根据粮农组织森林定义的微波和光学图像分析绘制的澳大利亚年度森林地图

遥感学报 Pub Date : 2021-08-23 DOI:10.34133/2021/9784657

Yuanwei Qin, Xiangming Xiao, J. Wigneron, P. Ciais, J. Canadell, M. Brandt, Xiaojun Li, L. Fan, Xiaocui Wu, Hao Tang, R. Dubayah, R. Doughty, Q. Chang, S. Crowell, Bo Zheng, K. Neal, J. Celis, B. Moore

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引用次数: 5

摘要

2010年，澳大利亚政府机构在联合国粮农组织(FAO)报告的森林总面积为1.49亿公顷，排名世界第六，这是基于树木高度为1.2米的森林定义。在这里，我们报告了一种新的森林覆盖数据产品，该产品使用粮农组织森林定义(观测时间或成熟时树木覆盖率为10%，树木高度为>5米)，来自微波(相控阵型l波段合成孔径雷达，PALSAR)和光学(中分辨率成像光谱仪，MODIS)图像，并使用非常高的空间分辨率图像，来自冰、云和陆地高程卫星(ICESat)的光探测和测距(LiDAR)数据进行验证。并在现场进行实地调查。新的PALSAR/MODIS森林地图估计2010年澳大利亚有3200万公顷的森林。PALSAR/MODIS森林地图基于森林和非森林覆盖类型的高空间分辨率影像的目视解译数据，总体精度可达95%左右。与来自ICESat激光雷达条的冠层高度和冠层覆盖数据相比，PALSAR/MODIS森林图有73%的森林像元符合粮农组织森林定义，远高于其他四种广泛使用的森林图(36%至52%)。PALSAR/MODIS森林图与国家植被信息系统森林图在空间上也有一定的一致性。利用粮农组织森林资源评估报告中的数据，澳大利亚新的年度森林地图可以支持跨国比较。

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Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height>2 meters. Here, we report a new forest cover data product that used the FAO forest definition (tree cover>10% and tree height>5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

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来源期刊

遥感学报 Social Sciences-Geography, Planning and Development

CiteScore

3.60

自引率

0.00%

发文量

3200

期刊介绍： The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China. As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.