The aims of this paper are to illustrate the meaning and method of comparing industrial land use efficiency in different regions by eliminating the industrial land area differences caused by industrial structures, thus to improve the level of industrial land use efficiency assessment. We select the land use intensity as an effective tool to eliminate the effect of industrial land area differences, and explain method of comparing industrial land use efficiency under the situation of no intensity differences. Then we build a“Volume rate index”(VRI) to reflect the land use intensity differences characterized by industrial output, and use it to calculate the comparable areas of industrial land in different regions, achieving the industrial land comparison with no intensity differences. Finally, in order to grasp the full extents of industrial land use in different regions, we make 30 provinces as macro DMUs to measure the industrial land use efficiency using sub-factor DEA method. The data used includes industrial input and output from 2001 to 2011. The results indicate that industrial land in many provinces has not been fully utilized, and there is a massive waste of industrial land area. The areas which have higher VRI distribute in southeast coastal region, and lower VRI areas generally locate in midwest region. After correction of industrial land area, the provinces whose industrial land use efficiency increase are mainly in the midwest region, and the provinces whose industrial land use efficiency decrease are mainly in the southeast coastal region. The changes of industrial land use efficiency and VRI have a negative correlation. China's industrial development has a significant regional gradient difference, that the industrial land use efficiency in the Yangtze River Delta and Pearl River Delta are significantly higher than that in the central and western regions. The coefficient of variation reflects that the imbalance of industrial output is greater than the imbalance of industrial land configuration among regions, while at the same time it reflects that regional gap of land use efficiency is gradually narrowing.
Changting County in Fujian Province is a typical reddish soil erosion region in southeastern China. The county has been called the flame mountains area due to severe soil erosion on barren terrains. After years of ecological restoration, the local ecosystem has been improved remarkably and the barren lands are now covered with forests. This paper used remote sensing techniques to study the fractional vegetation cover (FVC) changes and quantitatively evaluated the effects of ecological restoration in Changting during the period from 2001 to 2013 by using the remote sensing ecological index (RSEI). Two remote-sensing based models for estimating FVC have been compared in order to select a suitable model for the retrieval of the FVC in the study area. One is the commonly-used Linear Spectral Mixture Analysis (LSMA) model, and the other is the LSMAM model. The LSMAM is based on the LSMA but with a Normalized Difference Mountain Vegetation Index (NDMVI) derived band added into the model. The comparative analysis confirms that the LSMAM model has higher accuracy than the LSMA model, indicated by its lower root mean square error and higher correlation with referenced FVC data because the addition of the NDMVI band into the model could eliminate shadow problem caused by topographic relief in the mountainous areas. Therefore, the LSMAM model was selected to retrieve the FVC in this study. The result indicates that the 13-year effort for treatment of the soil loss in the county has led to a notable increase in county's FVC from 75.1% (2001) to 86.5% (2013). Meanwhile, the RSEI- based analysis also indicates a significant improvement of the county's ecological quality during the same period, because of the increase in RSEI value from 0.750 to 0.787, along with an increase in high-RSEI-level area from 85.83% to 90.59%. Regression analysis between FVC and RSEI suggests that each 10% increment in FVC could raise the RSEI by 10%. This clearly indicates a significant effect of FVC on county's ecological quality.
