Based on the NOAA AVHRR-NDVI monthly data from 1981 to 2001, the spatial distribution and dynamic change of land cover along the Qinghai-Tibet Highway and Railway were studied. The results of the analytical data indicate that the NDVI values in July, August and September are rather high during a year, and a linear trend by calculating NDVI of each pixel computed based on the average values of NDVI in July, August and September were obtained. The results are as follows: 1) Land cover of the study area by NDVI displays high at two sides of the area and low in the center, and agriculture area > alpine meadow > alpine grassland > desert grassland. 2) In the study area, the amount of pixels with high increase, slight increase, no change, slight decrease and high decrease account for 0.29%, 14.86%, 67.61%, 16.7% and 0.57% of the whole area, respectively. The increase of land cover pixels is mainly in the agriculture and alpine meadow and the decrease pixels mainly in the alpine grassland, desert grassland and hungriness. Grassland and hungriness contribute to the decrease mostly and artificial land and meadow contribute to the increase mostly. 3) In the area where human beings live, the changing trend is obvious, such as the valleys of Lhasa River and Huangshui River and area along the Yellow River; in the high altitude area with fewer people living, the changing trend is relatively low, like the area of Hoh Xil. 4) Human being’s behaviors are a key factor followed by the climate changes affecting land cover.
This paper examines the changing regional distribution of grain production in China. Based on the analysis of data from county statistics for the period 2000–2003, major differences in the main grain-output regions in China can be observed. The main grain-producing areas have shifted from the south to the north of China. New grain production regions have been also added to western China since the late 1990s. The per capita grain consumption in one third of China’s main grain-producing counties has fallen below 400 kg; most of these areas are located in southern China. In the new millennium, Northeast China, the central-south North China, and the arid and semi-arid regions of Northwest China produced three quarters of the surplus grains. Most of these areas are located in regions susceptible to environmental change. The amount of grain production in these regions shows high fluctuations. It is argued here that further studies of recent environmental changes as well as a risk assessment of China’s food security in main grain-output regions are needed.
Taking the typical karst agricultural region, Xiaojiang watershed in Luxi of Yunnan Province as a research unit, utilizing the groundwater quality data in 1982 and 2004, the aerial photos in 1982 and TM images in 2004, supported by the GIS, we probe into the law and the reason of its space-time change of the groundwater quality over the past 22 years in the paper. The results show: (1) There were obvious temporal and spatial changes of groundwater quality during the past 22 years. (2) Concentrations of,SO42-,NO3-,NO2-,Cl-,and the pH value, total hardness, total alkalinity increased significantly, in which NH4+,NO3-, and NO2- of groundwater exceeded the drinking water standards as a result of non-point pollution caused by the expansion of cultivated land and mass use of the fertilizer and pesticide. (3) Oppositely, Ca2+ and HCO3- showed an obvious decline trend due to forest reduction and degradation and stony desertification. Meantime, there was a dynamic relation between the groundwater quality change and the land use change.
The expansion of agriculture is posited as one of the main dynamics of forest landscape change globally, and the robust modeling of these processes is important for policy as well as academic concern. This paper concerns a relatively small area of Yiluo River catchment where considerable attention has been paid to slow down the process of the expansion of agriculture into the remaining natural forests. In the present study, we reconstructed the former forest landscape structure and elucidated the landscape change during a period of about 15 years. Three sets (1987, 1996 and 2002) of maps derived from Landsat-5 images were used for analyses. The result showed that there was a decrease in the area of the forest landscape from 995.60 km2 in 1987 to 650.50 km2 in 2002. Then we examined the degree to which forest landscape conversion could be attributed to a set of factors identified as significant at broader scales, namely topography, distribution of the village clusters (centroids), distance from villages (centroids), and distance from forest edge (1987). By using “spatial analysis” in Arc/gis 8.3, the correlation between forest landscape change and driving factors was constructed. This study found that forest landscape conversion in this region was largely explained by elevation, slope and proximity to village.
Neolithic site sections, natural sections and other proxy indicators like paleotrees and peat are collected for further understanding the environmental changes during the past 10,000 years in the Yangtze Delta region. The results indicate that cultural interruption in the Yangtze Delta was the result of water expansion induced by climatic changes like more precipitation. For fragile human mitigation to the natural hazards in the Neolithic cultural period, environmental changes usually exerted tremendous influences on human activities, havocking the human civilization, which is meaningful for human mitigation to natural hazards under the present global warming. At the same time, some uncertainties in reconstruction of paleo-environmental changes were discussed in the text.
Using foggy days and mean temperature and relative humidity data of 602 stations from January to December in the period 1961–2003 in China, the relationship between variations of foggy days and temperature and its possible reason for the 43 years were analyzed by regression, correlation and contrastive analysis methods. The results show that the higher (lower) the mean temperature and the lower (higher) the relative humidity correspond to less (more) foggy days, the relationship is the best in the western, northern and eastern Sichuan, Yunnan-Guizhou Plateau, and southeast highland in China. This induces a decrease in relative humidity when the climate becomes warmer, and eventually brings about a decrease in foggy days in China.
