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Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm



Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong Province of East China. The experimental results show that the RBF model produces a relatively accurate biomass estimation compared with multivariate linear regression (MLR), k-Nearest Neighbor (KNN), and backpropagation artificial neural network (BP ANN) models.



Total Pages: 8
Pages: 391-398


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Volume: 24
Issue: 2
Year: 2018

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Aguirre-Salado, Carlos A. et al. "Mapping Aboveground Biomass by Integrating Geospatial and Forest Inventory Data through a k-Nearest Neighbor Strategy in North Central Mexico." Journal of Arid Land 6.1 (2013): 80-96. Crossref. Web.

Bénié, G.B. et al. "Remote Sensing-Based Spatio-Temporal Modeling to Predict Biomass in Sahelian Grazing Ecosystem." Ecological Modelling 184.2-4 (2005): 341-354. Crossref. Web.

Dombi, George W. et al. "Prediction of Rib Fracture Injury Outcome by an Artificial Neural Network." The Journal of Trauma: Injury, Infection, and Critical Care 39.5 (1995): 915-921. Crossref. Web.

Dube, Timothy, and Onisimo Mutanga. "Evaluating the Utility of the Medium-Spatial Resolution Landsat 8 Multispectral Sensor in Quantifying Aboveground Biomass in uMgeni Catchment, South Africa." ISPRS Journal of Photogrammetry and Remote Sensing 101 (2015): 36-46. Crossref. Web.

Ene, Liviu Theodor et al. "Large-Scale Estimation of Aboveground Biomass in Miombo Woodlands Using Airborne Laser Scanning and National Forest Inventory Data." Remote Sensing of Environment 186 (2016): 626-636. Crossref. Web.

Fan, Yuanchao, Tatjana Koukal, and Peter J. Weisberg. "A Sun-crown-sensor Model and Adapted C-Correction Logic for Topographic Correction of High Resolution Forest Imagery." ISPRS Journal of Photogrammetry and Remote Sensing 96 (2014): 94-105. Crossref. Web.

Fernández-Manso, O., A. Fernández-Manso, and C. Quintano. "Estimation of Aboveground Biomass in Mediterranean Forests by Statistical Modelling of ASTER Fraction Images." International Journal of Applied Earth Observation and Geoinformation 31 (2014): 45-56. Crossref. Web.

Kronseder, Karin et al. "Above Ground Biomass Estimation Across Forest Types at Different Degradation Levels in Central Kalimantan Using LiDAR Data." International Journal of Applied Earth Observation and Geoinformation 18 (2012): 37-48. Crossref. Web.

Kumar R. International Journal of Ecology and Environmental Sciences

Liu, Jiping et al. "Extraction of Individual Tree Crowns from Airborne LiDAR Data in Human Settlements." Mathematical and Computer Modelling 58.3-4 (2013): 524-535. Crossref. Web.

Lumbres, Roscinto Ian C., and Young Jin Lee. "Aboveground Biomass Mapping of La Trinidad Forests in Benguet, Philippines, Using Landsat Thematic Mapper Data Andk-Nearest Neighbor Method." Forest Science and Technology 10.2 (2014): 104-111. Crossref. Web.

Karlson, Martin et al. "Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest." Remote Sensing 7.8 (2015): 10017-10041. Crossref. Web.

Cutler, M.E.J. et al. "Estimating Tropical Forest Biomass with a Combination of SAR Image Texture and Landsat TM Data: An Assessment of Predictions Between Regions." ISPRS Journal of Photogrammetry and Remote Sensing 70 (2012): 66-77. Crossref. Web.

Musavi, M.T. et al. "On the Training of Radial Basis Function Classifiers." Neural Networks 5.4 (1992): 595-603. Crossref. Web.

Seo H. Journal of Korean Forestry Society

Seo H. Journal of Tropical Forest Science

Skowronski, Nicholas S. et al. "Airborne Laser Scanner-Assisted Estimation of Aboveground Biomass Change in a Temperate Oak-pine Forest." Remote Sensing of Environment 151 (2014): 166-174. Crossref. Web.

Stümer, Wolfgang, Bernhard Kenter, and Michael Köhl. "Spatial Interpolation of in Situ Data by Self-Organizing Map Algorithms (neural Networks) for the Assessment of Carbon Stocks in European Forests." Forest Ecology and Management 260.3 (2010): 287-293. Crossref. Web.

Vaglio Laurin, Gaia et al. "Above Ground Biomass Estimation in an African Tropical Forest with Lidar and Hyperspectral Data." ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014): 49-58. Crossref. Web.

Vaglio Laurin, Gaia et al. "Above Ground Biomass and Tree Species Richness Estimation with Airborne Lidar in Tropical Ghana Forests." International Journal of Applied Earth Observation and Geoinformation 52 (2016): 371-379. Crossref. Web.

Webb, Andrew R, and David Lowe. "The Optimised Internal Representation of Multilayer Classifier Networks Performs Nonlinear Discriminant Analysis." Neural Networks 3.4 (1990): 367-375. Crossref. Web.

Xu, Xiaojun et al. "Estimation of Aboveground Carbon Stock ofMosobamboo (Phyllostachys Heterocyclavar.pubescens)forest with a Landsat Thematic Mapper Image." International Journal of Remote Sensing 32.5 (2011): 1431-1448. Crossref. Web.

Yilmaz, Işık, and Oguz Kaynar. "Multiple Regression, ANN (RBF, MLP) and ANFIS Models for Prediction of Swell Potential of Clayey Soils." Expert Systems with Applications 38.5 (2011): 5958-5966. Crossref. Web.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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