{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:04:23Z","timestamp":1774641863249,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T00:00:00Z","timestamp":1654300800000},"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 light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary.<\/jats:p>","DOI":"10.3390\/rs14112708","type":"journal-article","created":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T10:47:11Z","timestamp":1654426031000},"page":"2708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7070-0668","authenticated-orcid":false,"given":"Schyler","family":"Brown","sequence":"first","affiliation":[{"name":"College of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Dr., Auburn, AL 36849, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6125-7649","authenticated-orcid":false,"given":"Lana L.","family":"Narine","sequence":"additional","affiliation":[{"name":"College of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Dr., Auburn, AL 36849, USA"}]},{"given":"John","family":"Gilbert","sequence":"additional","affiliation":[{"name":"Solon Dixon Forestry Education Center, Auburn University, 12130 Dixon Road Center, Andalusia, AL 36420, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lister, A.J., Andersen, H., Frescino, T., Gatziolis, D., Healey, S., Heath, L.S., Liknes, G.C., McRoberts, R., Moisen, G.G., and Nelson, M. (2020). Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory. Forests, 11.","DOI":"10.3390\/f11121364"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liang, S., and Yang, L. (2019). A Review of Regional and Global Gridded Forest Biomass Datasets. Remote Sens., 11.","DOI":"10.3390\/rs11232744"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/0378-1127(84)90062-8","article-title":"Woody Mass of Forest Stands","volume":"8","author":"Cannell","year":"1984","journal-title":"For. Ecol. Manag."},{"key":"ref_4","unstructured":"Elledge, J., and Barlow, B. (2021, February 22). Basal Area: A Measure Made for Management. Available online: https:\/\/www.aces.edu\/wp-content\/uploads\/2018\/10\/ANR-1371.REV_.2.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wiant, H.V.W., Baumgras, J.E., and Knight, R. (1984). Relation of Biomass to Basal Area and Site Index on an Appalachian Watershed, Department of Agriculture, Forest Service, Northeastern Forest Experiment Station.","DOI":"10.2737\/NE-RN-315"},{"key":"ref_6","first-page":"e01025","article-title":"Allometric Relationships of Stand Level Carbon Stocks to Basal Area, Tree Height and Wood Density of Nine Tree Species in Bangladesh","volume":"22","author":"Khan","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_7","unstructured":"U.S. Fish and Wildlife Service (2003). Recovery Plan for the Red-Cockaded Woodpecker (Picoides Borealis): Second Revision, U.S. Fish and Wildlife Service."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1093\/ae\/tmv072","article-title":"Forest Inventory and Analysis Data in Invasive Insect Research","volume":"62","author":"Vogt","year":"2016","journal-title":"Am. Entomol."},{"key":"ref_9","unstructured":"Bond, B. (2011). PB1650 Understanding Log Scales and Log Rules, The University of Tennessee Institute of Agriculture."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Oswalt, C.M., and Conner, R.C. (2011). Southern Forest Inventory and Analysis Volume Equation User\u2019 s Guide, Department of Agriculture Forest Service, Southern Research Station.","DOI":"10.2737\/SRS-GTR-138"},{"key":"ref_11","first-page":"569","article-title":"Growth and Stem Profiles of Invasive","volume":"63","author":"Tian","year":"2017","journal-title":"For. Sci."},{"key":"ref_12","first-page":"12","article-title":"National-Scale Biomass Estimators for United States Tree Species","volume":"49","author":"Jenkins","year":"2003","journal-title":"For. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"095002","DOI":"10.1088\/1748-9326\/ab80ee","article-title":"Incorporating Canopy Structure from Simulated GEDI Lidar into Bird Species Distribution Models","volume":"15","author":"Burns","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_14","unstructured":"United Nations Framework Convention on Climate Change (2016). UNFCCC Key Decisions Relevant for Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+), United Nations Framework Convention on Climate Change."},{"key":"ref_15","unstructured":"Environmental Protection Agency (E.P.A) (2021). Inventory of U.S. Greenhouse Gas Emissions and Sinks, EPA."},{"key":"ref_16","first-page":"25","article-title":"How to Estimate Forest Carbon for Large Areas from Inventory Data","volume":"102","author":"Smith","year":"2004","journal-title":"J. For."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/07038992.2018.1461557","article-title":"Transferability of Lidar-Derived Basal Area and Stem Density Models within a Northern Idaho Ecoregion","volume":"44","author":"Fekety","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1750-0680-8-1","article-title":"Imputing Forest Carbon Stock Estimates from Inventory Plots to a Nationally Continuous Coverage","volume":"8","author":"Wilson","year":"2013","journal-title":"Carbon Balance Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.dendro.2014.01.002","article-title":"Toward Consistent Measurements of Carbon Accumulation: A Multi-Site Assessment of Biomass and Basal Area Increment across Europe","volume":"32","author":"Babst","year":"2014","journal-title":"Dendrochronologia"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1093\/wjaf\/20.2.134","article-title":"Observer Variation in Tree Diameter Measurements","volume":"20","author":"Elzinga","year":"2005","journal-title":"West. J. Appl. For."