Psychrobacter halodurans sp. nov. and Psychrobacter coccoides sp. nov., two new slightly halophilic bacteria isolated from marine sediment.

In this study, two bacterial strains designated F2608T and F1192T, isolated from marine sediment sampled in Weihai, PR China, were characterized using a polyphasic approach. Strains were aerobic, Gram-stain-negative and motile. According to the results of phylogenetic analyses based on their 16S rRNA genes, these two strains should be classified under the genus Psychrobacter and they both show <98.5% sequence similarity to their closest relative, Psychrobacter celer JCM 12601T. Moreover, strain F2608T showed 97.5% sequence similarity to strain F1192T. Strain F2608T grew at 4-37 °C (optimum, 30-33 °C) and at pH 6.0-9.0 (optimum, pH 6.5-7.0) in the presence of 0-12% (w/v) NaCl (optimum, 4.0-5.0%). Strain F1192T grew at 4-37 °C (optimum, 30 °C) and at pH 5.5-9.0 (optimum, pH 7.0-7.5) in the presence of 0.5-12% (w/v) NaCl (optimum, 3.0-4.0%). The genomic DNA G+C contents of strain F2608T and strain F1192T were 47.4 and 44.9 %, respectively. Genomic characteristics including average nucleotide identity and digital DNA-DNA hybridization values clearly separated strain F2608T from strain F1192T. The sole isoprenoid quinone in these two strains was ubiquinone 8 and the major cellular fatty acids (>10.0%) were C18:1 ω9c and C17:1 ω8c. The major polar lipids of these two strains were phosphatidylglycerol, phosphatidylethanolamine and diphosphatidylglycerol. Based on the results of polyphasic analysis, the two strains represent two novel species of the genus Psychrobacter, for which the names Psychrobacter halodurans sp. nov. and Psychrobacter coccoides sp. nov. are proposed. The type strains are F2608T (=MCCC 1K05774T=KCTC 82766T) and F1192T (=MCCC 1K05775T=KCTC 82765T), respectively.

[Spatial pattern dynamics of darkling beetle communities at small scale in a desert grassland of alluvial fans in Helan Mountain, Northwest China]. / è´ºå °å±±å†²ç§¯æ‰‡è’æ¼ è‰åœ°æ‹Ÿæ­¥ç”²ç¾¤è½å°å°ºåº¦ç©ºé—´æ ¼å±€åŠ¨æ€.

To understand the dynamics of spatial pattern of darkling beetle communities at the small scale, we surveyed the darkling beetle community using pitfall in a desert grassland of alluvial fans in Helan Mountain from May to October 2019. Based on the geostatistical analysis, we divided the 200 m×200 m study area equally into 100 grid squares and analyzed the spatial autocorrelation, spatial heterogeneity, spatial distribution pattern, and its relationship with topographic factors of the darkling beetle community. A total of 1086 individuals belonged to 10 species and 7 genera were collected. Community composition of darkling beetle had significant spatial and temporal variation. The diversity index of the community was the highest in May and lowest in July. The spatial autocorrelation of dominant species had obvious seasonal fluctuation, with a significantly spatial positive correlation in May, September, and October. Communities of darkling beetle and the dominant species showed strongly spatial heterogeneous, which were mainly determined by structural factors. The ordinary Kriging interpolation showed that the gradient distribution of beetle communities was obviously different among seasons, being the simplest in summer. The results of the cross variogram showed that the spatial relationships between different dominant species groups were mostly positive, and were mainly regulated by structural factors. Results of the canonical correspondence analysis (CCA) showed that the slope and elevation significantly affected the distribution of darkling beetles. Our results showed that the spatial heterogeneity of the darkling beetle showed significant seasonal variation, and thus provided a basis for understanding the mechanism and biodiversity of ground-dwelling beetle community in a desert grassland of alluvial fans.

[Efficacy of Phoslock® on the Reduction of Sediment Phosphorus Release in West Lake, Hangzhou, China].

