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Variation in winter temperature is less well understood than in annual and summer temperatures over long timescales, particularly in low-latitude (0–30° N) montane regions with large spatial and topographic heterogeneity. Understanding these variations could be critical for forest manageemnt in these important tree growing regions. We collected tree-ring cores from Yunnan Province in montane southwest China, to determine how winter temperature has varied in the past and to explore its possible drivers in this region. Six highly correlated site chronologies were combined into a long and well-replicated regional composite (RC) chronology. The RC chronology correlated strongest with mean early winter season temperature (EWST) from November to January during 1959–2015. We applied RC chronology to reconstruct EWST from 1653 to 2015. The reconstruction shows five relatively warm historic intervals (1658–1718, 1743–1755, 1771–1791, 1929–1959, and 1995–2015) and cold intervals (1720–1742, 1792–1852, 1860–1883, 1905–1928, and 1960–1994). The 1950s with six anomalous warm seasons and 1980s with five anomalous cold seasons were the warmest and coldest decades, respectively. The EWST reconstruction generally agreed with other winter temperature reconstructions from nearby areas and with historical documents. Atlantic Multidecadal Oscillation (AMO) might be a key forcing of multidecadal winter temperature variations in montane southwest China over the past three and half centuries. Both warm and cold temperature periods coincide with respective positive and negative phases of AMO. Besides, the cold intervals during the nineteenth and twentieth centuries may also have been influenced by large volcano eruptions in low-latitude regions of south and east Asia. Our EWST reconstruction not only improves our understanding of the trends and variations of winter climate history, but also supports planning for resilience in conservation, agriculture, and forestry management in montane southwest China into the future.

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and ...

Forests in low-latitude (0&deg, N&ndash, 30&deg, N) regions are important for greenhouse gas sequestration. They harbor around 25% of vegetation carbon stocks. The productivity of these forests is expected to change as the global climate changes. They may absorb less greenhouse gasses, with negative effects on the global climate. However, little is known about how exactly these forests will respond to climate change. Here, we focus on the largest pine forests in low-latitude forests areas in southwest China. These forests have experienced rapid climate warming over the past 60 years. We collected tree-ring cores from two naturally occurring key afforestation pine tree species: Pinus yunnanensis Franch. and Pinus armandii Franch. in Baoshan and Lijiang of Yunnan. We used basal area increment (BAI) to examine the productivity trends of these two species over long-time scales, and Partial Least Squares regression analysis together with Pearson correlation analysis to identify the critical climate periods for the growth of these two species. We found that: (1) regional pine tree productivity began to decline at the beginning of this century. (2) The radial growth of both species had the strongest positive correlation with the spring Palmer Drought Severity Index (PDSI) and precipitation and the strongest negative correlation with spring temperatures. (3) Climate responses differ by species with higher positive correlations between tree-ring width, PDSI, and precipitation in P. armandii than in P. yunnanensis at similar locations. (4) Climate responses differ by location with higher positive correlations between tree-ring width and PDSI in Lijiang than in Baoshan for P. yunnanensis. Our results suggest that spring moisture availability is the main limiting factor for pine tree growth, especially for P. yunnanensis in Lijiang and P. armandii in Baoshan. Future regional warming and warm-induced drought conditions may have negative effects on the growth of pine trees. These results provide reference data for the sustainable management of pine forests in low-latitude regions of southwest China and may be useful in assessing the sustainable carbon sink ability of pine forests in this region under climate change.

Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.

Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing emerged to provide crucial computing support to address these challenges. This chapter introduces Digital Earth data sources, analytical methods, and architecture for data analysis and describes how cloud computing supports big data processing in the context of Digital Earth.

Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

This paper presents a three-step approach for large scale point cloud ground extraction. Firstly, the ground is roughly extracted according to the local height in the grid, and then the region growth algorithm is used to find the ground region. Then, the principal component analysis method is used to obtain the point cloud distribution characteristics of each growth region. Constraints based on the height of the region are introduced, and then the ground region can be removed. The proposed ground extraction algorithm and other algorithms are experimented on the public data set. Through the comparison and analysis of the ground extraction results, it is proved that the proposed ground extraction method has better effect.