It is still unclear if the altitudinal pattern of aboveground biomass detected from small-scale transect resembles those at larger-scale landscapes such as county-level, and if the threshold vary among different scales. Based on NDVI values extracted from Landsat ETM images and the terrain factors detected from DEM, we first assessed the altitudinal patterns of NDVI (a surrogate of aboveground biomass) and validated it by aboveground biomass measurements sampled along a transect of grassland at a south slope in Damxung County, Tibet. We then analyzed the altitudinal pattern of NDVI of the whole county where NDVI >0.2. The results are as follows: 1) at the slope scale, the NDVI values calculated by different methods all showed a unimodal pattern that increased first and then decreased with the increase of altitude, with the maximum value appeared at the height of about 4950 m, and then decreased when altitude continually rose, which displays the same pattern as the investigated aboveground biomass with the peaked at 4950 m (313 g/m2); 2) at the whole county scale, the average NDVI showed a bimodal pattern: one peak appeared around 4700 m and the other below 4300 m. For the area where the altitude was above 4300 m and which accounted for more than 85% of the total area), the variation of NDVI with rising altitude also presented a unimodal pattern. For the peak below 4300 m, the high levels of NDVI came from wet meadows or wetlands which only accounted for a small part of total grassland area (≈10%). In conclusion, similar altitudinal patterns of NDVI at different space scales indicate that it is the elevation that primarily controls the spatial pattern of NDVI in Damxung, Tibet, and the altitudinal patterns of NDVI reflect the adaptations of grassland vegetation to varied combinations of temperature and precipitation. This is important for the ecological conservation at alpine regions and the protection and utilization of grassland resources reasonably under climate change scenarios.
This paper focuses on the elevation variation of urban and rural construction land expansion. It uses 1985, 1995, 2000 and 2010's land use data and 90 m resolution DEM data, and establishes evaluation indexes including density index, incremental proportion, sprawl rate, sprawl intensity index and evaluation index of construction land, to explore the characteristics of spatial heterogeneity and the evolution pattern of urban and rural construction land elevation changes in Taihu Lake Basin. The research results indicate that: the hotspot of construction land expansion has transferred from medium-elevated region to higher-elevated and lower-elevated region in the whole basin, of which the former trend is more significant. Spatially, construction lands distributing along the plain river network in the eastern downstream watershed, in the city and town concentrated areas along the Yangtze River in the north, and in the valley areas of upper reaches of Tiaoxi stream have the trend to expand to lower-elevated region. However, except for Tiaoxi stream basin, the other hill regions in the upstream basin are expanding gradually to higher-elevated zones. The development of new districts and cities has promoted a rapid expansion of urban and rural construction land. Contradiction between the limited supplies of suitable land for construction and the huge demand of land driven by land finance has forced the construction land to expand to higher and lower elevation areas, especially to lower areas in city and town concentrated areas in southern Jiangsu Province. Construction land expanding to lower altitude regions will inevitably occupy the natural water storage space, divide and obstruct the natural flood discharge channel, and lead to difficulties for flood to be discharged in the low-lying place. While construction land expanding to higher altitude regions will accelerate the confluence water and shorten the downstream flood peak time, which lead to regional hydrological security risks and frequent waterlogging disasters.
Using the daily precipitation data of 34 meteorological stations in Beijing and its surrounding areas, this paper attempts to describe the variation trends of drought-flood in Beijing in different seasons during 1960-2013. Based on Standardized Precipitation Index (SPI), correlation analysis, Morlet wavelet analysis and other climate diagnosis method, we analyzed the influence factors of drought-flood variations. It is found that the number of minor drought-flood events decreased, while that of the severe drought- flood events increased during that period, which indicates that drought and flood events were becoming more extreme. At short- time scales, SPI fluctuated greatly, which means that drought and flood alternated frequently. At long-time scales, droughts and floods alternated frequently before the 1980s; after the middle of 1980s, SPI was going down, so that the number of flood disasters decreased, while the number of drought disasters increased gradually. Since there was little precipitation during 1999- 2008, the continuous drought occurred during the past decade. The urbanization process had obvious effect on the intensity of droughts and floods, but it did not affect the interdecadal variation. The relationship between drought-flood and El Niño-Southern oscillation (ENSO) is unstable. During the El Niño before 1980s, precipitations in summer showed a decreasing trend, leading to a severe drought in Beijing. With the emergence of the anomalous convection over the western North Pacific after the 1980s, the relationship became weak. The western Pacific subtropical high and East Asian summer monsoon showed relatively stable relationship with the variation of droughts and floods in Beijing: when the East Asian summer monsoon was stronger than normal and the western Pacific subtropical high went more northward, continuous droughts occurred in Beijing, whereas the reverse would cause flood.