Famous as the world cultural heritage, West Lake in Hangzhou city has plenty of soft sediments with high organic matter content. To search the countermeasures for internal phosphorus release reduction from the sediment, the sediment core incubation was conducted to understand the efficacy of Phoslock® on internal phosphorus release in spring, summer and winter, respectively. The results showed that the internal phosphorus release fluxes in winter and spring were relatively low, with averaged values in the entire lake of 0.13 mg·(m2·d)-1 and 0.29 mg·(m2·d)-1, respectively, while the release flux was 3.29 mg·(m2·d)-1 in summer, more than ten times higher than those in spring and winter. It was estimated that 23.7 kg of phosphorus could be released from sediment in the entire lake every day in summer. Spatially, the phosphorus release flux was related to organic matter contents in sediments, but not the phosphorus or bioavailable phosphorus contents in sediments in West Lake. With Phoslock® added at the rate of 630 g·m-2, sediment phosphorus release was successfully controlled, which reduced the phosphorus concentration in the lake water to less than 0.010 mg·L-1. Especially during summer time, the sediment phosphorus release was reduced by 98% after Phoslock® application. The research suggested that Phoslock® is powerful for phosphorus control even for sediments with high organic matter content, which could be considered in ecological restoration of WEst Lake.

[Composition and Environmental Effects of LFOM and HFOM in "Incense-Ash" Sediments of West Lake, Hangzhou, China].

To understand the organic matter pollution characteristic and its relationship with nitrogen, phosphorus and other nutrients in sediments of high organic matter type of urban shallow lakes, the organic matter content, light fraction organic matter (LFOM), heavy fraction organic matter (HFOM), and nitrogen and phosphorus contents were investigated in eight different regions of West Lake, Hangzhou. The results showed that, the organic matter content of the west lake sediment was 28-251 g x kg(-1), belonging to typical high organic matter sediment. The difference of organic matter content in different lake sediments was very big. The sediments located at the input site of water diversion engineering had significantly lower organic content than the rest regions. The LFOM content of West Lake sediment ranged 0.57-9.17 g x kg(-1), which averagely occupied 2.83% of the total organic matter, and the HFOM content ranged 5.35-347.41 g x kg(-1), which occupied more than 90% of the total organic matter. Compared to other shallow lakes located in China, sediments of West Lake had significantly high percentage of HFOM/LFOM ratio. But the HFOM content was obviously on the high side, reflecting the west lake as an urban lake with a long history, as well as high organic matter pollution load and sediment humification degree. Both the content and the ratio of LFOM/HFOM in sediment were related to nitrogen and phosphorus contents in sediment. This suggested that the composition of organic matter in West Lake sediments had potential control ability for the internal loading of N and P of the lake.

[Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data].

The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0. 52, 0. 45 (p<0. 01) , respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0. 77(p<0. 000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy.

[Effect of 18-ß Glycyrrhetinic Acid on the Endoplasmic Reticulum of Nasal Epithelial Cells in Allergic Rhinitis Model Rats].

OBJECTIVE: To explore the effect of 18-ß glycyrrhetinic acid (GA) on the endoplasmic reticulum of nasal epithelial cells in allergic rhinitis (AR) model rats. METHODS: Totally 96 Wistar rats were randomly divided into the blank group, the AR model group, the loratadine group, the GA group, 24 in each group. AR models were established by peritoneally injecting ovalbumin (OVA). Morphological scoring was performed. GA at 21. 6 mg/kg was intragastrically administered to rats in the GA group. Nasal mucosal tissues were taken for electron microscopic examinations at the second, fourth, sixth, and tenth week after drug intervention. RESULTS: The overlapping score was 2.10 ± 0.45 in the blank group, 5.10 ± 0.56 in the loratadine group, 5.10 ± 0.56 in the AR model group, 5.20 ± 0.78 in the GA group, showing statistical difference when compared with the blank group (P < 0.01). Results under transmission electron microscope showed that the number of the endoplasmic reticulum increased in the AR model group, with obvious cystic dilatation, a lot of vacuole formation, and degranulation. A large number of free ribosomes could be seen in cytoplasm. With persistent allergen exposure, changes mentioned above was progressively aggravated in the endoplasmic reticulum of nasal mucosal epithelium in the AR model group. But the dilation of endoplasmic reticulum, vacuole formation, and degranulation were relieved in the GA group, and got close to those of the blank group. CONCLUSION: 18-ß GA could improve the expansion, vacuolization, and degranulation of the endoplasmic reticulum of nasal epithelial cells in AR model rats.

