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Chen J.,CAS Qingdao Institute of Oceanology | Quan W.,Shaanxi Remote Sensing Information Center for Agriculture
Journal of the Indian Society of Remote Sensing | Year: 2013

To understand the scale effects on chlorophyll-a (chl-a) concentration retrieved from satellite images, the two-band algorithm (TA) and three-band algorithm (TBA) were constructed for estimating chl-a from satellite images. Two synchronous images of Advanced Wide-Field Sensor (AWiFS) and Linear Imaging Self-Scanner (LISS) of Indian remote sensing satellite were used to assess and validate the scale errors of these two algorithms. They were collected at local time 02:55:46:471 and 02:58:25:053 on October 8, 2005 in Yellow River Estuary, and their spatial resolutions are 24 m and 56 m, respectively. From the results of this study, it was found that: (1) the relative scale error (RSE) of TA and TBA, caused by scale changing from LISS to AWiFS, varied from 0% to 100%; (2) the RSE was correlated with the spatial non-homogeneous degree of chl-a distribution; and (3) using TBA to estimate chl-a concentration in Yellow River Estuary decreased 2. 55% of model uncertainty, but increased 4. 97% of scale errors, in comparison with TA. Additionally, the study indicated that the performance of algorithms for chl-a estimation was greatly affected by the scale error. If the scale effects of chl-a retrieval algorithm were taken into consideration, TA had a superior performance to the TBA in this study. © 2012 Indian Society of Remote Sensing.


Shi X.-L.,National Climate Center | He H.-J.,Shaanxi Remote Sensing Information Center for Agriculture | Ren H.-C.,Nanjing University of Information Science and Technology
Advances in Climate Change Research | Year: 2013

The impacts of land cover changes on regional climate in Shaan-Gan-Ning (SGN) in western China were simulated with RegCM3. Sensitivity experiments were conducted by replacing crop grids with different new land cover types in the key area of SGN, where the returning cropland to tree/grass project has been carried out since 1999. The modified new land cover types include desert, forest, shrub and grass. They represent degraded, improved, and maintained vegetation cover with natural canopy in the key area. Results from three individual case studies show that the land cover change causes changes in temperature and terrestrial water variables especially within the key area, while changes in precipitation are found for a larger area. The strongest changes appear where the cropland is degraded to bare soil, leading to increasing temperature and decreases in rainfall, evaporation and soil water. Opposite changes occur when cropland changed into forests, especially with strong increases in soil water. When cropland changed to grass and shrub land, the climatic changes are closer to those with forest cover. This shows the importance of improving and maintaining the vegetation in SGN for the ecosystem and regional climate.


Sun Z.,Meteorological Bureau of Yanan | Lei Y.,Meteorological Bureau of Yanan | Zhuo J.,Shaanxi Remote Sensing Information Center for Agriculture | Cao X.,Meteorological Bureau of Yanan | And 2 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2010

A comprehensive analysis of the SPOT VGT, NOAA/AVHRR, EOS/MODIS, TM and DEM data by using the Geographic Information System (GIS) showed that the ecological construction project for changing cultivated land back into forest or grass land in seven counties of northern Yanan has achieved some effects. Examination of the NOAA/AVHRR and EOS/MODIS remote sensing monitoring data from 1998 to 2009 revealed that the area is highlighted in the remote sensing images, distinct from the adjacent areas to the north and west. All the results demonstrated that the vegetation coverage is improved and the vegetation recovery is better in the hilly gully region of northern Yanan. Evolution of the SPOT VGT NDVI from 1999 to 2007 illustrated that the difference between the NDVI in northern Yanan and the NDVI in secondary forest is significantly reduced. The difference of NDVI in Northern Yanan and that in grain-growing areas is obviously increasing. The NDVI is in a rapid increasing stage with the linear trend value being 0.0078, visibly higher than that of the surrounding area. Comparative interpretation of the TM images before and after the farmland reconstruction illustrated that the effect of converting farmland into forest is remarkable in recent years, with great improvement in the ecological environment. Compared with the situation in 1997, 68. 37% of arable land in northern Yanan is no longer farmed in 2007. The cultivated land is changed mainly into grassland, woodland, or orchard. The forest/grass coverage has markedly increased, with an attainment of 65. 3%, raised by 24. 3%. The grass area has increased pronouncedly in particular. By the year 2007, the area with vegetation coverage of 30% -50% is the largest, occupying nearly half (47. 2%) of the total area, and the area with tall vegetation has also dilatated a lot, from 6% in 1997 to 22% in 2007. TM images and DEM data analyses showed that the soil erosion intensity decreases in general. Areas of violent soil erosion, extremely heavy soil erosion, and heavy soil erosion have diminished notably, by more than 50%. However, the areas of violent soil erosion and extremely heavy soil erosion still account for 13. 3% of the total eroded soil land of China in 2007, so the situation is still formidable. Vegetation coverage is dominated by shrub and grass. The area covered by trees takes only a small percentage, and the forest coverage is only 22.4%. Therefore, strengthened governance is still imperative.