Three growing season indices, the growing season length, the starting/ending time of the growing season were defined based on the daily minimum and the daily average temperature data across Xinjiang, China. Spatiotemporal properties of the growing season indices were analyzed using Fuzzy Clustering Technique, robust regressive method and nonparametric Mann-Kendall trend test method. Besides, the implications of change of growing season indices on agricultural activities were discussed. The results indicate that there was a great change of growing season length in mid-1990s across Xinjiang. The agricultural activities in southern Xinjiang were not heavily influenced by the low temperature. However hazards were examined in the northern parts of Xinjiang. It is also found that the growing season length is in close relations with the occurrence of low temperature at the end of spring (before and after April) and in the early autumn (before and after October). The increase of the growing season length may improve the preliminary production and annual total evaporation, and it may further impact the hydrological, ecological and geochemical processes across Xinjiang. In this case, our study is of practical and theoretical merits in terms of planning and management of agricultural activities over Xinjiang, China.
To investigate the silicon (Si) storage in sympodial bamboo ecosystem and its spatial distribution in China, samples were collected from the sympodial bamboo forest in Guangdong, Fujian, Zhejiang, Yunnan, and Sichuang provinces. The eight selected sympodial bamboo species in this study were Bambusa textilis McClure (BTM), Bambusa chungii McClure (BCM), Bambusa burmanica McClure (BBM), Dendrocalamus latiflorus Munro (DLM), Dendrocalamopsis oldhami (Munro) Keng f. (DOK), Dendrocalamus membranceus Munro (DMM), Dendrocalamus giganteus Munro (DGM) and Neosinocalamus affinis (Rendle) Keng f. (NAK), respectively. They cover more than 0.64 million hm2 and approximately account for 80% of the total sympodial bamboo forest area in China. The above-ground Si storage of the eight sympodial bamboo forests was initially estimated by measuring the Si content and dry biomass in leaf, branch, culm and litter, respectively. The results showed that: 1) the Si contents in different organs of the eight sympodial bamboo forests ranked as follows: leaf (12.47-62.71 g·kg-1) > branch (7.66- 29.26 g·kg-1) > culm (1.12-11.77 g·kg-1), and the average Si content in litter was higher than that in organs. Meanwhile, among the different species, the Si content of the leaf and branch was significantly higher in DOK than that in other species. 2) The Si storages of the eight different sympodial bamboo species ranked in the following order: DGM > DMM > NAK > DOK > BTM > BBM > BCM > DLM. In addition, the total Si storage in above-ground part of the eight sympodial bamboo species was about 5082.93 kg·hm-2, in which the Si storage in the litter accounted for more than 51%. And 3) through calculating the area and the Si storage, the storage of total Si in the above-ground part of the eight selected sympodial bamboo species was approximately 41.55×104 t Si and the storage of the total Si in the whole sympodial bamboo in China was approximately 51.94×104 t Si. The initial estimation of Si storage in the whole sympodial bamboo ecosystem in this study has a vital significance and provides basic data for estimating the Si storage in the whole bamboo ecosystem, and even that in subtropical forest ecosystem.
The change of soil organic carbon (SOC) pool in cropland ecosystem and its effecting factors were little investigated in the alpine area of Qinghai Province in recent decades. Using soil type as the unit, combining the second soil survey data (1982) and the recent (2011) repeatedly collected soil samplings, the spatio-temporal distribution characteristics of SOC pool at the surface layer (0-20 cm) of cropland ecosystem were studied at a county scale in Qinghai Province. The results clearly showed that, in Ledu County, the SOC density (SOCD) at the surface layer of cropland was 3.8 and 2.8 kg ·m- 2 in 1982 and 2011 respectively. At a county scale, SOCD decreased by 26% during the period from 1982 to 2011, however it only decreased in the northeast and south of the county while increased in the northwest of the county. The SOC storage at surface layer of cropland was 1.8×106 t in 1982 and 1.4×106 t in 2011, decreasing by 24% in recent 30 years. In all soil types, meadow soil, chestnut soil and chernozem soil showed carbon losing at the rates of -137.3, -35.0 and -91.0 g C·m-2·a-1 respectively, while fluvo-aquic soil and sierozem soil showed carbon sequestration at the rates of 9.7 and 7.3 g C ·m-2 ·a-1 respectively. Furthermore, the changing rates of SOCD in recent 30 years had a negative linear correlation with the SOCD in 1982 (y=0.35-0.13x).
