Multidimensional diversity of bird communities across spatial variation of land cover in Zoige on the eastern Qinghai-Tibetan Plateau

Background:Spatial variation of land cover can result in the changes of community similarities and biotic homog-enization,whereby the increasing similarity would reduce the adaptive capacity of biotic assemblages to further disturbance,and degenerate ecosystem services they offer.However,it remains scarce to integrate multidimensional diversity for unveiling how variations in land cover may influence the patterns and processes of biotic homogeniza-tion in the Anthropocene.In this study,we examined how spatial variation of land cover could alter taxonomic,phy-logenetic and functional homogenization of bird communities simultaneously in a compound ecosystem of Zoige Marsh on the eastern Qinghai-Tibetan Plateau.Acting as the largest alpine marsh and peatland in the world,Zoige Marsh has undergone great changes in the land cover pattern due to climate change and anthropogenic activities.Methods:We conducted transect surveys for bird communities over six years(2014‒2019)during breeding sea-sons in four main land cover types(meadow,woodland,village and marsh),representing the spatial variation of land covers in the study area.We compared multidimensional diversity(taxonomic,phylogenetic and functional diver-sity)among land covers to assess the effects of spatial variation in land cover type on bird communities,particularly whether this variation has homogenized biotic communities.Results:Bird communities during breeding seasons were different and complementary in the four land covers.Taxonomic,phylogenetic and functional similarities were significantly lower in meadow than in the other three types,i.e.woodland,village and marsh.However,when we controlled for the effects of taxonomic similarities,the pattern of phylogenetic similarities almost reversed,with the highest standardized effect size(SES)phylogenetic similarity in meadow;and we found no significant difference in SES functional similarity among land covers.Conclusions:Our results suggest that spatial variation of land cover can play a crucial role in regulating multiple dimensions of bird diversity in Zoige Marsh.The findings indicate that taxonomic,phylogenetic and functional homogenization of bird communities may differently response to the variation of land covers.It thus highlights not only the relative roles of different land covers in maintaining biodiversity and community structures of birds,but also the urgency of retarding ecosystem degradations on the eastern Qinghai-Tibetan Plateau.

Effects of Land Cover on Soil Organic Carbon Stock in a Karst Landscape with Discontinuous Soil Distribution

Land cover type is critical for soil organic carbon （SOC） stocks in territorial ecosystems. However, impacts of land cover on SOC stocks in a karst landscape are not fully understood due to discontinuous soil distribution. In this study, considering soil distribution, SOC content and density were investigated along positive successional stages （cropland, plantation, grassland, scrubland, secondary forest, and primary forest） to determine the effects of land cover type on SOC stocks in a subtropical karst area. The proportion of continuous soil on the ground surface under different land cover types ranged between 0.0% and 79.8%. As land cover types changed across the positive successional stages, SOC content in both the 0-20 cm and 20-50 cm soil layers increased significantly. SOC density （SOCD） within O-lOO cm soil depth ranged from 1.45 to 8.72 kg m^-2, and increased from secondary forest to primary forest, plantation, grassland, scrubland, and cropland, due to discontinuous soil distribution. Discontinuous soil distribution had a negative effect on 8OC stocks, highlighting the necessity for accurate determination of soil distribution in karst areas. Generally, ecological restoration had positive impacts on SOC accumulation in karst areas, but this is a slow process. In the short term, the conversion of croplandto grassland was found to be the most efficient way for SOC sequestration.

Response of ecosystem service value to land use/cover change in the northern slope economic belt of the Tianshan Mountains, Xinjiang, China

Land use/cover change(LUCC)is becoming more and more frequent and extensive as a result of human activities,and is expected to have a major impact on human welfare by altering ecosystem service value(ESV).In this study,we utilized remote sensing images and statistical data to explore the spatial-temporal changes of land use/cover types and ESV in the northern slope economic belt of the Tianshan Mountains in Xinjiang Uygur Autonomous Region,China from 1975 to 2018.During the study period,LUCC in the study region varied significantly.Except grassland and unused land,all the other land use/cover types(cultivated land,forestland,waterbody,and construction land)increased in areas.From 1975 to 2018,the spatial-temporal variations in ESV were also pronounced.The total ESV decreased by 4.00×10^(8) CNY,which was primarily due to the reductions in the areas of grassland and unused land.Waterbody had a much higher ESV than the other land use/cover types.Ultimately,understanding the impact of LUCC on ESV and the interactions among ESV of different land use/cover types will help improve existing land use policies and provide scientific basis for developing new conservation strategies for ecologically fragile areas.

Study on the Remote Sensing of Surface Albedo in the Horqin Sand Land

The surface albedo which is affected by the earth surface coverage or other surface characteristics is one of the important factors impacting remote sensing image information and therefore it can be calculated by integrating land coverage types with information of remote sensing images.Horqin sand land which was taken as an experimental area for study on Landsat-TM topography and atmospheric correction,then the Landsat-TM data inversion formula established by Liang was used to calculate the experimental zone albedo map;correlation analysis was performed to the surface albedo map and the land-use maps which was acquired by supervision and classification.The results revealed significant relations between land-use types and the surface albedo of study area.Additionally,the surface albedo and NDVI of the study area were statistically analyzed to obtain the study area's surface albedo and NDVI dependent equation.