[Estimating Winter Wheat Nitrogen Vertical Distribution Based on Bidirectional Canopy Reflected Spectrum].

The vertical distribution of crop nitrogen is increased with plant height, timely and non-damaging measurement of crop nitrogen vertical distribution is critical for the crop production and quality, improving fertilizer utilization and reducing environmental impact. The objective of this study was to discuss the method of estimating winter wheat nitrogen vertical distribution by exploring bidirectional reflectance distribution function (BRDF) data using partial least square (PLS) algorithm. The canopy reflectance at nadir, +/-50 degrees and +/- 60 degrees; at nadir, +/- 30 degrees and +/- 40 degrees; and at nadir, +/- 20 degrees and +/- 30 degrees were selected to estimate foliage nitrogen density (FND) at upper layer, middle layer and bottom layer, respectively. Three PLS analysis models with FND as the dependent variable and vegetation indices at corresponding angles as the explicative variables were. established. The impact of soil reflectance and the canopy non-photosynthetic materials, was minimized by seven kinds of modifying vegetation indices with the ratio R700/R670. The estimated accuracy is significant raised at upper layer, middle layer and bottom layer in modeling experiment. Independent model verification selected the best three vegetation indices for further research. The research result showed that the modified Green normalized difference vegetation index (GNDVI) shows better performance than other vegetation indices at each layer, which means modified GNDVI could be used in estimating winter wheat nitrogen vertical distribution

[The new method monitoring crop water content based on NIR-Red spectrum feature space].

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

[Assessment of chlorophyll content using a new vegetation index based on multi-angular hyperspectral image data].

The fast estimation of chlorophyll content is significant for understanding the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study gets the hyperspectral imagery data by using a self-developed multi-angular acquisition system during the different maize growth period, the reflectance of maize canopy was extracted accurately from the hyperspectral images under different view angles in the principal plane. The hot-dark-spot index (HDS) of red waveband was calculated through the analysis of simulated values by ACRM model and measured values, then this index was used to modify the vegetation index (TCARI), thus a new vegetation index (HD-TCARI) based on the multi-angular observation was proposed. Finally, the multi-angular hyperspectral imagery data was used to validate the vegetation indexes. The result showed that HD-TCARI could effectively reduce the LAI effects on the assessment of chlorophyll content. When the chlorophyll content was greater than 30 µg x cm(-2), the correlation (R2) between HD-TCARI and LAI was only 26.88%-28.72%. In addition, the HD-TCARI could resist the saturation of vegetation index during the assessment of high chlorophyll content. When the LAI varled from 1 to 6, the linear relation between HD-TCARI and chlorophyll content could be improved by 9% compared with TCARI. The ground validation of HD-TCARI by multi-angular hyperspectral image showed that the linear relation between HD-TCARI and chlorophyll content (R2 = 66.74%) was better than the TCARI (R2 = 39.92%), which indicated that HD-TCARI has good potentials for estimating the chlorophyll content.

[Winter wheat GPC estimation based on leaf and canopy chlorophyll parameters].

