{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T06:09:28Z","timestamp":1763705368847,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T00:00:00Z","timestamp":1508716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Par\u00e1, Brazil. Field-derived AGB was computed at 85 square 50 \u00d7 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses\u00b7m\u22122 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses\u00b7m\u22122. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of &gt;20 Mg\u00b7ha\u22121 when pulse density decreased from 12 to 0.2 pulses\u00b7m\u22122. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg\u00b7ha\u22121. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.<\/jats:p>","DOI":"10.3390\/rs9101068","type":"journal-article","created":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T12:20:24Z","timestamp":1508761224000},"page":"1068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos","family":"Silva","sequence":"first","affiliation":[{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"},{"name":"Department of Natural Resources and Society, College of Natural Resources, University of Idaho, (UI), 875 Perimeter Drive, Moscow, ID 83843, USA"},{"name":"US Forest Service (USDA), Rocky Mountain Research Station, RMRS, 1221 South Main Street, Moscow, ID 83843, USA"}]},{"given":"Andrew","family":"Hudak","sequence":"additional","affiliation":[{"name":"US Forest Service (USDA), Rocky Mountain Research Station, RMRS, 1221 South Main Street, Moscow, ID 83843, USA"}]},{"given":"Lee","family":"Vierling","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Society, College of Natural Resources, University of Idaho, (UI), 875 Perimeter Drive, Moscow, ID 83843, USA"}]},{"given":"Carine","family":"Klauberg","sequence":"additional","affiliation":[{"name":"US Forest Service (USDA), Rocky Mountain Research Station, RMRS, 1221 South Main Street, Moscow, ID 83843, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6260-5791","authenticated-orcid":false,"given":"Mariano","family":"Garcia","sequence":"additional","affiliation":[{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"},{"name":"Department of Geography, Centre for Landscape and Climate Research, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Ant\u00f3nio","family":"Ferraz","sequence":"additional","affiliation":[{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0253-3359","authenticated-orcid":false,"given":"Michael","family":"Keller","sequence":"additional","affiliation":[{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"},{"name":"USDA Forest Service, International Institute of Tropical Forestry, San Juan, PR 00926, USA"},{"name":"Brazilian Agricultural Research Corporation\u2014Embrapa, Campinas, SP 13070-115, Brazil"}]},{"given":"Jan","family":"Eitel","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Society, College of Natural Resources, University of Idaho, (UI), 875 Perimeter Drive, Moscow, ID 83843, USA"}]},{"given":"Sassan","family":"Saatchi","sequence":"additional","affiliation":[{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1126\/science.1248525","article-title":"Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains","volume":"344","author":"Nepstad","year":"2014","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1016\/j.rse.2010.01.001","article-title":"Assessment of tropical forest degradation by selective logging and fire using Landsat imagery","volume":"114","author":"Matricardi","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foreco.2016.06.003","article-title":"Recovery of biomass and merchantable timber volumes twenty years after conventional and reduced-impact logging in amazonian Brazil","volume":"376","author":"Vidal","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1126\/science.1118051","article-title":"Selective logging in the brazilian Amazon","volume":"310","author":"Asner","year":"2005","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1002\/2016GB005465","article-title":"Aboveground biomass variability across intact and degradedforests in the Brazilian Amazon","volume":"30","author":"Longo","year":"2016","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_7","first-page":"479","article-title":"Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon","volume":"124","author":"Reutebuch","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2013.08.049","article-title":"Monitoring selective logging in western Amazonia with repeat lidar flights","volume":"151","author":"Andersen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.rse.2013.09.023","article-title":"Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric","volume":"140","author":"Asner","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(01)00281-4","article-title":"Estimation of tropical forest structural characteristics using large-footprint lidar","volume":"79","author":"Drake","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/S0034-4257(02)00013-5","article-title":"Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest","volume":"81","author":"Drake","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., and Hurtt, G.C. (2010). Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. J. Geophys. Res., 115.","DOI":"10.1029\/2009JG000933"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5421","DOI":"10.5194\/bg-10-5421-2013","article-title":"Detecting tropical forest biomass dynamics from repeated airborne lidar measurements","volume":"10","author":"Meyer","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/forestry\/cpw016","article-title":"Principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne lidar data","volume":"89","author":"Silva","year":"2016","journal-title":"Forestry"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Silva, C.A., Klauberg, C., Hudak, A.T., Vierling, L.A., Jaafar, W.S.W.M., Mohan, M., Garcia, M., Ferraz, A., Cardil, A., and Saatchi, S. (2017). Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest. Forests, 8.","DOI":"10.3390\/f8070254"},{"key":"ref_16","first-page":"619","article-title":"Effects on estimation accuracy of forest variables using different pulse density","volume":"53","author":"Magnusson","year":"2007","journal-title":"For. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2012.11.024","article-title":"Tradeoffs between lidar pulse density and forest measurement accuracy","volume":"130","author":"Jakubowski","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"349","DOI":"10.5194\/essd-7-349-2015","article-title":"Global Carbon Budget","volume":"7","author":"Moriarty","year":"2015","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13021-015-0013-x","article-title":"Airborne lidar-based estimates of tropical forest structure in complex terrain: Opportunities and trade-offs for REDD+","volume":"10","author":"Leitold","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1007\/s10310-015-0504-3","article-title":"Estimating aboveground carbon using airborne lidar in Cambodian tropical seasonal forests for REDD+ implementation","volume":"20","author":"Ota","year":"2015","journal-title":"J. For. