Anthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land‐atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario‐based projections of global spatially explicit LULC at 1‐km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.
Recent market share statistics show that mobile device traffic has overtaken that of traditional desktop computers. Users spend an increasing amount of time on their smartphones and tablets, while the web continues to be the platform of choice for delivering new applications to users. In this environment, it is necessary for web applications to utilize all the tools at their disposal to protect mobile users against popular web application attacks. In this paper, we perform the first study of the support of popular web-application security mechanisms (such as the Content-Security Policy, HTTP Strict Transport Security, and Referrer Policy) across mobile browsers. We design 395 individual tests covering 8 different security mechanisms, and utilize them to evaluate the security-mechanism support in the 20 most popular browser families on Android. Moreover, by collecting and testing browser versions from the last seven years, we evaluate a total of 351 unique browser versions against the aforementioned tests, collecting more than 138K test results. By analyzing these results, we find that, although mobile browsers generally support more security mechanisms over time, not all browsers evolve in the same way. We discover popular browsers, with millions of downloads, which do not support the majority of the tested mechanisms, and identify design choices, followed by the majority of browsers, which leave hundreds of popular websites open to clickjacking attacks. Moreover, we discover the presence of multi-year vulnerability windows between the time when popular websites start utilizing a security mechanism and when mobile browsers enforce it. Our findings highlight the need for continuous security testing of mobile web browsers, as well as server-side frameworks which can adapt to the level of security that each browser can guarantee.
In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China’s future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020–2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at 10.6084/m9.figshare.14776128.v1) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality.
Grasslands play a very important role in the water and carbon cycle of arid and semi‐arid areas, where they are the main type of steppe globally. With this said, research into grassland ecological processes mostly consists of single‐factor controlled experiments such as precipitation, temperature or grazing, and few studies have investigated the effects of synergistic interactions between multiple factors on grassland hydrological, soil and vegetation processes. In this study, we set up a prohibited grazing area in a typical area of the Xilingole Steppe in Inner Mongolia, China. Vegetation (species richness (SR), above‐ground biomass (AGB), below‐ground biomass (BGB), etc.), precipitation and the soil moisture of 5 cm, 10 cm, 15 cm and 30 cm depths were observed continuously from 2015 to 2018. The results indicate that the species number in areas where grazing is prohibited is higher than where it is grazed, and the number and species of dominant species changes with grazing prohibition time. The AGB and BGB of prohibited areas is higher than grazed areas, and the variance rate of AGB increased rapidly (20% and 45%) at the first 2 years and then stabilized (52% and 55%), but BGB's variance rate increased slowly from 12% to 20%. The soil moisture content in the study area is higher in the surface layer than in the deeper layers. In the grazing prohibition zone, the above ground biomass (AGB) and belowground biomass (BGB) were both significantly correlated with volumetric water content (VWC) at the 0.01 level. In grazing areas, there was no significant correlation between AGB and soil moisture, and the coefficient of determination between BGB and VWC was 0.6127 (p < 0.01). SR did not have a significant relationship with soil moisture but indirectly response to it through BGB, especially in prohibited site. These results are important for understanding water cycle processes, grazing management and address food security issues across steppe in arid and semi‐arid regions.
Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha−1. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha−1 of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development.
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