Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA

Shi, Yi; Choi, Sungho; Ni, Xiliang; Ganguly, Sangram; Zhang, Gong; Duong, Hieu V.; Lefsky, M. A.; Simard, Marc; Saatchi, Sassan; Lee, Shihyan; Ni‐Meister, Wenge; Piao, Shilong; Cao, Chunxiang; Nemani, Ramakrishna R.; Myneni, Ranga B.

doi:10.3390/rs5010284

Cited by 19 publications

(29 citation statements)

References 65 publications

(106 reference statements)

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“…However, the ASRL model with universal allometric coefficients and scaling exponents shows less predictive power when applied to diverse eco-climatic regimes and forest types of varying age classes [16,17]. Thus, a synergistic combination of physical/physiological mechanism (i.e., ASRL approach) and 3D remote sensing techniques (i.e., GLAS) has recently been explored using parametric optimizations [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this third study aims to test the optimized ASRL model over the CONNA at both site and continental scales. In this study, we produced the modeled heights for Chinese forests using the same manner used in previous articles [16,17]. The evaluations were made against field-measured and GLAS-derived tree heights.…”

Section: Introductionmentioning

confidence: 99%

“…The first [16] in a multi-article series has provided a thorough description of the optimized ASRL model. The GLAS altimetry is implemented to iteratively adjust three selected ASRL parameters (area of single leaf, α, exponent for canopy radius, η, and root absorption efficiency, γ).…”

Section: Introductionmentioning

confidence: 99%

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Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China

Park

Choi

et al. 2014

Remote Sensing

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Abstract:The ultimate goal of our multi-article series is to demonstrate the Allometric Scaling and Resource Limitation (ASRL) approach for mapping tree heights and biomass. This third article tests the feasibility of the optimized ASRL model over China at both site (14 meteorological stations) and continental scales. Tree heights from the Geoscience Laser Altimeter System (GLAS) waveform data are used for the model optimizations. Three selected ASRL parameters (area of single leaf, α; exponent for canopy radius, η; and OPEN ACCESS Remote Sens. 2014, 6 3534 root absorption efficiency, γ) are iteratively adjusted to minimize differences between the references and predicted tree heights. Key climatic variables (e.g., temperature, precipitation, and solar radiation) are needed for the model simulations. We also exploit the independent GLAS and in situ tree heights to examine the model performance. The predicted tree heights at the site scale are evaluated against the GLAS tree heights using a two-fold cross validation (RMSE = 1.72 m; R 2 = 0.97) and bootstrapping (RMSE = 4.39 m; R 2 = 0.81). The modeled tree heights at the continental scale (1 km spatial resolution) are compared to both GLAS (RMSE = 6.63 m; R 2 = 0.63) and in situ (RMSE = 6.70 m; R 2 = 0.52) measurements. Further, inter-comparisons against the existing satellite-based forest height maps have resulted in a moderate degree of agreements. Our results show that the optimized ASRL model is capable of satisfactorily retrieving tree heights over continental China at both scales. Subsequent studies will focus on the estimation of woody biomass after alleviating the discussed limitations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China

Park

Choi

et al. 2014

Remote Sensing

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“…For example, Shi et al (2013) and Choi et al (2013) used the Allometric Scaling and Resource Limitations (ASRL) model, which predicts potential maximum tree height using a combination of allometric scaling laws and energy budgets constrained by local resource limitations. Shi et al (2013) mapped tree height over forested land in the continental United States at a spatial resolution of 1 km using the optimized ASRL model. We compared our tree height map with that of Shi et al and found that their results are generally larger than ours and that correlation at the pixel level is low.…”

Section: Comparison With Potential Maximum Tree Height In the Usamentioning

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

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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).

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“…TRMM has been extensively used for inter-disciplined investigations and applications, such as land surface modeling [17,20], climatic prediction [21], and hydrological simulation [22]. Nevertheless, the large pixel size (i.e., 0.25°) of the TRMM precipitation data is too coarse for many regional and continental models, which generally need higher resolution inputs [23]. The above precipitation products, thus, need to be, first, downscaled to meet the requirement of being in a high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

et al. 2015

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Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km 2 ) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions.

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Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA

Cited by 19 publications

References 65 publications

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China

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

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

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