کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6346421 | 1621246 | 2014 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
ترجمه فارسی عنوان
اهمیت اندازه نمونه، نوع داده ها و روش پیش بینی برای برآورد های مبتنی بر سنجش از راه دور بیوماس جنگل های بیرونی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
چکیده انگلیسی
In conclusion, our literature review revealed that different methods for biomass estimation are currently used, with no general agreement on best practices. In our case studies, we found substantial accuracy differences between those methods, with LiDAR data, in combination with a random forest algorithm and a large number of reference sample units on the ground yielding the lowest error for biomass predictions. The comparatively high importance of the statistical prediction method seems particularly relevant, as they suggest that choosing the appropriate statistical method may be more effective than obtaining additional field data for obtaining good biomass estimates. Considering the costs of improving accuracy of global and regional biomass estimates by ground measurements, it seems sensible to invest in further comparative studies, preferably with a wider range of sites and including also RADAR sensors, to establish robust best-practice recommendations for obtaining regional and global biomass estimates from remote-sensing data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 154, November 2014, Pages 102-114
Journal: Remote Sensing of Environment - Volume 154, November 2014, Pages 102-114
نویسندگان
F.E. Fassnacht, F. Hartig, H. Latifi, C. Berger, J. Hernández, P. Corvalán, B. Koch,