Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds
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Fábio Guimarães Gonçalves | André Almeida | Robert N. Treuhaft | Gilson Silva | Rodolfo Souza | Weslei Santos | Diego Loureiro | Márcia Fernandes | R. Treuhaft | F. Gonçalves | R. Souza | A. Almeida | Weslei Santos | Diego Loureiro | M. Fernandes | Gilson Silva | R. Souza
[1] Joanne C. White,et al. Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment , 2015 .
[2] Terje Gobakken,et al. Inventory of Small Forest Areas Using an Unmanned Aerial System , 2015, Remote. Sens..
[3] Eduardo González-Ferreiro,et al. Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities , 2012 .
[4] Zuyuan Wang,et al. A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds , 2015 .
[5] H. Haberl,et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass , 2017, Nature.
[6] Jeffrey Q. Chambers,et al. Tree damage, allometric relationships, and above-ground net primary production in central Amazon forest , 2001 .
[7] R. McRoberts,et al. A questionnaire-based review of the operational use of remotely sensed data by national forest inventories , 2016 .
[8] Xin Shen,et al. Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests , 2019, Forests.
[9] Stefano Puliti,et al. Use of UAV Photogrammetric Data for Estimation of Biophysical Properties in Forest Stands Under Regeneration , 2019, Remote. Sens..
[10] E. Næsset,et al. Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data , 2015 .
[11] João Roberto dos Santos,et al. Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry , 2017 .
[12] Nicholas C. Coops,et al. Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds , 2017 .
[13] Fabio Remondino,et al. State of the art in high density image matching , 2014 .
[14] W. Cohen,et al. Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .
[15] J. Chambers,et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.
[16] M. dos-Santos,et al. Quantification of selective logging in tropical forest with spaceborne SAR interferometry , 2018, Remote Sensing of Environment.
[17] Guoqing Sun,et al. Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America1 , 2015 .
[18] Pablo J. Zarco-Tejada,et al. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .
[19] Jan-Peter Mund,et al. UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring , 2019, Remote. Sens..
[20] Erle C. Ellis,et al. Remote Sensing of Vegetation Structure Using Computer Vision , 2010, Remote. Sens..
[21] Shengli Tao,et al. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data , 2015 .
[22] Tetsuji Ota,et al. Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest , 2015 .
[23] Giles M. Foody,et al. Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development , 2003 .
[24] Satoshi Tsuyuki,et al. Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass , 2019, Remote. Sens..
[25] Susan G. Letcher,et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics , 2016, Science Advances.
[26] Fausto W. Acerbi-Junior,et al. Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[27] Marc Olano,et al. Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..
[28] Terje Gobakken,et al. Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System , 2017, Remote. Sens..
[29] Bisheng Yang,et al. Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries , 2019, Remote. Sens..
[30] Nicholas C. Coops,et al. Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions , 2019, Current Forestry Reports.
[31] João Roberto dos Santos,et al. Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations , 2017, Remote. Sens..
[32] Y. Shimabukuro,et al. Multi-scale approach to estimating aboveground biomass in the Brazilian Amazon using Landsat and LiDAR data , 2019, International Journal of Remote Sensing.
[33] Luciano Vieira Dutra,et al. Biomass estimation in a tropical wet forest using Fourier transforms of profiles from lidar or interferometric SAR , 2010 .
[34] Erle C. Ellis,et al. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery , 2015 .
[35] M. L. Guillén-Climent,et al. Testing the quality of forest variable estimation using dense image matching: a comparison with airborne laser scanning in a Mediterranean pine forest , 2018 .
[36] S. Reutebuch,et al. A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods , 2006 .
[37] Campbell O. Webb,et al. Regional and phylogenetic variation of wood density across 2456 Neotropical tree species. , 2006, Ecological applications : a publication of the Ecological Society of America.
[38] S. Running,et al. Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century , 2006 .
[39] L. Dutra,et al. Tropical Forest Measurement by Interferometric Height Modeling and P-Band Radar Backscatter , 2005, Forest Science.
[40] Ariel E. Lugo,et al. Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data , 1989, Forest Science.
[41] Martina L. Hobi,et al. Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing , 2015 .
[42] Erle C. Ellis,et al. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .
[43] Xin Shen,et al. Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data , 2017, Remote. Sens..
[44] B. Nelson,et al. Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.
[45] Tetsuji Ota,et al. Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests , 2017 .
[46] Nikolay S. Strigul,et al. Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring , 2018, Remote. Sens..
[47] Lars T. Waser,et al. Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests , 2013 .
