Wang X.,Lanzhou University |
Li X.,Lanzhou University |
Zhang J.,Lanzhou University |
Feng G.,Lanzhou University |
And 4 more authors.
Australian Journal of Crop Science | Year: 2011
Alfalfa (Medicago sativa L.) is one of the most important forage species worldwide. In order to discover the mechanism of low seed production, two cytological experiments were carried out to investigate ovule numbers per floret and ovule fertility among nine different alfalfa varieties. Results showed that there was significant difference in ovule numbers per floret among the nine alfalfa varieties (P<0.05), and the number of ovules per floret ranged from 7 to 13. The highest ovule number was Huangyangzhen while the lowest one was Ladak. Results also showed that there was significant difference in ovule fertility among different alfalfa varieties (P<0.05), and the average percentage of ovule sterility was 34.90%. The highest percentage of ovule sterility was observed in Russia with 49.07%, while the lowest percentage sterility was 25.20% in Zhungeer. Alfalfa seed set under artificial-pollination showed a significantly linear correlation with ovules per floret (P<0.05) but no significant correlation under self-pollination treatments. The results showed that the fertile ovules per pistil accounted for 51-75% with total ovules per pistil, but the percentages of actual seed yields with potential seed yields of the nine varieties were only 2.49-6.06%, which suggested that ovule sterility maybe just one of the limiting factors for alfalfa seed production. Our results indicated that female fertility of alfalfa was remained in the nature reproductive ability, which was probably due to the breeding selection program of alfalfa that was mainly focused on yield or quality, but seldom on seed production.
Wang X.-Y.,CAS Institute of Remote Sensing Applications |
Guo Z.-F.,CAS Institute of Remote Sensing Applications |
Pang Y.,Chinese Academy of Forest |
Qin W.-H.,NASA |
And 2 more authors.
Zhongguo Kuangye Daxue Xuebao/Journal of China University of Mining and Technology | Year: 2011
The study of forest composition, structure and dynamic changes in the forestry remote sensing has been a hot issue. Forest canopy structure has an important effect on the energy distribution of solar radiation and the biophysical parameters of vegetation canopies. It is difficult for the computer simulation method to study radiation regime and estimate the structural parameters of forest canopies at large-scale. Simplified coniferous model, Radiosity-graphics combined model (RGM) was applied to investigate the radiance distribution of forest canopy in this study. Combined the forest growth model ZELIG and L-systems to render 3D forest scenarios, and the RGM model was used to calculate bidirectional reflectance factor(BRF) at visible and near-infrared regions. Finally, the spectra was compared between the simulated by RGM model and multi-angle the compact high resolution imaging spectrometer(CHRIS) data in the experimental field of Changbai mountain. The results show that they were both agreed well. It has an important value for inversion of the structure parameters of forest canopy with RGM model and multi-angle CHRIS data.
Liang Z.,Fuzhou University |
Ling F.,Fuzhou University |
Chen E.,Chinese Academy of Forest
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2013
Considering the influence of the posterior and the statistic distributions of full-polarimetric SAR data, we proposed a new classification method of full polarimetric SAR data. First, the covariance matrix of polarization SAR data was converted to nine intensity quantities with normal distribution. Then, the probability of occurance for each class was calculated with iterative initial classification. Finally, the nine intensity images were classified with maximum likelihood classification method taking the probabilities of occurance for the classes into account. We applied the developed method to the ALOS PALSAR full-polarimetric data of Xunke County, Heilongjiang Province. The overall accuracy is 81.34% and the Kappa coefficient 0.84. The developed method showed higher accuracy than that from the traditional maximum likelihood classifier. This indicates that our method can improve the accuracy of classification.
Wang X.,Lanzhou University |
Song Y.,Lanzhou University |
Ma Y.,Hebei Normal University of Science and Technology |
Zhuo R.,Chinese Academy of Forest |
Jin L.,Lanzhou University
Environmental Pollution | Year: 2011
In order to evaluate Cd tolerance in wide-ranging sources of alfalfa (Medicago sativa) and to identify Cd tolerant genotypes which may potentially be useful for restoring Cd-contaminated environments, thirty-six accessions of alfalfa were screened under hydroponic culture. Our results showed that the relative root growth rate varied from 0.48 to 1.0, which indicated that different alfalfa accessions had various responses to Cd stress. The candidate fragments derived from differentially expressed metallothionein (MT) genes were cloned from leaves of two Cd tolerant genotypes, YE and LZ. DNA sequence and the deduced protein sequence showed that MsMT2a and MsMT2b had high similarity to those in leguminous plants. DDRT-PCR analysis showed that MsMT2a expressed in both YE and LZ plants under control and Cd stress treatment, but MsMT2b only expressed under Cd stress treatment. This suggested that MsMT2a was universally expressed in leaves of alfalfa but expression of MsMT2b was Cadmium (Cd) inducible. © 2011 Elsevier Ltd. All rights reserved.
Xu D.,Chinese Institute of Scientific and Technical Information |
Li C.,Chinese Academy of Forest |
Song X.,Kaifeng Academy of Agriculture and Forestry science |
Song X.,Henan Agricultural University
2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012 - Proceedings | Year: 2012
China is one of the countries that seriously suffered desertification in the world, and it is very meaningful to develop a quantitative method to assess desertification at large scale. In this study, the MODIS images were selected as the data resources, and NDVI, land surface albedo, soil water index (the reflectance of MODIS band 7) were selected as the indicators for assessing desertification. Based on building the indicator rule sets of different desertification grades in different sub-regions, the authors developed a quantitative method for desertification assessment by using decision tree model. The results showed that, the method developed in this study that can reflect the heterogeneity of land surface at large scale, and the overall accuracy of the method can reach 85.5%, which was suitable to assess desertification at large scale. Based on using this method to assess the desertification in farming-pastoral region of north China in 2000 and 2010, the authors found that the areas of lands that experienced desertification reversion and desertification expansion were almost consistent, and the spatial distribution of these regions existed obvious differences. © 2012 IEEE.