Isorugosiformins A-F, six ent-kaurane diterpenoids from Isodon rugosiformis.

Six new ent-kaurane diterpenoids, isorugosiformins A-F (1-6), were isolated from the aerial parts of Isodon rugosiformis Hand.-Mazz. Hara. Their structures were elucidated by spectroscopic data interpretation, single crystal X-ray diffraction, and quantum chemical calculation of NMR parameters. The absolute configuration of 5 as 6R was the first case in the known 6,7:8,15-diseco-7,20-olide-6,8-cyclo-ent-kaurane diterpenoids.

[Temporal stage and method selection of tree species classification based on GF-2 remote sensing image]. / åŸºäºŽé«˜åˆ†äºŒå·é¥æ„Ÿå½±åƒæ ‘ç§åˆ†ç±»çš„æ—¶ç›¸åŠæ–¹æ³•é€‰æ‹©.

It's important to master tree species composition and distribution in forests for the study of forest ecosystems. To promote the application of domestic Gaofen data in the classification of tree species and to explore the effects of different combining images, classification features and classifier on tree species classification results, three kinds of single temporal data and four kinds of multi-temporal data were constructed. Based on three GF-2 images, according to the multi-scale segmentation, C5.0 feature optimization as well as two classifiers including support vector machine (SVM) and random forest (RF), we finished the object-based classification of eight tree species of different temporal and feature dimensions respectively, and finally achieved good results with overall accuracy between 63.5% and 83.5% and the Kappa coefficient between 0.57 and 0.81. The results showed that the choice of temporal stage would affect the classification results. The results based on multi-temporal were generally better than that on single temporal stage. There were obvious precision differences between different image combinations of multi-temporal as well as different single temporal stage. It is notable that feature optimization played a positive role in the improvement of classification accuracy. SVM was relatively stable across different temporal and feature dimensions, which should be given priority when single temporal and classification features are difficult to distinguish tree species directly, while it should be noted that SVM was easy to overfit. RF was not easy to overfit, but it was more dependent on the quality of classification features and would get good results under favorable image combination.

[Estimation of forest vegetation carbon storage in Hunan Province, China based on k-NN method and domestic high-resolution data]. / åŸºäºŽåŸºå‡†æ ·åœ°æ³•å’Œå›½äº§é«˜åˆ†æ•°æ®çš„æ¹–å—çœæ£®æž—æ¤è¢«ç¢³å‚¨é‡ä¼°æµ‹.

To promote the application of domestic high-resolution satellite data in large-scale carbon storage estimation and measurement, a total of 206 high-resolution remote sensing images covering Hunan Province were used as the data source, and the estimated minimum unit was fixed as a 0.06 hm2 square composed of multiple pixels. Through the establishment and purification of the interpretation marks, in the extraction of forest information, the pixel-based method and object-oriented classification method were used to compare. In the estimation of carbon storage of arbor forest, the robust estimate, partial least squares method and k-NN estimate were used to compare. Finally, we estimated forest carbon storage in Hunan Province and generated the distribution map of carbon density levels. The results showed that the interpretation mark based on the automatic extraction of plots could increase the extraction accuracy of arbor forest after purification. For the estimation of forest carbon storage at large-scale, the k-NN algorithm embodied a large advantage in forest information extraction and arbor forest carbon storage modeling. The average classification accuracy of the 206 scene images was 76.8%, the average RMSE was 8.95 t·hm-2, the average RRMSE was 19.1%, and the total carbon stock in Hunan Province was 22.28 Mt. The results provided effective reference for the estimation and measurement of forest carbon storage at the provincial and national scales.