| Research Articles |
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| Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years |
| Feng YAN1, Bo WU1*, YanJiao WANG2 |
1 Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China;
2 National Climate Center, China Meteorological Administration, Beijing 100081, China |
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Abstract Remote sensing is a valuable and effective tool for monitoring and estimating aboveground biomass (AGB) in large areas. The current international research on biomass estimation by remote sensing technique mainly focused on forests, grasslands and crops, with relatively few applications for desert ecosystems. In this paper, Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images from 1988 to 2007 and the data of 283 AGB samples in August 2007 were used to estimate the AGB for Mu Us Sandy Land over the past 30 years. Moreover, temporal and spatial distribution characteristics of AGB and influencing factors of climate and underlying surface were also studied. Results show that: (1) Differences of correlations exist in the fitted equations between AGB and different vegetation indices in desert areas. The modified soil adjusted vegetation index (MSAVI) and soil adjusted vegetation index (SAVI) show relatively higher correlations with AGB, while the correlation between normalized difference vegetation index (NDVI) and AGB is relatively lower. Error testing shows that the AGB- MSAVI model established can be used to accurately estimate AGB of Mu Us Sandy Land in August. (2) AGB in Mu Us Sandy Land shows the fluctuant characteristics over the past 30 years, which decreased from the 1980s to the 1990s, and increased from the 1990s to 2007. AGB in 2007 had the highest value, with a total AGB of 3.352×106 t. Moreover, in the 1990s, AGB had the lowest value with a total AGB of 2.328×106 t. (3) AGB with relatively higher values was mainly located in the middle and southern parts of Mu Us Sandy Land in the 1980s. AGB was low in the whole area in the1990s, and relatively higher AGB values were mainly located in the southern parts of Uxin. In 2007, AGB in the whole area was relatively higher than those of the last twenty years, and higher AGB values were mainly located in the northern, western and middle parts of Mu Us Sandy Land.
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Received: 26 October 2012
Published: 06 December 2013
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| Fund: The National Nonprofit Institute Research Grant of Chinese Academy of Forestry (CAFYBB2011003, CAFYBB2011002), the Key Laboratory of Agrometeorological Support and Applied Technique of China Meteorological Administration (AMF201107, AMF201204), and the National Natural Science Foundation of China (40801173). |
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Corresponding Authors:
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| Barnett J L, Thompson D R. 1983. Large area relation of Landsat MSS and NOAA-6 AVHRR spectral data to wheat yield. Remote Sensing of Environment, 13(4): 277–290.Boyd D S, Foody G M, Curran P J. 1999. The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0–5.0 μm). International Journal of Remote Sensing, 20(5): 1017–1023.Burrows S N, Gower S T, Norman J M, et al. 2003. Spatial variability of aboveground net primary production for a forested landscape in northern Wisconsin. Canadian Journal of Forest Research, 33(10): 2007–2018.Claverie M, Demarez V, Duchemin B, et al. 2012. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, 124: 844–857.Crow T R. 1978. Biomass and production in three contiguous forests in northern Wisconsin. Ecology, 59(2): 265–273.Curran P J, Dungan J L, Gholz H L. 1992. Seasonal LAI in slash pine estimated with landsat TM. Remote Sensing of Environment, 39(1): 3–13.Fang J Y, Chen A P, Peng C H, et a1. 2001. Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 292(5525): 2320–2322.Hame T, Salli A, Andersson K, et al. 1997. A new methodology for the estimation of biomass of conifer dominated boreal forest using NOAA AVHRR data. International Journal of Remote Sensing, 18(15): 3211–3243.Houghton R A, Skole D L, Nobre C A, et al. 2000. Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon. Nature, 403: 301–304.Huete A, Didan K, Shimabokuro Y, et al. 2000. Regional Amazon Basin and global analysis of MODIS vegetation indices: early results and comparisons with AVHRR. Proceeding of IGARSS, 6(2): 536–538.Huete A, Didan K, Miura T, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2): 195–213.Huete A R. 1988. A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295–309.Huete A R, Hua G, Qi J, et al. 1992. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment, 41(2–3): 143–154.Ikeda H, Okamoto K, Fukuhara M. 1999. Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data. International Journal of Remote Sensing, 20(11): 2283–2294.Major D J, Baret F, Guyot G. 1990. A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing, 11(5): 727–740.Moreaua S, Bossenob R, Gu X F, et al. 2003. Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR. Remote Sensing of Environment, 85(4): 516–529.Muukkonen P, Heiskanen J. 2005. Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sensing of Environment, 99(4): 434–447.Muukkonen P, Heiskanen J. 2007. Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: a possibility to verify carbon inventories. Remote Sensing of Environment, 107(4): 617–624.Nezlina N P, Kostianoyb A G, Li B L. 2005. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. Journal of Arid Environments, 62(4): 677–700.Propastin P A, Kappas M W, Herrmann S M, et al. 2012. Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing. International Journal of Remote Sensing, 33(5–6): 1465–1487.Qi J, Chehbouni A, Huete A R. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2): 119–126.Roerink G J, Menenti M, Soepboer W, et al. 2003. Assessment of climate impact on vegetation dynamics by using remote sensing. Physics and Chemistry of the Earth, 28(1–3): 103–109.Rouse J W, Haas R H, Schell J A, et al. 1973. Monitoring vegetation systems in the Great Plains with ERTS. In: Freden S C, Mercanti E P, Becker M. Third Earth Resources Technology Satellite-1 Sympo-sium. Technical Presentations, Section A, Vol. I. Washington, DC: National Aeronautics and Space Administration, 309–317. Schmidt H, Karnileli A. 2000. Remote sensing of the seasonal variability of vegetation in a semi-arid environment. Journal of Arid Environ-ments, 45(1): 43–59.Singh R, Goyal R C, Saha S K, et al. 1992. Use of satellite spectral data in crop yield estimation surveys. International Journal of Remote Sensing, 13(14): 2583–2592.Todd S W, Hoffer R M, Milchunas D G. 1998. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing, 19(3): 427–438.Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127–150.Verbesselt J, Somers B, van Aardt J, et al. 2006. Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems. Remote Sensing of Environment, 101(3): 399–414.Zhang L, Wylie B, Loveland T, et a1. 2007. Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands. Remote Sensing of Environment, 106(2): 173–189.Zheng D L, Rademacher J, Chen J Q, et al. 2004. Estimating above-ground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Envi-ronment, 93(3): 402–411. |
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