Detecting Global Vegetation Changes Using Mann-Kendal(MK) Trend Test for 1982–2015 Time Period

Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index(NDVI) from the Advanced Very High Resolution Radiometer(AVHRR) Global Inventory Modeling and Mapping Studies(GIMMS) time series(1982–2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal(MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends(P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.

Urban surface heat fluxes infrared remote sensing inversion and their relationship with land use types

Using ASTER （Advanced Spaceborne Thermal Emission and Reflection Radiometer） infrared remote sensing data we inversed the parameters of urban surface heat fluxes applying the PCACA model and theoretical position algorithm, and then we analyzed the influence of different land use types on the surface heat fluxes and energy balance. In this study Kumagaya, a city in Saitama Prefecture, Japan, was selected as the experimental area. The result shows that the PCACA model is feasible for the surface heat fluxes estimation in urban areas because this model requires less parameters in the procedure of heat fluxes estimation in urban areas with complicated surface structure and can decrease the uncertainty. And we found that different land-use types have indicated the height heterogeneity on the surface heat fluxes significantly. The magnitudes of Bowen ratio in descending order are industrial, residential, transportation, institutional, dry farmland, green space, and water body. Under the same meteorological condition, there are distinct characteristics and regional differences in Bowen ratios among different surface covers, indicating higher sensible heat flux and lower latent heat flux in the urban construction land, while lower sensible heat flux and higher latent heat flux in the vegetation-covered area, the outskirt of the urban area. The increase of urban impervious surface area caused by the urban sprawl can enlarge the sensible heat flux and the Bowen ratio, so that it causes the increasing of urban surface temperature and air tem- perature, which is the mechanism of the so-called heat island effect.

Spatio-temporal variations of vegetation carbon use efficiency and potential driving meteorological factors in the Yangtze River Basin

Understanding of the vegetation dynamics is essential for addressing the potential threats of terrestrial ecosystem.In recent years,the vegetation coverage of the Yangtze River Basin(YRB)has increased significantly,yet the spatio-temporal variations and potential driving meteorological factors of carbon use efficiency(CUE)under the context of global warming are still not clear.In this study,MODIS-based public-domain data during 2000–2015 was used to analyze these aspects in the YRB,a large river basin with powerful ecological functions in China.Spatio-temporal variations of CUE in different sub-basins and land cover types were investigated and the correlations with potential driving meteorological factors were examined.Results revealed that CUE in the YRB had strong spatiotemporal variability and varied remarkably in different land cover types.For the whole YRB,the average CUE of vegetated land was 0.519,while the long-term change trend of CUE was obscure.Along the rising altitude,CUE generally showed an increasing trend until the altitude of 3900 m and then followed by a decreasing trend.CUE of grasslands was generally higher than that of croplands,and then forest lands.The inter-annual variation of CUE in the YRB is likely to be driven by precipitation as a strong positive partial correlation between the inter-annual variability of CUE and precipitation was observed in most of sub-basins and land cover types in the YRB.The influence of temperature and relative humidity is also outstanding in certain regions and land cover types.Our findings are useful from the view point of carbon cycle and reasonable land cover management under the context of global warming.

Human activity intensity of land surface: Concept, methods and application in China

Human activity intensity is a synthesis index for describing the effects and influences of human activities on land surface. This paper presents the concept of human activity intensity of land surface and construction land equivalent, builds an algorithm model for human activity intensity, and establishes a method for converting different land use/cover types into construction land equivalent as well. An application in China based on the land use data from 1984 to 2008 is also included. The results show that China＇s human activity intensity rose slowly before 2000, while rapidly after 2000. It experienced an increase from 7.63% in 1984 to 8.54% in 2008. It could be generally divided into five levels： Very High, High, Medium, Low, and Very Low, according to the human activity intensity at county level in 2008, which is rated by above 27%, 16%-27%, 10%-16%, 6%-10%, and below 6%. China＇s human activity intensity was spatially split into eastern and western parts by the line of Helan Mountains-Longmen Mountains-Jinghong. The eastern part was characterized by the levels of Very High, High, and Medium, and the levels of Low and Very Low were zonally distributed in the mountainous and hilly areas. In contrast, the western part was featured by the Low and Very Low levels, and the levels of Medium and High were scattered in Gansu Hexi Corridor, the east of Qinghai, and the northern and southern slopes of Tianshan Mountains in Xinjiang.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.

Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta,China

High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance,urban climate change,and so on.This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer（MODIS）land surface temperature（LST）to estimate air temperature at a high resolution over the Yangtze River Delta region,China.It is found that daytime LST is highly correlated with maximum air temperature,and the linear regression coefficients vary with the type of land surface.The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models.The estimated air temperature shows a clear spatial structure of urban heat islands.Spatial patterns of LST and air temperature differences are detected,indicating maximum differences over urban and forest regions during summer.Validations are performed with independent data samples,demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C,and the uncertainty is about 3.1°C,if using all valid LST data.The error is reduced by 0.4°C（15%）if using best-quality LST with errors of less than 1 K.The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.