کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6345651 1621227 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass
چکیده انگلیسی


- The distinction between design-based and model-based inference is expounded.
- The statistical basis for rigorous variance estimation using the precepts of survey sampling is presented.
- Examples of design-based and model-based inference for a large LiDAR-assisted studies is presented.

For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 173, February 2016, Pages 98-108
نویسندگان
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