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The two successive earthquakes of magnitude 7.8 in Turkey on February 6, 2023, were a remarkable tectonic movement and a particularly typical scientific experiment scene. At present, various observational data are abundant, providing a perfect opportunity for scholars around the world to verify their conjectures, models and conduct experiments. By using the thermal infrared annual mean line difference amplitude enhancement ratio method and the sliding annual moving average method of TEC (Total Electron Content) microscopic anomaly events，which are both created by the writer, we found that there were high intensity thermal infrared anomalies in the east and west sides of the epicenter area in late April to early May and in August 2022, respectively. About one month before the earthquake, the synchronous thermal infrared activity of the east and west sides of the epicenter and the northern region was significantly enhanced. In particular, the annual mean line difference amplitude enhancement ratio of the northe

On December 18, 2023, a M6.2 earthquake occurred in Jishishan County of Gansu Province, and on January 1, 2024, a M7.6 earthquake occurred in Noto, Japan, with a short interval between the two earthquakes. They caused a large number of deaths and huge property losses with causing great concern in the seismic community. By using the mean line difference amplitude enhancement ratio method, it was found that there were significant thermal infrared anomalies before the two earthquakes. About 110 to 90 days before the Jishishan earthquake, the activity of long-wave radiant thermal infrared was 1.5 to 5 times higher than the historical average. Thermal infrared activity levels were 3 to 4 times higher than the historical average for about 140 to 100 days before the Noto earthquake in Japan. These results once again prove that there are thermal infrared anomalies before strong earthquakes, and also indicate that the mean line difference amplitude enhancement ratio method has strong ability to extract thermal infrare

TP391; 大多数基于目标检测的红外图像目标跟踪算法采取基于时空一致性在线模型更新策略.然而,当所跟踪的目标发生形变、快速运动和受到遮挡时,在线模型更新过程会受到不同程度的干扰而导致目标跟踪失败.基于孪生网络的离线模型跟踪策略则能够在目标发生扰动的情况下保持其外观模型的不变性.然而,在跟踪速度上与在线模型更新策略差距较大.提出了目标跟踪过程中的跟踪错误检测方法将在线和离线目标模型更新方法相结合,该检测方法通过基于联合响应图的离散度测量来联合2类模型更新方法,并能根据当前目标跟踪状态自动在2种模型更新方法中切换,有效地解决了跟踪算法实时性与鲁棒性的平衡问题.所提出算法在VOT-TIR-2015数据库的实验结果显示相比原有算法Staple和SiamFC在跟踪成功率上分别提高3.3％和3.6％,在跟踪精度上分别提高3.8％和5％,同时保证跟踪的实时性.

Based on the thermal infrared phenomena before and after the 2008 Wenchuan MS8.0 earthquake，the 2013 Yaan Lushan MS7.0 earthquake and the 2017 Jiuzhaigou MS7.0 earthquake，the amplitude fluctuation level of the mean difference of the original MODIS satellite 1 km high-resolution brightness temperature data was analyzed. ① Before these three earthquakes，there were significant difference amplitude changes in the area near the epicenter，which were low value anomalies，lower than 50% of the historical average，and then developed into high value anomalies，and the peak values of high value anomalies were all more than 3 times of the historical average. ② Wenchuan earthquake，Yaan Lushan earthquake and Jiuzhaigou earthquake occurred 55 days，35 days and 22 days after the anomalous peak value，respectively. ③ The anomalous predominance is not located in the epicenter：The anomalous preseismic area of Wenchuan earthquake is mainly located in the Qaidam block in the northwest of the epicenter，and the anomalous preseismic area