Pollen analysis of 23 surface samples in the east of Qaidam Basin reveals the characteristics of pollen assemblages and their relationships with vegetation and climate. In pollen assemblages, Chenopodiaceae and Artemisia are preponderant types in all the samples, and Ephedra, Gramineae and Compositae are common types. The results of DCA (Detrended Correspondance Analysis) and Correlation Analysis show different pollen assemblages indicate different vegetations, coincided with respective vegetation types. A/C (Artemisia/Chenopodiaceae) in the desert can indicate the aridity. Depending on the aridity, the vegetation communities are divided into four groups: severe drought group, moderate drought group, slight drought group and tropophilous group. A/C value is less 0.2 in the severe drought group, 0.2–0.5 in the moderate drought group, 1.63 in the slight drought group and 5.72 slight-wetness group.
The spatial and temporal variability of primary productivity in the China seas from 2003 to 2005 was estimated using a size-fractionated primary productivity model. Primary productivity estimated from satellite-derived data showed spatial and temporal variability. Annual averaged primary productivity levels were 564.39, 363.08, 536.47, 413.88, 195.77, and 100.09 gCm-2a-1 in the Bohai Sea, northern Yellow Sea (YS), southern YS, northern East China Sea (ECS), southern ECS, and South China Sea (SCS), respectively. Peaks of primary productivity appeared in spring (April–June) and fall (October and November) in the northern YS, southern YS, and southern ECS, while a single peak (June) appeared in the Bohai Sea and northern ECS. The SCS had two peaks in primary productivity, but these peaks occurred in winter (January) and summer (August), with the winter peak far higher than the summer peak. Monthly averaged primary productivity values from 2003 to 2005 in the Bohai Sea and southern YS were higher than those in the other four seas during most months, while those in the southern ECS and SCS were the lowest. Primary productivity in spring (March–June in the southern ECS and April–July in the other five areas) contributed approximately 41% on average to the annual primary productivity in all the study seas except the SCS. The largest interannual variability also occurred in spring (average standard deviation = 6.68), according to the satellite-derived estimates. The contribution during fall (October–January in the southern ECS and August–November in the other five areas) was approximately 33% on average; the primary productivity during this period also showed interannual variability. However, in the SCS, the winter (December–March) contribution was the highest (about 42%), while the spring (April–July) contribution was the lowest (28%). The SCS did share a feature with the other five areas: the larger the contribution, the larger the interannual variability. Spatial and temporal variability of satellite-derived ocean primary productivity may be influenced by physicochemical environmental conditions, such as the chlorophyll-a concentration, sea surface temperature, photosynthetically available radiation, the seasonally reversed monsoon, river discharge, upwelling, and the Kuroshio and coastal currents.
A study was conducted in the Taihu Lake with the aim of deriving a model for the retrieval of suspended sediment (SS) concentrations from Landsat TM images and in situ sampled data. The correlation between suspended sediment concentrations of lake and the reflectance obtained from the TM images is significant. By TM images and in situ sampled data in summer and winter, we obtained a comparative uniform model for the retrieval of suspended sediment concentrations in the Taihu Lake, that is lnSS = a*(R3/R1) + b, where lnSS is the natural logarithm of the suspended sediment concentration, R1 and R3 are the reflectance coincident with the 1st band and the 3rd band in TM images, a and b are the regression coefficients. Furthermore, we analysed the errors particularly to make sure the model is valid. The model is accurate to within 0.33(RMSE), suggesting that this model may be applicable to predict suspended sediment in the Taihu Lake from TM image throughout the year.
A 6-m ice core was recovered in 2004 from the Naimona’Nyi Glacier, the middle Himalayas. Empirical orthogonal function (EOF) analysis on the major ion reveals that EOF1 represents the variations of majority of ions which may be originated from crustal aerosols. Comparing the calcium concentrations from the Naimona’Nyi with these from Dasuopu, East Rongbuk and Guliya ice cores, it is observed that calcium, a good indicator of the input of crustal aerosol in snow, concentrates mostly in the Guliya ice core located on the northern Tibetan Plateau, and gradually decreases from west to east in the Himalayas.
Vertical cycle karst zone has been studied for more than 100 years, however karst subzones in the zone have never been divided and affected depth of CO2 from rainwater in the zone has never been studied. On the basis of field observation, survey and chemical analysis, the difference of karst processes indicated by CaCO3and pH values in fine and loose sedimentary strata as well as limestone strata, and the vertical cycle zone ascertained by predecessors can be divided into three subzones, that is, the upper first subzone, characterized by unsaturated water solution and strong dissolution processes, the middle second subzone, characterized by supersaturated water solution and precipitation, and the lower third subzone, characterized by unstable water solution and weak dissolution or weak precipitation. The three subzones can indicate the vertical co2 cycle. In fine and loose sediment strata, the bottom of the first subzone is the lower boundary strongly influenced by co2 from rainwater, soil and air; all co2 from rainwater, soil and air is almost exhausted in the second subzone. In the early developmental period of karst process in limestone strata, karst funnels and vertical caves do not form, vertical seeping of rainwater and soil water is very slow, and co2 from soil, rainwater and air almost can reach the third subzone, but in the middle and late developmental periods, karst funnels and vertical caves occur, co2 from soil, rainwater and air can reach deep seasonal change zone and horizontal cycle zone and quicken development of karst morphology. Deep karst morphology near groundwater level under vertical cycle zone develops better in the middle and late periods of karst process.