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2006.09.034","article-title":"Remote Sensing Support for National Forest Inventories","volume":"110","author":"Mcroberts","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s10584-012-0686-z","article-title":"A Statistical Power Analysis of Woody Carbon Flux from Forest Inventory Data","volume":"118","author":"Westfall","year":"2013","journal-title":"Clim. Change"},{"key":"ref_23","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":"2011","journal-title":"J. For."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"378","DOI":"10.5558\/tfc2014-072","article-title":"Validating Estimates of Merchantable Volume from Airborne Laser Scanning (ALS) Data Using Weight Scale Data","volume":"90","author":"White","year":"2014","journal-title":"For. Chron."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chuvieco, C. (2020). Fundamentals of Satellite Remote Sensing an Environmnetal Approach, CRC Press. [3rd ed.].","DOI":"10.1201\/9780429506482"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1590\/0001-3765201720160324","article-title":"Modeling and Mapping Basal Area of Pinus Taeda L. Plantation Using Airborne LiDAR Data","volume":"89","author":"Silva","year":"2017","journal-title":"An. Acad. Bras. Cienc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Spriggs, R.A., Vanderwel, M.C., Jones, T.A., Caspersen, J.P., and Coomes, D.A. (2019). A Critique of General Allometry-Inspired Models for Estimating Forest Carbon Density from Airborne LiDAR. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215238"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/02827580410019472","article-title":"Prediction of Tree Height, Basal Area and Stem Volume in Forest Stands Using Airborne Laser Scanning","volume":"19","author":"Holmgren","year":"2004","journal-title":"Scand. J. For. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/S0034-4257(98)00091-1","article-title":"Use of Large-Footprint Scanning Airborne Lidar to Estimate Forest Stand Characteristics in the Western Cascades of Oregon","volume":"67","author":"Means","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D., and Woods, M. (2013). A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach, Canadian Wood Fibre Centre.","DOI":"10.5558\/tfc2013-132"},{"key":"ref_32","unstructured":"Laes, C.D., Reutebuch, S.E., Mcgaughey, R.J., and Mitchell, B. (2021, March 03). Guidelines to Estimate Forest Inventory Parameters from Lidar and Field Plot Data, Available online: https:\/\/fsapps.nwcg.gov\/gtac\/CourseDownloads\/Reimbursables\/FY21\/Lidar_Material\/GTAC_Guidelines%20to%20estimate%20forest%20inventory%20parameters%20from%20lidar%20and%20field%20plot%20data.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.01.037","article-title":"Estimating Aboveground Biomass and Forest Canopy Cover with Simulated ICESat-2 Data","volume":"224","author":"Narine","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hogland, J., Anderson, N., St. Peter, J., Drake, J., and Medley, P. (2018). Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040140"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.rse.2007.10.009","article-title":"Nearest Neighbor Imputation of Species-Level, Plot-Scale Forest Structure Attributes from LiDAR Data","volume":"112","author":"Hudak","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9469","DOI":"10.1080\/01431161.2020.1800125","article-title":"Vegetation Mapping of No Name Key, Florida Using Lidar and Multispectral Remote Sensing","volume":"41","author":"Kim","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1080\/07038992.2016.1196582","article-title":"Imputation of Individual Longleaf Pine (Pinus Palustris Mill.) Tree Attributes from Field and LiDAR Data","volume":"42","author":"Silva","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_39","unstructured":"Hudak, A.T., Evans, J.S., Crookston, N.L., Falkowski, M.J., Steigers, B.K., Taylor, R., and Hemingway, H. (2007, January 13\u201315). Aggregating Pixel-Level Basal Area Predictions Derived from LiDAR Data to Industrial Forest Stands in North-Central Idaho. Proceedings of the Third Forest Vegetation Simulator Conference, Fort Collins, CO, USA."},{"key":"ref_40","unstructured":"Hudak, A.T., Evans, J.S., Falkowski, M.J., Crookston, N.L., Gessler, P.E., Morgan, P., and Smith, A.M.S. (2005, January 14\u201316). Predicting Plot Basal Area and Tree Density in Mixed-Conifer Forest from Lidar and Advanced Land Imager (ALI) Data. Proceedings of the 26th Canadian Symposium on Remote Sensing, Wolfville, NS, Canada."},{"key":"ref_41","first-page":"551","article-title":"Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot-Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA","volume":"50","author":"Popescu","year":"2004","journal-title":"For. