Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor

被引:161
作者
Ramoelo, A. [1 ,2 ]
Skidmore, A. K. [2 ]
Cho, M. A. [1 ]
Schlerf, M. [3 ]
Mathieu, R. [1 ]
Heitkonig, I. M. A. [4 ]
机构
[1] CSIR, Earth Observat Res Grp, Nat Resource & Environm Unit, ZA-0001 Pretoria, South Africa
[2] Univ Twente UT ITC, Fac Geoinformat Sci & Earth Observat, NL-7500 AE Enschede, Netherlands
[3] Publ Res Ctr, L-4422 Belvaux, Luxembourg
[4] Wageningen Univ, Resource Ecol Grp, NL-6708 PB Wageningen, Netherlands
基金
新加坡国家研究基金会;
关键词
Grass nitrogen; Savanna ecosystem; Integrated modeling; Red-edge band; RapidEye; Vegetation indices; HYPERSPECTRAL VEGETATION INDEXES; LEAF-AREA INDEX; CHLOROPHYLL CONTENT; BROAD-BAND; BIOCHEMICAL CONTENT; FORAGING BEHAVIOR; CROSS-VALIDATION; PREDICTION; BIOMASS; QUALITY;
D O I
10.1016/j.jag.2012.05.009
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The regional mapping of grass nutrients is of interest in the sustainable planning and management of livestock and wildlife grazing. The objective of this study was to estimate and map foliar and canopy nitrogen (N) at a regional scale using a recent high resolution spaceborne multispectral sensor (i.e. RapidEye) in the Kruger National Park (KNP) and its surrounding areas, South Africa. The RapidEye sensor contains five spectral bands in the visible-to-near infrared (VNIR), including a red-edge band centered at 710 nm. The importance of the red-edge band for estimating foliar chlorophyll and N concentrations has been demonstrated in many previous studies, mostly using field spectroscopy. The utility of the red-edge band of the RapidEye sensor for estimating grass N was investigated in this study. A two-step approach was adopted involving (i) vegetation indices and (ii) the integration of vegetation indices with environmental or ancillary variables using a stepwise multiple linear regression (SMLR) and a non-linear spatial least squares regression (PLSR). The model involving the simple ratio (SR) index (R-805/R-710) defined as SR54, altitude and the interaction between SR54 and altitude (SR54* altitude) yielded the highest accuracy for canopy N estimation, while the non-linear PLSR yielded the highest accuracy for foliar N estimation through the integration of remote sensing (SR54) and environmental variables. The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the red edge waveband with a high spatial resolution. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:151 / 162
页数:12
相关论文
共 90 条
[1]   Amphibians do not follow Bergmann's rule [J].
Adams, Dean C. ;
Church, James O. .
EVOLUTION, 2008, 62 (02) :413-420
[2]  
[Anonymous], KRUGER EXPERIENCE EC
[3]   Variability in leaf and litter optical properties: Implications for BRDF model inversions using AVHRR, MODIS, and MISR [J].
Asner, GP ;
Wessman, CA ;
Schimel, DS ;
Archer, S .
REMOTE SENSING OF ENVIRONMENT, 1998, 63 (03) :243-257
[4]   POTENTIALS AND LIMITS OF VEGETATION INDEXES FOR LAI AND APAR ASSESSMENT [J].
BARET, F ;
GUYOT, G .
REMOTE SENSING OF ENVIRONMENT, 1991, 35 (2-3) :161-173
[5]   THE RELATIONSHIPS BETWEEN SOIL FACTORS, GRASS NUTRIENTS AND THE FORAGING BEHAVIOR OF WILDEBEEST AND ZEBRA [J].
BENSHAHAR, R ;
COE, MJ .
OECOLOGIA, 1992, 90 (03) :422-428
[6]   Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density [J].
Broge, NH ;
Leblanc, E .
REMOTE SENSING OF ENVIRONMENT, 2001, 76 (02) :156-172
[7]   BOOTSTRAP AND CROSS-VALIDATION ESTIMATES OF THE PREDICTION ERROR FOR LINEAR-REGRESSION MODELS [J].
BUNKE, O ;
DROGE, B .
ANNALS OF STATISTICS, 1984, 12 (04) :1400-1424
[8]   Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs [J].
Çamdevyren, H ;
Demyr, N ;
Kanik, A ;
Keskyn, S .
ECOLOGICAL MODELLING, 2005, 181 (04) :581-589
[9]   A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method [J].
Cho, MA ;
Skidmore, AK .
REMOTE SENSING OF ENVIRONMENT, 2006, 101 (02) :181-193
[10]   INTEGRATING REMOTE SENSING AND ANCILLARY DATA FOR REGIONAL ECOSYSTEM ASSESSMENT: EUCALYPTUS GRANDIS AGRO-SYSTEM IN KWAZULU-NATAL, SOUTH AFRICA [J].
Cho, Moses ;
van Aardt, Jan ;
Main, Russell ;
Majeke, Bongani ;
Ramoelo, Abel ;
Mathieu, Renaud ;
Norris-Rogers, Mark ;
Du Plessis, Marius .
2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5, 2009, :2644-+