Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.

The upcoming space-borne LiDAR satellite Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2018. Different from the waveform LiDAR system onboard the ICESat, ICESat-2 will use a micro-pulse photon-counting LiDAR system. Thus new data processing algorithms are required to retrieve vegetation canopy height from photon-counting LiDAR data. The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height. The new proposed method consists of three key steps: 1) filtering out the noise photons by an effective noise removal algorithm based on localized statistical analysis; 2) separating ground returns from canopy returns using an iterative photon classification algorithm, and then determining ground surface; 3) generating canopy-top surface and calculating vegetation canopy height based on canopy-top and ground surfaces. This automatic vegetation height estimation approach was tested to the simulated ICESat-2 data produced from Sigma Space LiDAR data and Multiple Altimeter Beam Experimental LiDAR (MABEL) data, and the retrieved vegetation canopy heights were validated by canopy height models (CHMs) derived from airborne discrete-return LiDAR data. Results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data (R2 and RMSE values ranging from 0.639 to 0.810 and 4.08 m to 4.56 m respectively). This means our new proposed approach is appropriate for retrieving vegetation canopy height from micro-pulse photon-counting LiDAR data.

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement.

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.

Phyllosphere bacterial community dynamics in response to bacterial wildfire disease: succession and interaction patterns.

Plants interact with complex microbial communities in which microorganisms play different roles in plant development and health. While certain microorganisms may cause disease, others promote nutrient uptake and resistance to stresses through a variety of mechanisms. Developing plant protection measures requires a deeper comprehension of the factors that influence multitrophic interactions and the organization of phyllospheric communities. High-throughput sequencing was used in this work to investigate the effects of climate variables and bacterial wildfire disease on the bacterial community's composition and assembly in the phyllosphere of tobacco (Nicotiana tabacum L.). The samples from June (M1), July (M2), August (M3), and September (M4) formed statistically separate clusters. The assembly of the whole bacterial population was mostly influenced by stochastic processes. PICRUSt2 predictions revealed genes enriched in the M3, a period when the plant wildfire disease index reached climax, were associated with the development of the wildfire disease (secretion of virulence factor), the enhanced metabolic capacity and environmental adaption. The M3 and M4 microbial communities have more intricate molecular ecological networks (MENs), bursting with interconnections within a densely networked bacterial population. The relative abundances of plant-beneficial and antagonistic microbes Clostridiales, Bacillales, Lactobacillales, and Sphingobacteriales, showed significant decrease in severally diseased sample (M3) compared to the pre-diseased samples (M1/M2). Following the results of MENs, we further test if the correlating bacterial pairs within the MEN have the possibility to share functional genes and we have unraveled 139 entries of such horizontal gene transfer (HGT) events, highlighting the significance of HGT in shaping the adaptive traits of plant-associated bacteria across the MENs, particularly in relation to host colonization and pathogenicity.

Responses of the bacterial community of tobacco phyllosphere to summer climate and wildfire disease.

Both biotic and abiotic factors continually affect the phyllospheric ecology of plants. A better understanding of the drivers of phyllospheric community structure and multitrophic interactions is vital for developing plant protection strategies. In this study, 16S rRNA high-throughput sequencing was applied to study how summer climatic factors and bacterial wildfire disease have affected the composition and assembly of the bacterial community of tobacco (Nicotiana tabacum L.) phyllosphere. Our results indicated that three time series groups (T1, T2 and T3) formed significantly distinct clusters. The neutral community model (NCM) and beta nearest taxon index (betaNTI) demonstrated that the overall bacterial community assembly was predominantly driven by stochastic processes. Variance partitioning analysis (VPA) further showed that the complete set of the morbidity and climatic variables together could explain 35.7% of the variation of bacterial communities. The node numbers of the molecular ecological networks (MENs) showed an overall uptrend from T1 to T3. Besides, Pseudomonas is the keystone taxa in the MENs from T1 to T3. PICRUSt2 predictions revealed significantly more abundant genes of osmoprotectant biosynthesis/transport in T2, and more genes for pathogenicity and metabolizing organic substrate in T3. Together, this study provides insights into spatiotemporal patterns, processes and response mechanisms underlying the phyllospheric bacterial community.

Development of a lateral-flow assay (LFA) for rapid detection of Soybean mosaic virus.

Soybean mosaic virus (SMV) is the most common virus in soybean and poses a serious threat to crop production and germplasm recession in many countries worldwide. In this study, a highly practical and rapid lateral-flow assay (LFA) was developed for the detection of SMV. The SMV coat protein (CP) was prokaryotically expressed and purified to immunize mice. After generation of hybridoma cell lines, four anti-SMV monoclonal antibodies were selected. The LFA-strip was then assembled using a double-antibody sandwich strategy. When the SMV-infected leaf sample was assayed using the assembled LFA-strip, the positive pink color appeared in the test line within 5-10min. The strip only gave positive results with SMV and not other viruses tested and could be used to detect 800 fold dilutions of infected leaf samples. The LFA could be used to detect SMV in infected leaf tissue as well as soybean seeds. To our knowledge, this is the first report of the development of a LFA for the detection of SMV. The practical, rapid and specific assay that was developed in this study can be widely applied to the diagnosis and surveillance of SMV in the laboratory and the field.