Articles | Volume 11, issue 10
https://doi.org/10.5194/bg-11-2793-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-11-2793-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data
J. Zhang
Department of Renewable Resources, University of Alberta, Edmonton, Alberta, T6G 2H1, Canada
S. Huang
Forest Management Branch, Alberta Department of Environment and Sustainable Resource Development, 8th Floor, 9920-108 Street, Edmonton, Alberta, T5K 2M4, Canada
E. H. Hogg
Canadian Forest Service, Natural Resources Canada, Northern Forestry Centre, 5320-122 Street, Edmonton, Alberta, T6H 3S5, Canada
V. Lieffers
Department of Renewable Resources, University of Alberta, Edmonton, Alberta, T6G 2H1, Canada
Y. Qin
Geographic Information Science Center of Excellence, South Dakota State University, Brookings, South Dakota, 57007 USA
F. He
Department of Renewable Resources, University of Alberta, Edmonton, Alberta, T6G 2H1, Canada
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- Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China X. He et al. 10.3390/su14095580
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- Modeling temporal patterns of methane effluxes using multiple regression and random forest in Poyang Lake, China L. Liu et al. 10.1007/s11273-017-9558-7
- Gains and losses of plant species and phylogenetic diversity for a northern high‐latitude region J. Zhang et al. 10.1111/ddi.12365
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- Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data W. Shen et al. 10.3390/rs8070595
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- Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets T. Dube et al. 10.1080/01431161.2015.1070316
- Custo, tempo e precisão: uma otimização do inventário florestal pré-corte em um povoamento de eucalipto L. Pinto et al. 10.4336/2024.pfb.44e202102251
- Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data X. Wang et al. 10.1007/s11676-017-0504-6
- Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach N. Jaiswal & S. Jayakumar 10.1007/s10661-024-12591-5
- Evaluating the performance of airborne and spaceborne lidar for mapping biomass in the United States' largest dry woodland ecosystem M. Campbell et al. 10.1016/j.rse.2024.114196
- Hazard rating of coastal pine forests for a black pine bast scale using self-organizing map (SOM) and random forest approaches Y. Nam et al. 10.1016/j.ecoinf.2014.11.001
- Integration of Landsat time series and field plots for forest productivity estimates in decision support models C. Boisvenue et al. 10.1016/j.foreco.2016.06.022
- A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables J. García-Gutiérrez et al. 10.1016/j.neucom.2014.09.091
50 citations as recorded by crossref.
- Modeling fire hazards for the maintenance of long-term forest inventory plots in Alberta, Canada K. Xu et al. 10.1016/j.foreco.2022.120206
- Supervised terrestrial to airborne laser scanner model calibration for 3D individual-tree attribute mapping using deep neural networks Z. Xi et al. 10.1016/j.isprsjprs.2024.02.010
- Fire disturbance data improves the accuracy of remotely sensed estimates of aboveground biomass for boreal forests in eastern Canada D. Irulappa Pillai Vijayakumar et al. 10.1016/j.rsase.2017.07.010
- Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass C. Wu et al. 10.1007/s11676-017-0404-9
- Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico C. López-Sánchez et al. 10.3832/ifor1891-009
- Combining Sentinel-2 and diverse environmental data largely improved aboveground biomass estimation in China’s boreal forests P. Liu et al. 10.1038/s41598-024-78615-9
- Remote sensing of subtropical tree diversity: The underappreciated roles of the practical definition of forest canopy and phenological variation Y. Liu et al. 10.1016/j.fecs.2023.100122
- Calculation of the aboveground carbon stocks with satellite data and statistical models integrated into the climatic parameters in the Alborz Mountain forests (northern Iran) M. Ghanbari Motlagh et al. 10.17221/107/2019-JFS
- Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China X. He et al. 10.3390/su14095580
- Integrating multiple scales of remote sensing measurement – from satellites to kites K. Anderson 10.1177/0309133316639175
