Baolin Xue

Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

As a large carbon pool, global forest ecosystems are a critical component of the global carbon cycle. Accurate estimations of global forest aboveground biomass (AGB) can improve the understanding of global carbon dynamics and help to quantify anthropogenic carbon emissions. Light detection and ranging (LiDAR) techniques have been proven that can accurately capture both horizontal and vertical forest structures and increase the accuracy of forest AGB estimation. In this study, we mapped the global forest AGB density at a 1-km resolution through the integration of ground inventory data, optical imagery, Geoscience Laser Altimeter System/Ice, Cloud, and Land Elevation Satellite data, climate surfaces, and topographic data. Over 4000 ground inventory records were collected from published literatures to train the forest AGB estimation model and validate the resulting global forest AGB product. Our wall-to-wall global forest AGB map showed that the global forest AGB density was 210.09 Mg/ha on average, with a standard deviation of 109.31 Mg/ha. At the continental level, Africa (333.34 ± 63.80 Mg/ha) and South America (301.68 ± 67.43 Mg/ha) had higher AGB density. The AGB density in Asia, North America and Europe were 172.28 ± 94.75, 166.48 ± 84.97, and 132.97 ± 50.70 Mg/ha, respectively. The wall-to-wall forest AGB map was evaluated at plot level using independent plot measurements. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) between our predicted results and the validation plots were 0.56 and 87.53 Mg/ha, respectively. At the ecological zone level, the R2 and RMSE between our map and Intergovernmental Panel on Climate Change suggested values were 0.56 and 101.21 Mg/ha, respectively. Moreover, a comprehensive comparison was also conducted between our forest AGB map and other published regional AGB products. Overall, our forest AGB map showed good agreements with these regional AGB products, but some of the regional AGB products tended to underestimate forest AGB density.

Global patterns, trends, and drivers of water use efficiency from 2000 to 2013

Water use efficiency (WUE; gross primary production [GPP]/evapotranspiration [ET]) estimates the tradeoff between carbon gain and water loss during photosynthesis and is an important link of the carbon and water cycles. Understanding the spatiotemporal patterns and drivers of WUE is helpful for projecting the responses of ecosystems to climate change. Here we examine the spatiotemporal patterns, trends, and drivers of WUE at the global scale from 2000 to 2013 using the gridded GPP and ET data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results show that the global WUE has an average value of 1.70 g C/kg H2O with large spatial variability during the 14-year period. WUE exhibits large variability with latitude. WUE also varies much with elevation: it first remains relatively constant as the elevation varies from 0 to 1000 m and then decreases dramatically. WUE generally increases as precipitation and specific humidity increase; whereas it decreases after reaching maxima as temperature and solar radiation increases. In most land areas, the temporal trend of WUE is positively correlated with precipitation and specific humidity over the 14-year period; while it has a negative relationship with temperature and solar radiation related to global warming and dimming. On average, WUE shows an increasing trend of 0.0025 g C·kg−1 H2O·yr−1 globally. Our global-scale assessment of WUE has implications for improving our understanding of the linkages between the water and carbon cycles and for better projecting the responses of ecosystems to climate change.

Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

Derivation, Validation, and Sensitivity Analysis of Terrestrial Laser Scanning-Based Leaf Area Index

Abstract. Terrestrial laser scanning (TLS) is a promising tool, used to retrieve leaf area index (LAI). However, the accuracy of LAI estimations based on TLS is still difficult to validate, because high-fidelity destructive measurements of leaf area are lacking. A comprehensive analysis of the sensitivity of TLS-based LAI estimates against various influencing factors (e.g., noise points, woody points, and voxel size) has yet to be reported. We acquired the true LAI by destructively measuring all leaves of 17 magnolia trees. We also improved a voxel-based method to estimate the LAI from the TLS data. We further assessed the sensitivity of LAI estimates against denoising, separation of woody points from foliage points, and voxel size. Our results showed that TLS-based LAI estimations were significantly related to the destructively sampled LAI (R2 = 0.832, RMSE = 0.693). Denoising improved the TLS-based LAI accuracy with a decrease of 0.415 in RMSE. Conversely, wood-leaf separation showed little effect on the accuracy of LAI estimation. The voxel size was an important parameter affecting the accuracy of TLS-based LAI, and our new method for determining voxel size (R2 = 0.832) proved to be more effective than the existing 2 methods (R2 = 0.661 and 0.581).

Global patterns of woody residence time and its influence on model simulation of aboveground biomass

Woody residence time (τw) is an important parameter that expresses the balance between mature forest recruitment/growth and mortality. Using field data collected from the literature, this study explored the global forest τw and investigated its influence on model simulations of aboveground biomass (AGB) at a global scale. Specifically, τw was found to be related to forest age, annual temperature, and precipitation at a global scale, but its determinants were different among various plant function types. The estimated global forest τw based on the filed data showed large spatial heterogeneity, which plays an important role in model simulation of AGB by a dynamic global vegetation model (DGVM). The τw could change the resulting AGB in tenfold based on a site-level test using the Monte Carlo method. At the global level, different parameterization schemes of the Integrated Biosphere Simulator using the estimated τw resulted in a twofold change in the AGB simulation for 2100. Our results highlight the influences of various biotic and abiotic variables on forest τw. The estimation of τw in our study may help improve the model simulations and reduce the parameters uncertainty over the projection of future AGB in the current DGVM or Earth System Models. A clearer understanding of the responses of τw to climate change and the corresponding sophisticated description of forest growth/mortality in model structure is also needed for the improvement of carbon stock prediction in future studies.