Xiaoling Pan
Xinjiang University
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Featured researches published by Xiaoling Pan.
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture | 2004
Xiwu Zhan; Wei Gao; Jiaguo Qi; Paul R. Houser; James R. Slusser; Xiaoling Pan; Zhiqiang Gao; Yingjun Ma
With the large volume of satellite remote sensing data of the earth terrestrial surface becoming available, precisely monitoring the dynamics of the land surface state variables for agricultural and land use management becomes possible. Currently, the moderate resolution imaging spectroradiometers on board NASA’s Earth Observing Satellites (EOS) Terra and Aqua make it possible to derive a global coverage of land surface vegetation indices, leaf area index, and surface temperature data products at 1 km spatial resolution every day. The advanced microwave scanning radiometers (AMSR) on board Aqua and Japans ADEOS satellites start sending back a global coverage of rainfall and land surface soil moisture data products at up to 25km spatial resolution every two to three days. It is also well known that these land surface remote sensing products contain uncertainties due to imperfect instrumentation calibration and inversion algorithms, geophysical noise, representativeness error, communication breakdowns, and other sources while land surface model can continuously simulate these land surface state or storage variables for all time steps and all covered areas. Therefore a combination of satellite remote sensing products and land surface model simulations may provide more continuous, precise and comprehensive depiction of the dynamics of the land surface states. This paper introduces the state-of-the-arts technologies in the development of NASAs Land Data Assimilation System, and then proposes a procedure to combine the simulations of a simple land surface model and the remote sensing products from MODIS and AMSR. After the results of testing the procedure for an arid area in Southwest USA are presented, the application of the procedure for the oases in Fukang Count of Xinjiang Autonomous Region is proposed.
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture | 2004
Jie Zhang; Xiaoling Pan; Zhiqiang Gao; Wei Gao; Suling Zhao; Qingdong Shi; Guanghui Lu
This study was conducted to develop an appropriate assessment technique to define impact of mountain-desert-oasis ecosystem on net primary productivity (NPP) in northern foothills of Tianshan Mountains. Geographic Information System (GIS) was used to estimate land use/land cover of the mountain, desert and oasis zones. An ecological process model was used to estimate NPP by using data entirely derived from satellite. The results show that landscape heterogeneity was important factor to affect NPP values in mountain-desert-oasis ecosystem. Simulated results indicated a total annual NPP of 1.5081×1014 g C for selected transect in 2002. There was 32.67% of total NPP which came from oasis areas, 28.16% from alpine meadows areas, 9.15% from forests area. Mean NPP values over the selected transect was 150.29 g C m-2 year-1 in 2002. However, NPP values varied greatly with different geography and season.
Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space | 2003
Wei Gao; Zhiqiang Gao; James R. Slusser; Xiaoling Pan; Yingjun Ma
The scientific community has been interested in the responses of vegetation to global change at regional and global scales. Many models have been developed to study the responses in terms of primary productivity. The ecosystem model, Biome-BGC, simulates the storage and fluxes of water, carbon, and nitrogen within the vegetation, litter, and soil components of a terrestrial ecosystem and can be used to quantify effect on net primary production (NPP) under different climate scenarios. This study was conducted in oasis areas along the Tianshan Mountains in Xinjiang, China with an arid climate. Ten sites were selected to test Biome-BGC model for its feasibility in the study areas. The model was proven not suitable to the desert ecosystem. After ecological and meteorological parameters were modified for each of the vegetation covers we applied the model to four sites that present agricultural, shrub, grasslands and mixed forest ecosystems. By using modified Biome-BGC model, we simulated the response of NPP with different land surface covers to four designed climate scenarios.
Proceedings of SPIE | 2005
Jie Zhang; Xiaoling Pan; Zhiqiang Gao; Qingdong Shi; Guanghui Lv; Wei Gao
Urban sprawl has sparked a new debate over land-use policy in Beijing metropolitan area in China during past three decades. Increasing populations and economics intensify the urban growth and cropland encroachment. The metropolitan area has gone through a rapid urban growth and transformation from rural to developed land over a short period of time and provided an excellent study area for this study. Using historical land use maps and a spatially explicit dynamic cellular automata urban sprawl model we present applications of a spatially explicit model of land use change. The use of the results for environmental assessments is illustrated by calculating spatial indices to assess the impact of land use change on forest fragmentation. It is concluded that spatially explicit modeling of land use change yields important information for environmental management and land use planning. We quantify the urban sprawl and model the spatial landscape pattern change in Beijing metropolitan area, China. These results constitute a foundation for spatial and ecosystem models to predict long-term environmental impacts of land use change in China.
