Urbanization is a global problem with emergent properties. The difference in temperature between urban and rural surfaces is one such property that affects health, energy consumption budgets, ...regional planning and climate. We used remotely sensed datasets and gridded population to estimate the magnitude of thermal differentials (urban heat islands and/or sinks), the timing of heat differential events, and the controlling variables. The global scope of the study provides a consistent analytical environment that enables identification of the key factors that contribute to deleterious heat differentials. We propose new indices of thermal differential and use them to show particular prevalence of heat islands and sinks in arid regions. A variable ranking analysis indicates that development intensity, vegetation amount and the size of the urban metropolis are the most important urban variables to predict heat differentials. Population was of lesser importance in this study. Urban structure indices were also ranked lower, though a different measurement scale qualifies this conclusion. The results support the paradigm of compact development and incorporation of vegetation to the urban infrastructure.
•We characterize and map surface urban heat islands and sinks globally for 2010.•Alteration of thermal inertia patterns is implicated in the creation of heat islands and sinks.•Urban area, vegetation and development are within-city controls of urban heat islands and sinks.
Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services
. As ...a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred
. However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats-one of the most extensive coastal ecosystems-remain unknown
. Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984-2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation
, occupy at least 127,921 km
(124,286-131,821 km
, 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series-which included East Asia, the Middle East and North America-we estimate that 16.02% (15.62-16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development
, reduced sediment delivery from major rivers
, sinking of riverine deltas
, increased coastal erosion and sea-level rise
signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems.
Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ...ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining's extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005. Here we use Google Earth Engine and Landsat imagery to map the yearly extent of surface coal mining in Central Appalachia from 1985 through 2015, making our processing models and output data publicly available. We find that 2,900 km2 of land has been newly mined over this 31-year period. Adding this more-recent mining to surface mines constructed prior to 1985, we calculate a cumulative mining footprint of 5,900 km2. Over the study period, correlating active mine area with historical surface mine coal production shows that each metric ton of coal is associated with 12 m2 of actively mined land. Our automated, open-source model can be regularly updated as new surface mining occurs in the region and can be refined to capture mining reclamation activity into the future. We freely and openly offer the data for use in a range of environmental, health, and economic studies; moreover, we demonstrate the capability of using tools like Earth Engine to analyze years of remotely sensed imagery over spatially large areas to quantify land use change.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Although it is well established that transpiration contributes much of the water for rainfall over Amazonia, it remains unclear whether transpiration helps to drive or merely responds to the seasonal ...cycle of rainfall. Here, we use multiple independent satellite datasets to show that rainforest transpiration enables an increase of shallow convection that moistens and destabilizes the atmosphere during the initial stages of the dry-to-wet season transition. This shallow convection moisture pump (SCMP) preconditions the atmosphere at the regional scale for a rapid increase in rain-bearing deep convection, which in turn drives moisture convergence and wet season onset 2–3 mo before the arrival of the Intertropical Convergence Zone (ITCZ). Aerosols produced by late dry season biomass burning may alter the efficiency of the SCMP. Our results highlight the mechanisms by which interactions among land surface processes, atmospheric convection, and biomass burning may alter the timing of wet season onset and provide a mechanistic framework for understanding how deforestation extends the dry season and enhances regional vulnerability to drought.
Land cover in Beijing experienced a dramatic change due to intensive human activities, such as urbanization and afforestation. However, the spatial patterns of the dynamics are still unknown. The ...archived Landsat images provide an unprecedented opportunity to detect land cover changes over the past three decades. In this study, we used the Normalized Difference Vegetation Index (NDVI) trajectory to detect major land cover dynamics in Beijing. Then, we classified the land cover types in 2015 with the Google Earth Engine (GEE) cloud calculation. By overlaying the latest land cover types and the spatial distribution of land cover dynamics, we determined the main types where a land cover change occurred. The overall change detection accuracy for three types (vegetation loss associated with negative change in NDVI, vegetation gain associated with positive change in NDVI, and no changes) is 86.13%. We found that the GEE is a fast and powerful tool for land cover mapping, and we obtained a classification map with an overall accuracy of 86.61%. Over the past 30years, 1402.28km2 of land was with vegetation loss and 1090.38km2 of land was revegetated in Beijing. The spatial pattern of vegetation loss and vegetation gain shows significant differences in different zones from the center of the city. We also found that 1162.71km2 of land was converted to urban and built-up, whereas 918.36km2 of land was revegetated to cropland, shrub land, forest, and grassland. Moreover, 202.67km2 and 156.75km2 of the land was transformed to forest and shrub land in the plain of Beijing that were traditionally used for cropland and housing.
