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The scale and severity of forest disturbances across the globe are increasing due to climate change and human activities. Remote sensing analysis using time series data is a powerful approach for detecting large-scale forest disturbances and describing detailed forest dynamics. Various large-scale forest disturbance detection algorithms have been proposed, but most of them are only suitable for detecting high-magnitude forest disturbances (e.g., fire, harvest). Conversely, more continuous, subtle, and gradual lower-magnitude forest disturbances (e.g., thinning, pests, and diseases) have been subject to less focus. Deep learning (DL) can distinguish subtle differences in information within time series data, offering new opportunities to capture forest disturbances in a complete and detailed way. This study proposes an approach for analyzing forest dynamics across large areas and long time periods by combining DL time series classification and prior knowledge constraint. The approach consists of two stages: (1) an improved self-attention model used for time series classification to identify sequences with forest disturbance characteristics; (2) developed skip-disturbance recovery index (S-DRI) characterizing the temporal context, using prior knowledge constraint to identify forest disturbance years in time series with disturbance characteristics. In this study, the year of forest disturbances in five study areas located in the United States, Canada, and Poland from 2001 to 2020 was mapped. A total of 3082 manually interpreted test data with different disturbance causal agents (such as fire, harvest, conversion, hurricane, and pests) were sampled from five research areas for validation. Our approach was also evaluated against two forest disturbance benchmark datasets derived from LandTrendr and the Global Forest Change (GFC) dataset. The results demonstrate that our approach achieved an overall accuracy of 87.8%, surpassing the accuracy of LandTrendr (84.6%) and the Global Forest Change dataset (81.4%). Furthermore, our approach demonstrated lower omission rates (ranging from 10.0% to 67.4%) in detecting subtle to severe causal agents of forest disturbance, in comparison to LandTrendr (with a range of 18.0% to 81.6%) and GFC (with a range of 15.0% to 88.8%). This study, which involved mapping large-scale and long-term forest disturbance in multiple regions, revealed that our approach can be applied to new areas without a requirement for complex parameter adjustments. These results demonstrate the potential of our approach in generating comprehensive and detailed forest disturbance data, thus providing a new and effective method in this domain.

Imaging with different spectrums often leads to significant nonlinear differences in the intensity, which brings a great challenge to the automatic matching of multispectral images. Considering that there are often limitations to only using intensity information, this article studies multispectral image matching based on the structure consistency. First, an extended log-Gabor filter is constructed to build phase congruency maps, which encodes the structure information and provides rich and robust features. Then, a nonlinear diffusion-based algorithm is developed to detect the salient feature points on the phase congruency map, which are expected to be illumination and contrast invariant. Finally, the structure descriptors are built according to the orientation of the maximum log-Gabor filter responses, and the image matching is achieved by computing the correspondence. Extensive experiments are carried out on various multispectral image datasets. The results show that the proposed method holds a stable performance over the nonlinear intensity variance across spectrums, and outperforms the comparison methods in terms of the number of correct matches and the matching precision.

Since the emergence of the new corona virus Corona Virus Disease 19 (COVID-19), it has spread rapidly around the world and has had a serious impact on world economic and social development. In this article, we reviewed the pathogenesis, development process, and the impact and loss of COVID-19 on global human health. Combined with the GIS method, the global distribution of COVID-19 was visualized at multiple scales, and the core areas of the COVID-19 epidemic at different scales were determined. And use the global real-time updated infection data to judge the overall development trend of COVID-19. After visualizing and analyzing the global epidemic data, it was found that the number of patients with COVID-19 at the global scale is still growing at a relatively rapid rate, but the global average mortality rate is drop rapidly. The country with the highest mortality rate at the national scale is 16.24% in Belgium, followed by France and Italy, with mortality rates of 15.31% and 14.4%, respectively. Changes in the number of COVID-19 cases in the United States at the regional scale have a major impact on the global epidemic. Although the mortality rate in the United States is gradually declining, the number of infections and deaths is still increasing at a relatively rapid rate. At the regional scale, the New York epidemic is a hot spot in the United States and should be given high priority.

