Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations

Monitoring forest height is crucial to determine the structure and biodiversity of forest ecosystems. However, detailed spatial patterns of forest height from 30 m resolution remotely sensed data are currently unavailable. In this study, we present a new method for mapping forest height by combining spaceborne Light Detection and Ranging (LiDAR) with imagery from multiple remote sensing sources, including the Landsat 5 Thematic Mapper (TM), the Phased Array L-band Synthetic Aperture Radars (PALSAR), and topographic data. The nationwide forest heights agree well with results obtained from 525 independent forest height field measurements, yielding correlation coefficient, root mean square error (RMSE), and mean absolute error (MAE) values of 0.92, 4.31 m, and 3.87 m, respectively. Forest heights derived from remotely sensed data range from 1.41 m to 38.94 m, with an average forest height of 16.08 ± 3.34 m. Mean forest heights differ only slightly among different forest types. In natural forests, conifer forests have the greatest mean forest heights, whereas in plantations, bamboo forests have the greatest mean forest heights. Important predictors for modeling forest height using the random forest regression tree method include slope, surface reflectance of red bands and HV backscatter. The uncertainty caused by the uneven distribution of Geoscience Laser Altimeter System (GLAS) footprints is estimated to be 0.64 m. After integrating PALSAR data into the model, the uncertainty associated with forest height estimation was reduced by 4.58%. Our finer-resolution forest height could serve as a benchmark to estimate forest carbon storage and would greatly contribute to better understanding the roles of ecological engineering projects in China.

• Publication year

  2019

• Venue

  Remote Sensing

• Publication date

  2019-07-24

• Fields of study

  Geology, Geography, Computer Science, Environmental Science

• Identifiers
  DOI 10.3390/RS11151740
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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