Li D.K.,Shaanxi Remote Sensing Information Center for Agriculture | Fan J.-Z.,Shaanxi Remote Sensing Information Center for Agriculture | Wang J.,Shaanxi Remote Sensing Information Center for Agriculture
Chinese Journal of Applied Ecology | Year: 2010

Based on the sub-pixel analysis model, and by using 2000-2009 MODIS NDVI (250 m resolution), this paper quantitatively analyzed the spatiotemporal change characteristics and their causes of fractional vegetation coverage (FVC) in Shannxi Province. From 2000 to 2009, the FVC in the Province had a significant increasing trend, with the great magnitude of 35.0%. During that period, the vegetation coverage increased from 56.9% in 2000 to 68.9% in 2009 in the provincial scale, and the increment was much higher in northern Shannxi, being 21.6% in Yulin and 22.0% in Yan' an. Though the vegetation coverage had an overall increase, it was locally degraded in some areas. The areas with improved vegetation coverage at the significance levels of <0.01, and <0.05 were 37.8% and 11.9% , while those with non-improved and degraded vegetation coverage were 46.1% and 4.2% , respectively. The areas whose vegetation coverage had a change rate of 200% , 200% -100%, 100%-10%, 10%-10%, and < -10% occupied 12.2%, 13.3%, 38.8%, 29.3% , and 6.4% of the total, respectively. During the study period, the structure of vegetation coverage in the Province also improved. The areas with high and normal vegetation density increased significantly by 10% and 8.4% , respectively, while the area with low vegetation density decreased significantly by 18.4%. The improvement of the FVC in Shaanxi Province was the interactive effect of natural factors and human activities, but the main cause was the implementation of a series of ecological construction projects such as closing hill for forestation and restoring farmland into forestland and grassland.


Li D.-k.,Shaanxi Remote Sensing Information Center for Agriculture | Fan J.-z.,Shaanxi Remote Sensing Information Center for Agriculture | Wang J.,Shaanxi Remote Sensing Information Center for Agriculture
Chinese Journal of Ecology | Year: 2011

A quantitative analysis was made on the spatiotemporal characteristics of vegetation net primary productivity (NPP) in Shaanxi Province, based on the 2000-2006 average annual vegetation NPP data of MOD17A3 dataset and by using GIS technology. In the Province, the annual NPP ranged from 340 to 434 g C · m-2 · a-1 with an average of 383 g C · m-2 · a-1. The average annual NPP was higher in the south and lower in the north part of the Province, and higher in the west and lower in the east part of central and south Shaanxi, being 0-200 g C · m-2 · a-1 in the wind-eroded area along Great Wall, 200-300 g C · m-2 · a-1 in the hilly and gully area of Loess Plateau, 300-400 g C · m-2 · a-1 in the dry-farming area of Weibei plateau, 400-500 g C · m-2 · a-1in the forest area of central part, and 400-500 g C m-2 · a-1 in the forest area of Qinba mountains region. As compared with that in 2000, the annual NPP in most part of the Province in 2006 had an increase, and the area with increased annual NPP accounted for 90. 52% of the territory of the Province. The increase of the annual NPP in the Province was mainly in linear type, and the area with an increment of >10% occupied 50. 6% of the territory of the Province, mainly distributed in the north parts from Yan' an, which suggested that through the implementation of a series of ecological construction projects such as closing hill for forestation and restoring farmland into forestland, the vegetation in these parts was improved.