This paper explores the methodology for compiling the torrent hazard and risk zonation map by means of GIS technique for the Red River Basin in Yunnan province of China, where is prone to torrent. Based on a 1:250,000 scale digital map, six factors including slope angle, rainstorm days, buffer of river channels, maximum runoff discharge of standard area, debris flow distribution density and flood disaster history were analyzed and superimposed to create the torrent risk evaluation map. Population density, farmland percentage, house property, and GDP as indexes accounting for torrent hazards were analyzed in terms of vulnerability mapping. Torrent risk zonation by means of GIS was overlaid on the two data layers of hazard and vulnerability. Then each grid unit with a resolution of 500 m × 500 m was divided into four categories of the risk: extremely high, high, moderate and low. Finally the same level risk was combined into a confirmed zone, which represents torrent risk of the study area. The risk evaluation result in the upper Red River Basin shows that the extremely high risk area of 13,150 km2 takes up 17.9% of the total inundated area, the high risk area of 33,783 km2 is 45.9%, the moderate risk area of 18,563 km2 is 25.2% and the low risk area of 8115 km2 is 11.0%.
The endangerment of aeolian sand is one of the most serious eco-environmental problems facing the southern suburb of Beijing. To control wind erosion is thus a matter of great urgency. This paper chooses typical land-use types, i.e., cultivated land, wild grassland, drifting sandy land, etc. based on the fixed experimental observation and quantitative analysis, to conduct studies on the principles and characteristics of aeolian activities and measures controlling wind erosion in Daxing District of Beijing. The results show that: the near ground wind velocity yielded the logarithmic distribution with height; there are significant differences among different ground cover roughness; there are significant differences in the corresponding friction velocities caused by different properties of the underlying surface; there are differences in the threshold wind velocities speed in different land use types; and there are minus exponential relations between the sediment discharge percentage and the height at a range of 0–20 cm air flow layer. Different land use types result in various degrees of coarse component. There is a significant exponential relation between sediment transport concentration and wind velocity.
By wind tunnel experiment, we studied the deflation rates of 8 different clastic sediments in the arid regions of China, discussed the sources of aeolian sand and their influence on the development of sand dunes and formation of sand deserts from the view of dynamics of wind erosion. The average deflation rates of 8 typical clastic sediments in the arid regions of China can be arranged in the order of lacustrine sand > alluvial sand > weathered sandstone and shale > pluvial sediments > fluvioglacial sand > weathered granite > slope deposit > glacial sediments. The deflation rates exhibited strong positive correlations with the erodible particle (0.063–2mm) content and sorting features. In contrast, the deflation rates had obvious negative correlations with the contents of silt clay (<0.063 mm) and gravel (>2 mm). According to the deflation rates, the 8 typical clastic sediments can be divided into four categories: (1) lacustrine and alluvial sand, which are readily prone to wind erosion, assumed to be the main source of aeolian sand; (2) weathered sandstone and shale, pluvial sediments and fluvioglacial sand with considerable deflation rates, might be the secondary source of aeolian sand; (3) weathered granite and slope deposit having the lower deflation rates, could supply a little aeolian sand; and (4) glacial sediments with a strong anti-erodibility, could hardly offer any aeolian sand. In addition to the strong wind conditions, the exposure of extensive lacustrine sand induced by the desiccation of inland lake basin, as well as the pre-sorting of clastic sediments by flowing water should be the key factors influencing the development of sand deserts in China. The possible reason the sand deserts in China being mostly distributed around the inland lake basins and along riverbanks could be better understood through sand source analysis.
“Ejin Section” found in a typical vegetation-covered sand dune in Ejin Oasis was investigated. In this study, 263 samples were taken from the section for grain-size analysis, 25 for chemical analysis, 11 for 14C dating and 6 for scanning electron microscope (SEM). The results of the study indicate that 3 types of the sediments in the section can be identified, YS, LS and ST. YS, homogeneous yellow-brown dune sands, is equal to those of inland deserts, LS, loess-like sandy soils, is the same as the sandy loess in the middle Yellow River and modern falling dusts, and ST, sandy sediments interbeded with the deadwood and defoliation of Tamarix spp, represents the depositional process of the section interrupted by abrupt changes in climate. The Ejin Section has recorded the repeated dust-storms or sandstorms since 2500 yr BP and the peak periods of the dust-storms or sandstorms revealed by the section are consistent with the records of “dust rains” in historical literatures, indicating that the change of climate is a key factor to increase sandstorms or dust-storms, whereas, “artificial” factor may only be an accelerating one for desertification.
