Atlas of Global Surface Water Dynamics

It is impossible to overstate the importance of freshwater in our daily lives – for proof, try going without it for any length of time. Surface waterbodies (lakes, ponds, rivers, creeks, estuaries… it doesn't matter what name they go under) are particularly important because they come into direct contact with us and our biophysical environment. But our knowledge concerning where and when waterbodies might be found was, until recently, surprisingly sparse. The paucity of information was because trying to map a moving target is actually very difficult – and waterbodies undeniably move, in both geographical space and time. By 2013 the U.S. Geological Survey and NASA were making petabyte scale archives of satellite imagery freely available, archives that covered the entire planet's surface and stretched back decades. Other's such as the European Commission / European Space Agency Copernicus programme were also putting full free and open data access policies into place, and Google's Earth Engine had become a mature, powerful cloud-based platform for processing very large geospatial datasets. Back in 2013 a small team working at the European Commission's Joint Research Centre were looking at ways satellite imagery could be used to capture surface waterbody dynamics, and create new maps that accurately incorporated time dimensions. Concurrently the Google Earth Engine team were focussing their massive computational capabilities on major issues facing humanity, such as deforestation, food security, climate change - and water management. The two teams came together in a partnership based not on financial transactions but on a mutual exchange of complementary capabilities, and devoted thousands of person hours and thousands of CPU years into turning petabytes of Landsat satellite imagery into unique, validated surface water maps, first published in 2016, and made available to everyone through a dedicated web portal, the Global Surface Water Explorer. Since then satellites have continued to image the Earth, surface water has continued to change and the JRC Goole Earth Engine partnership has continued to work on improving our knowledge of surface water dynamics and making sure this knowledge benefits as many people as possible. This Atlas is part of the outreach; it is not a guide to the Global Surface Water Explorer, it is not a Google Earth Engine tutorial (though if it inspires you to visit either of these resources then it has achieved one of its objectives), but it is a stand-alone window into how people and nature affect, and are affected by the 4.46 million km2 of the Earth's landmass that have been under water at some time over the past 35 years.JRC.D.5-Food Securit

Global Dam Watch: curated data and tools for management and decision making

Dams, reservoirs, and other water management infrastructure provide benefits, but can also have negative impacts. Dam construction and removal affects progress toward the UN sustainable development goals at local to global scales. Yet, globally-consistent information on the location and characteristics of these structures are lacking, with information often highly localised, fragmented, or inaccessible. A freely available, curated, consistent, and regularly updated global database of existing dams and other instream infrastructure is needed along with open access tools to support research, decision-making and management needs. Here we introduce the Global Dam Watch (GDW) initiative (www.globaldamwatch.org ) whose objectives are: (a) advancing recent efforts to develop a single, globally consistent dam and instream barrier data product for global-scale analyses (the GDW database); (b) bringing together the increasingly numerous global, regional and local dam and instream barrier datasets in a directory of databases (the GDW directory); (c) building tools for the visualisation of dam and instream barrier data and for analyses in support of policy and decision making (the GDW knowledge-base) and (d) advancing earth observation and geographical information system techniques to map a wider range of instream structures and their properties. Our focus is on all types of anthropogenic instream barriers, though we have started by prioritizing major reservoir dams and run-of-river barriers, for which more information is available. Our goal is to facilitate national-scale, basin-scale and global-scale mapping, analyses and understanding of all instream barriers, their impacts and their role in sustainable development through the provision of publicly accessible information and tools. We invite input and partnerships across sectors to strengthen GDW’s utility and relevance for all, help define database content and knowledge-base tools, and generally expand the reach of GDW as a global hub of impartial academic expertise and policy information regarding dams and other instream barriers

An Erosion-Based Approach Using Multi-Source Remote Sensing Imagery for Grassland Restoration Patterns in a Plateau Mountainous Region, SW China

