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Remote Sens. 2015, 7(7), 8436-8452; doi:10.3390/rs70708436

Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data

1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Survey Planning and Design Institute, State Forest Administration of China, Beijing 100714, China
4
School of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China
5
Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad Thenkabail
Received: 30 March 2015 / Revised: 19 June 2015 / Accepted: 25 June 2015 / Published: 30 June 2015
View Full-Text   |   Download PDF [6094 KB, uploaded 30 June 2015]   |  

Abstract

Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R2 = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass. View Full-Text
Keywords: tree height; Geoscience Laser Altimeter System (GLAS); artificial neural Network (ANN); China Meteorological Data (CMD); nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) tree height; Geoscience Laser Altimeter System (GLAS); artificial neural Network (ANN); China Meteorological Data (CMD); nadir bidirectional reflectance distribution function adjusted reflectance (NBAR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ni, X.; Zhou, Y.; Cao, C.; Wang, X.; Shi, Y.; Park, T.; Choi, S.; Myneni, R.B. Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data. Remote Sens. 2015, 7, 8436-8452.

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