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Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural network) is proposed for optical remote sensing videos based on a correlation filter and deep neural networks. The pipeline is used to simultaneously track ships and planes in videos. There are many target tracking methods for general video data, but they suffer some difficulties in remote sensing videos with low resolution and those influenced by weather conditions. The tracked targets are usually misty. Therefore, in TDNet, we propose a new multi-target tracking method called MT-KCF and a detecting-assisted tracking (i.e., DAT) module to improve tracking accuracy and precision. Meanwhile, we also design a new target recognition (i.e., NTR) module to recognise newly emerged targets. In order to verify the performance of TDNet, we compare our method with several state-of-the-art tracking methods on optical video remote sensing data sets acquired from the Jilin No. 1 satellite. The experimental results demonstrate the effectiveness and the state-of-the-art performance of the proposed method. The proposed method can achieve more than 90% performance in terms of precision for single-target tracking tasks and more than 85% performance in terms of MOTA for multi-object tracking tasks. [ABSTRACT FROM AUTHOR]

On-orbit absolute radiometric calibration is not only a prerequisite for the quantitative application of optical remote sensing satellite data but also a key step in ensuring the accuracy and reliability of satellite observation data. Due to the diversity of imaging conditions for optical remote sensing satellite sensors, on-orbit absolute radiometric calibration usually requires a large number of imaging tasks and manual labor to calibrate each imaging condition. This seriously limits the timeliness of on-orbit absolute radiometric calibration and is also an urgent problem to be solved in the context of the explosive growth of satellite numbers. Based on this, we propose a general on-orbit absolute radiometric calibration method compatible with multiple imaging conditions. Firstly, we use a large amount of laboratory radiometric calibration data to explore the mathematical relationship between imaging conditions (row transfer time, integration level and gain), radiance, and DN, and successfully build an imaging condition compatibility model. Secondly, we combine the imaging condition compatibility model with cross calibration to achieve a general on-orbit absolute radiometric calibration method. We use cross calibration to obtain the reference radiance and corresponding DN of the target satellites, which calculates the general coefficient by using row transfer time, integration level, and gain, and use the general coefficient to calibrate all imaging conditions. Finally, we use multiple imaging tasks of the JL1GF03D11 satellites to verify the effectiveness of the proposed method. The experiments show that the average relative difference was reduced to 2.79% and the RMSE was reduced to 1.51, compared with the laboratory radiometric calibration method. In addition, we also verify the generality of the proposed method by using 10 satellites of the Jilin-1 GF03D series. The experiment shows that the goodness of fit of the general coefficient is all greater than 95%, and the average relative difference between the reference radiance and the calibrated radiance of the proposed method is 2.46%, with an RMSE of 1.67. To sum up, by using the proposed method, all imaging conditions of optical remote sensing satellite sensor can be calibrated in one imaging task, which greatly improves the timeliness and accuracy of on-orbit absolute radiometric calibration. [ABSTRACT FROM AUTHOR]

The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region. [ABSTRACT FROM AUTHOR]

Vaccinium corymbosum L., commonly known as blueberry, is a valuable small fruit tree in terms of its economic significance and is primarily found in China within the regions of Heilongjiang and Jilin provinces. Additionally, it thrives in the areas spanning the middle and lower reaches of the Yangtze River. Beyond their economic value, blueberries play a crucial role within forest ecosystems, serving as a significant energy source and source of nutrients. Their presence contributes significantly to fostering stability, enhancing biodiversity, and acting as an indicator of environmental quality within forest ecosystems. Since August 2022, an unknown leaf disease has been found on a large scale in blueberry fields in Nanjing, Jiangsu Province, China. The disease causes leaf curling, wilting, and even early defoliation, severely reducing the yield and production value of blueberries. The pathogenicity test confirmed the virulence of the isolates (NG5-1, NG5-2, NG5-3, NG5-4, N2-1, and N2-2) against V. corymbosum. The two pathogens were identified as Colletotrichum fructicola and C. aenigma by observing the morphological characteristics of the isolates and combined with multilocus phylogenetic analyses (ITS, CAL, ACT, TUB2, ApMat, and GAPDH). Blueberry anthracnose, caused by C. aenigma, is the first report of this disease in China. The biological characteristics of C. aenigma were investigated under different conditions, including temperature, pH, light conditions, culture medium, and carbon and nitrogen sources. The optimal temperature for growth was determined to be within the range of 25–30 °C; C. aenigma exhibits optimal growth at a pH of 7–8. Mycelial growth is favored under conditions of partial light, whereas complete darkness promotes spore production. It was found that PDA medium was the most favorable medium for C. aenigma mycelial growth, and MM medium was the best medium for spore production; the most suitable carbon sources for colony growth and spore production were sorbitol and glucose, respectively, and the most suitable nitrogen source was peptone. This study furnishes a theoretical foundation for a more scientifically informed approach to the prevention and control of anthracnose on V. corymbosum. [ABSTRACT FROM AUTHOR]