【目的】研究多源光谱反演大田夏玉米叶面积指数（LAI）的效果。【方法】以大田夏玉米为研究对象，利用无人机获取试验区不同生育期热红外以及多光谱影像，提取热红外冠层温度（TC）以及多光谱植被指数，结合地面实测LAI数据，分析光谱数据与实测LAI之间的相关关系，并将TC与筛选出的11种植被指数作为输入变量，LAI作为输出变量利用多元线性回归、支持向量机和随机森林3个算法模型训练学习，建立了夏玉米LAI的反演模型。【结果】多光谱植被指数以及TC均与夏玉米LAI在P<0.000 1水平上显著相关，相关系数均在0.5以上；RF算法于拔节期、喇叭口期、以及吐丝期3个生育期的LAI预测值与实测值的R2均高于MLR算法和SVM算法，对应的RMSE及NRMSE均低于MLR算法和SVM算法；融合热红外TC后的RF模型反演精度均有不同程度的提升，各生育期LAI预测值与实测值R2均大于同时期未融合TC的LAI反演模型。【结论】多光谱植被指数以及TC均与夏玉米LAI具有较强的相关性，且RF算法构建的夏玉米LAI反演模型精度优于MLR和SVM算法，同时TC的加入可以有效提升夏玉米LAI反演精度。

交通在人民生活和社会经济中有着举足轻重的作用。车辆速度检测是智能交通管理系统的重要组成部分。本文提出了一种基于无人机（UAV）多源影像数据进行车辆速度检测的方法，首先，搭建小型无人机多源数据采集平台，获取可见光影像与热红外影像。然后，针对采集的多源数据，采用深度学习框架YOLO（you only look once）进行车辆检测。最后，基于卡尔曼滤波进行车辆跟踪，并根据跟踪结果计算车辆速度。本文利用无人机平台增加监测车辆的灵活性，同时综合使用多源数据，不仅提高车辆检测精度，还可以不依赖光照条件跟踪车辆。试验结果表明，本文方法具有有效性和稳健性，为道路监控管理部门提供一种高效率、机动灵活的监测模式。

High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T < 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes.

This paper deals with two aspects of photogrammetric processing of thermal images: image quality and 3D reconstruction quality. The first aspect of the paper relates to the influence of day light on Thermal InfraRed (TIR) images captured by an Unmanned Aerial Vehicle (UAV). Environmental factors such as ambient temperature and lack of sun light affect TIR image quality. We acquire image sequences of the same object during day and night and compare the generated orthophotos according to different metrics like contrast and signal-to-noise ratio (SNR). Our experiments show that performing TIR image acquisition during night time provides a better thermal contrast, regardless of whether we compute contrast over the whole image or over small patches. The second aspect investigated in this work is the potential of using TIR images for photogrammetric tasks such as the automatic generation of Digital Surface Models (DSM) and orthophotos. Due to the low geometrical resolution of a TIR camera and the low image quality in terms of contrast and noise compared to RGB images, the TIR DSM suffers from reconstruction errors and an orthophoto generated using the TIR DSM and TIR images is visibly influenced by those errors. We therefore include measurements of the UAVs positions during image capturing provided by a Global Navigation Satellite System (GNSS) receiver to retrieve position and orientation of TIR and RGB images in the same world coordinate system. To generate an orthophoto from TIR images, they are projected onto the DSM reconstructed from RGB images. This procedure leads to a TIR orthophoto of much higher quality in terms of geometrical correctness.

NASA’s Mars 2020 (M2020) rover mission includes a suite of sensors to monitor current environmental conditions near the surface of Mars and to constrain bulk aerosol properties from changes in atmospheric radiation at the surface. The Mars Environmental Dynamics Analyzer (MEDA) consists of a set of meteorological sensors including wind sensor, a barometer, a relative humidity sensor, a set of 5 thermocouples to measure atmospheric temperature at ∼1.5 m and ∼0.5 m above the surface, a set of thermopiles to characterize the thermal IR brightness temperatures of the surface and the lower atmosphere. MEDA adds a radiation and dust sensor to monitor the optical atmospheric properties that can be used to infer bulk aerosol physical properties such as particle size distribution, non-sphericity, and concentration. The MEDA package and its scientific purpose are described in this document as well as how it responded to the calibration tests and how it helps prepare for the human exploration of Mars. A comparison is also presented to previous environmental monitoring payloads landed on Mars on the Viking, Pathfinder, Phoenix, MSL, and InSight spacecraft.