The Light Detection and Ranging （LiDAR） has been widely used in forest inventory. It is quite difficulty to describe the complex vertical structures of forest using the terrestrial or Unmanned Aerial Vehicle （UAV） LiDAR or laser scanning， individually. The complete spatial structure of forest can be obtained by combing the Terrestrial Laser Scanning （TLS） and UAV Laser Scanning （ULS）. The TLS and ULS point cloud were registered and fused to extract the trunks of individual trees. The random Hough transform was used to fit the point cloud of the trunk in segments. The taper equation was fitted using the diameters of trunk segments and the differential quadrature method was used to calculate the volumes of individual trees. The volumes of individual trees were accumulate to get plot volume. Compared with the calculated value of the binary volume model， the results showed that the accuracy of calculating the volume of individual tree based on the fusion point cloud was better than that of the terrestrial point cloud， the R² can be increased by more than 2%， and the RMSE can be reduced by 0.01 m³. The R² and RMSE were 0.98 and 0.87m³ for the plot volume， which calculated by the combination of taper equation and differential quadrature method. Among them， the R² and RMSE of Cunninghamia lanceolata volume were 0.96 and 0.07 m³， for Eucalyptus， the R² and RMSE were 0.93 and 0.07 m³. Among the three types of plots： easu， medium， and difficult， the volume R² of Cunninghamia lanceolata and Eucalyptus in easy and medium plots were all above 0.94， the RMSE was about 0.07 m³， but the R² of the volume results in difficult plot was below 0.9. The TLS and ULS fusion point cloud can more finely measure the forest spatial structure， and better meet the needs of forest resource survey applications.

Vegetation phenology is an important biological indicator for monitoring terrestrial ecosystems and global climate change. The monitoring of land surface phenology based on classical remote sensing vegetation indices is more challenging in terms of accurate analysis of different vegetation types. Solar-Induced Chlorophyll Fluorescence （SIF） is more sensitive to the seasonal dynamics of vegetation and can more accurately portray the interannual variability of vegetation. Based on the 2001~2020 GOSIF dataset， this study extracted the vegetation phenology parameters in Northeast China by D-L fitting function and dynamic threshold method， combined with unitary linear regression analysis， stability and sustainability analysis， this study analyzed the spatiotemporal evolution characteristics， stability and sustainability of vegetation phenology in Northeast China from 2001 to 2020 at multiple spatiotemporal scales， and explored the response mechanism of vegetation phenology to climate change. The results showed that SOS， EOS， LOS， and POP showed advanced， delayed， prolonged and advanced， respectively. The trend of SOS advance and EOS delay in grassland was significant， and EOS of coniferous forests was advanced. The advance of SOS and the delay of EOS led to the prolongation of LOS. Except for coniferous forest， all vegetation types showed an extended trend of LOS. All vegetation types POP showed an advance trend， except for grassland and steppe. The changes of SOS， EOS， LOS and POP were relatively stable in the past 20 years， and the coefficients of variation were all less than 0.1. The H values of SOS， EOS， LOS and POP in most regions ranged between 0.35 and 0.5， indicating that the trend was opposite to the past and would show a slight trend of delay， advance， shortening and delay. Overall， the influence mechanism of temperature and precipitation was opposite on vegetation phenology， that is， higher temperature （increased precipitation） led to advance （delay） of SOS and POP， delay （advance） of EOS， and lengthen （shorten） of LOS. There was a negative correlation between relative humidity and vegetation phenological parameters. The results of this study help to understand the spatiotemporal pattern changes of photosynthesis in vegetation and the response mechanism to climate change， and also provide reference for the assessment and management of ecological environment in Northeast China.

Remote sensing technology has played an important role in the emergency response of major natural disasters. However， the existing global emergency response mechanism based on remote sensing data still has problems such as insufficient data sharing， complex start-up procedures， and low response efficiency. It is urgent to establish an efficient， stable and sustainable remote sensing disaster emergency mechanism. This paper systematically reviews the value of multi-source remote sensing data in disaster emergency response and the shortcomings of the current international emergency response mechanism for major natural disasters. Based on the shared resource database of large-scale multi-source remote sensing data and the one-stop service collaboration method， the theoretical framework of China GEO Collaborative network of Disaster Data Response （CDDR） mechanism is proposed. It has also been applied in emergency response to disasters such as Tonga volcanic eruption and Turkey earthquake. Through two representative application cases， it can be seen that the mechanism has improved the efficiency of disaster emergency response services from various aspects such as data collection， download， analysis and application， and effectively supplemented the shortcomings of the existing mechanism. The new mechanism has simplified start-up procedures， enhanced data aggregation capabilities， more professional disaster assessment capabilities， and more accurate sharing capabilities， which is expected to provide stable and sustainable sharing services for the international community.

As a "New Blue Ocean" for urban consumption to increase employment， boost consumption and drive regional development， the night economy has gradually become a research hotspot in the "Post-Pandemic era". The “nighttime economic agglomeration center” is the core and foundation of the nighttime economic development， and there is no systematic studies available at home and abroad. As a social economic element， it has the characteristics of non-uniform symmetry and "grey clustering"， so it cannot simply apply the traditional geographical clustering center identification method. Based on the theory of "point-axis system"， this paper proposes a method to identify and extract the nighttime economic agglomeration centers， and uses the Geo-information Tupu of generalized symmetric to deconstruct the spatial pattern and differentiation mechanism of the nighttime economic agglomeration centers in downtown Shanghai. For the first time， this paper provides a systematic method reference for rapidly and accurately identifying the nighttime economic agglomeration centers and scientifically exploring their spatial distribution characteristics， and provides decision support for promoting the prosperity and sustainable development of nighttime economy. The results show that： within the central city of Shanghai， a total of 12 first-class nighttime economic agglomeration centers and 26 second-class nighttime economic agglomerations were extracted， and the average vitality values of the night-time economy were： 0.49 and 0.24， respectively. Conclusions （1） The method proposed in this paper can quickly identify and extract nighttime economic agglomeration centers； （2） The Shanghai nighttime economic agglomeration centers present a "center-periphery" spatial distribution pattern， forming a distinct hierarchical system； （3） The degree of infrastructure perfection and the distance from the city center are the main driving factors for the differentiation of Shanghai nighttime economic agglomeration； （4） The "Color Symmetry" distribution spatial pattern of the agglomeration center indicates that the night economy of Shanghai in a reasonable and sustainable development stage. In the future planning，it can be expanded and filled internally along Metro Line 2. The central connecting line of the agglomeration symmetry can also be used as the development axis to upgrade the nighttime economic agglomeration effect in the form of "Agglomeration Area".