Biological soil crusts (biocrusts) are non- ignorable influence factors of soil and water loss in the Loess Plateau region of China, however the impact of biocrusts on slope runoff generating is poorly understood so far. In Liudaogou small catchment that locates in Shenmu County, north of Shaanxi Province, the impact of four types of biocrusts and bare soil (no biocrusts) on runoff generating were studied in the runoff plots through simulated overland flow experiment. The four types of biocrusts were light cyanobacterial crust, dark cyanobacterial crust, cyanobacterial with moss crust (mixed crust hereafter) and moss dominated crust, which represent four different succession stages of biocrusts in the region. The results showed that: 1) compared with bare land, both light, dark cyanobacterial crusts and mixed crusts significantly reduced the time of runoff initiation by 89.0%, 96.2% and 96.0%; While the moss dominated crust markedly increased the time of runoff initiation. 2) The duration of runoff recession on light cyanobacterial crust and mixed crust were respectively 2.28 and 2.13 times higher than that on bare land; while that on dark cyanobacterial crust had no significant difference with that on bare land plot. 3) The runoff velocity was reduced by 29.1% by the dark cyanobacterial crust and 67.3% by the moss dominated crust compared with that on bare land; The runoff velocity on moss dominated crust was markedly slower than that on the other biocrust land. The runoff depth in biocrust plots did not show significant difference with that in bare land plot; The runoff depth on light cyanobacterial crust was significantly deeper than that on dark cyanobacterial crust. 4) The process and the amount of runoff in plots with different types of biocrusts showed obvious differences with that in bare land plots; The runoff coefficient in light cyanobacterial crust plot was significantly higher than that in bare land plots, while the runoff coefficient in dark cyanobacterial crust plots and mixed crust plots had no significant difference with thatin bare land plot. The runoff coefficient in light cyanobacterial crust plot was 2.44 times higher than that in dark cyanobacterial crust plots; No runoff generated on moss dominated crust during the runoff period. The results suggested that biocrusts are important influence factors on runoff generation, and the extend of influence was related to the succession stages and the composition of the biocrusts.
Using the hyperspectral reflectance data of typical objects and TM remote sensing images, the paper extracts the moss-dominated biological soil crusts at vigorous growth stage (from July to August) in Mu Us Desert. We collected the reflectance spectra of six typical ground features including bare sand, algae crust, dry vegetation, green vegetation, the moss crusts of 33% coverage and 100% coverage. Remote sensing data include twelve Landsat TM images acquired from July to August in 2010 and 2011. The results are as follows: 1) Moss crust in vigorous growth stage has similar spectral reflectance curves with green plants. There are obvious reflection peak and“red edge”phenomenon in visible light band. However, in the red band (680-760 nm), they have differences in all“red edge”parameters including red edge amplitude, red edge width and red edge area, except red edge position. In the near- infrared shortwave bands(760-900 nm), the reflectance of moss crust is much lower than that of green plants. Their average reflectance is 0.198 and 0.424 respectively. Moss crust has no reflection peak at the wavelength of 550 nm, but at the wavelength of 625 nm. The moss crust of 33% coverage, algae crust and dry vegetation have similar spectral curves between the wavelength of 400 nm and 900 nm. Their spectral reflectance is lower than that of bare sand which is 48%. 2) Based on the Landsat TM images, the method extracts biological crust by combing biological soil crust index (BSCI), normalized difference vegetation index (NDVI), supervised classification and slope classification. 3) The pixel area of moss crusts is 7200 km2, which takes 6.43% of the total study area.
