Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions
Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions
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DOI：
10.1016/j.isprsjprs.2012.03.011
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发表时间：
2012-06-01
影响因子：
12.7
通讯作者：
Vetrivel, A.
中科院分区：
文献类型：
--
作者：
Cutler, M. E. J.;Boyd, D. S.;Vetrivel, A.
量化热带森林的地上生物量对于了解陆地生态系统与大气之间的碳通量动态以及监测生态系统对环境变化的反应至关重要。遥感仍然是估算热带森林生物量的一个有吸引力的工具，但是在一个地点使用的关系和方法并不总是证明适用于其他地点。这种缺乏广泛适用的一般关系限制了遥感作为生物量估计方法的实际使用，特别是在高生物量生态系统中。本研究将多光谱Landsat TM和JERS-1 SAR数据一起用于估算巴西、马来西亚和泰国三个不同地理位置的热带森林生物量。利用小波分析和灰度共生矩阵方法从JERS-1 SAR数据中获得纹理度量，并与多光谱数据相结合，为人工神经网络提供输入，该神经网络在四种不同的训练场景下进行训练，并使用144个野外样地测量的生物量进行验证。在对同一地点采集的数据进行训练和测试时，在多光谱数据中添加SAR纹理与地上生物量具有很强的相关性（泰国、马来西亚和巴西的r分别为0.79、0.79和0.84）。此外，当网络使用来自所有三个站点的数据进行训练和测试时，相关性强度（r = 0.55）比先前报道的仅使用多光谱数据的相同站点的结果更强。还测试了从不同异速生长方程估计AGB的不确定性，但发现对所观察到的关系的强度影响不大。结果表明，在多光谱数据中包含SAR纹理可以在某种程度上提供跨时间和空间可转移的关系，但如果卫星遥感要为诸如减少森林砍伐和退化排放（REDD+）等倡议提供强大而可靠的方法，则需要进一步的工作。(C) 2012国际摄影测量与遥感学会（ISPRS）Elsevier B.V.版权所有。
Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). (C) 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.