SCIENCE CHINA Earth Sciences. Forest canopy height mapping over China using GLAS and MODIS data

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1 SCIENCE CHINA Earth Sciences RESEARCH PAPER doi: /s Forest canopy height mapping over China using GLAS and MODIS data YANG Ting 1,2, WANG Cheng 1*, LI GuiCai 3, LUO SheZhou 1, XI XiaoHuan 1 GAO Shuai 4 & HongCheng ZENG 5 1 Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing , China; 2 University of Chinese Academy of Sciences, Beijing , China; 3 National Satellite Meteorological Center, China Meteorological Administration, Beijing , China; 4 National Key Laboratory of Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing , China; 5 Faculty of Foretry, University of Toronto, Ontario M5S3B3, Canada Received November 27, 2013; accepted March 3, 2014 The Geoscience Laser Altimeter System (GLAS) accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems. Since the GLAS footprints are discontinuously distributed with a relativity low density, continuous vegetation height distributions cannot be mapped with a high accuracy using GLAS data alone. The MODIS BRDF product provides more forest structural information than other optical remote sensing data. This study aimed to map forest canopy heights over China from the GLAS and MODIS BRDF data. Firstly, the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis, and the terrain index was calculated using the ASTER GDEM data. Secondly, the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index. Thirdly, the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data, and applied to get the continuous canopy height map over China. Finally, the map was validated by the measured data and the airborne LiDAR data, and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data. GLAS, waveform decomposition, terrain index, canopy height model Citation: Yang T, Wang C, Li G C, et al Forest canopy height mapping over China using GLAS and MODIS data. Science China: Earth Sciences, 57, doi: /s Global climate change has attracted significant attention in the international community. The forest carbon cycle is one of important global climate change research components. About 77% of the terrestrial ecosystem carbon is stored in forests. The canopy height, leaf area index (LAI), and diameter at breast height (DBH) are important biophysical parameters in forest survey. Canopy height has been successfully used as a surrogate for diameter at breast height in *Corresponding author ( chengwang@ceode.ac.cn) Corresponding author ( ligc@cma.gov.cn) terrestrial carbon studies (Wang et al., 2011). Therefore, estimation of accurate canopy height is essential for the research on forest carbon cycle and the global climate change (De Jong et al., 2003). The mapping of canopy height in China intuitively reflects the distribution of vegetation. It also provides scientific data for the research of the terrestrial carbon cycle. The traditional ground-based field measurement of forest canopy height is easy and accurate, but also time-consuming and expensive, as well as impossible to perform at the large Science China Press and Springer-Verlag Berlin Heidelberg 2014 earth.scichina.com link.springer.com