The establishment of the arable land fertility inversion model based on vegetation index from TM remote sensing image provides a scientific basis for resource management and sustainable use of regional farmland. The study used the field survey of the arable land fertility and TM remote sensing data to screen vegetation index which can better reflect the arable land fertility. We chose the counties of Tancheng and Dongping in Shandong Province as study area, where the arable land fertilities are similar. Regression analysis was used to establish the model of arable land fertility-vegetation index with data of Tancheng, and the data of Dongping were used to validate the inversion model. The results showed that the positive correlation between enhanced vegetation index (EVI) and evaluation results of cultivated land is the most significant one, and the correlation coefficient was 0.82. The best fitted model was the Quadratic model with EVI as independent variable whose decisive factor was 0.69. The conformity degree between the result of inversion model and the result of original evaluation were tested by use of four indicators which include the decisive coefficient (R2), root mean square error (RMSE), precision and accuracy. The results showed that the Quadratic model built by EVI was the best inversion model of arable land fertility. The accuracy of it was the highest which is 95.84%, and the RMSE and precision was the lowest which are 5.21 and 0.04 respectively. Through the comparison of the result of inversion model and conventional evaluation of arable land fertility in Dongping, we can see that the fertility levels obtained by the inversion model agree with the actual farmland productivity levels in space. Classifying the arable land fertility levels into three grades of high, medium and low, it was found that the inconsistent areas of the three grades all took less than 3.3% of the area of the grade. The inversion effect was good and accorded with the actual situation. This study proved the feasibility of estimating farmland productivity by quantitative remote sensing, and provided an effective tool for monitoring and utilizing farmland resources.
As the important component of the global water cycle, precipitation is the key parameter in hydrology, meteorology and climate. Conventional interpolated observed precipitation data cannot reflect the spatial variation due to the limitation of the number of stations. In recent decades, with the development of remote sensing and meteorological satellite technology, satellite remote sensing images has become an important source of spatial precipitation data to detect rainfall information. In this study, APHRODITE precipitation data in Northeast China from 2000 to 2007 are used. We adjust the TRMM precipitation data based on GWR method, validate the accuracy of the adjusted data, and analyze the spatial and temporal distribution characteristics of precipitation in Northeast China based on the adjusted TRMM precipitation data. The conclusions are: 1) Correlation coefficient between APHRODITE and observed data is higher, and the root mean square error is smaller, so APHRODITE data have a higher accuracy. 2) The adjusted TRMM precipitation data have a higher correlation coefficient and a smaller RMSE value. Overall, the TRMM precipitation is higher than that of observed data. 3) High value of R mainly exists in the northern, eastern and southeastern regions, low-values mainly exist in the western and central regions. 4) The BIAS of adjusted TRMM precipitation data is relatively small from May to October. Overall, the BIAS of most areas ranges in 0-30%. 5) The distribution of precipitation is uneven in Northeast China, which reduces from the southeast to the northwest. The rainfall mostly happens in summer while less happens from November to the following March. The largest rainfall happens in July.
Intercropping has the characteristics of being high efficient and environmental friendly. It might be an important part of the sustainable agriculture in the future. The present study reviewed the semi-empirical and theoretical models for estimating light interception of intercropped canopies, and then analyzed the mechanisms responsible for high yield of intercrops from the perspective of light utilization. Intercropping could increase light interception, or promote the efficiency of converting captured light, or change the harvest index of crop. For the intercrops with relative long co-growth period, yield advantage is mainly attained from the efficient light capture by the dominant crop and the efficient light use by the subordinate crop, while for the intercropping systems with shorter co-growth period, the increase of light interception by both crops is the primary drive force for the intercropping benefit.