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture | 2004
Shunli Chang; Qingdong Shi; Xiaoling Pan; Guanghui Lu; Suling Zhao; Zhiqiang Gao; Wei Gao
Heat, precipitation and humidity affect vegetation types and their distribution. However, their degree of effects is highly spatial and temporal dependent. When we study the major factors which affect vegetation cover, we need define a specific region and a time period. In order to study land cover and vegetation change in Xinjiang and to probe its driving force from 1992 to 2000, we analyzed sensitivity of land-cover to climate change using Remote Sensing (RS) and Geographic Information System (GIS) with multi-temporal NOAA/AVHRR NDVI images. Major factors we considered in this study were temperature, precipitation, humidity and their long-term and seasonal impacts on land cover and vegetation change. Results provided different sensitive levels as following: bare lands, partially vegetated lands, agriculture uses and water bodies. Concerning meteorological parameters impact we found in eastern Xinjiang humidity was more important than temperature and precipitation, in southern Xinjiang precipitation had more impact than temperature and humidity, and in both northern Xinjiang and Ili area temperature was more important than precipitation and humidity.
Remote Sensing and Modeling of Ecosystems for Sustainability | 2004
Qifeng Lu; Wei Gao; Xiaoling Pan; Zhiqiang Gao; Jifeng Liu
To make better use of the land, the regional climate response to vegetation change with oasis-desert-nesting structure in sensitive climatic area was studied in this paper. Three vegetation change experiments with different oasis-desert-nesting structure were designed and the differential charts between control and sensitivity simulation run were made. The results indicated that for oasis-desert-nesting structure, whose width reached certain width so as to engender local circulation, such as EXP1 and EXP2, its width change mainly affected intensity of climatic response while its complexity affected spatial distribution of climatic response. For commensurate mosaic oasis-desert nested structures in the same sensitive area, they had the same distribution type of climatic response but different intensity. EXP1, EXP2, and EXP3 presented that certain complexities can make nesting fields appear with a whole-field characteristic; there were different climate response to vegetation change between sensitive climatic area and ordinary one, the former with greater response than the latter.
Remote Sensing and Modeling of Ecosystems for Sustainability | 2004
Wei Gao; Zhiqiang Gao; Xiaoling Pan; James R. Slusser; Jiaguo Qi; Xiwu Zhan; Yingjun Ma
This study presented the temporal and spatial variation patterns of the seasonal NPP, temperature and precipitation. The NPP simulated by using the GLO-PEM. A semi-mechanistic model of plant photosynthesis and respiration driven entirely by the satellite observations was combined with climate data in Xinjiang of China over the past 20 years to study the impact of seasonal climate changes on the seasonal NPP. The higher correlation coefficients between the seasonal NPP and the corresponding seasonal temperature and precipitation over the past 20 years happened in the areas covered with forest lands, grasslands, oasis and croplands in the northern and southern foothills of Tianshan Mountain, Iili River Valley, Tarim Basin and Junggar Basin. In these areas, the vegetation growth was greatly influenced by interannual changes of seasonal temperature and precipitation. The spatial patterns of the correlation coefficients in Xinjiang showed that the higher correlation coefficients between seasonal NPP and seasonal temperature and precipitation in 1990s than in 1980s. With the increased temperature and precipitation, the areas of grasslands and oasis in Xinjiang were expanded over the last 20 years.
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture | 2004
Wei Gao; Zhiqiang Gao; Xiaoling Pan; James R. Slusser; Mingkui Cao; Jiaguo Qi; Jie Zhang; Xiwu Zhan; Yingjun Ma
In the last several decades, the responses of vegetation to global changes at regional and global scales have been studied with many mathematical models primarily driven by point meteorological observations. In this study, the net primary productivity (NPP) of Xinjiang, China is simulated using the GLObal Production Efficiency Model (GLO-PEM) which is a semi-mechanistic model of plant photosynthesis and respiration and driven entirely by satellite observations. With the available satellite observation data acquired from NOAA’s Advanced Very High Resolution Radiometer (AVHRR), the seasonal and inter-annual changes of NPP in the Xinjiang area are analyzed for the time period of 20 years from 1981 to 2000. Large spatial variability of NPP is found in this area. The temporal trends of NPP in different regions of the area differed significantly. However, for the whole area the mean annual NPP decreased in the 1980s and increased in the 1990s. Seasonal variations of NPP are large and inter-annual changes are moderate. The correlations between the simulated NPP and the precipitation and temperature suggested that precipitation and temperature played major roles in the variations of NPP.
Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space | 2003
Xiaoling Pan; Wei Gao; Fengxue Gu; Weiqing Li; Shunli Chang; Yuandong Zhang; Qian Ye; Subai Anabiek
This article discusses the relationship between desert ecosystem structures, succession and environment factors which include soil moisture, salt content and pH values in Fukang of Xinjiang. Some preliminary conclusions have been drawn as following: (1) In the study area the niche breadth of typical species in desert vegetation/ecosystem is closely related to environment factors, such as soil moisture, soil pH and salinity. The biggest niche breadth species are Haloxylon ammodendron (1.412) and Reaumuria soongorrica (1.399), which are dominant species in climax communities of the region, and they have very strong adaptability to the arid desert environment. The niche breadths of Nitraria rovorowskii, Kalidium foliatum and Suaeda acuminata range from 0.8 to 1.2. The smallest niche breadth species are Tamarix spp. and Anabasis spp., ranging from 0.4 to 0.8, and both of them show sensitivity to drought and salinity. (2) Low species diversity in desert vegetation/ecosystem of Fukang was found. In general, the grade of community diversity from high to low is defined as: Tamarix soongorica community, Kalidium foliatum community, Suaeda physophora community, Halocnemum strobilaceum community, Haloxylon ammodendron community, Salsola passerina community, Reaumuria soongorica community, Bassia spp. community and Suaeda acuminate community. The most important factors that influence the species diversity of communities are soil salinity and pH values. Because of saline-sodic environment desert vegetation has developed a saline-sodic endurance ecological type. The main effects of salinity on vegetation are observed in the change of dominant and constructive species in communities, and halophyte becomes the dominant species gradually. (3) The limit factor on secondary succession in regional ecosystem is soil salinization. The trend and phase of community succession are in accordance with soil salinization development. There are three soil types: non-salinity, saline soil, and strong saline soil. Communities of Bassia spp., Suaeda acuminnata, Petrosimonia sibirica, Suaeda physophora, Anabasis spp., Kalidium foliatum, Haloxylon ammodendron. Haloxylon ammodendron distributed in non-saline soil and Reaumuria soongorica community is regarded as a climax community in this area and distributed in all kinds of saline soils. The appearance of Tamarix spp. is the result of succession development responding to salinity and increased water content. The plant communities are distributed, in turn, within the entire basin because groundwater table and soil salinity changes from the foothill of mountain to desert, and this sequence is the same as the secondary succession serial of the vegetation / ecosystem of this area.
Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space | 2003
Zhiqiang Gao; Wei Gao; James R. Slusser; Xiaoling Pan; Yingjun Ma
The continuing rise in atmospheric CO2 is considered as a main cause of the future changes in global climate. Predicted climate changes include an increase in mean annual air temperature and alterations in precipitation pattern and cloud cover. Net primary productivity (NPP) measures products of major economic and social importance, such as agricultural crop yield and forest production. It is important to understand the response of vegetation to the possible climate changes. While the Global NPP is hard to be measured directly, its global spatial and temporal dynamics can be investigated by a combination of ecosystem process modeling and monitoring by remote sensing (RS). NPP has been linked to climatic patterns by approaches ranging from simple correlations to sophisticated simulation models. This study was conducted in a range where the productivity and climate exist along an east-west transect in northern China. We used modified Common Land Surface Model (CLM) to simulate the NPP combined with satellite data and assessed the response of NPP under different climate change controls with different land surface vegetation types in study areas. The feasibility of the CLM model was tested and parameterized based on the ecological characteristics. The response of NPP to increased temperature was more sensitive to the doubled CO2 climate because the temperature is the limited factor to vegetation growth in study areas. The responses of NPP to different climate controls were also influenced by different vegetation types and ecological characteristics.