Multi-temporal remote sensing imagery has been regarded as an effective tool to monitor cropland. But optical sensors often miss key stages for crop growth because of clouds, which poses challenges to many studies. The synergistic of SAR and optical data is expected to lift this problem, especially in areas with persistent cloud cover. However, due to the different characteristics of optical and SAR sensors, it is difficult to build a relationship between the two with most existing methods, let alone construct the long-time correlations to fill optic observation gaps using SAR data. Inspired by deep learning, this study presents a novel strategy to learn the relationship between optical and SAR time series based on the sequence of contextual information. To be specific, we extended the conventional CNN-RNN to build Multi-CNN-Sequence to Sequence (MCNN-Seq) model, and formulate the correlation between the optic and SAR time series sequences. We verified the MCNN-Seq model and found that the accuracy of the predicted optical image was determined by crop types and phenological stages, both in the spatial and temporal domain, respectively. For several crops, such as onion, winter wheat, corn, and sugar beet, our predictions are fitting well with R2 0.9409, 0.9824,0.9157, and 0.9749, respectively. Compared to CNN and RNN, the simulation accuracy achieved by the MCNN-Seq model is much better in terms of R2 and RMSE. In general, results demonstrate that deep learning models have the potential to synergize SAR and optical data and provide replaceable information when the optical data has a long data gap due to the persistent clouds.

Timely and accurate agricultural information is essential for food security assessment and agricultural management. Synthetic aperture radar (SAR) systems are increasingly available in crop mapping, as they provide all-weather imagery. In particular, the Sentinel-1 sensor provides dense time-series data, thus offering a unique opportunity for crop mapping. However, in most studies, the Sentinel-1 complex backscatter coefficient was used directly which limits the potential of the Sentinel-1 in crop mapping. Meanwhile, most of the existing methods may not be tailored for the task of crop classification in time-series polarimetric SAR data. To solve the above problem, we present a novel deep learning strategy in this research. To be specific, we collected Sentinel-1 time-series data in two study areas. The Sentinel-1 image covariance matrix is used as an input to maintain the integrity of polarimetric information. Then, a depthwise separable convolution recurrent neural network (DSCRNN) architecture is proposed to characterize crop types from multiple perspectives and achieve better classification results. The experimental results indicate that the proposed method achieves better accuracy in complex agricultural areas than other classical methods. Additionally, the variable importance provided by the random forest (RF) illustrated that the covariance vector has a far greater influence than the backscatter coefficient. Consequently, the strategy proposed in this research is effective and promising for crop mapping.

WMS (Web Map Service) is the standard that is formulated by OGC (Open Geospatial Consortium), and TMS (Tile Map Service) is the specification that is formulated by OSGeo (Open Source Geospatial Foundation), the objective of both are to realize the sharing and interoperability of geographical information, using those standards, we can build the network that is composed of distributed map servers, the client can use them to request layers from the server, so we can release remote sensing image data into WMS or TMS in order to realize the sharing of remote sensing information. Through the comparative analysis, the speed of TMS requests and generates layers is greatly faster than WMS, which has an important meaning for improving the response speed of the traditional WebGIS server and ameliorating users' experience, therefore, this paper mainly studies the segmentation and release of remote sensing images that are based on Tile Map Service (TMS).

Surface albedo is the ratio of the light-flux reflected in all directions of the ground and the total flux incident. It is a very important surface parameters. In this paper, atmospheric correction algorithm based on dark pixel method was introduced, and corrected surface reflectance in study area was obtained after pre-processing of the CCD data of HJ-1 Satellite. The surface albedo of study area was combined by the inversion formula which created by Liang Shunlin to converts albedo from narrowband to broadband, and then a correlation analysis of the results and accuracy given. The result presents that: the retrieval accuracy is reliable basically.

Production-study-research cooperation educational mode can make students not only learn theoretical knowledge, and combine with the productive practice, the scientific research projects for use, which is vital important that improving students’ each aspect of quality, cultivating innovative ability and team spirit, better adapting to society. Cultivating high-quality talents with innovative spirit, practical ability and entrepreneurial spirit who adapt to the new century’s need is the tenet of this paper, carrying on research and practice of comprehensive reform to talents’ training mode, teaching content, teaching method, teaching means of surveying major, will certainly promote the educational reform to develop to the depth.

As a major part of information infrastructure planning and construction and an important regional level of digital earth, digital city will provide a new approach to satisfy information society and play important roles in digital engineering. Though there has made some strides in digital projects in some cities, the effective application service framework still has not fully been constructed. This paper centered on intelligent spatial information service system in digital city, and digital city and spatial data resource is studied. We also explore spatial data sharing service and the mechanism of spatial data sharing. Intelligent spatial information service system is presented in this paper, and we describe three parts of the system at last.

The demands for increasingly new spatial information application are becoming a social and economic imperative in many urban today. Urban spatial information platform is an important part of geospatial fundamental framework construction in digital city as an integrated shared environment. There requires novel technologies for urban construction, planning, even a higher level scientific support. Web services have received much interest in industry due to their potential in facilitating seamless business-to-business or enterprise application integration. So this paper focused on urban spatial information platform construction on the basis of a variety of geographic information services and data services. Spatial information service and spatial information platform are discussed, and integrated spatial information platform is proposed. An approach to achieve spatial information sharing is presented, and we also talked our related works. At last we draw our conclusion.