2 2 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? scale across China. As remote sensing technology develops, the optical remote sensing and synthetic aperture radar (SAR) have become an important method to estimate forest canopy height (Brown et al., 1999). Optical remote sensing is commonly used for mapping forest canopy height at the regional scale. Kimes et al. (2006) predicted the forest vegetation height near Howland Maine, using data collected by the MISR sensor. Heiskanen (2006) estimated the canopy height in the Fennoscandian tundra-taiga transition zone from MISR BRF data. However, optical data are restricted by the requirement of cloud-free daylight conditions, and only provide the surface information of the vegetation (Zhang et al., 2006). Since SAR is not affected by weather conditions, it can better reflect the canopy height characteristics and produce more accurate estimates of canopy height. Cloude et al. (1998) proposed a method to estimate forest vegetation height using single baseline POLINSAR based on the RVOG model. Neumann et al. (2009) adopted a simple polarimetric scattering model to estimate the vegetation height with high accuracy from Polarimetric SAR interferometry data. However, the complex scattering mechanisms of SAR make it hard to construct the canopy height model (Hyde et al., 2007). LiDAR (Light Detection and Ranging) is a new, rapidly developing remote sensing technology that is especially useful for detecting the spatial structure of vegetation. Clapk et al. (2004) used small-footprint LiDAR to estimate the canopy height in a tropical rain forest landscape in La Selva. Pang et al. (2008a) extracted the canopy height from airborne LiDAR in a tree farm in Shandong Province of China. The cost for acquiring airborne LiDAR data is relatively high. By contrast, space-borne LiDAR has advantages associated with the satellite platform, such as high orbit and wide-field view, and thus is suitable for acquiring the large-scale canopy height information. Lefsky et al. (2005) and Sun et al. (2006) extracted the canopy height with high accuracy from GLAS data, respectively. NASA created an accurate, high-resolution forest height map by using GLAS, MODIS and TRMM data. The map was compared against data from a network of about 70 ground sites across the globe, and its accuracy was validated (RMSE=6.1 m, R 2 = 0.5) ( Combining the GLAS data with MERSI data, Dong et al. (2011) estimated the forest canopy height in Jiangxi Province of China and the square correlation coefficient (R 2 ) between remotely-sensed and ground-measured canopy heights is Topography is one major factor affecting the accuracy of the constructed forest height map from GLAS data. Lefsky et al. (2005) extracted the terrain index from Shuttle Radar Topography Mission (SRTM) data to reduce the influence of the topography on GLAS waveforms. Xing et al. (2010) proposed an improved model based on Lefsky s model to predict maximum canopy height using the logarithmic transformation of waveform extent and elevation change as independent variables. Since the GLAS points are discontinuous with relativity low density, they should be combined with other continuous remotely sensed data to obtain a continuous high-precision canopy height distribution. The MODIS BRDF data reflect the canopy structure information through the bidirectional reflectance model (Jiao et al., 2011). Using geometric optics and radiation transfer theory, Deng et al. (2006) proposed a BRDF-based global algorithm to estimate the global LAI. The MODIS BRDF data is calculated by semi-empirical kernel-driven models and canopy height can be estimated through physical models (Gao et al., 2003). Wang et al. (2011) used multivariate linear regression to estimate the canopy height of the Howland Research Forest in central Maine, USA from MODIS BRDF data. The R 2 in Wang et al. (2011) ranged from 0.54 to In summary, large-scale continuous canopy height can be effectively estimated by combining LiDAR with optical remote sensing data. In this study, GLAS data were processed in three steps: Data preprocessing, waveform decomposition, and waveform characterization parameters extraction. Then, the terrain index estimated from the ASTER GDEM data, was combined with the waveform characterization parameters (e.g., waveform length) to reduce the topographic influence on canopy height estimation. Finally, the GLAS data and MODIS BRDF data were combined to establish a neural network model for mapping forest canopy height. The proposed methods were applied to produce a continuous forest canopy height distribution map over China, and the results were validated through comparing with field measurements and airborne LiDAR estimated canopy height data, respectively. 1 Data used 1.1 GLAS data GLAS is a LiDAR sensor launched on January 12, It has an orbit altitude about 600 km with a repeat cycle of 183 days, covering the area between 86 N and 86 S. The system transmits 40 pulses per second and obtains the distance between the satellite and target by calculating the time difference between the transmitted and received signals. The signal forms a ground footprint with a diameter of about 70 m, and a distance between two adjacent footprints of about 170 m. The distance between the track changes with latitude, reducing from 15 km near the equator to 2.5 km at 80 S and 80 N (Abshire et al., 2005). GLAS records the vertical information of targets within a footprint, and from which the forest canopy height can be estimated. GLAS has produced 15 data products. The GLA01 records the waveforms for each laser shot, and the GLA14 records the latitude, longitude, and elevation. NASA also provides waveform decomposition parameters for each laser