•We used annual NDVI time-series to detect the land cover dynamics in Beijing.•We used cloud computation in the GEE to map the most recent land cover in 2015.•We obtained a classification map with an overall accuracy of 86.61%.•We found vegetation loss and vegetation gain patterns over the past three decades.•1402.28km2 of land was with vegetation loss and 1090.38km2 was revegetated.
Land cover is the physical material at the surface of the Earth. As the cause and result of global environmental change, land cover
change (LCC) influences the global energy balance and ...biogeochemical cycles.
Continuous and dynamic monitoring of global LC is urgently needed. Effective
monitoring and comprehensive analysis of LCC at the global scale are rare.
With the latest version of GLASS (Global Land Surface Satellite) CDRs
(climate data records) from 1982 to 2015, we built the first record of
34-year-long annual dynamics of global land cover (GLASS-GLC) at 5 km
resolution using the Google Earth Engine (GEE) platform. Compared to earlier
global land cover (LC) products, GLASS-GLC is characterized by high consistency, more
detail, and longer temporal coverage. The average overall accuracy for the
34 years each with seven classes, including cropland, forest, grassland,
shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431
test sample units. We implemented a systematic uncertainty analysis and
carried out a comprehensive spatiotemporal pattern analysis. Significant
changes at various scales were found, including barren land loss and
cropland gain in the tropics, forest gain in the Northern Hemisphere, and
grassland loss in Asia. A global quantitative analysis of human factors
showed that the average human impact level in areas with significant LCC was
about 25.49 %. The anthropogenic influence has a strong correlation with
the noticeable vegetation gain, especially for forest. Based on GLASS-GLC,
we can conduct long-term LCC analysis, improve our understanding of global
environmental change, and mitigate its negative impact. GLASS-GLC will be
further applied in Earth system modeling to facilitate research on global
carbon and water cycling, vegetation dynamics, and climate change. The
GLASS-GLC data set presented in this article is available at
https://doi.org/10.1594/PANGAEA.913496 (Liu et al., 2020).
Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems ...are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China.
Eutrophication is an emerging global issue associated with increasing anthropogenic nutrient loading. The impacts and extent of eutrophication are often limited to regions with dedicated monitoring ...programmes. Here we introduce the first global and Google Earth Engine-based interactive assessment tool of coastal eutrophication potential (CEP). The tool evaluates trends in satellite-derived chlorophyll-a (CHL) to devise a global map of CEP. Our analyses suggest that, globally, coastal waters (depth ≤200 m) covering ∼1.15 million km
are eutrophic potential. Also, waters associated with CHL increasing trends-eutrophication potential-are twofold higher than those showing signs of recovery. The tool effectively identified areas of known eutrophication with severe symptoms, like dead zones, as well as those with limited to no information of the eutrophication. Our tool introduces the prospect for a consistent global assessment of eutrophication trends with major implications for monitoring Sustainable Development Goals (SDGs) and the application of Earth Observations in support of SDGs.
Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global ...scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km2, is 809 664 km2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m2) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.
Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and ...low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.
•An approach is proposed to map built-up land cover at a large geographical scale.•Our data fusion approach utilizes nighttime-lights data and Landsat imagery.•The approach overcomes the lack of extensive ground-reference data for urban research.•Hexagonal tessellation partition improves classification of heterogeneous land cover.•High quality maps of built-up LC are produced for 3 geographically diverse countries.
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