benzoic acid-(4-dichloromethyl-4-methyl-cyclohexa-2,5-dienyl ester) | 100968-54-5

• 分子结构分类: 有机化合物 - 苯类化合物 - 苯和取代衍生物
• 英文名称: benzoic acid-(4-dichloromethyl-4-methyl-cyclohexa-2,5-dienyl ester)
• 英文别名: Benzoesaeure-(4-dichlormethyl-4-methyl-cyclohexa-2,5-dienylester)；[4-(Dichloromethyl)-4-methylcyclohexa-2,5-dien-1-yl] benzoate
• CAS: 100968-54-5
• 化学式: C₁₅H₁₄Cl₂O₂
• 分子量: 297.181
• InChiKey: ODLBTFVMXWTJOE-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  5
• 重原子数：
  19
• 可旋转键数：
  4
• 环数：
  2.0
• sp3杂化的碳原子比例：
  0.27
• 拓扑面积：
  26.3
• 氢给体数：
  0
• 氢受体数：
  2

反应信息

• 作为产物：
  描述：

  4-dichloromethyl-4-methyl-cyclohexa-2,5-dienol 、 苯甲酰氯 生成 benzoic acid-(4-dichloromethyl-4-methyl-cyclohexa-2,5-dienyl ester)
  参考文献：
  名称：

  Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: A comparison of aerial photography, Landsat TM and SPOT satellite imagery

  摘要：

  The tropical wetland environments of northern Australia have ecological, social, cultural and economic values. Additionally, these areas are relatively pristine compared to the many other wetland environments in Australia, and around the world, that have been extensively altered by humans. However, as the remote northern coastline of Australia becomes more populated, environmental problems are beginning to emerge that highlight the need to manage the tropical wetland environments. Lack of information is currently considered to be a major factor restricting the effective management of many ecosystems and for the expansive wetlands of the Northern Territory, this is especially the case, as these areas are generally remote and inaccessible. Remote sensing is therefore an attractive technique for obtaining relevant information on variables such as land cover and vegetation status. In the current study, Landsat TM, SPOT (XS and PANT) and large-scale, true-colour aerial photography were evaluated for mapping the vegetation of a tropical freshwater swamp in Australia's Top End. Extensive ground truth data were obtained, using a helicopter survey method. Fourteen cover types were delineated from 1:15 000 air photos (enlarged to 1:5000 in an image processing system) using manual interpretation techniques, with 89% accuracy. This level of detail could not be extracted from any of the satellite image data sets, with only three broad land-cover types identified with accuracy above 80%. The Landsat TM and SPOT XS data provided similar results although superior accuracy was obtained from Landsat, where the additional spectral information appeared to compensate in part for the coarser spatial resolution. Two different classification algorithms produced similar results.

  DOI：

  10.1080/01431160119174

表征谱图