Next Article in Journal
Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018
Previous Article in Journal
Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1
Previous Article in Special Issue
Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images
Open AccessFeature PaperLetter

Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China

by Guiying Li 1,2, Zhuli Xie 3,4, Xiandie Jiang 3,4, Dengsheng Lu 1,2,* and Erxue Chen 5
1
State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 350007, China
2
School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
3
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
4
School of Environmental & Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China
5
Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2328; https://doi.org/10.3390/rs11192328
Received: 19 August 2019 / Revised: 4 October 2019 / Accepted: 7 October 2019 / Published: 8 October 2019
Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem. View Full-Text
Keywords: aboveground biomass estimation; data saturation; ZY-3 data; canopy height; larch plantations aboveground biomass estimation; data saturation; ZY-3 data; canopy height; larch plantations
Show Figures

Graphical abstract

MDPI and ACS Style

Li, G.; Xie, Z.; Jiang, X.; Lu, D.; Chen, E. Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China. Remote Sens. 2019, 11, 2328.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop