Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data

Wang, Zhuosen; Schaaf, Crystal B.; Lewis, P.; Knyazikhin, Yuri; Schull, Mitchell A.; Strahler, Alan H.; Yao, Tian; Myneni, Ranga B.; Chopping, Mark; Blair, Bryan

doi:10.1016/j.rse.2011.02.010

Cited by 47 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such an attempt for the LAI retrieval may initially demonstrates the feasibility of PROSAIL inversion by linking the two models. The three coefficients f iso , f vol and f geo included in the MODIS BRDF product are promising for estimating vegetation parameters, such as in the CI [25,26] and canopy height [28], as discussed in the introduction. In addition, the AFX has been applied to extract prior BRDF archetypes and can capture the main BRDF variety, which is potentially related to the vegetation architecture [38][39][40].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

et al. 2018

View full text Add to dashboard Cite

Abstract:Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate) the bidirectional reflectance factor (BRF) in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite) and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS). In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL) with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs) are 0.0092 and 0.0355, respectively, although some discrepancy in the simulated BRFs can be found at large average leaf angle (ALA) values. Unsurprisingly, albedos generated by the method are quite consistent, and 99.98% and 97.99% of the simulated white sky albedo (WSA) has a divergence less than 0.02. For the field measurements, the kernel-driven model presents somewhat better model-observation congruence than the PROSAIL model. The results show that these models have an overall good consistency for both field-measured and model-simulated BRFs. Therefore, there is potential for linking these two models for looking into the retrieval of canopy biophysical/structural variables through a simulation method, particularly from the current archive of the global routine MODIS BRDF parameters that were produced by the kernel-driven BRDF model; however, erectophile vegetation must be further examined.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Likewise, by simulating customized directional reflectance as the training dataset for a physical process Remote Sens. 2018, 10, 437 3 of 24 in regard to directional escape probabilities, canopy height at three typical forest regions was accurately estimated [28].…”

Section: Introductionmentioning

confidence: 99%

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Although a direct physical relationship between plant height and reflectance is absent, alternative structural parameters (e.g. biomass and canopy densification) may serve as a proxy for the former (Wang et al, 2011). Resultantly, the employing of near-infrared wavelengths regarding plant height possibly followed from A. sativa growing taller, while gradually disclosing additional leaves that enhanced near-infrared scattering (Christenson et al, 2013).…”

Section: Relevant Wavelengths For Plant Trait Predictionsmentioning

confidence: 99%

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

View full text Add to dashboard Cite

Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are shortterm and small-scale due to practical constraints for fieldscale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R 2 = 0.79), chlorophyll content (RMSE = 0.11 g m −2 , R 2 = 0.80), Ncontent (RMSE = 1.94 g m −2 , R 2 = 0.68), and fresh biomass (RMSE = 0.72 kg m −2 , R 2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m −2 , respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m −2 , respectively), while the lowest values (76 cm, 0.41 g m −2 , respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAVbased optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

show abstract

“…Although a shadow-affected spectral signal is related to tree height, spectral data still cannot provide explicit information on canopy three-dimensional structure (Chopping et al, 2008, Chopping, 2011. Wang et al (2011) examined the capability of the MODIS BRDF product to estimate the height of the Harvard forest with directional escape probabilities and suggested that multi-angular observations of MODIS have the potential to be used synergistically with GLAS data. However, to our knowledge, no researcher has combined MODIS BRDF data and GLAS data to map global forest canopy height.…”

Section: Objectives Of This Studymentioning

confidence: 99%

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Wang

Ding

et al. 2016

Remote Sensing of Environment

View full text Add to dashboard Cite

Mapping the global distribution of forest canopy height is important for estimating forest biomass and terrestrial carbon flux. In this study, we present a global map of mean forest canopy height at 500 m spatial resolution obtained by combining Geoscience Laser Altimeter System (GLAS) data acquired from 2005 to 2006 and 13 ancillary variables, including seven climatic variables and six remote sensing variables (nadir BRDF-adjusted reflectance at red and NIR bands, tree cover, anisotropic factor, accumulated Enhanced Vegetation Index, and elevation). The original contributions of this study include the following: (1) The wavelet method was applied to complement the GLA14 product to identify the ground peak and the top-canopy peak. We found that it was useful for dealing with waveforms with low reconstruction accuracy. (2) GLAS data from the leafless season were not used for nonevergreen forest because the height retrieval results exhibited underestimation and strong variations. (3) The anisotropic factor (ANIF), an indicator related to surface structure, was included as an ancillary variable for the first time and was determined to be important for height modeling in the Asian and North American regions. (4) The balanced random forest (BRF) algorithm was applied to register GLAS mean forest canopy height to a 500 m grid considering the small proportion of extreme height classes (tall and short trees), and it achieved good performance in terms of modeling accuracy (RMSE = 2.75 to 4.45 m) and preserving data variation. An inter-comparison among three global forest height maps [the present study, Lefsky (2010), andSimard et al. (2011)] was implemented in a pixel-by-pixel manner. High agreement (R 2 = 0.73, RMSE = 4.49 m) was determined between the present study and Simard et al., whereas the result from Lefsky was notably different from the other two results (R 2 = 0.14, RMSE = 8.92 m, compared with the present study; R 2 = 0.11, RMSE = 11.19 m, compared with Simard et al.). Large disparities were generally associated with evergreen broadleaf forests in South America, deciduous needleleaf forests in Europe and Russian North Asia, and evergreen needleleaf forests on the West Coast of North America. Differences in the height metric were a main factor affecting the disparities among the three results. Validation against field survey data acquired from the Distributed Active Archive Center indicated the accuracy of our mean forest canopy height map (R 2 = 0.63, RMSE = 4.68 m, n = 59).

show abstract

Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data

Cited by 47 publications

References 28 publications

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Contact Info

Product

Resources

About