Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage

IntroductionLeaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.MethodsTaking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.ResultsThe ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).DiscussionThis study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing

Crowdsourcing geospatial data for Earth and human observations: a review

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors

Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images

Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data

Identification of Potential Biomarkers for Rhegmatogenous Retinal Detachment Associated with Choroidal Detachment by Vitreous iTRAQ-Based Proteomic Profiling

Rhegmatogenous retinal detachment associated with choroidal detachment (RRDCD) is a complicated and serious type of rhegmatogenous retinal detachment (RRD). In this study, we identified differentially expressed proteins in the vitreous humors of RRDCD and RRD using isobaric tags for relative and absolute quantitation (iTRAQ) combined with nano-liquid chromatography-electrospray ion trap-mass spectrometry-mass spectrometry (nano-LC-ESI-MS/MS) and bioinformatic analysis. Our result shows that 103 differentially expressed proteins, including 54 up-regulated and 49 down-regulated proteins were identified in RRDCD. Gene ontology (GO) analysis suggested that most of the differentially expressed proteins were extracellular．The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis suggested that proteins related to complement and coagulation cascades were significantly enriched. iTRAQ-based proteomic profiling reveals that complement and coagulation cascades and inflammation may play important roles in the pathogenesis of RRDCD. This study may provide novel insights into the pathogenesis of RRDCD and offer potential opportunities for the diagnosis and treatment of RRDCD

Immediate Recanalization of Large‐Vessel Occlusions by Tissue Plasminogen Activator Occurs in 28% of Patients Treated in a Mobile Stroke Unit

Background Recanalization of cerebral large‐vessel occlusions (LVOs) by intravenous thrombolysis is infrequent but has been relatively unexplored with ultraearly treatment. We evaluated prehospital treatment with tissue plasminogen activator (tPA) in a mobile stroke unit to explore the recanalization rate in patients with LVOs and its effect on early clinical improvement and long‐term disability. Methods Prospectively collected data were analyzed from Houston mobile stroke unit patients who were treated with tPA and had LVOs identified by either hyperdense arteries on computed tomography or arterial occlusion on computed tomography angiography while on board the mobile stroke unit. The primary outcome was immediate recanalization (IRC), categorized as resolution of LVO on repeat vascular imaging in the emergency department (ED) or on emergent angiography. The secondary outcome was change in National Institutes of Health Stroke Scale from baseline and modified Rankin score at 90 days. Results Sixty‐nine patients with anterior or posterior circulation LVOs were enrolled; the median time from last known normal to tPA bolus was 64.0 minutes (interquartile range, 52.0–89.0). Nineteen patients (28%) had IRC, with 11 based on computed tomography angiography on ED arrival and 8 based on first run of emergent angiography. Median time from tPA bolus to documentation of IRC was 61.0 minutes (interquartile range, 42.0–111.0). IRC was associated with improvement in median National Institutes of Health Stroke Scale from baseline (17.0 [14.0–22.0]) to ED arrival (10.0 [5.5–16.5]) and to 24 hours (4.0 [0.5–10.5]). Of the non‐IRC patients, 41 had recanalization after endovascular thrombectomy and 9 did not receive recanalization. The IRC group, earlier last known normal to tPA bolus, greater baseline National Institutes of Health Stroke Scale, and M1 and M2 middle cerebral artery occlusion locations were independently associated with greater improvement in National Institutes of Health Stroke Scale from baseline to ED arrival. The 90‐day modified Rankin score distribution was best in the IRC group, followed by the delayed recanalization group, and both had significantly less disability than the no recanalization group (P=0.002). Conclusions Recanalization by ED arrival occured in 28% of patients with LVO who received tPA treatment in a mobile stroke unit and results in early clinical improvement and less disability at 90 days

Grain Boundary Structures and Electronic Properties of Hexagonal Boron Nitride on Cu(111)

Grain boundaries (GBs) of hexagonal boron nitride (h-BN) grown on Cu(111) were investigated by scanning tunneling microscopy/spectroscopy (STM/STS). The first experimental evidence of the GBs composed of square-octagon pairs (4|8 GBs) was given, together with those containing pentagon-heptagon pairs (5|7 GBs). Two types of GBs were found to exhibit significantly different electronic properties, where the band gap of the 5|7 GB was dramatically decreased as compared with that of the 4|8 GB, consistent with our obtained result from density functional theory (DFT) calculations. Moreover, the present work may provide a possibility of tuning the inert electronic property of h-BN via grain boundary engineering

Crowdsourcing Geospatial Data for Earth and Human Observations: A Review

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors

Direct Formation of C–C Double-Bonded Structural Motifs by On-Surface Dehalogenative Homocoupling of <i>gem</i>-Dibromomethyl Molecules

Conductive polymers are of great importance in a variety of chemistry-related disciplines and applications. The recently developed bottom-up on-surface synthesis strategy provides us with opportunities for the fabrication of various nanostructures in a flexible and facile manner, which could be investigated by high-resolution microscopic techniques in real space. Herein, we designed and synthesized molecular precursors functionalized with benzal <i>gem</i>-dibromomethyl groups. A combination of scanning tunneling microscopy, noncontact atomic force microscopy, high-resolution synchrotron radiation photoemission spectroscopy, and density functional theory calculations demonstrated that it is feasible to achieve the direct formation of C–C double-bonded structural motifs <i>via</i> on-surface dehalogenative homocoupling reactions on the Au(111) surface. Correspondingly, we convert the sp³-hybridized state to an sp²-hybridized state of carbon atoms, <i>i</i>.<i>e</i>., from an alkyl group to an alkenyl one. Moreover, by such a bottom-up strategy, we have successfully fabricated poly­(phenylenevinylene) chains on the surface, which is anticipated to inspire further studies toward understanding the nature of conductive polymers at the atomic scale