3 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? 3 footprint. In this study, the canopy height was estimated from GLA01 and GLA14 data collected in China in February 2008 (Figure 1). 1.2 MODIS data The MODIS (Moderate-resolution Imaging Spectroradiometer) is an important sensor mounted on the Terra and Aqua satellites with 36 spectral bands, and provides real-time monitoring of global large-scale dynamics of land, oceans, and the troposphere. The MODIS BRDF product is a fourthgrade product at a spatial resolution of 500 m. The MODIS BRDF products reflect the anisotropy of each pixel better and have the ability to retrieve the vegetation structures (Gao et al., 2003). The main algorithm of the MODIS BRDF represents the volumetric scattering and geometric-optical scattering by the Ross-Thick kernel and Li-Sparse Reciprocal kernel, named RTLSR. R,,, fiso fvol Kvol,, (1) f K,,, geo where f iso, f vol, and f geo are weights of isotropic scattering, volumetric scattering, and geometric-optical scattering, respectively; K geo and K vol are trigonometric functions of view zenith, illumination zenith, and relative azimuth ; these functions provide shapes for the surface-scattering and volume-scattering BRDFs; R (,,, ) is the bidirectional reflectance distribution function for the waveband (Jiao et al., 2011). The sub-product of the MODIS BRDF/Albedo data, termed MCD43A1, provides the weighting parameters for geo the RTLSR model, i.e., the weight of isotropic scattering, the weight of geometrical-optical scattering, and the weight of volumetric scattering. To synchronize with the GLAS data, the MCD43A1 products in February 2008 were used in this study. 1.3 Vegetation classification data Typically, differences between the canopy type and spatial structure lead to differences in echo signal (Lefsky et al., 2007). To improve the accuracy of the inversion result, three different vegetation types (i.e., broadleaf forest, coniferous forest, and mixed forest) were considered in our work to construct the neural network models. The land use and land cover classification data, GLC2000 (Global Land Cover 2000) with a spatial resolution of 1000m produced by the European Commission s Joint Research Centre were used in this study, and the GLC2000 map had the highest accuracy in China compared with other three global land cover datasets, i.e., IGBPDISCover, UMd, and MOD12Q1 (Ran et al., 2010, 2012; Loveland et al., 2000; Hansen et al, 2000; Friedl et al., 2002; Bartholomé et al., 2005). Based on the GLC2000, forest vegetation types in China were classified into three types, i.e., broadleaf forest, coniferous forest, and mixed forest (Figure 2). 1.4 Field measurements The main forests in China are located in the southwest and northeast regions, in which some field measurements of canopy height were collected in this study. The location of each selected GLAS footprint was positioned in the field by Figure 1 Spatial distribution of the GLAS points in China (February 2008).

4 4 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? Figure 2 Classification map of the three forest vegetation types in China. GPS. All trees with DBH exceeding 5 cm were measured within a circular area with a diameter of 30 m and centered at the center of each selected GLAS footprint. Ninety-three GLAS footprints were surveyed in Yunnan Province during January 2007 (Xishuangbanna and Kunming), and 21 footprints were measured in Jilin Province during June 2007 (Figure 3). In this study, field measured canopy heights in 76 GLAS footprints (28 in Xishuangbanna, 38 in Kunming, and 10 in Jilin) were used to develop a canopy height estimation Figure 3 Location of the test points.

5 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? 5 model, and field measurements in the remaining 38 footprints (11 in Xishuangbanna, 16 in Kunming, and 11 in Jilin) were used for model validation. 2 Methods 2.1 GLAS data processing There are six steps involved in GLAS data processing for mapping spatial distribution of forest canopy height over China: (1) the GLAS data were preprocessed; (2) the waveform characterization parameters (e.g., waveform length, leading and trailing edge lengths) were extracted; (3) the terrain index was estimated from the ASTER GDEM data (Tighe et al., 2009); (4) the GLAS-footprint canopy height model was developed using the waveform characterization parameters and terrain index; (5) three canopy height models (i.e., broadleaf forest, coniferous forest, and mixed forest) for different forest types were built by the neural network method using the GLAS and MODIS BRDF data, and (6) the canopy height models developed in this study were applied to map the forest canopy height over China Data preprocessing Data preprocessing includes four steps: error elimination, data decompression, voltage conversion, and filtering. The extreme data influenced by cloud and system noise were eliminated. To ensure that 544 samples completely comprise the vertical information within a footprint, the GLAS system records waveforms in the form of subsection compression, and the waveform data was decompressed based on the compression ratio. GLA01 and GLA14 were recorded as digital numbers in binary format (the conversion formula is given in the ANC07 documents) ( data/docs/daac/glas_ancillary_products.html). To obtain highquality waveform data, the mean filtering method was applied to the GLAS waveform data in this study (Wang et al., 2013). adopted for waveform characterization (Wang et al., 2013). The basic Gaussian wavelet function with different scales was selected to best fit the targets within the footprint. The optimal peak position was determined by comparing the Gaussian peak around the same position at different scales. The waveform length is defined as the distance between the first and last peaks. The leading edge of the waveform is the distance between the start of the effective signal and the first peak. The trailing edge of the waveform is the distance between the last peak and the end of the effective signal (Figure 4) (Wang et al., 2013; Pang et al., 2008b) Forest canopy estimation over GLAS footprints Several models have been proposed for estimating the forest canopy height. These models differ markedly by their input parameters. Nilsson (1996) estimated canopy height using the distance between the top of the canopy and ground within a footprint. Wang et al. (2013) estimated canopy height using the waveform length on flat surfaces. Lefsky et al. (2005) included the terrain index into their model for canopy height estimation in areas with steep terrain slope. Lefsky et al. (2007) added the leading and trailing edges of the waveform to their model. In this study, we used all parameters (waveform length w, leading edge of the waveform l, trailing edge of the waveform length t, terrain index g, and terrain standard deviation s) to construct seven models (Table 1) and selected the best one for canopy height estimation through comparison. The data used here were the GLAS data, DEM data, and the 76 field measurements (28 in Xishuangbanna, 38 in Kunming, and 10 in Jilin). Table 1 lists the GLAS-footprint canopy height estimation Waveform decomposition The distance between the start of the effective signal and the last peak of a GLAS waveform is the height of the tallest tree within the footprint, while it cannot represent the height information of all trees within the footprint. In this study, the heights of all vegetation within the footprint are represented by the average canopy height. Previous studies had used the waveform length, leading edge of the waveform, and trailing edge of the waveform to estimate the average canopy height (Lefsky et al., 2005, 2007; Pang et al., 2008b; Wang et al., 2013). The GLA01 waveform data is the superposition of reflection signals from different targets, and each signal is assumed as a Gaussian function with different scales. The Gaussian characteristics (mean and standard deviation ) depend on target size. In this study, wavelet analysis was Figure 4 Characteristic parameters of GLAS echo waveform.