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00704-012-0796-6","article-title":"Modeling monthly mean air temperature for Brazil","volume":"113","author":"Alvares","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_22","unstructured":"RADAMBRASI (1983). Projeto RADAMBRASIL: 1973\u20131983\u2014Levantamento de Recursos Naturais, Minist\u00e9rio das Minas e Energia, Departamento Nacional de Produ\u00e7\u00e3o Mineral."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_24","unstructured":"McGauchey, R.J. (2016, October 15). FUSION\/LDV: Software for LiDAR Data Analysis and Visualization. Available online: http:\/\/forsys.cfr.washington.edu\/fusion\/FUSION_manual.pdf."},{"key":"ref_25","unstructured":"Isenburg, M. (2016, October 03). LAStools\u2014Efficient Tools for Lidar Processing. Available online: http:\/\/www.cs.unc.edu\/~isenburg\/lastools\/."},{"key":"ref_26","unstructured":"R Core Team (2016, May 15). R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.r-project.org\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rse.2006.03.005","article-title":"A model-based approach to estimating forest area","volume":"103","author":"McRoberts","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/sys.21275","article-title":"A systems engineering approach to estimating uncertainty in above-ground biomass (AGB) derived from remote-sensing data","volume":"17","author":"Weisbin","year":"2014","journal-title":"Syst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1002\/2015JG003315","article-title":"Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne lidar and Landsat OLI data","volume":"122","author":"Garcia","year":"2017","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Maltamo, M., Naesset, E., and Vauhkonen, J. (2014). Modeling and estimating change. Forestry Applications of Airborne Laser Scanning, Springer. Concepts and Case Studies.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8453","DOI":"10.3390\/rs70708453","article-title":"Effects of pulse density on digital terrain models and canopy metrics using airborne laser scanning in a tropical rainforest","volume":"7","author":"Hansen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.rse.2009.11.007","article-title":"Remote sensing of environment reliability of lidar derived predictors of forest inventory attributes: A case study with Norway spruce","volume":"114","author":"Magnussen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"644","DOI":"10.5589\/m12-052","article-title":"Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables","volume":"38","author":"Strunk","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.isprsjprs.2014.12.021","article-title":"Effects of LiDAR point density and landscape context on estimates of urban forest biomass","volume":"101","author":"Singh","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"126","DOI":"10.5589\/m06-007","article-title":"Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return LiDAR and multispectral satellite data","volume":"32","author":"Hudak","year":"2006","journal-title":"Can. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/S0034-4257(01)00228-0","article-title":"Estimating tree heights and number of stems in young forest stands using airborne laser scanner data","volume":"78","author":"Bjerknes","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","first-page":"591","article-title":"Mapping aboveground carbon stocks using LiDAR data in Eucalyptus spp. plantations in the state of S\u00e3o Paulo, Brazil","volume":"42","author":"Silva","year":"2014","journal-title":"Sci. For."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1016\/j.rse.2008.03.004","article-title":"Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser","volume":"112","author":"Naesset","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13021-017-0081-1","article-title":"Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data","volume":"12","author":"Silva","year":"2017","journal-title":"Carbon Balance Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2012.02.023","article-title":"Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys","volume":"123","author":"Hudak","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1080\/07038992.2016.1196582","article-title":"Imputation of individual longleaf pine ( Mill.) Tree attributes from field and LiDAR Data","volume":"42","author":"Silva","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13021-017-0073-1","article-title":"Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR","volume":"12","author":"Garcia","year":"2017","journal-title":"Carbon Balance Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1080\/01431160903144086","article-title":"Investigating the impact of discrete-return LiDAR point density on estimations of mean and dominant plot-level tree height in Eucalyptus grandis plantations","volume":"31","author":"Tesfamichael","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.foreco.2004.07.077","article-title":"Simulation study for finding optimal lidar acquisition parameters for forest height retrieval","volume":"214","author":"Lovell","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40490-014-0018-3","article-title":"The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions","volume":"44","author":"Watt","year":"2014","journal-title":"N. Z. J. For. Sci."},{"key":"ref_47","first-page":"625","article-title":"Understanding the effects of ALS pulse density for metric retrieval across diverse forest types. Photogramm","volume":"81","author":"Wilkes","year":"2015","journal-title":"Eng. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.foreco.2015.08.009","article-title":"Fates of treesdamaged by logging in Amazonian Bolivia","volume":"357","author":"Shenkin","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_49","first-page":"267","article-title":"A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur national forest","volume":"109","author":"Hummel","year":"2015","journal-title":"J. For."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/1068\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:13Z","timestamp":1760208493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/1068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,23]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["rs9101068"],"URL":"https:\/\/doi.org\/10.3390\/rs9101068","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2017,10,23]]}}}