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth\u2019s Forests and Topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_43","unstructured":"Smith, L., and Sidney, L. (2021, March 03). Ecoregions of Alabama and Georgia. Available online: http:\/\/ecologicalregions.info\/data\/ga\/alga_front.pdf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1111\/j.1467-8306.1987.tb00149.x","article-title":"Map Supplement Ecoregions of the Conterminous United","volume":"77","author":"Omernik","year":"2014","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_45","unstructured":"Range, B., and Lawrence, S. (2021, March 03). Level III Ecoregions of Alaska, Available online: https:\/\/gaftp.epa.gov\/EPADataCommons\/ORD\/Ecoregions\/ak\/ak_eco.pdf."},{"key":"ref_46","unstructured":"(2021, April 04). ESRI \u201cWorld Imagery\u201d [Basemap]. Available online: https:\/\/pro.arcgis.com\/en\/pro-app\/2.8\/help\/mapping\/map-authoring\/author-a-basemap.htm."},{"key":"ref_47","unstructured":"Matney, T. (2021, February 24). TCruise. Available online: https:\/\/landmarkspatialsolutions.com\/forest-inventory-software."},{"key":"ref_48","unstructured":"(2021, March 09). United States Geolotical Survey (USGS); US Department of Interior 3D Elevation Program, Available online: https:\/\/www.usgs.gov\/core-science-systems\/ngp\/3dep."},{"key":"ref_49","unstructured":"(2021, February 26). UGET. Available online: https:\/\/ugetdm.com\/."},{"key":"ref_50","unstructured":"Drusch, M., Gascon, F., and Berger, M. (2010). Sentinel-2 Mission Requirements Document, ESA."},{"key":"ref_51","unstructured":"Isenburg, M. (2021, April 20). LAStools: Award-Winning Software for Rapid LiDAR Processing. Available online: http:\/\/lastools.org\/."},{"key":"ref_52","unstructured":"Mcgaughey, R.J. (2017). Aagriculture FUSION\/LDV: Software for LIDAR Data Analysis and Visualization, U.S. Department of Agriculture, Forest, Service, Pacific Northwest Research Station, University of Washington."},{"key":"ref_53","unstructured":"(2021, February 03). ESRI ArcGIS Pro. Available online: https:\/\/www.esri.com\/en-us\/arcgis\/products\/arcgis-pro\/overview."},{"key":"ref_54","unstructured":"(2021, May 22). L3Harris Exelis Visual Information Solutions. Available online: https:\/\/www.l3harrisgeospatial.com\/Software-Technology\/ENVI."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Dinstein","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_56","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Core Team."},{"key":"ref_57","unstructured":"Lumley, T. (2021, June 14). Package \u2018Leaps\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/leaps\/leaps.pdf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.2307\/1911963","article-title":"A Simple Test for Heteroscedasticity and Random Coefficient Variation","volume":"47","author":"Breusch","year":"1979","journal-title":"Econometrica"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3390\/rs70100229","article-title":"Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest","volume":"7","author":"Sheridan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_60","unstructured":"Freeman, A.E., Frescino, T., and Freeman, M.E. (2021, June 16). Package \u2018ModelMap\u2019. Available online: https:\/\/cran.r-project."},{"key":"ref_61","unstructured":"Gareth, J., Witten, D., Hastie, T., and Tibshirani, R. (2014). An Introduction to Statistical Learning: With Applications in R., Springer Publishing Company, Incorporated."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Dorado-Roda, I., Pascual, A., Godinho, S., Silva, C.A., Botequim, B., Rodr\u00edguez-Gonz\u00e1lvez, P., Gonz\u00e1lez-Ferreiro, E., and Guerra-Hern\u00e1ndez, J. (2021). Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens., 13.","DOI":"10.3390\/rs13122279"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1016\/j.rse.2009.03.006","article-title":"Lidar-Based Mapping of Leaf Area Index and Its Use for Validating GLOBCARBON Satellite LAI Product in a Temperate Forest of the Southern USA","volume":"113","author":"Zhao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_64","unstructured":"Van Etten, J., Sumner, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., Busetto, L., Canty, M., Fasoli, B., and Forrest, D. (2021, November 15). Package \u2018Raster\u2019 R Topics Documented. Available online: https:\/\/rspatial.org\/raster."},{"key":"ref_65","first-page":"42","article-title":"A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area","volume":"4","author":"Gagliasso","year":"2014","journal-title":"Open J. For."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"S99","DOI":"10.5589\/m13-027","article-title":"Predicting Live and Dead Tree Basal Area of Bark Beetle Affected Forests from Discrete-Return Lidar","volume":"39","author":"Bright","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1139\/cjfr-2014-0405","article-title":"Temporal Transferability of LiDAR-Based Imputation of Forest Inventory Attributes","volume":"45","author":"Fekety","year":"2015","journal-title":"Can. J. For. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2708\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:24:38Z","timestamp":1760138678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,4]]},"references-count":67,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112708"],"URL":"https:\/\/doi.org\/10.3390\/rs14112708","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,4]]}}}