- Using low-density discrete Airborne Laser Scanning data to assess the potential carbon dioxide emission in case of a fire event in a Mediterranean pine forest A. Montealegre-Gracia et al. 10.1080/15481603.2017.1320863
- Modeling temporal patterns of methane effluxes using multiple regression and random forest in Poyang Lake, China L. Liu et al. 10.1007/s11273-017-9558-7
- Gains and losses of plant species and phylogenetic diversity for a northern high‐latitude region J. Zhang et al. 10.1111/ddi.12365
- Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China W. Shen et al. 10.1016/j.agrformet.2018.04.005
- Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data W. Shen et al. 10.3390/rs8070595
- Spatiotemporal Assessment of Forest Biomass Carbon Sinks: The Relative Roles of Forest Expansion and Growth in Sichuan Province, China R. Li et al. 10.2134/jeq2016.07.0261
- Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches T. Shi et al. 10.1016/j.envpol.2020.116041
- Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada N. Badreldin & A. Sanchez-Azofeifa 10.3390/rs70302832
- Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods A. dos Reis et al. 10.1186/s40490-017-0108-0
- Comprehensive Analysis of Gap Formation in the Canopy of an Old-Growth Broadleaved Forest A. Portnov et al. 10.1134/S1062359023602719
- Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images J. Meng et al. 10.3390/rs8090719
- Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms Y. Zhu et al. 10.3390/rs9101060
- Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions A. Shen et al. 10.3390/f9120778
- Woody Biomass Mobilization for Bioenergy in a Constrained Landscape: A Case Study from Cold Lake First Nations in Alberta, Canada N. Mansuy et al. 10.3390/en13236289
- Regional and historical factors supplement current climate in shaping global forest canopy height J. Zhang et al. 10.1111/1365-2745.12510
- Carbon storage and sequestration rates of trees inside and outside forests in Great Britain F. Zellweger et al. 10.1088/1748-9326/ac74d5
- Estimation and Spatial Distribution of Individual Tree Aboveground Biomass in a Chinese Fir Plantation in the Dabieshan Mountains of Western Anhui, China A. Chen et al. 10.3390/f15101743
- Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods J. Zhu et al. 10.3390/rs9030241
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- A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data J. Song et al. 10.3390/su16167232
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- Modeling Carbon Emissions of Post-Selective Logging in the Production Forests of Ulu Jelai, Pahang, Malaysia S. Saad et al. 10.3390/rs15041016
- Quantifying the evidence for co-benefits between species conservation and climate change mitigation in giant panda habitats R. Li et al. 10.1038/s41598-017-12843-0
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- Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR F. Fassnacht et al. 10.1016/j.rse.2018.05.007
- Comprehensive analysis of gap formation in the canopy of an old-growth broadleaved forest A. Portnov et al. 10.31857/S1026347024010136
- Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring J. Zhang et al. 10.1016/j.biocon.2016.03.027
- Change Analysis of Aboveground Forest Carbon Stocks According to the Land Cover Change Using Multi-Temporal Landsat TM Images and Machine Learning Algorithms J. LEE 10.11108/kagis.2015.18.4.081
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- Custo, tempo e precisão: uma otimização do inventário florestal pré-corte em um povoamento de eucalipto L. Pinto et al. 10.4336/2024.pfb.44e202102251
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- Evaluating the performance of airborne and spaceborne lidar for mapping biomass in the United States' largest dry woodland ecosystem M. Campbell et al. 10.1016/j.rse.2024.114196
- Hazard rating of coastal pine forests for a black pine bast scale using self-organizing map (SOM) and random forest approaches Y. Nam et al. 10.1016/j.ecoinf.2014.11.001
2 citations as recorded by crossref.
- Integration of Landsat time series and field plots for forest productivity estimates in decision support models C. Boisvenue et al. 10.1016/j.foreco.2016.06.022
- A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables J. García-Gutiérrez et al. 10.1016/j.neucom.2014.09.091
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