Satellite remote sensing of grassland ecosystem restoration requires considering both the above-ground biomass and soil information, and the latter is even more crucial due to the value and restoration difficulty of soil productivity. In this study, we proposed an approach to support the restoration pattern for mountainous grasslands at regional scale. The approach integrates different aspects and key processes, including degradation status, restoration potential and recovery capability, compared to a reference state. Specifically, we illustrated the method with the case of grasslands in southwestern China from a conservation perspective. Soil erosion conditions, net primary productivity and regrowth rate of grasslands were selected as indicators to reveal restoration possibilities. The results showed that the method proposed for remote sensing identification of grassland distribution has an overall accuracy of 88.21% at the regional scale. 59.54% of grasslands in Zhaotong are being eroded with an unsustainable erosion rate greater than the tolerant soil loss, and the average annual soil erosion rate is 952.17 t/(km2·a). Meanwhile, there is obvious spatial heterogeneity in soil erosion factors, vegetation restoration potential and regrowth rate, and the dry–hot valley of Jinsha River in the southwest is much more sensitive to climate change and vulnerable than other regions. The grassland vegetation cover revealed a fluctuating trend and protection of grassland vegetation on soil from erosion has an obvious lag, restoration efforts should be focused on the months before the arrival of the rainy season. In light of various grassland types, the overlay zoning results suggest various restoration patterns of natural repair and manual intervention should be employed for different grasslands. Urgent action is needed to face the challenge and process of grassland degradation and restore its sustainability with shared understanding by taking the stakeholders, collaborations and mutual relationships among different roles into account (e.g., scientist, government and herdsman)

Quantitative Assessment of Soil Erosion Based on CSLE and the 2010 National Soil Erosion Survey at Regional Scale in Yunnan Province of China

Regional soil loss assessment is the critical method of incorporating soil erosion into decision-making associated with land resources management and soil conservation planning. However, data availability has limited its application for mountainous areas. To obtain a clear understanding of soil erosion in Yunnan, a pixel-based estimation was employed to quantify soil erosion rate and the benefits of soil conservation measures based on Chinese Soil Loss Equation (CSLE) and data collected in the national soil erosion survey. Results showed that 38.77% of the land was being eroded at an erosion rate higher than the soil loss tolerance, the average soil erosion rate was found to be 12.46 t∙ha−1∙yr−1, resulting in a total soil loss of 0.47 Gt annually. Higher erosion rates mostly occurred in the downstream areas of the major rivers as compared to upstream areas, especially for the southwest agricultural regions. Rain-fed cropland suffered the most severe soil erosion, with a mean erosion rate of 47.69 t∙ha−1∙yr−1 and an erosion ratio of 64.24%. Lands with a permanent cover (forest, shrub, and grassland) were mostly characterized by erosion rates an order of magnitude lower than those from rain-fed cropland, except for erosion from sparse woods, which was noticeable and should not be underestimated. Soil loss from arable land, woodland and grassland accounted for 52.24%, 35.65% and 11.71% of the total soil loss, respectively. We also found significant regional differences in erosion rates and a close relationship between erosion and soil conservation measures adopted. The CSLE estimates did not compare well with qualitative estimates from the National Soil Erosion Database of China (NSED-C) and only 47.77% of the territory fell within the same erosion intensity for the two approaches. However, the CSLE estimates were consistent with the results from a national survey and local assessments under experimental plots. By advocating of soil conservation measures and converting slope cropland into grass/forest and terraced field, policy interventions during 2006–2010 have reduced soil erosion on rain-fed cropland by 20% in soil erosion rate and 32% in total soil loss compared to the local assessments. The quantitative CSLE method provides a reliable estimation, due to the consideration of erosion control measures and is potentially transferable to other mountainous areas as a robust approach for rapid assessment of sheet and rill erosion

Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images

Southwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-scale mountainous grassland distribution data to support the development of sustainable grassland husbandry. Here, we proposed a sample selection method and comprehensively utilized multi-source data to obtain the quasi-10 m southwest grassland distribution data. The sample selection method was to first determine the sample selection range based on multi-source land use/cover database, and then to randomly select the samples under the constraint of secondary land use types, multiple factors of terrain and pure pixels. This method can deal with the difficulty in identifying the fragmented grassland distribution caused by steep mountains and hills. In addition, a multispectral time series dataset was constructed based on the fusion of Landsat 8 OLI and Sentinel-2A/B data due to cloudy and rainy weather and was used as one of the input features along with synthetic aperture radar Sentinel-1 time series data and the terrain multi-factor data. Finally, a remote sensing method to accurately identify grassland distribution in southwest China was constructed based on the Google Earth Engine (GEE) platform. Taking Zhaotong City, a prefecture-level city in Yunnan Province, as an example, a thematic map of grassland distribution with an overall accuracy of 88.21% was obtained using the above method. This map has been used by the local government of Zhaotong City in their planning of the development of sustainable grassland husbandry