Planetary Defense (PD) has become a critical effort of protecting our home planet by discovering potentially hazardous objects (PHOs), simulating the potential impact, and mitigating the threats. Due to the lack of structured architecture and framework, pertinent information about detecting and mitigating near earth object (NEO) threats are still dispersed throughout numerous organizations. Scattered and unorganized information can have a significant impact at the time of crisis, resulting in inefficient processes, and decisions made on incomplete data. This PD Mitigation Gateway (pd.cloud.gmu.edu) is developed and embedded within a framework to integrate the dispersed, diverse information residing at different organizations across the world. The gateway offers a home to pertinent PD-related contents and knowledge produced by the NEO mitigation team and the community through (1) a state-of-the-art smart-search discovery engine based on PD knowledge base, (2) a document archiving and understanding mechanism for managing and utilizing the results produced by the PD science community, (3) an evolving PD knowledge base accumulated from existing literature, using natural language processing and machine learning, and (4) a 4D visualization tool that allows the viewers to analyze near-Earth approaches in a three-dimensional environment using dynamic, adjustable PHO parameters to mimic point-of-impact asteroid deflections via space vehicles and particle system simulations. Along with the benefit of accessing dispersed data from a single port, this framework is built to advance discovery, collaboration, innovation, and education across the PD field-of-study, and ultimately decision support.

This paper investigates spatiotemporal changes of nitrogen dioxide (NO2) tropospheric vertical column density due to the COVID-19 pandemic using satellite observations before, during and after the lockdown (hereafter referred as the pre-, peri- and post-periods) in six different countries: China, South Africa, Brazil, India, the UK and the US, and compare these periods with 2019 as well as mean climatology from 2010 to 2019. We observe significant declines in relative differences (RDs) from the pre- to peri-period (as compared with the 10-year climatology) in most study countries including China, South Africa, India, and the UK by 15, 17, 8 and 7% respectively. The US does not demonstrate significant decline with RD difference relatively small at just 2%. Meanwhile, although the 2020 RD of Brazil is 7% lower than 2010–2019, this trend is quite similar to that of 2019 (20% vs 23%). In the post-period of 2020, the NO2 columns rebound in most target countries: China, US, South Africa, Brazil and UK, with similar RDs relative to the corresponding pre-period as compared with 2019 and 2010–2019. In contrast, NO in India continues to be influenced by the ongoing COVID-19 crisis with pre-to-post RD 8% lower than the average of previous 10 years., Graphical abstract Unlabelled Image

Pollution weather may cause serious damages to human life and property. Meteorological conditions like large-scale circulation can affect the formation and dissipation of pollution weather. We apply the hierarchical clustering method to classify circulation patterns based on the National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) sea level pressure (SLP) daily data and investigate their features during the formation and dissipation process of the defined 31 continuous pollution weather (CPW) cases in the Sichuan Basin using the meteorological data from 104 surface observation stations during 2007–2017. We find that the Sichuan Basin during the formation process is controlled by the large scale high-pressure circulation at sea level, and the SLP can be divided into high-pressure front type FC1 (32%) with the high pressure located in the west of the Basin, weak high-pressure type FC2 (35%) with the weak high pressure located in the Basin, and uniform pressure field type FC3 (33%) with the near homogeneous pressure distributed in the Basin. The Sichuan Basin during the dissipation process is dominated by the low-pressure circulation at sea level, and the SLP can be classified as low-pressure type DC1 (35%) with the low pressure centralize in the Basin, low-pressure front type DC2 (38%) with the low pressure center in the west of the Basin, and low-pressure bottom type DC3 (27%) with the low pressure in the north of the Basin. Besides, the wind, relative humidity, geopotential height, and temperature data are used to explore the formation and dissipation mechanisms of the CPW. The wind speed and temperature are lower during the formation phase of the CPW compared to those from the dissipation phase. Furthermore, we estimate the effects of the CPW on pollutants based on the air quality index and Particulate Matter (PM10 and PM2.5) concentration from environmental protection monitoring data during 2013–2017. The FC2 type shows the strongest enhancement of pollutants (36%), and the DC3 circumstance generates the most efficient scavenging mechanism to dispel pollutants (−57%).

Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.