National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

Chi, Huisheng; Sun, Guoqing; Huang, Jinliang; Guo, Zichao; Ni, Wenjian; Fu, Anmin

doi:10.3390/rs70505534

Cited by 66 publications

(45 citation statements)

References 72 publications

(104 reference statements)

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“…With reference to the screening methods proposed by Chi [29], Wu [30] and Baghdadi [31], GLAS data with no cloud and a signal-to-noise ratio (SNR) greater than 60 were retained. Cloudless GLAS data were identified using the cloud detection flag (i_FRir_qaflag = 15) in the GLA14 product [29,31]. In addition, the SNR values (Equation (1)) can be calculated using the fields i_max-RecAmp and i_sDevNsOb1 in the GLA14 product [30].…”

Section: Icesat/glas Datamentioning

confidence: 99%

“…Gaussian Decomposition A Gaussian decomposition was applied to the filtered waveform using Levenberg-Marquardt nonlinear least-squares fitting [29,34,37]. To compare this method with the GLA14 product, the Pearson's correlation coefficient (r) was calculated between the original waveform and the fitted waveforms obtained by the method described above and by the GLA14 product separately.…”

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confidence: 99%

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Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

et al. 2017

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Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing'anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R 2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R 2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.

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Section: Icesat/glas Datamentioning

confidence: 99%

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confidence: 99%

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

et al. 2017

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“…The climate is dominated by a continental monsoon climate. Seasonal changes and annual variability of temperature and precipitation are significant in most regions of China, which are major factors in the formation of complex and diverse climate as well as topography (Chi et al 2015).…”

Section: Study Areamentioning

confidence: 99%

Potential impact of land use change on ecosystem services in China

Wang

Sun

2016

Environ Monit Assess

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Land use change can greatly alter spatial pattern and overall ecosystem service values (ESV). The goal of this study was to explore the likely effects of land use change on ESV in China. In this paper, the spatially explicit land use changes across China from 2010 to 2020 under the 2000s trend scenario and the planning scenario were projected using the Dyna-CLUE model. The ESV evaluation method was improved by adjusting the ESV coefficients using biomass data to reduce the generalization error of proxy-based method. The results revealed that between 2010 and 2020, total ESV increased by 1798 and 2215 billion RMB a −1 under the 2000s trend scenario and the planning scenario, respectively. The spatial pattern of ESV in 2010 and 2020 presented a logical geographic distribution. The areas with ESV of 50,000 RMB ha −1 a −1 and higher occurred primarily in northeastern and southern China, while the areas with ESV of 5000 RMB ha −1 a −1 and lower were mainly located in northwestern China. The spatial differences between the two scenarios were insignificant except that the increase of ESV in southwestern China was more prominent in the planning scenario than that in the 2000s trend scenario, while the total ESV in 2020 under the planning scenario was larger than that in the 2000s trend scenario. The increase of ESV occurred mainly in northeastern, southern, and southeastern China due to forest growth and woodland expansion in 2020 compared with 2010. The results of this study can provide useful information for the public and land managers to consider.

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“…The conventional ways to obtain borderland data are field surveying, mapping and census. Moreover, we can also extract information from remote sensing images, including multi-source remote sensing image geometric correction and terrain information extraction [14]. Furthermore, the way to capture border information online is public network information integration, including the process of network search, data extraction, matching and mapping transformation [1].…”

Section: Borderland Information Ascquisitionmentioning

confidence: 99%

A Geoweb-Based Tagging System for Borderlands Data Acquisition

Xing

Zhou

2015

IJGI

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Borderlands modeling and understanding depend on both spatial and non-spatial data, which were difficult to obtain in the past. This has limited the progress of borderland-related research. In recent years, data collection technologies have developed greatly, especially geospatial Web 2.0 technologies including blogs, publish/subscribe, mashups, and GeoRSS, which provide opportunities for data acquisition in borderland areas. This paper introduces the design and development of a Geoweb-based tagging system that enables users to tag and edit geographical information. We first establish the GeoBlog model, which consists of a set of geospatial components, posts, indicators, and comments, as the foundation of the tagging system. GeoBlog is implemented such that blogs are mashed up with OpenStreetMap. Moreover, we present an improvement to existing publish/subscribe systems with support for spatio-temporal events and subscriptions, called Spatial Publish/Subscribe, as well as the event agency network for routing messages from the publishers to the subscribers. A prototype system based on this approach is implemented in experiments. The results of this study provide an approach for asynchronous interaction and message-ordered transfer in the tagging system.

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National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

Cited by 66 publications

References 72 publications

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

Potential impact of land use change on ecosystem services in China

A Geoweb-Based Tagging System for Borderlands Data Acquisition

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