The present study focused on the wheat harvest grain protein content (GPC) estimation based on wheat leaf and canopy chlorophyll parameters, SPAD and SFR, which were acquired by two hand-held instruments, SPAD and Multiplex 3. The wheat GPC estimate experiment was applied on a wheat field of the Scientific Observation and Experiment Field Station for Precision Agriculture at suburb of Beijing in 2012. The wheat leaf SPAD and canopy SFR value were measured in field for all 110 wheat sample points at five different wheat growth stages from April to June. The wheat plant sample for each point was then collected after the SPAD and SFR measurement and sent to lab for leaf nitrogen content (LNC) and canopy nitrogen density (CND) analysis. Analysis results showed that the correlation coefficients of wheat GPC with wheat CND were much higher than that from wheat tillering stage to early milking stage. They were similar at the wheat middle milking stage. While the wheat leaf SPAD value was highly correlated with wheat LNC at wheat tillering, heading and early milking stage. Wheat canopy chlorophyll parameters SFR were highly correlated with wheat CND at wheat tillering, jointing, heading and milking stage. It can be seen from the study that SFR is more sensitive to the wheat CND compared with wheat LNC. The analysis also indicated that leaf SPAD value at wheat tillering, heading and milking stage was highly correlated with wheat GPC and yield of grain protein (YGP). The wheat canopy parameters, SFR_G and SFR_R were significantly correlated with wheat GPC and YGP at wheat milking stage. Then the optimal GPC and YGP estimation model was established. The R2 of GPC estimation models established by SPAD and SFR_R are 0.426 and 0.497, and the standard errors of the estimate are 0.060% and 0.055%, respectively. The R2 of YGP estimation models established by SPAD and SFR_R are 0.366 and 0.386 and the standard errors of the estimate are 125.367 and 123.454 kg x ha(-1), respectively. The study reveals that SPAD value is a good indicator of single plant activity while SFR_G and SFR_R are better indicators for the wheat group activity. Wheat leaf SPAD value and canopy chlorophyll fluorescence information SFR are all feasible and valuable for GPC estimation before wheat harvesting.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

[Comparative study on remote sensing invertion methods for estimating winter wheat leaf area index].

The present study aims to explore capability of different methods for winter wheat leaf area index inversion by integrating remote sensing image and synchronization field experiment. There were four kinds of LAI inversion methods discussed, specifically, support vector machines (SVM), discrete wavelet transform (DWT), continuous wavelet transform (CWT) and principal component analysis (PCA). Winter wheat LAI inversion models were established with the above four methods respectively, then estimation precision for each model was analyzed. Both discrete wavelet transform method and principal component analysis method are based on feature extraction and data dimension reduction, and multivariate regression models of the two methods showed comparable accuracy (R2 of DWT and PCA model was 0. 697 1 and 0. 692 4 respectively; RMSE was 0. 605 8 and 0. 554 1 respectively). While the model based on continuous wavelet transform suffered the lowest accuracy and didn't seem to be qualified to inverse LAL It was indicated that the nonlinear regression model with support vector machines method is the most eligible model for estimating winter wheat LAI in the study area.

[Research on universality of least squares support vector machine method for estimating leaf area index of winter wheat].

Leaf area index (LAI) is one of the most important parameters for evaluating winter wheat growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of vegetation LAI at large scale. This study aims to explore the capability of least squares support vector machines (LS-SVM) method to winter wheat LAI estimation with hyperspectral data. After the compression of PHI airborne data with principal component analysis (PCA), the sample set based on the measured LAI data and hyperspectral reflectance data was established. Then the method of LS-SVM was developed respectively to estimate winter wheat LAI under four different conditions, to be specific, different plant type cultivars, different periods, different nitrogenous fertilizer and water conditions. Compared with traditional NDVI model estimation results, each experiment of LS-SVM model yielded higher determination coefficient as well as lower RMSE value, which meant that the LS-SVM method performed better than the NDVI method. In addition, NDVI model was unstable for winter wheat under the condition of different plant type cultivars, different nitrogenous fertilizer and different water, while the LS-SVM model showed good stability. Therefore, LS-SVM has high accuracy for learning and considerable universality for estimation of LAI of winter wheat under different conditions using hyperspectral data.

Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain.

Improving winter wheat water use efficiency in the North China Plain (NCP), China is essential in light of current irrigation water shortages. In this study, the AquaCrop model was used to calibrate, and validate winter wheat crop performance under various planting dates and irrigation application rates. All experiments were conducted at the Xiaotangshan experimental site in Beijing, China, during seasons of 2008/2009, 2009/2010, 2010/2011 and 2011/2012. This model was first calibrated using data from 2008/2009 and 2009/2010, and subsequently validated using data from 2010/2011 and 2011/2012. The results showed that the simulated canopy cover (CC), biomass yield (BY) and grain yield (GY) were consistent with the measured CC, BY and GY, with corresponding coefficients of determination (R(2)) of 0.93, 0.91 and 0.93, respectively. In addition, relationships between BY, GY and transpiration (T), (R(2) = 0.57 and 0.71, respectively) was observed. These results suggest that frequent irrigation with a small amount of water significantly improved BY and GY. Collectively, these results indicate that the AquaCrop model can be used in the evaluation of various winter wheat irrigation strategies. The AquaCrop model predicted winter wheat CC, BY and GY with acceptable accuracy. Therefore, we concluded that AquaCrop is a useful decision-making tool for use in efforts to optimize wheat winter planting dates, and irrigation strategies.

[Estimating canopy water content in wheat based on new vegetation water index].

Moisture content is an important indicator for crop water stress condition, timely and effective monitoring crop water content is of great significance for evaluate crop water deficit balance and guide agriculture irrigation. In order to improve the saturated problems of different forms of typical NDWI (Normalized Different Water Index), we tried to introduce EVI (Enhanced Vegetation Index) to build new vegetation water indices (NDWI#) to estimate crop water content. Firstly, PROSAIL model was used to study the saturation sensitivity of NDWI, and NDWI# to canopy water content and LAI (Leaf Area Index). Then, the estimated model and verified model were estimated using the spectral data and moisture data in the field. The result showed that the new indices have significant relationships with canopy water content. In particular, by implementing modified standardized for NDWI1450, NDWI1940, NDWI2500. The result indicated that newly developed indices with visible-infrared and shortwave infrared spectral feature may have greater advantage for estimation winter canopy water content.

[Band depth analysis and partial least square regression based winter wheat biomass estimation using hyperspectral measurements].

The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550-750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2 = 0.792, RMSE = 0.164 kg x m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.

[Advance of the imaging spectral technique in diagnosis of the information of crop].

Labor intensive, time consuming, high technical requirements in operation and much affected by human factors is the limitation of diagnosing the crop information with conventional method, which could not make diagnosis real-time and rapid. Imaging spectral technique could simultaneously obtain the image and spectral information of crops. It could diagnose the growth and insects information of crop rapidly and non-destructively. In recent years, imaging spectroscopy has been widely used in diagnosis of the information of crop, so it provides technical support for agricultural informatization. In the present study, the principle of imaging spectroscopy was presented. The application progress of imaging spectroscopy technique in crop detection was investigated, including seed component detection, seed variety discrimination, seed disease and insect pest detection, field crop growth monitoring and field crop disease and insects monitoring. Then the paper analyzed difficulty of imaging spectroscopy for crop measurement, and the prospect of this technique was also discussed.

[Monitoring of winter wheat stripe rust based on the spectral knowledge base for TM images].