Monitoring Grassland Growth Based on Consistency-Corrected Remote Sensing Image

Monitoring grassland growth in large areas usually needs multiple images from different sensors or on different dates to cover the study area completely. Images from different sensors or on different dates need consistency correction to eliminate the sharp differences between images. The main contribution of this study is to promote a method for consistency correction of images on different days by constructing a linear regression equation of land cover types and the classification pixel mean. Taking a prefecture-level area in China as a test area, the consistency corrected images were applied for monitoring grassland growth. The results showed the following. First, compared with the normal correction equation constructed for two images, taking all features into account, the coefficient of determination of the equation corrected by the land cover types was improved, and the root mean square error was also significantly reduced. Secondly, the areas of consistency in the corrected image were improved compared with the original image, with an improvement rate of 21% for images from the same sensor and 25% for images from different sensors. The pixel average was much closer to the benchmark images, indicating that the corrected image was more consistent than the original image. Thirdly, when applied for monitoring grassland growth, consistency correction can solve the problem of misjudging grassland degradation. Grassland that was judged to be degraded using direct imagery, in fact, showed stable growth after consistency correction, and this type accounted for 7.33% of the regional grassland area. The seasonal characteristics of grass growth in the region were also obtained by monitoring the growth of grass in the region throughout the year. The application test showed that an effective image consistency correction method can improve the accuracy of grassland growth monitoring across a large area

An Integrated Land Cover Mapping Method Suitable for Low-Accuracy Areas in Global Land Cover Maps

In land cover mapping, an area with complex topography or heterogeneous land covers is usually poorly classified and therefore defined as a low-accuracy area. The low-accuracy areas are important because they restrict the overall accuracy (OA) of global land cover classification (LCC) data generated. In this paper, low-accuracy areas in China (extracted from the MODIS global LCC maps) were taken as examples, identified as the regions having lower accuracy than the average OA of China. An integrated land cover mapping method targeting low-accuracy regions was developed and tested in eight representative low-accuracy regions of China. The method optimized procedures of image choosing and sample selection based on an existent visually-interpreted regional LCC dataset with high accuracies. Five algorithms and 16 groups of classification features were compared to achieve the highest OA. The support vector machine (SVM) achieved the highest mean OA (81.5%) when only spectral bands were classified. Aspect tended to attenuate OA as a classification feature. The optimal classification features for different regions largely depends on the topographic feature of vegetation. The mean OA for eight low-accuracy regions was 84.4% by the proposed method in this study, which exceeded the mean OA of most precedent global land cover datasets. The new method can be applied worldwide to improve land cover mapping of low-accuracy areas in global land cover maps

The Preparation and Application of Dendrimer Modified CdTe/CdS Near Infrared Quantum Dots for Brain Cancer Cells Imaging

The most notable obstacle of brain cancer diagnosis is the inability of delivering imaging agents across the blood-brain barrier (BBB). Recently, quantum dots (QDs) has been demonstrated as an ideal image agent for brain imaging due to their ultra-small size for crossing BBB. The plolyamidoamine dendrimers modified CdTe/CdS core/shell near-infrared (NIR) region QDs was successfully synthesized in aqueous solution, and then was characterized by UV-vis absorption, photoluminescence (PL) spectroscopy, dynamic light scattering (DLS), X-ray powder diffraction (XRD) and high-resolution transmission electron microscopy (HR-TEM), etc. Our results reveal that the dendrimers modified CdTe/CdS QDs exhibits good water-dispersity and stable NIR fluorescence in various biological environments. In addition, this NIR QDs demonstrates a good biocompatibility and sensitive photoluminescence responses in brain tumor cell imaging. In a word, this type of dendrimers modified NIR CdTe/CdS QDs has huge potential applications in brain imaging

Identifying hydro-geomorphological conditions for state shifts from bare tidal flats to vegetated tidal marshes

High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the restoration of tidal marsh ecosystems in particular. However, the number of studies investigating the conditions for state shifts from bare mudflats to vegetated marshes remains relatively low. We developed a GIS approach to identify the locations of expected shifts from bare intertidal flats to vegetated marshes along a large estuary (Western Scheldt estuary, SW Netherlands), by analyzing the interactions between spatial patterns of vegetation biomass, elevation, tidal currents, and wind waves. We analyzed false-color aerial images for locating marshes, LIDAR-based digital elevation models, and spatial model simulations of tidal currents and wind waves at the whole estuary scale (~326 km2). Our results demonstrate that: (1) Bimodality in vegetation biomass and intertidal elevation co-occur; (2) the tidal currents and wind waves change abruptly at the transitions between the low-elevation bare state and high-elevation vegetated state. These findings suggest that biogeomorphic feedback between vegetation growth, currents, waves, and sediment dynamics causes the state shifts from bare mudflats to vegetated marshes. Our findings are translated into a GIS approach (logistic regression) to identify the locations of shifts from bare to vegetated states during the studied period based on spatial patterns of elevation, current, and wave orbital velocities. This GIS approach can provide a scientific basis for the management and restoration of tidal marshes