Predicting lidar measured forest vertical structure from multi-angle spectral data

Kimes, D. S.; Ranson, K.J.; Sun, Guoqing; Blair, J. B.

doi:10.1016/j.rse.2005.11.004

Cited by 84 publications

(56 citation statements)

References 29 publications

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“…Assessments of gaseous (CO 2 , CO) and particulate (aerosol) emissions from forest fires via estimates of biomass loss are likely to be more accurate than those from nadir-pointing passive instruments, since the vertical dimension is taken into account. Forest parameters retrieved using MISR data with a simple GO model might also prove useful when combined in a predictive modeling approach such as that used by the US Forest Service in its mapping work (e.g., Blackard & Moisen, 2005) alongside temporal-spectral remote sensing measures, or in a synergistic approach where data from active instruments are used to calibrate or train the passive multiangle observations (e.g., Kimes et al, 2006). The results reported here also show that the development of an operational MISR forest data product with utility in a wide range of mapping applications is feasible.…”

Section: Discussionmentioning

confidence: 99%

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

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Moisen

et al. 2008

Remote Sensing of Environment

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A rapid canopy reflectance model inversion experiment was performed using multi-angle reflectance data from the NASA Multi-angle Imaging Spectro-Radiometer (MISR) on the Earth Observing System Terra satellite, with the goal of obtaining measures of forest fractional crown cover, mean canopy height, and aboveground woody biomass for large parts of south-eastern Arizona and southern New Mexico (N200,000 km 2 ). MISR red band bidirectional reflectance estimates in nine views mapped to a 250 m grid were used to adjust the Simple Geometric-optical Model (SGM). The soil-understory background signal was partly decoupled a priori by developing regression relationships with the nadir camera blue, green, and nearinfrared reflectance data and the isotropic, geometric, and volume scattering kernel weights of the LiSparse-RossThin kernel-driven bidirectional reflectance distribution function (BRDF) model adjusted against MISR red band data. The SGM's mean crown radius and crown shape parameters were adjusted using the Praxis optimization algorithm, allowing retrieval of fractional crown cover and mean canopy height, and estimation of aboveground woody biomass by linear rescaling of the dot product of cover and height. Retrieved distributions of crown cover, mean canopy height, and aboveground woody biomass for forested areas showed good matches with maps from the United States Department of Agriculture (USDA) Forest Service, with R 2 values of 0.78, 0.69, and 0.81, and absolute mean errors of 0.10, 2.2 m, and 4.5 tons acre -1 (10.1 Mg ha -1 ), respectively, after filtering for high root mean square error (RMSE) on model fitting, the effects of topographic shading, and the removal of a small number of outliers. This is the first use of data from the MISR instrument to produce maps of crown cover, canopy height, and woody biomass over a large area by seeking to exploit the structural effects of canopies reflected in the observed anisotropy patterns in these explicitly multiangle data.

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Section: Discussionmentioning

confidence: 99%

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

Chopping

Moisen

et al. 2008

Remote Sensing of Environment

132

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“…Quartile heights calculated by subtracting the ground elevation from elevation at which 25% or 75% of the returned energy occurs [34,39].…”

Section: H25 H75mentioning

confidence: 99%

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

et al. 2017

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Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing'anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R 2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R 2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.

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“…The potential of using a multi-angle spectral sensor to predict forest vertical structure were researched, the results showed the multi-angle spectral provided a relatively direct measure of vertical structure(D.S. Kimes 2006). The anisotropic scattering of both the vegetation canopy and the background is taken into consideration in a two-layer model of the bidirectional reflectance of homogeneous vegetation canopies(S.Liangrocapart, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…The direction spectrum includes a great deal of crop canopy information. Multiangle data significantly improved the accuracy of recovering forest parameters when inverting 3-D optical models (Kimes et al, 2002) but forest vertical structure can not be captured accurately using only the 4 spectral bands in the nadir view or all view angles with a single spectral band (Kimes et al,2006). Directional radiances in or near the principal plane of the sun provides information that leads to more accurate prediction of canopy structure parameters than from other azimuth planes (Gobron et al, 2000).…”

Section: * Corresponding Authormentioning

confidence: 99%

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

Xiao¹,

Li²,

Wang³

et al. 2011

Computer and Computing Technologies in Agriculture IV

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Abstract. The spectral signatures of crop canopies in the field provide much information relating morphological or quality characteristics of crops to their optical properties. This experiment was conducted using two winter-wheat (Triticum aestivum) cultivars, Jingdong8 (with erect leaves) and Zhongyou9507 (with horizontal leaves). We analysiced the relation between the direction spectral characteristics and the laeves nitrogen content(LNC). The result showed that the spectral information observed at the 0° angle mainly provided information on the upper canopy and the lower layer had little impact on their spectra. However, the spectral information observed at 30° and 60° angles reflected the whole canopy information and the status of the lower layer of the canopy had great effects on their spectra. Variance analysis indicated that the ear layer of canopy and the topmost leaf blade made greater contributions to CDS. The predicted grain protein content (GPC) model by leaf layers spectra using 0° view angle was the best with root mean squares (RMSE) of 0.7500 for Jingdong8 and 0.6461 for Zhongyou9507. The coefficients of determination, R 2 between measured and estimated grain protein contents were 0.7467 and 0.7599. Thus, grain protein may be reliably predicted from the leaf layer spectral model.

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Predicting lidar measured forest vertical structure from multi-angle spectral data

Cited by 84 publications

References 29 publications

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

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