In most cases, the reversion model for monitoring the severity degree of stripe rust based on the hyperspectral information can not be directly applied by the satellite images with relatively broad bandwidth, while the airborne hyperspectral images can not be applied for large-scale monitoring either, due to the scale limitation of its data and high cost. For resolving this dilemma, we developed a monitoring method based on PHI images, which relies on the construction of spectral knowledge base of winter wheat stripe rust. Three PHI images corresponding to the winter wheat experimental field that included different severity degree of stripe rust were used as a medium to establish the spectral knowledge base of relationships between disease index (DI) and the simulated reflectance of TM bands by using the empirical reversion model of DI(%) and the relative spectral response (RSR) function of TM-5 sensor. Based on this, we can monitor and identify the winter wheat stripe rust by matching the spectral information of an untested pixel to the spectral knowledge base via Mahalanobis distance or spectral angle mapping (SAM). The precision of monitoring was validated by simulated TM pixels, while the effectiveness of identification was tested by pixels from TM images. The results showed that the method can provide high precision for monitoring and reasonable accuracy for identification in some certain growth stages of winter wheat. Based on the simulated TM pixels, the model performed best in the pustulation period, yielded a coefficient of determination R2 = 0.93, while the precision of estimates dropped in the milk stage, and performed worst in the jointing stage, which is basically inappropriate for monitoring. Moreover, by using the pixels from TM images, the infected pixels could be identified accurately in pustulation and milk stages, while failed to be identified in jointing stage. For matching algorithms, the Mahalanobis distance method produced a slightly better result than SAM method.

[The analysis of consistency between HJ-1B and Landsat 5 TM for retrieving LST based on the single-channel algorithm].

To ascertain whether the thermal infrared image of HJ-1B which has the similar sensor parameter and setting to Landsat 5 TM6 image is applicable for retrieving the land surface temperature (LST), a comparison of retrieved LST between two types of sensors was conducted. Two scenes of thermal infrared images that came from different sensors were acquired in 5th, Apr 2009, which covered the same region in Beijing. To retrieve LST, a generalized single-channel algorithm developed by Jiménez-Muñoz and Sobrino was applied. The LST of study area for both images was thus generated. Based on the LST mapping results and corresponding statistics, an apparent trend could be observed which indicated the consistency in both LST value and its spatial distribution. Consequently, the performance of HJ-IB IRS serving as the data source for LST retrieval was assessed and illustrated in this study. Besides, a high temporal resolution as well as wide swath of the HJ-IRS data suggested its potential in application.

[Darkling beetle community structure and its relations with environmental factors in Sidunzi of Yanchi, Ningxia, China].

From March to October 2009, a field survey was conducted on the darkling beetle community structure and related environmental factors in the desert grasslands with different vegetation cover and human disturbance intensity in Sidunzi of Yanchi, Ningxia, China. By using diversity index and canonical correspondence analysis (CCA) , the relationships between the beetle community structure and related environmental factors were analyzed. A total of 5431 individuals were collected, belonging to 20 species and 10 genera. Blaps femoralis femoralis, Microdera kraatzi kraatzi, and Platyope mongolica were the dominant species, accounting for 47.30%, 39.90%, and 3.59% of the total, respectively. CCA explained 100% of the correlations between the beetle species and related environmental factors, suggesting that the occurrence of the beetle species had close relations to the changes of related environmental factors. Among the environmental factors, the Shannon diversity index of plant community (HP), plant biomass (BP), and soil water content (SW) affected the beetle species occurrence most. The occurrence frequency of Mantichorula semenowi, Anatolica amoenula, A. sternalis, and A. gravidula was negatively correlated with BP and plant coverage (CP), and that of B. gobiensis, Cyphogenia chinensis, Gonocephalum reticuluatum, and Crypticus rufipes was positively correlated with plant density (DP) and SW. The distribution of P. mongolica, M. kraatzi kraatzi, Scytosoma pygmaeum, and B. kiritshenkoi showed a positive correlation to HP, and that of Eumylada oberbergeri, B. femoralis femoralis, and B. davidea showed a positive correlation to BP and CP. There was a significant positive correlation (r = 0.943, P = 0.005) between the beetle activity density and SW. The CCA ordination showed that the darkling beetles had different demands for multidimensional ecological resources in desert and semi-desert ecosystems.