胸径
碳储量
常绿
常绿森林
林业
森林砍伐(计算机科学)
环境科学
登录中
非法日志记录
农林复合经营
减少毁林和森林退化造成的排放
数学
地理
生态学
生物
气候变化
计算机科学
程序设计语言
作者
Eriko Ito,Naoyuki Furuya,Bora Tith,Samkol Keth,Ly Chandararity,Sophal Chann,Mamoru Kanzaki,Y. Awaya,Kaoru Niiyama,Yasuhiro Ohnuki,Makoto Araki,Tamotsu Sato,Mitsuo Matsumoto,Yoshiyuki Kiyono
出处
期刊:Jarq-japan Agricultural Research Quarterly
[Japan International Research Center for Agricultural Sciences]
日期:2010-01-01
卷期号:44 (4): 435-446
被引量:5
摘要
The Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) initiative requires accurate estimates of carbon stock changes in forested areas. However, estimating carbon emissions from stumps of various heights left by illegal loggers is difficult. To remedy this problem, we examined two methods of estimating diameter at breast height (DBH) from a reference diameter observation measured at any stump height. The one-reference diameter (OD) observation model estimates DBH from a single diameter observation using empirical coefficients derived mainly from emergent dipterocarp trees. The two-reference diameter (TD) observation model estimates DBH from two diameter observations and assumes a logarithmic relationship between diameter and height. Prediction data to establish the models were collected in Cambodian lowland evergreen forests that are undergoing intensive illegal logging of emergent dipterocarp trees for timber. The OD model performed better than the TD model in predicting DBH and is extremely practical, as it requires only a single diameter observation. Validation data previously collected in the Southeast Asian tropical forests established the general validity of the OD model. This study may improve the reliability of the REDD scheme by providing a reliable method to assess carbon emissions from Southeast Asian tropical forests.
科研通智能强力驱动
Strongly Powered by AbleSci AI