Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

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Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

Remote Sensing ◽

10.3390/rs13142755 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2755

Author(s):

Peng Fang ◽

Nana Yan ◽

Panpan Wei ◽

Yifan Zhao ◽

Xiwang Zhang

Keyword(s):

Aboveground Biomass ◽

Production Management ◽

Vegetation Index ◽

Net Primary Productivity ◽

Vegetation Indices ◽

Biophysical Parameters ◽

Edge Information ◽

Casa Model ◽

Red Edge ◽

Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

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Assessment of clogging in constructed wetlands by saturated hydraulic conductivity measurements

Water Science & Technology ◽

10.2166/wst.2019.045 ◽

2019 ◽

Vol 79 (2) ◽

pp. 314-322 ◽

Cited By ~ 6

Author(s):

F. Licciardello ◽

R. Aiello ◽

V. Alagna ◽

M. Iovino ◽

D. Ventura ◽

...

Keyword(s):

Sensitivity Analysis ◽

Hydraulic Conductivity ◽

Constructed Wetlands ◽

Spatial Scales ◽

Pilot Scale ◽

Laboratory Scale ◽

Test Method ◽

Conductivity Measurements ◽

Multiple Spatial Scales ◽

Ks Values

Abstract This study aims at defining a methodology to evaluate Ks reductions of gravel material constituting constructed wetland (CW) bed matrices. Several schemes and equations for the Lefranc's test were compared by using different gravel sizes and at multiple spatial scales. The falling-head test method was implemented by using two steel permeameters: one impervious (IMP) and one pervious (P) on one side. At laboratory scale, mean K values for a small size gravel (8–15 × 10−2 m) measured by the IMP and the P permeameters were equal to 19,466 m/d and 30,662 m/d, respectively. Mean Ks values for a big size gravel (10–25 × 10−2 m) measured by the IMP and the P permeameters were equal to 12,135 m/d and 20,866 m/d, respectively. Comparison of Ks values obtained by the two permeameters at laboratory scale as well as a sensitivity analysis and a calibration, lead to the modification of the standpipe equation, to evaluate also the temporal variation of the horizontal Ks. In particular, both permeameters allow the evaluation of the Ks decreasing after 4 years-operation and 1–1.5 years' operation of the plants at full scale (filled with the small size gravel) and at pilot scale (filled with the big size gravel), respectively.

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FORMULATION OF THE HEAT CONDUCTION EQUATION FOR HETEROGENEOUS MEDIA WITH MULTIPLE SPATIAL SCALES USING REITERATED HOMOGENIZATION

Revista de Engenharia Térmica ◽

10.5380/reterm.v15i1.62165 ◽

2016 ◽

Vol 15 (1) ◽

pp. 96

Author(s):

E. Iglesias-Rodríguez ◽

M. E. Cruz ◽

J. Bravo-Castillero ◽

R. Guinovart-Díaz ◽

R. Rodríguez-Ramos ◽

...

Keyword(s):

Heat Conduction ◽

Small Parameter ◽

Heat Conduction Equation ◽

Heterogeneous Media ◽

Spatial Scales ◽

Conduction Equation ◽

Small Scale ◽

Multiple Spatial Scales ◽

Effective Coefficients ◽

Reiterated Homogenization

Heterogeneous media with multiple spatial scales are finding increased importance in engineering. An example might be a large scale, otherwise homogeneous medium filled with dispersed small-scale particles that form aggregate structures at an intermediate scale. The objective in this paper is to formulate the strong-form Fourier heat conduction equation for such media using the method of reiterated homogenization. The phases are assumed to have a perfect thermal contact at the interface. The ratio of two successive length scales of the medium is a constant small parameter ε. The method is an up-scaling procedure that writes the temperature field as an asymptotic multiple-scale expansion in powers of the small parameter ε . The technique leads to two pairs of local and homogenized equations, linked by effective coefficients. In this manner the medium behavior at the smallest scales is seen to affect the macroscale behavior, which is the main interest in engineering. To facilitate the physical understanding of the formulation, an analytical solution is obtained for the heat conduction equation in a functionally graded material (FGM). The approach presented here may serve as a basis for future efforts to numerically compute effective properties of heterogeneous media with multiple spatial scales.

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