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Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest, California

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Abstract

Context

Spatial scale and pattern play important roles in forest aboveground biomass (AGB) estimation in remote sensing. Changes in the accuracy of satellite images-estimated forest AGBs against spatial scales and pixel distribution patterns has not been evaluated, because it requires ground-truth AGBs of fine resolution over a large extent, and such data are difficult to obtain using traditional ground surveying methods.

Objectives

We intend to quantify the accuracy of AGB estimation from satellite images on changing spatial scales and varying pixel distribution patterns, in a typical mixed coniferous forest in Sierra Nevada mountains, California.

Methods

A forest AGB map of a 143 km2 area was created using small-footprint light detection and ranging. Landsat Thematic Mapper images were chosen as typical examples of satellite images, and resampled to successively coarser resolutions. At each spatial scale, pixels forming random, uniform, and clustered spatial patterns were then sampled. The accuracies of the AGB estimation based on Landsat images associated with varying spatial scales and patterns were finally quantified.

Results

The changes in the accuracy of AGB estimation from Landsat images are not monotonic, but increase up to 60–90 m in spatial scale, and then decrease. Random and uniform spatial patterns of pixel distributions yield better accuracy for AGB estimation than clustered spatial patterns. The corrected NDVI (NDVIc) was the best predictor of AGB estimation.

Conclusions

A spatial scale of 60–90 m is recommended for forest AGB estimation at the Sierra Nevada mountains using Landsat images and those with similar spectral resolutions.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grants Nos. 41471363 and 41401505), the National Key Basic Research Program of China (2013CB956604), and the Sierra Nevada Adaptive Management Project (SNMAP). We are grateful for the constructive comments from the anonymous reviewers of an earlier version of the manuscript.

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Authors and Affiliations

  1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China

    Shengli Tao, Qinghua Guo, Fangfang Wu, Baolin Xue & Jin Liu

  2. Department of Ecology, College of Environmental Sciences, and Key Laboratory of Earth Surface Processes of the Ministry of Education, Peking University, Beijing, 100871, China

    Shengli Tao, Zhiyao Tang & Jingyun Fang

  3. School of Engineering, Sierra Nevada Research Institute, University of California at Merced, Merced, CA, 95343, USA

    Qinghua Guo

  4. State Key Laboratory of Earth Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

    Le Li

  5. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Shaopeng Wang

  6. Institute of Ecology, Friedrich Schiller University Jena, Jena, Germany

    Shaopeng Wang

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  1. Shengli Tao
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  2. Qinghua Guo
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Correspondence to Qinghua Guo.

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Tao, S., Guo, Q., Wu, F. et al. Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest, California. Landscape Ecol 31, 1711–1723 (2016). https://doi.org/10.1007/s10980-016-0357-y

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