu, S.; Shimono, A.; Chen, J. Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau. J. Plant. Ecol 2008, 1, 247–257.
[8]
Chen, J.; Gu, S.; Shen, M.G.; Tang, Y.H.; Matsushita, B. Estimating aboveground biomass of grassland having a high canopy cover: An exploratory analysis of in situ hyperspectral data. Int. J. Remote Sens 2009, 30, 6497–6517.
[9]
Zhang, C.; Guo, X. Monitoring northern mixed prairie health using broadband satellite imagery. Int. J. Remote Sens 2008, 29, 2257–2271.
[10]
Richardson, A.J.; Wiegand, C. Distinguishing vegetation from soil background information (by gray mapping of Landsat MSS data). Photogramm. Eng. Remote Sens 1977, 43, 1541–1552.
[11]
Fox, G.A.; Sabbagh, G.J.; Searcy, S.W.; Yang, C. An automated soil line identification routine for remotely sensed images. Soil Sci. Soc. Am. J 2004, 68, 1326–1331.
[12]
Baret, F.; Jacquemoud, S.; Hanocq, J.F. About the soil line concept in remote-sensing. Adv. Space Res 1993, 13, 281–284.
[13]
Galvao, L.S.; Vitorello, I. Variability of laboratory measured soil lines of soils from southeastern Brazil. Remote Sens. Environ 1998, 63, 166–181.
[14]
Jaishanker, R.; Thomaskutty, A.V.; Senthivel, T.; Sridhar, V.N. Soil line transformation based relative radiometric normalization. Int. J. Remote Sens 2006, 27, 5103–5108.
[15]
Yoshioka, H.; Miura, T.; Demattê, J.A.; Batchily, K.; Huete, A.R. Derivation of soil line influence on two-band vegetation indices and vegetation isolines. Remote Sens. 2009, 1, 842–857.
[16]
Huete, A.; Jackson, R.; Post, D. Spectral response of a plant canopy with different soil backgrounds. Remote Sen. Environ 1985, 17, 37–53.
[17]
Baret, F.; Guyot, G. Potentials and limits of vegetation indexes for LAI and APAR assessment. Remote Sens. Environ 1991, 35, 161–173.
[18]
Baret, F.; Guyot, G.; Major, D. TSAVI—A Vegetation Index which Minimizes Soil Brightness Effects on LAI and APAR Estimation. Proceedings of 12th Canadian Symposium on Remote Sensing and 1989 International Geoscience and Remote Sensing Symposium, IGARSS’89, Vancouver, BC, Canada, 10–14 July 1989; pp. 1355–1358.
[19]
Gitelson, A.A.; Stark, R.; Grits, U.; Rundquist, D.; Kaufman, Y.; Derry, D. Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. Int. J. Remote Sens 2002, 23, 2537–2562.
[20]
Thoma, D.; Gupta, S.; Bauer, M. Evaluation of optical remote sensing models for crop residue cover assessment. J. Soil Water Conserv 2004, 59, 224–233.
[21]
Fox, G.A.; Sabbagh, G.J. Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line Euclidean distance technique. Soil Sci. Soc. Am. J 2002, 66, 1922–1929.
[22]
Fox, G.A.; Metla, R. Soil property analysis using principal components analysis, soil line, and regression models. Soil Sci. Soc. Am. J 2005, 69, 1782–1788.
[23]
Wang, J.; Li, Y.H.; Chen, Y.Q.; He, T.; Lv, C.Y. Hyperspectral Degraded Soil Line Index and Soil Degradation Mapping in Agriculture-Pasture Mixed Area in Northern China. Proceedings of International Workshop on Earth Observation and Remote Sensing Applications, 2008 (EORSA 2008), Beijing, China, 30 June–2 July 2008; pp. 1–10.
[24]
Stabile, M.C.C.; Searcy, S.W. Validation of the soil line transformation technique. Trans. ASABE 2009, 52, 633–640.
[25]
Paz-Pellat, F.; Reyes, M.; Medrano, E. Design of spectral vegetation indexes using iso-soil curves. Agrociencia 2011, 45, 121–134.
[26]
Guo, X.L.; Wilmshurst, J.F.; Li, Z.Q. Comparison of laboratory and field remote sensing methods to measure forage quality. Int. J. Environ. Res. Public Health 2010, 7, 3513–3530.
[27]
Black, S.C.; Guo, X. Estimation of grassland CO2 exchange rates using hyperspectral remote sensing techniques. Int. J. Remote Sens 2008, 29, 145–155.
[28]
Li, Z.; Guo, X. Detecting climate effects on vegetation in northern mixed prairie using NOAA AVHRR 1-km time-series NDVI data. Remote Sens 2012, 4, 120–134.
[29]
Fargey, K.S.; Larson, S.D.; Grant, S.J.; Fargey, P.; Schmidt, C. Grasslands National Park Field Guide; Prairie Wind & Silver Sage, Friends of Grasslands Inc.: Val Marie, SK, Canada, 2000.
[30]
Guo, X.; Wilmshurst, J.; McCanny, S.; Fargey, P.; Richard, P.; Richard, P. Measuring spatial and vertical heterogeneity of grasslands using remote sensing techniques. J. Environ. Inform 2004, 3, 24–32.
[31]
Koenker, R.; Bassett, G. Regression quantiles. Econometrica 1978, 46, 33–50.
[32]
Koenker, R.; Machado, J.A.F. Goodness of fit and related inference processes for quantile regression. J. Am. Stat. Assoc 1999, 94, 1296–1310.
[33]
Tsionas, E.G. Bayesian quantile inference. J. Stat. Comput. Simul 2003, 73, 659–674.
[34]
Young, T.M.; Shaffer, L.B.; Guess, F.M.; Bensmail, H.; Leon, R.V. A comparison of multiple linear regression and quantile regression for modeling the internal bond of medium density fiberboard. For. Prod. J 2008, 58, 39–48.
[35]
Cade, B.S.; Terrell, J.W.; Neely, B.C. Estimating geographic variation in allometric growth and body condition of blue suckers with quantile regression. Trans. Am. Fish. Soc 2011, 140, 1657–1669.
[36]
Mills, A.J.; Fey, M.V.; Grongroft, A.; Petersen, A.; Medinski, T.V. Unravelling the effects of soil properties on water infiltration: Segmented quantile regression on a large data set from arid south-west Africa. Australian J. Soil Res 2006, 44, 783–797.
[37]
Mills, A.; Fey, M.; Donaldson, J.; Todd, S.; Theron, L. Soil infiltrability as a driver of plant cover and species richness in the semi-arid Karoo, South Africa. Plant. Soil 2009, 320, 321–332.
[38]
Zhang, X.Y.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ 2003, 84, 471–475.
[39]
Rouse, J.; Haas, R.; Schell, J.; Deering, D.; Harlan, J. Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation Greenbelt, MD; NASA/GSFC: College Station, TX, USA, 1974.
[40]
Yoshioka, H.; Miura, T.; Demattê, J.A.; Batchily, K.; Huete, A.R. Soil line influences on two-band vegetation indices and vegetation isolines: A numerical study. Remote Sens 2010, 2, 545–561.
