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利用来自陆地、空中和太空的多种独立测量方法对红树林冠层高度进行的比较。

A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space.

作者信息

Lagomasino David, Fatoyinbo Temilola, Lee SeungKuk, Feliciano Emanuelle, Trettin Carl, Simard Marc

机构信息

Universities Space Research Association/GESTAR, 7178 Columbia Gateway Dr., Columbia, MD 21046, USA.

NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.

出版信息

Remote Sens (Basel). 2016 Apr;8(4):327. doi: 10.3390/rs8040327. Epub 2016 Apr 14.

Abstract

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.

摘要

树冠高度是森林生态系统中生物量和碳含量的最强预测指标之一。此外,红树林生态系统是最集中的碳库之一,由于森林砍伐、开发和水文操纵,该碳库正在迅速退化。因此,红树林森林树冠高度模型(CHM)的准确性可为监测和核查协议提供关键信息。我们比较了从独立遥感影像中得出的四种CHM,并确定了测量类型之间的潜在误差和偏差。CHM来自三个星载数据集;超高分辨率(VHR)立体摄影测量、用于数字高程测量的TerraSAR-X附加组件以及航天飞机雷达地形测绘任务(TanDEM-X),以及从机载平台获取的激光雷达数据。每个数据集都表现出与空间分辨率、传感器灵敏度和参考框架相关的不同误差特征。所有CHM都能准确预测10米以上的树冠,而VHR CHM对树冠高度分布的预测最佳。根据监测和核查活动所需的指导方针和策略,粗分辨率CHM可用于在区域和全球尺度上跟踪树冠高度,而高分辨率影像则用于验证和监测正在发生快速变化的关键区域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0359/5884677/9333455f2faa/nihms913098f1.jpg

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