A Simple Method for Retrieving Understory NDVI in Sparse Needleleaf Forests in Alaska Using MODIS BRDF Data

Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory properties in LAI estimation algorithms. Compared with the conventional data bank method, estimation of forest understory properties from satellite data is superior in studies at a global or continental scale over long periods. However, implementation of the current remote sensing method based on multi-angular observations is complicated. As an alternative, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the fact that the bidirectional variation in NDVIu is smaller than that in canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using pixels within a 5 × 5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position in which the stand NDVI had the smallest angular variation. Validation by a noise-free simulation data set yielded high agreement between estimated and true NDVIu, with R2 and RMSE of 0.99 and 0.03, respectively. Using the MODIS BRDF data, we achieved an estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively) and reasonable seasonal patterns of NDVIu in 2010 to 2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests and ultimately to benefit studies on carbon cycle modeling over high-latitude areas.

[1]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[2]  A. Rango,et al.  Mapping shrub abundance in desert grasslands using geometric-optical modeling and multi-angle remote sensing with CHRIS/Proba , 2006 .

[3]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[4]  Sylvain G. Leblanc,et al.  Investigation of directional reflectance in boreal forests with an improved four-scale model and airborne POLDER data , 1999, IEEE Trans. Geosci. Remote. Sens..

[5]  Scott J. Goetz,et al.  SNF Leaf Optical Properties: TMS , 1996 .

[6]  Jan Pisek,et al.  Algorithm for global leaf area index retrieval using satellite imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[7]  A. Kuusk,et al.  A Directional Multispectral Forest Reflectance Model , 2000 .

[8]  Hideki Kobayashi,et al.  A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape , 2008 .

[9]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[10]  Ray R. Hicks,et al.  Oak establishment and canopy accession strategies in five old-growth stands in the central hardwood forest region , 2003 .

[11]  Jing M. Chen,et al.  Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data , 2009 .

[12]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

[13]  Andres Kuusk,et al.  Simulation of the reflectance of ground vegetation in sub-boreal forests , 2004 .

[14]  F. R. Schiebe,et al.  Canopy attributes of desert grassland and transition communities derived from multiangular airborne imagery , 2003 .

[15]  Michel M. Verstraete,et al.  Toward a direct comparison of field and laboratory goniometer measurements , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Tetsuya Hiyama,et al.  NDVI responses to the forest canopy and floor from spring to summer observed by airborne spectrometer in eastern Siberia , 2011 .

[17]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[18]  Richard A. Fournier,et al.  Seasonal change in understory reflectance of boreal forests and influence on canopy vegetation indices , 1997 .

[19]  Keiji Kushida,et al.  Remote sensing of net ecosystem productivity based on component spectrum and soil respiration observation in a boreal forest, interior Alaska , 2004 .

[20]  J. Pisek,et al.  Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America , 2007 .

[21]  Kyoichi Otsuki,et al.  Influences of canopy structure and physiological traits on flux partitioning between understory and overstory in an eastern Siberian boreal larch forest , 2011 .

[22]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[23]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[24]  Miina Rautiainen,et al.  Retrieval of seasonal dynamics of forest understory reflectance in a Northern European boreal forest from MODIS BRDF data , 2012 .

[25]  John R. Miller,et al.  Mapping Forest Background Reflectance in a Boreal Region Using Multiangle Compact Airborne Spectrographic Imager Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Hideki Kobayashi,et al.  Spatial Scale and Landscape Heterogeneity Effects on FAPAR in an Open-Canopy Black Spruce Forest in Interior Alaska , 2014, IEEE Geoscience and Remote Sensing Letters.

[27]  Ronggao Liu,et al.  Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties , 2013 .

[28]  A. Rango,et al.  Modelling the reflectance anisotropy of Chihuahuan Desert grass–shrub transition canopy–soil complexes , 2004 .

[29]  Ronggao Liu,et al.  Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data , 2012 .

[30]  J. Chen,et al.  Retrieving forest background reflectance in a boreal region from Multi-angle Imaging SpectroRadiometer (MISR) data , 2007 .

[31]  Sylvain G. Leblanc,et al.  A four-scale bidirectional reflectance model based on canopy architecture , 1997, IEEE Trans. Geosci. Remote. Sens..

[32]  Nadine Gobron,et al.  Partitioning the Solar Radiant Fluxes in Forest Canopies in the Presence of Snow , 2008 .

[33]  M. Rautiainen,et al.  BRDF measurement of understory vegetation in pine forests: dwarf shrubs, lichen, and moss , 2005 .