6 6 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? Table 1 Parameters fitting the vegetation height Model Parameters R 2 1 w w, l w, g w, l, t w, l, g w, l, t, g w, l, t, s conversion, image mosaics, and image fusion (Salomonson et al., 2002). The MCD43A1 products are retrieved daily and represent the best BRDF based on 16 days data. In this study, four periods of the MCDA1 product were used. In some regions, some data lost by the influence of fog and cloud, and the data gaps were filled with the data of the other three periods. Few pixels with missing data were filled with their neighboring pixels value. The distribution of NIR isotropic scattering kernel parameters of the MCD43A1 during February 2008 is shown in Figure 6. models with different parameters and coefficient of determinations (R 2 ). Among these models, Model 6 with four variables, i.e., the waveform length, leading edge length of the waveform, trailing edge length of the waveform and terrain index, has the highest accuracy and was selected to estimate GLAS-footprint canopy height in this study. The equation of Model 6 is H 0.536w 0.021l 1.193t 0.047g, (2) where H is the canopy height; w is the wave length; l and t are the leading and trailing edge lengths of the waveform, and g is the terrain index. Figure 5 shows the measured and estimated (eq. (2)) canopy height. 2.2 MODIS data processing Some bands of MODIS data are well-correlated with chlorophyll, e.g., the red and blue bands are absorbed strongly by chlorophyll, and the near-infrared band is reflected strongly by chlorophyll (Fang et al., 2003). Therefore, we selected the red, blue, and near-infrared bands for the study. The MODIS data was processed in three steps: data format 2.3 Mapping the forest canopy height from GLAS and MODIS data The model combining the MODIS BRDF data and GLAS were constructed by the BP neural network (Figure 7). The input layer contains nine nodes, including three BRDF parameters in the red, blue, and near-infrared bands. The output layer is the canopy height. The maximum training number was set to 6000, and the error threshold was The neural network models were consructed based on three forest types (broadleaf forest, coniferous forest, and mixed forest). The BP neural network was trained using 3267 GLAS points carefully screened (1887 in broadleaf forest regions, 1308 in coniferous forest regions, and 72 in mixed regions), and verified by 1089 sample points (629 in broadleaf forest regions, 436 in coniferous forest regions, and 24 in mixed regions). The results are shown in Figure 8. The R 2 of the validation is 0.676, and the Root Mean Squared Error (RMSE) is 4.80 m. This study indicates that the average canopy height can be estimated with a reliable result through the neural network models. 3 Results and verification 3.1 Results Following the methods described in section 3, we estimated the forest canopy height in China (see Figure 9) using the GLAS, MODIS BRDF, ASTER GDEM, GLC2000, and the field-measured data. 3.2 Verification Figure 5 Validation results of the GLAS-footprint average canopy height estimation model. The field measured canopy heights at the other thirty-eight footprints (11 in Xishuangbanna, 16 in Kunming, and 11 in Jilin) were used to validate the results. Among these 38 footprints, 22 points are in broadleaf forest regions and 16 points are in coniferous forest regions. In addition, the model was also verified by comparing estimated canopy heights with airborne LiDAR estimated canopy heights collected in Genhe City, Inner Mongolia. The diameter of the field-measured points was approximately 30 m, and the sample locations relatively clustered.

