Engineering topics
Zhao, Feng
Publications and source records attributed to Zhao, Feng.
Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements
Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Additionally, compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposure of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R- squared = 0.63, RMSE = 19.11%). The IS+LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Furthermore, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.
Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques
Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.
Physiological Effect of XoxG(4) on Lanthanide-Dependent Methanotrophy
ABSTRACT A recent surprising discovery of the activity of rare earth metals (lanthanides) as enzyme cofactors as well as transcriptional regulators has overturned the traditional assumption of biological inertia of these metals. However, so far, examples of such activities have been limited to alcohol dehydrogenases. Here we describe the physiological effects of a mutation in xoxG , a gene encoding a novel cytochrome, XoxG(4), and compare these to the effects of mutation in XoxF, a lanthanide-dependent methanol dehydrogenase, at the enzyme activity level and also at the community function level, using Methylomonas sp. strain LW13 as a model organism. Through comparative phenotypic characterization, we establish XoxG as the second protein directly involved in lanthanide-dependent metabolism, likely as a dedicated electron acceptor from XoxF. However, mutation in XoxG caused a phenotype that was dramatically different from the phenotype of the mutant in XoxF, suggesting a secondary function for this cytochrome, in metabolism of methane. We also purify XoxG(4) and demonstrate that this protein is a true cytochrome c , based on the typical absorption spectra, and we demonstrate that XoxG can be directly reduced by a purified XoxF, supporting one of its proposed physiological functions. Overall, our data continue to suggest the complex nature of the interplay between the calcium-dependent and lanthanide-dependent alcohol oxidation systems, while they also suggest that addressing the roles of these alternative systems is essential at the enzyme and community function level, in addition to the gene transcription level. IMPORTANCE The lanthanide-dependent biochemistry of living organisms remains a barely tapped area of knowledge. So far, only a handful of lanthanide-dependent alcohol dehydrogenases have been described, and their regulation by lanthanides has been demonstrated at the transcription level. Little information is available regarding the concentrations of lanthanides that could support sufficient enzymatic activities to support specific metabolisms, and so far, no other redox proteins involved in lanthanide-dependent methanotrophy have been demonstrated. The research presented here provides enzyme activity-level data on lanthanide-dependent methanotrophy in a model methanotroph. Additionally, we identify a second protein important for lanthanide-dependent metabolism in this organism, XoxG(4), a novel cytochrome. XoxG(4) appears to have multiple functions in methanotrophy, one function as an electron acceptor from XoxF and another function remaining unknown. On the basis of the dramatic phenotype of the XoxG(4) mutant, this function must be crucial for methanotrophy.
Y-Doped NASICON-type LiZr 2 (PO 4 ) 3 Solid Electrolytes for Lithium-Metal Batteries
Not provided.
The WFIRST Coronagraph Instrument Optical Design Update
The WFIRST Coronagraph Instrument will perform direct imaging of exoplanets via coronagraphy of the host star. It uses both the Hybrid Lyot and Shaped Pupil Coronagraphs to meet the mission requirements. The Phase A optical design fits within the allocated instrument enclosure and accommodates both coronagraphic techniques. It also meets the challenging wavefront error requirements. We present the optical performance including throughput of the imaging and IFS channels, as well as the wavefront errors at the first pupil and the imaging channel. We also present polarization effects from optical coatings and analysis of their impacts on the performance of the Hybrid Lyot coronagraph. We report the results of stray light analysis of our Occulting Mask Coronagraph testbed.
Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem
As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.
Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem
As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.