Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get the accurate number of individual trees, this paper took coniferous forest and mixed coniferous and broad-leaved forest as experimental samples, as well as Digital Orthophoto Map and airborne LiDAR Point Cloud as research data. We propose a deep Learning individual tree segmentation method based on RetinaNet model and PCS algorithm by doing comparative analysis (classical Watershed Algorithm and Layer Stacking Algorithm) at the plots (with high, medium, and low densities). The experimental results show that the method proposed in this paper can solve the problem of individual tree segmentation in high density forest and improve its degree of automation. Compared with Watershed algorithm and Layer stacking algorithm, F-Measure is improved by 6%-29% and 7%-20%, respectively. In other words, the results of individual tree segmentation presented in this paper can not only improve the precision of individual tree segmentation, but also maintain a high detection rate, which can meet the accuracy and high efficiency of individual tree extraction, so as to meet the needs for modern forestry investigation.

<!-- *** Custom HTML *** --> More and more human activities have caused varying degrees of interference with the ecosystem. As an effective quantitative means of eco-environmental quality, remote sensing has been widely used. The remote sensing ecological index (RSEI) is the most popular ecological evaluation model at present. However, the model is affected by the direction of the eigenvector, and two completely opposite results will appear in the calculation of the model. In the past, researchers often manually judged whether the two results met the expectations according to a priori knowledge to select the appropriate calculation results. With the development of remote sensing big data, applying this artificial discrimination method to eco-environmental monitoring under the background of big data is difficult. Therefore, we test the evolution of the RSEI model eigenvector in time series through large samples. It is found that the results of the two models are completely opposite in space. For any model, changing the order of input bands may result in RSEI also being the opposite. Through the large sample test in different time periods, the eigenvectors of each ecological factor affect the direction of the corresponding RSEI. Therefore, we propose an improved model to judge the characteristic contribution direction of the first principal component by selecting the wet factor, which is less affected by seasonal changes. The model direction can be automatically modified without the intervention of researchers according to subjective experience. The improved model can adapt different periods of RSEI calculation. At the same time, no matter how the input band order changes, the final result direction is correct. The improved model makes it possible to monitor and calculate the eco-environmental quality of remote sensing big data and provides a solid scientific basis for the development of the model through research on the mechanism of the model.

Since the 21st century, the rapid development of China's mega-city clusters has posed a major threat to the healthy and coordinated development of cities. Therefore, it is necessary to be develop the comprehend the state of coupling coordination among mega-city cluster and EEQ under mesoscale. In this study, the largest Yangtze River Delta urban agglomeration is taken as the research object, NS-RSEI is constructed to evaluate the EEQ of the Yangtze River Delta, and the coupling coordination mechanism on the long-time series of the Yangtze River Delta in recent 20 years is explored by means of spatio-temporal analysis. The outcome verify that CCD of the Yangtze River Delta growth with a strong spatial dependence from 2001 to 2020, showing a spatial distribution pattern of " East West high-low". Above all, this study shows that urbanization is the main factor determining the development of CCD. In addition, compared with the traditional remote sensing eco-environment monitoring model, NS-RSEI proposed in this study shows better ability in mesoscale environmental monitoring, and provides great convenience for mesoscale EEQ evaluation. This study fills the research gap of the interactive coupling mechanism between urbanization and eco-environment quality of mesoscale mega-city group, and provides a new perspective on the sustainable development of megacity clusters.

This paper presents a novel hyperspectral image (HSI) classification approach that integrates the Sparse Inducing Variational Gaussian Process (SIVGP) with a spatially adaptive Markov random field (SAMRF), termed G-MDRF. Variational inference is employed to obtain a sparse approximation of the posterior distribution, modeling the spectral field within the latent function space. Subsequently, SAMRF is utilized to model the spatial prior within the function space, while the alternating direction method of multipliers (ADMM) is employed to enhance computational efficiency. Experimental results on three datasets with varying complexity show that the proposed algorithm improves computational efficiency by approximately 152 times and accuracy by about 7% to 26% compared to the current popular Gaussian process methods. Compared to classical random field methods, G-MDRF rapidly achieves a convergent solution with only one ten-thousandth to one hundredthousandth of the iterations, improving accuracy by about 5% to 18%. Particularly, when the number of classes in the dataset increases and the scene becomes more complex, the proposed method demonstrates a greater advantage in both computational efficiency and classification accuracy compared to existing methods. This paper releases the source code and datasets: https://github.com/dabaihuaya/G MDRF.