کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10113980 1621183 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data
ترجمه فارسی عنوان
خصوصیات پوشش گیاهی زیرزمینی در جنگل های مدیترانه با استفاده از داده های اسکن لیزر هوایی با شکل موج کامل
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی
The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75 m square subplots and circular plots with a radius of 15 m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2 = 0.957, maximum height: R2 = 0.771, cover: R2 = 0.871, and volume: R2 = 0.951). The proportion of explained variance was slightly lower at 3.75 m side cells (mean height: R2 = 0.633, maximum height: R2 = 0.470, cover: R2 = 0.581, and volume R2 = 0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 217, November 2018, Pages 400-413
نویسندگان
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