Yan N.,Shaanxi Normal University | Li D.-K.,Shaanxi Meteorological Administration | Du J.-W.,Shaanxi Normal University | Du J.-W.,Shaanxi Remote Sensing Information Center for Agriculture | Yan J.-P.,Shaanxi Normal University
Journal of Natural Disasters | Year: 2010

This paper chooses Shaanxi Province as a study area. Using normalized difference vegetation index (NDVI) , enhanced vegetation index (EVI) and land surface temperature (LST) gained from MODIS synthetic products data MODISHC3 and MODIS13C3 in April 2005, the TS -NDVI and TS -EVI characteristic space were established and the conditional temperature vegetation dryness index (TVDI) and the spatial distribution map of drought situation grade were obtained. In order to monitor and evaluate the drought in Shaanxi Province, a comparative analysis of results was necessary at the same time. Finally, based on combining air temperature and precipitation anomaly data from 94 meteorological stations the correlation was analyzed. The results show that, the linear correlation between drought situation-monitoring model,which is set up on surfdce temperature and enhanced vegetation indes, and precipitation anomaly is significant. The correlation coefficient is 0.537 and gets through 0.05 level test.


Fan J.,Shaanxi Remote Sensing Information Center for Agriculture | Li D.,Shaanxi Remote Sensing Information Center for Agriculture | Dong J.,Shaanxi Remote Sensing Information Center for Agriculture
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

Based on pixel decomposition model, this paper managed to get the fractional vegetation cover (FVC) of Shaanxi province form 2000 to 2010 with MODIS NDVI at a resolution of 250 m, and analyzed the temporal and spatial variation characteristics of FVC in key ecological construction areas (grain for green Program, desertification prevention and control project, natural forest protection project) of Shaanxi province. The research results indicated that: 1) The FVC in key ecological construction areas of Shaanxi province in 2010 increased 8.3% to 23.2% compared with 2000.2) The FVC in key ecological construction areas showed an upward trend with fluctuation (P<0.01). The percentage change in linear trend of the desertification prevention and control project areas (83.8%) was the highest, and grain for green program in north Shaanxi (61.1%) was the next. 3) The FVC in each ecological construction areas mainly increased, and the areas with an upward vegetation coverage linear trend accounted for 82.8% to 98.2%. 4) The percentage of high FVC areas had a notable increase trend, and the percentage of the low FVC areas had a notable decrease trend. The FVC in the ecological construction areas were improved noticeably though the construction of key ecological construction, especially in desertification prevention and control project areas and grain for green program in north Shaanxi.


Liu Y.,Northwest University, China | Gao M.,Northwest University, China | Gao M.,Shaanxi Remote Sensing Information Center for Agriculture | Wu W.,Northwest University, China | And 3 more authors.
Soil and Tillage Research | Year: 2013

Six tillage practices were studied in an apple orchard located in the Loess Plateau from 2007 to 2009. The objective was to investigate the effects of tillage practices on the soil water-holding capacity in a non-irrigated orchard in China. The results showed that different tillage practices had varied effects on the water-holding properties. Subsoil tillage with straw mulching, plow tillage with straw mulching and no tillage with straw mulching showed a decrease in the soil bulk density and an increase in the soil porosity, soil saturated water content and soil moisture relative to plow tillage in bare soil (i.e., conventional tillage). Among these three tillage practices, the subsoil tillage with straw mulching and no tillage with straw mulching treatments had a significant effect on the soil porosity, soil saturated water content and soil moisture compared to the plow tillage with straw mulching treatment. These results indicate that the subsoil tillage with straw mulching, plow tillage with mulching and no tillage with mulching treatments improved the soil structure and water-holding capacity of the apple orchard. However, the no tillage with bare soil and no tillage with grass treatments increased the soil bulk density and decreased the soil porosity and soil moisture content compared to conventional tillage. This finding demonstrates that no tillage with bare soil and no tillage with grass had adverse effects on the soil structure and water-holding capacity. From the results of this study, we concluded that the subsoil tillage with straw mulching treatment is the optimum practice of the six studied treatments for improving the soil water-holding capacity in this non-irrigated apple orchard in the Loess Plateau of China. © 2013 Elsevier B.V.

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