MODEL-BASED ESTIMATION OF LARGE AREA FOREST CANOPY HEIGHT AND BIOMASS USING RADAR AND OPTICAL REMOTE SENSING WITH LIMITED LIDAR DATA

被引：0

作者：

Benson, Michael ^{[1
]}

Pierce, Leland ^{[1
]}

Sarabandi, Kamal ^{[1
]}

机构：

[1] Univ Michigan, Radiat Lab, 1301 Beal Ave, Ann Arbor, MI 48109 USA

来源：

2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年

关键词：

Forest Parameter Estimation; SAR; Li-DAR; Virtual Lidar; LandSAT; database; BOREAS; large area;

D O I：

暂无

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Data synergy or fusion is a mechanism whereby discrete types of data are used together to achieve a better understanding than was possible with each individually. Spanning over 30% of the Earth's landmass, the global forest plays a role in numerous planetary systems including the carbon cycle. The objective of this study is to couple simulated forest stands with measured datasets from various instruments to estimate a forest's mean canopy height and aboveground dry-biomass in large regions spanning many square kilometers. We present a method to combine measured datasets with our sensor models to develop a feature estimation algorithm that fuses multi-modal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height in a region spanning over 60 km(2).

引用

页码：1016 / 1019

页数：4