[49] Timothy Dube,et al. Landscape-Scale Aboveground Biomass Estimation in Buffer Zone Community Forests of Central Nepal: Coupling In Situ Measurements with Landsat 8 Satellite Data , 2018, Remote. Sens..
[50] M. Keller,et al. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon , 2016 .
[51] R. McRoberts,et al. Remote sensing support for national forest inventories , 2007 .
[52] Terje Gobakken,et al. Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland , 2019, Remote. Sens..
[53] Christine Stone,et al. Comparing yield estimates derived from LiDAR and aerial photogrammetric point-cloud data with cut-to-length harvester data in a Pinus radiata plantation in Tasmania , 2018 .
[54] Michael A. Wulder,et al. Estimating Time Since Forest Harvest Using Segmented Landsat ETM+ Imagery , 2004 .
[55] Yi Lin,et al. Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography , 2018, Remote. Sens..
[56] D. W. MacFarlane,et al. Carbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variables , 2017 .
[57] Ralf Ludwig,et al. Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta , 2019, Remote. Sens..
[58] Frank Vermeulen,et al. Mapping by matching: a computer vision-based approach to fast and accurate georeferencing of archaeological aerial photographs , 2012 .
[59] Mariela Soto-Berelov,et al. Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data , 2020, Int. J. Appl. Earth Obs. Geoinformation.
[60] Katarzyna Zielewska-Büttner,et al. Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery , 2016, Remote. Sens..
[61] Emmanuel P. Baltsavias,et al. A comparison between photogrammetry and laser scanning , 1999 .
[62] Peter Axelsson,et al. Processing of laser scanner data-algorithms and applications , 1999 .
[63] G. Baskerville. Use of Logarithmic Regression in the Estimation of Plant Biomass , 1972 .
[64] Joanne C. White,et al. Comparison of airborne laser scanning and digital stereo imagery for characterizing forest canopy gaps in coastal temperate rainforests , 2018 .
[65] Takeo Kanade,et al. Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.
[66] Pablo J. Zarco-Tejada,et al. High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..
[67] R. Noss. Beyond Kyoto: Forest Management in a Time of Rapid Climate Change , 2001 .
[68] Timo Tokola,et al. Effect of field plot location on estimating tropical forest above-ground biomass in Nepal using airborne laser scanning data , 2014 .
[69] Scott J. Goetz,et al. The role of science in Reducing Emissions from Deforestation and Forest Degradation (REDD) , 2010 .
[70] Richard Condit,et al. Error propagation and scaling for tropical forest biomass estimates. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[71] Jean Paul Metzger,et al. The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation , 2009 .
[72] R. McGaughey,et al. Evaluation of pushbroom DAP relative to frame camera DAP and lidar for forest modeling , 2020 .
[73] David A. Coomes,et al. Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion , 2019, Remote. Sens..
[74] S. Goetz,et al. Importance of biomass in the global carbon cycle , 2009 .
[75] C CoopsNicholas,et al. Unmanned aerial systems for precision forest inventory purposes: A review and case study , 2017 .
[76] L. Tang,et al. Drone remote sensing for forestry research and practices , 2015, Journal of Forestry Research.
[77] Arko Lucieer,et al. A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[78] J. Saldarriaga,et al. LONG-TERM CHRONOSEQUENCE OF FOREST SUCCESSION IN THE UPPER RIO NEGRO OF COLOMBIA AND VENEZUELA , 1988 .
[79] Fábio Guimarães Gonçalves,et al. Vegetation profiles in tropical forests from multibaseline interferometric synthetic aperture radar, field, and lidar measurements , 2009 .
[80] Terje Gobakken,et al. Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland , 2016, Remote. Sens..
[81] Carlos Cabo,et al. Structure from Motion Photogrammetry in Forestry: a Review , 2019, Current Forestry Reports.
[82] Juha Hyyppä,et al. Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables , 2013 .
[83] Gregory Asner,et al. Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems , 2016, Remote. Sens..
[84] Fausto Weimar Acerbi Júnior,et al. Modelling aboveground biomass in forest remnants of the Brazilian Atlantic Forest using remote sensing, environmental and terrain-related data , 2021, Geocarto International.
[85] D. Roberts,et al. Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors , 2011 .
[86] Marcelo Tabarelli,et al. Tree species impoverishment and the future flora of the Atlantic forest of northeast Brazil , 2000, Nature.
[87] O. Mutanga,et al. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa , 2015 .
[88] S. Stephens,et al. Climate change and forests of the future: managing in the face of uncertainty. , 2007, Ecological applications : a publication of the Ecological Society of America.
[89] M. Keller,et al. Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest , 2018 .