Google Earth (GE), a large Earth-observation data-based geographical information computer application, is an intuitive three-dimensional virtual globe. It enables archaeologists around the world to communicate and share their multisource data and research findings. Different from traditional geographical information systems (GIS), GE is free and easy to use in data collection, exploration, and visualization. In the past decade, many peer-reviewed articles on the use of GE in the archaeological cultural heritage (ACH) research field have been published. Most of these concern specific ACH investigations with a wide spatial coverage. GE can often be used to survey and document ACH so that both skilled archaeologists and the public can more easily and intuitively understand the results. Based on geographical tools and multi-temporal very high-resolution (VHR) satellite imagery, GE has been shown to provide spatio-temporal change information that has a bearing on the physical, environmental, and geographical character of ACH. In this review, in order to discuss the huge potential of GE, a comprehensive review of GE and its applications to ACH in the published scientific literature is first presented; case studies in five main research fields demonstrating how GE can be deployed as a key tool for studying ACH are then described. The selected case studies illustrate how GE can be used effectively to investigate ACH at multiple scales, discover new archaeological sites in remote regions, monitor historical sites, and assess damage in areas of conflict, and promote virtual tourism. These examples form the basis for highlighting current trends in remote sensing archaeology based on the GE platform, which could provide access to a low-cost and easy-to-use tool for communicating and sharing ACH geospatial data more effectively to the general public in the era of Digital Earth. Finally, a discussion of the merits and limitations of GE is presented along with conclusions and remaining challenges.

Context Human demands for ecosystem services (ES) have tremendously changed the landscape and led to degradation of ecosystems and associated services. The resolving of current eco-environmental problems calls for better understanding of the spatially explicit ES interactions to guide targeted landuse policy-making. Objectives We propose a framework to map ES in continuous time-series, based on which we further quantify interactions among multiple ES. Methods The supply of three key ES-soil conservation (SC), net primary production (NPP) and water yield (WY)-were quantified and mapped at fineresolution from 2000 to 2013 using easily-accessible spatial data. Pairwise ES interactions were quantified using a spatio-temporal statistical method. Results Spatio-temporal analyses of ES dynamics illustrated that the supply of the three ES increased over the past 14 years in northern Shaanxi, where land cover dramatically changed owing to the wide-range ecological restoration projects. Our results also revealed that ES interactions varied across locations due to landscape heterogeneity and climate difference. In the arid and semi-arid area, synergies among ES (e.g., SC vs. WY) tended to dominate in grassland, while in artificial lands ES were prone to show tradeoffs. In the semi-humid area, pairwise ES (e.g., NPP vs. WY) in woodland tended to present synergies. Conclusions The spatio-temporal variation of ES and their interactions resulted from coupling effect of human-induced climate and land-use change. In the long-term, spatially explicit quantification of ES Electronic supplementary material The online version of this article (

Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services (ES) conservation. However, little research has identified and tested the compactness and efficiency of their ES hotspots and coldspots, which may weaken the effectiveness of ecological conservation. In this study, based on the RUSLE model and Getis-Ord Gi * statistics, we quantified the variation of annual soil conservation services (SC) and identified the statistically significant hotspots and coldspots in Shaanxi Province of China from 2000 to 2013. The results indicate that, 1) areas with high SC presented a significantly increasing trend as well, while areas with low SC only changed slightly; 2) SC hotspots and coldspots showed an obvious spatial differentiation-the hotspots were mainly spatially aggregated in southern Shaanxi, while the coldspots were mainly distributed in the Guanzhong Basin and Sand-windy Plateau; and 3) the identified hotspots had the highest capacity of providing SC, with 29.6% of the total area providing 59.7% of the total service. In contrast, the coldspots occupied 46.3% of the total area, but only provided 17.2% of the total SC. In addition to conserving single ES, the Getis-Ord Gi * statistics method can also help identify multi-functional priority areas for conserving multiple ES and biodiversity.

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