7 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? 7 Figure 6 MODIS data of China in February Weighting parameter of isotropic scattering of the NIR band. Figure 7 The used neural network for canopy height estimation. The spatial resolution of the vegetation height map over China created in this study (Figure 9) is 500 m, so several measured points were located in the same pixels (Figure 9). In this case, an average height of the measured points was calculated to represent the pixel value. As shown in Table 2, the canopy height can be estimated with a relatively high accuracy. To assess the estimation accuracy of canopy height, we calculated the mean error (ME) and RMSE using

8 8 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? RMSE 1 n 2 zobserved z. (4) predicted n i 1 Figure 8 Validation results of the neural network model. Airborne LiDAR data are characterized by high density, small footprint size, and high accuracy in the vegetation canopy height estimation. Using airborne LiDAR data, Wang et al. (2008) estimated the canopy height of a study site in Idaho with an error of ±0.51 m. Therefore, airborne LiDAR data was also used to verify the canopy height in this study and the validation area was a 32 km 2 transect located in Genhe city, Inner Mongolia. The View Laser module embedded in the TerraSolid software package (TerraSolid, Finland) was used to extract the canopy height. A comparison between the LiDAR-derived canopy height and the canopy height map (Figure 9) was carried out, and as shown in Table 3, the continuous canopy heights can be accurately estimated using our method (ME= 3.85 m, RMSE=4.81 m). eqs. (3) and (4), respectively. The terrain correction produced a more accurate result (ME= 2.31 m, RMSE=3.2 m) than the result without terrain correction (ME= 4.47 m, RMSE=3.41 m). n 1 ME zobserved zpred icted, (3) n i 1 where Z observed is the field measured value, Z predicted is the predicted value, n is the number of test points, i is an integer from 1 to n. 4 Conclusions In this study, we used satellite data (GLAS, MODIS, ASTER DEM, and GLC2000) and field measurements to build models to estimate the canopy height of broadleaf forests, coniferous forests, and mixed forests. The field measured canopy heights in southwest and northeast forest regions in China and the airborne LiDAR measured canopy heights collected in Inner Mongolia were used for validation. The mean errors in the estimated tree heights comparing with the field-measured and the airborne LiDAR extracted canopy Figure 9 Forest canopy height map of China.

9 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? 9 Table 2 Validation Results by the measured data Measured points Measured points Value before Value after terrain The error before Position of the number in value terrain correct correct terrain correct measured points one pixel (m) (m) (m) (m) N/ E Number of the pixel N/ E The error after terrain correct (m) N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E N/ E Table 3 Validation Results by the airborne lidar data Latitude and longitude The data before terrain correct N N E E The data after terrain correct N N E E Number of pixels The canopy height (m) ME (m) RMSE (m)

10 10 Yang T, et al. Sci China Earth Sci January (2014) Vol.57 No.? heights are 2.31 and 3.85 m, respectively, and the RMSEs are 3.20 and 4.81 m. Our results indicated that: (1) combining the GLAS data and MODIS data, we can map canopy height over China with a relatively high accuracy; and (2) the accuracy of the estimated canopy height strongly influenced by topographic factors, and thus including the terrain index in the canopy height estimation model can greatly improve the accuracy. Because the study area of this study extended over China and large amounts of data were involved, the data were divided into a number of blocks and separately processed. The differences in the sample GLAS points among blocks may cause mismatches along the boundaries of each block. Although we have considered the impact of topography on canopy height estimation in this study, it needs further study to completely eliminate the terrain impact. The LiDAR data produced by the ICESat2 satellite, scheduling to be launched by NASA in 2016, may solve this problem. Furthermore, this study used the GLAS data collected in February 2008 and measured data collected in June 2007 and November The time difference might influence the estimation and validation of canopy height. This work was supported by the Major International Cooperation and Exchange Project of National Natural Science Foundation of China (Grant No ), the National Basic Research Program of China (Grant NO. 2013CB733405), the National Natural Science Foundation of China (Grant Nos , ), the 100 Talents Program of the Chinese Academy of Sciences and Beijing Natural Science Foundation (Grant No ). We also thank the National Snow and Ice Data Center for providing the GLAS data and two anonymous reviewers for many constructive comments on the manuscript. 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