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

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. OPEN ACCESSRemote Sens. 2015, 7 8437The 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 (R 2 = 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.

Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship

“…The ICESat/GLAS is designed to obtain characteristics of the earth surface structures in three dimensions with high accuracy [26,38].…”

“…At the same time, GLAS footprints over non-forest should be filtered. The GLAS footprints processing steps for selecting valid GLAS waveforms can be depicted as follows [37,38,47,48]. Firstly, the GLAS data of the approximate growing season from 2003 to 2006 were considered.…”

Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network

“…In the BTH region, daily meteorological data from eight stations have been used in the present study for the period 2014-2016. In order to generate the gridded maps of variables from these stations, the ordinary kriging method has been used to interpolate meteorological parameters [30][31][32].…”

Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data

“…The climatic variables used in this study mainly include precipitation and temperature, from China Meteorological Data Sharing Service System (CMDSSS), which include hourly and daily meteorological records observed from 754 benchmark surface weather stations and automatic weather station since 1951 [30][31][32]. The accuracy of these meteorological station data has been found to be over 99.9%.…”

“…The time of the maximum (minimum) LAI change ratio can be considered as the onset dates of vegetation green up(dormancy) [11,31].We firstly detected the time T with the maximum LAI ratio , and then used the time T as the onset date of green-up. Likewise, we detected the time T with the minimum LAI ratio , and then used the time T + 1 as the onset date of vegetation dormancy.…”