Abstract The uncertainties of China's gross primary productivity ( GPP ) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year 2 from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend ( p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP . The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability ( IAV ) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP . The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.
By the GIS technology, this paper studied the impact of groundwater resource temporal-spatial change in the lake area of Minqin Oasis on the ecological security of irrigation region landscape. The results showed that the depth of groundwater was descending continuously, the velocity of groundwater line change in the center of irrigation region was faster than that in the edge region, and consequently, the area of the descending funnel of groundwater line was enlarged. From 1987 to 2001, the area with a groundwater depth deeper than 3 m was increased from 81.2% to 97.4%. The descending of groundwater line due to soil water reducing was the main reason that resulted in the destruction and deadness of forests. When the depth of groundwater was deeper than 8 m and soil water content was less than 12%, the die-back rate of tree was morn than 90%, and that of shrub was more than 50%. Impacted by the descend of groundwater depth, the area of forest land, shrub land and open-canopy land was decreased by 67%, 54% and 31%, and the number of their patches was decreased by 35, 42 and 50, respectively. The mineralization degree of groundwater in northern irrigation region increased obviously, and changed the safe pattern of crop growth. The benefit of agriculture decreased, and the adjustment of planting construction was restricted seriously, which became the main impact factor on the ecological security of irrigation region. Reasonably distributing water resource between upper and lower river basin, reducing unreasonable land use, decreasing farmland area, and constructing safety landscape pattern could lead to the balance between the exploitation and replenishment of groundwater resource, prevent the descend of groundwater depth and the increase of groundwater mineralization, and improve the ecological security of the irrigation region.
Abstract Climate change has a large impact on vegetation dynamics. A series of statistical analyses were employed to demonstrate the relationship between Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data with an 8 × 8 km resolution and meteorological data, during the period 1982–2005. Rainfall has a great impact on vegetation with varying time lags. The sensitivity of NDVI to the threshold of accumulated temperature varies regionally. To identify a 'best factor' for each meteorological station simple and partial correlation analyses were carried out. Multiple correlation analysis was used to validate the association between the two climatic factors and monthly maximum NDVI (MNDVI). This study led to the conclusion that good correlations between MNDVI and two climatic factors are prevalent in China. It also indicated that the 'best factors' for some regions identified by partial correlation analysis are better than those selected by simple correlation analysis. The partial correlation coefficients of MNDVI and each climate factor were calculated to describe the singular influence of each meteorological variable. The results indicated that the impact of other variables on vegetation should be considered in the 'best factor' selection for one climatic variable. Temperature has a significant positive influence on vegetation growth in China. Precipitation is the most important climatic factor that closely correlates with MNDVI, particularly in arid and semi-arid environments. However, in some wet regions, precipitation is not a limiting factor on vegetation growth. A trend analysis was carried out to study climate change and its impacts on vegetation. The annual accumulated temperature had an increasing trend in China during 1982–2005. Temperature increases had different influences on vegetation dynamics in different parts of China. The results coincided with those of the multiple and partial correlation analysis. Acknowledgements This work was supported by the CAS (Chinese Academy of Sciences) Action Plan for West Development Project 'Watershed Airborne Telemetry Experimental Research (WATER)' (grant number: KZCX2-XB2-09) and 'Western Light' Talents Training Program of CAS (CACX O728501001). This work was also supported by the innovation project of CAREERI (Cold and Arid Regions Environmental and Engineering Research Institute), CAS (CACX0650446001).
The use of normalized difference vegetation index (NDVI) data acquired with multiple satellite sensors has become a necessity in research fields such as agriculture, land-use and land-cover change and changes in the natural environment, where fast changes are taking place. A good understanding of these changes is a strong requirement of long-time-series monitoring programmes. In this paper, VEGETATION 10-day composite (VGT-S10) NDVI data with a 1 × 1 km resolution, covering the period from April 1998 to December 2006 and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data with a 8 × 8 km resolution, covering the period form April 1998 to December 2003 are used. The differences between the datasets were analysed to enable an unbiased comparison between the two datasets and to enable the description of the characteristics of non-system related differences between the NDVI values acquired from the VGT and AVHRR sensors. A correlation analysis was applied to validate a linear relationship between the two types of NDVI products. This study led us to conclude that most of the Chinese land surfaces elicit good linearity between the VGT and GIMMS NDVI values. It also indicated that the correlations partly depend on vegetation density. A pixel-based one-dimensional linear regression was used to describe the relationship between the two datasets. Significance testing demonstrates that the model is valid for most land-cover types occurring in China. Finally, the VGT NDVI covering the period from 2003 to 2006 is converted to the GIMMS NDVI for the same period. A comparison of the trends calculated with the VGT NDVI and the GIMMS NDVI from the period 1998 to 2006 demonstrates the validity of the regression model when evaluated in detail.
This paper introduces the use of Remote Sensing (RS) technology and GIS, combining with multidisciplinary methods, at the Zhangye and Jiuquan Oases in northwest China. Results show that: The integration of RS and GIS, Landscape Ecology, Settlement Geography, Community Ecology and social science is an efficient way to analyze landscape pattern and human impacts; The combination of Human Impact Index (HIM) and Human Pressure Index (HPI) can illustrate the influencing intensity of human beings explicitly; Nearest Distribution Pattern Index (NDPI) and spatial neighboring proportion can describe landscape pattern superior to partial traditional indices. NDPI is a potential index to replace Contagion. The neighboring proportion can reveal the neighboring relation more clearly than the Interspersion & Juxtaposition Index; The conclusions obtained from the Landsat TM data are well in accordance with the field survey and multidisciplinary analysis: Farmland and settlement are the key components in the both oases; At the patch types level, the distributing pattern is quite complex in the both oases. At the landscape level, the both oases are distributed randomly; Because of higher population pressure, more developed agriculture and communication, and higher density of manual corridor, the spatial pattern is more homogeneous and the human impacts on Zhangye Oasis are stronger than Jiuquan Oasis.
Greenhouse gases emitted from soil play a crucial role in the atmospheric environment and global climate change. The theory and technique of detecting stable isotopes in the atmosphere has been widely used to an investigate greenhouse gases from soil. In this paper, we review the current literature on greenhouse gases emitted from soil, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). We attempt to synthesize recent advances in the theory and application of stable isotopes in greenhouse gases from soil and discuss future research needs and directions.
Abstract Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth's “third pole,” is a unique region for studying the long‐term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low‐level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l'Observation de la Terre Vegetation (SPOT‐VEG) NDVI data set (1999–2013), and the Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green‐up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground‐based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.
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