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As major urban agglomerations with strong urbanization, global bay areas are seldom detected and compared in detail regarding the spatiotemporal evolution of their urban expansion. In this work, a framework was applied for detecting and comparing the spatiotemporal evolution of urban agglomerations in four major bay areas: the San Francisco Bay Area and the New York Bay Area in the US, the Tokyo Bay Area in Japan, and the Guangdong-Hong Kong-Macau (GHM) Bay Area in China. Landsat images from 1987, 1997, 2007 and 2017 were employed to derive the four urban bay areas using the object-oriented support vector machine (O-SVM) classification method, and a multi-scale spatial analysis method was applied to detect the landscape characteristics and types of growth in the urban expansions. The results showed that: (1) the O-SVM classification method exhibited a high accuracy in urban area extraction, especially for classifying large-scale images; (2) the urban areas of the San Francisco Bay Area, the New York Bay Area, the Tokyo Bay Area and the GHM Bay Area from 1987 to 2017 expanded from 1686.82, 5315.93, 3765.09 and 605.71 km2 to 2714.7, 8359.18, 5351.06 and 7568.19 km2, respectively, with a corresponding annual average increase of 1.60%, 1.52%, 1.18% and 8.82%; (3) the GHM Bay Area had the largest expansion area and rate among the four bay areas; (4) both the San Francisco Bay Area and the New York Bay Area successively formed a multi-nuclei ribbon model, and the Tokyo Bay Area and the GHM Bay Area formed a multinuclear fan-shaped model and a triangle zonal expansion pattern, respectively; and (5) the spatial patterns of urban expansions in these bay areas shifted from outlying to edge-expansion and infilling, in which the Tokyo Bay Area and the New York Bay Area experienced the largest infilling growth, and the San Francisco Bay Area followed closely thereafter; all were ahead of the GHM Bay Area. These results will be helpful for the understanding and sustainable development of these bay areas. Unlabelled Image • Urban growth was detected and compared in four major bay areas of US, China and Japan. • Urban expansion rate showed a considerable regional disparity. • Four bay areas demonstrated different urban expansion patterns. • Extensive outlying and edge-expansion growth confirmed the suburbanization of China bay area. • Landscape responses to urban growth varied with different spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Abstract The magnitudes, spatial distributions and contributions to global carbon budget of the global carbonate carbon sink (CCS) still remain uncertain, allowing the problem of national measurement of CCS remain unresolved which will directly influence the fairness of global carbon markets and emission trading. Here, based on high spatiotemporal resolution ecological, meteorological raster data and chemical field monitoring data, combining highly reliable machine learning algorithm with the thermodynamic dissolution equilibrium model, we estimated the new CCS of 0.89 ± 0.23 petagrams of carbon per year (Pg C yr−1), amounting to 74.50% of global net forest sink and accounting for 28.75% of terrestrial sinks or 46.81% of the missing sink. Our measurement for 142 nations of CCS showed that Russia, Canada, China and the USA contribute over half of the global CCS. We also presented the first global fluxes maps of the CCS with spatial resolution of 0.05°, exhibiting two peaks in equatorial regions (10°S to 10°N) and low latitudes (10°N to 35°N) in Northern Hemisphere. By contrast, there are no peaks in Southern Hemisphere. The greatest average carbon sink flux (CCSF), i.e., 2.12 tC ha−1 yr−1, for 2000 to 2014 was contributed by tropical rainforest climate near the equator, and the smallest average CCSF was presented in tropical arid zones, showing a magnitude of 0.26 tC ha−1 yr−1. This research estimated the magnitudes, spatial distributions, variations and contributions to the global carbon budget of the CCS in a higher spatiotemporal representativeness and expandability way, which, via multiple mechanisms, introduced an important sink in the terrestrial carbon sink system and the global missing sink and that can help us further reveal and support our understanding of global rock weathering carbon sequestration, terrestrial carbon sink system and global carbon cycle dynamics which make our understanding of global change more comprehensive. Graphical abstract Unlabelled Image Highlights • Carbonate carbon sink is an important part of terrestrial sinks and missing sink. • CCS amounts to nearly 75% of net forest sink have huge contribution to carbon budget. • Tropical rainforest climate owns the greatest carbonate carbon sink flux. • Global new production of multi-years' CCFS maps in a spatial resolution of 0.05° • Separately carry out national measurement of 142 countries or regions for CCS [ABSTRACT FROM AUTHOR]

Analyses of the chlorophyll a concentration (chla) from satellite ocean color products have suggested the decadal-scale variability of chla linked to the climate change. The decadal-scale variability in chla is both spatially and temporally non-uniform. We need to understand the spatiotemporal evolution of chla in decadal or multi-decadal timescales to better evaluate its linkage to climate variability. Here, the spatiotemporal evolution of the chla trend in the North Atlantic Ocean for the period 1997–2016 is analyzed using the multidimensional ensemble empirical mode decomposition method. We find that this variable trend signal of chla shows a dipole pattern between the subpolar gyre and along the Gulf Stream path, and propagation along the opposite direction of the North Atlantic Current. This propagation signal has an overlapping variability of approximately twenty years. Our findings suggest that the spatiotemporal evolution of chla during the two most recent decades is part of the multidecadal variations and possibly regulated by the changes of Atlantic Meridional Overturning Circulation, whereas the mechanisms of such evolution patterns still need to be explored. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization and a growing population, the tropical forest in southwestern China has experienced a dramatic shrinkage, which threatens its biodiversity and imposes limitations to sustainable development. Spatiotemporal change analysis and ecological sensitivity assessment are the important prerequisites for investigating the relationship between eco-environmental quality and human activities. In this study, the tropical forest and other land cover types in Jinghong, China were firstly classified by a machine learning classification algorithm (support vector machine, SVM) with 7 pairs of remote sensing (RS) data (from 1989 to 2018). Then the spatiotemporal change patterns were analyzed. The ecological sensitivity was evaluated based on an index system method (ISM) in which a weighted combination of eleven indicators were produced using an analytic hierarchy process (AHP) method and GIS. Meanwhile four individual sensitivity indicators, including biodiversity sensitivity (BS), water resources sensitivity (WRS), geological hazard sensitivity (GHS) and soil erosion sensitivity (SES) were assessed respectively to create a multi-perspective understanding of the entire ecological sensitivity. The results suggest that the tropical forest experienced a continual decrease from 5631.78 km2 in 1999 to 4216.23 km2 in 2018 with an average change rate of −1.49%. The decreased area was mainly encroached on by human settlements and agriculture, particularly in the south of Jinghong. Furthermore, it could be seen that urbanization is the key driver for the changes to ecological sensitivity with both positive and negative impacts. In Jinghong, the region covered by a tropical forest has a relatively higher comprehensive ecological sensitivity (CES) than that of an urban area. This work shows RS and GIS to be powerful tools providing profound insights to researchers with regard to the spatiotemporal evolution of tropical forests and ecological sensitivity. The results are significant for improving policies in order to keep a sustainable balance in regional ecosystem management. Unlabelled Image • Spatiotemporal evolution of tropical forests in Jinghong was analyzed. • Four individual indicators were constructed to describe ecological sensitivity. • Ecological sensitivity was evaluated by a weight model with ISM and AHP. • Urbanization is the key driver for the changes to ecological sensitivity in Jinghong. [ABSTRACT FROM AUTHOR]