{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:32:06Z","timestamp":1775179926550,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T00:00:00Z","timestamp":1534982400000},"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>Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha      \u2212 1      with a standard error of 11.17 Mgha      \u2212 1      (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha      \u2212 1      with a slightly smaller standard error of 10.57 Mgha      \u2212 1      (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha      \u2212 1     . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision.<\/jats:p>","DOI":"10.3390\/rs10091344","type":"journal-article","created":{"date-parts":[[2018,8,24]],"date-time":"2018-08-24T03:42:31Z","timestamp":1535082151000},"page":"1344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Evaluating the Potential of ALS Data to Increase the Efficiency of Aboveground Biomass Estimates in Tropical Peat\u2013Swamp Forests"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3777-5478","authenticated-orcid":false,"given":"Paul","family":"Magdon","sequence":"first","affiliation":[{"name":"Forest Inventory and Remote Sensing, Burckhardt Institute, Faculty of Forest Sciences and Forest Ecology, University of G\u00f6ttingen, B\u00fcsgenweg 5, 37077 G\u00f6ttingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4565-2155","authenticated-orcid":false,"given":"Eduardo","family":"Gonz\u00e1lez-Ferreiro","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Geom\u00e1tica e Ingenier\u00eda Cartogr\u00e1fica (GI 202-GEOINCA)\u2014Departamento de Tecnolog\u00eda Minera, Topograf\u00eda y de Estructuras, Universidad de Le\u00f3n, Av. de Astorga s\/n, 24401 Ponferrada, Spain"},{"name":"Unidade de Xesti\u00f3n Forestal Sostible (UXFS)\u2014Departamento de Enxe\u00f1ar\u00eda Agroforestal, Universidade de Santiago de Compostela, R\/Benigno Ledo s\/n, 27002 Lugo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9878-7678","authenticated-orcid":false,"given":"C\u00e9sar","family":"P\u00e9rez-Cruzado","sequence":"additional","affiliation":[{"name":"Forest Inventory and Remote Sensing, Burckhardt Institute, Faculty of Forest Sciences and Forest Ecology, University of G\u00f6ttingen, B\u00fcsgenweg 5, 37077 G\u00f6ttingen, Germany"},{"name":"Unidade de Xesti\u00f3n Forestal Sostible (UXFS)\u2014Departamento de Producti\u00f3n Vexetal e Proxectos de Enxe\u00f1ar\u00eda, Universidade de Santiago de Compostela, R\/Benigno Ledo s\/n, 27002 Lugo, Spain"}]},{"given":"Edwine Setia","family":"Purnama","sequence":"additional","affiliation":[{"name":"Forest Inventory and Remote Sensing, Burckhardt Institute, Faculty of Forest Sciences and Forest Ecology, University of G\u00f6ttingen, B\u00fcsgenweg 5, 37077 G\u00f6ttingen, Germany"}]},{"given":"Damayanti","family":"Sarodja","sequence":"additional","affiliation":[{"name":"Forest Inventory and Remote Sensing, Burckhardt Institute, Faculty of Forest Sciences and Forest Ecology, University of G\u00f6ttingen, B\u00fcsgenweg 5, 37077 G\u00f6ttingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8625-3492","authenticated-orcid":false,"given":"Christoph","family":"Kleinn","sequence":"additional","affiliation":[{"name":"Forest Inventory and Remote Sensing, Burckhardt Institute, Faculty of Forest Sciences and Forest Ecology, University of G\u00f6ttingen, B\u00fcsgenweg 5, 37077 G\u00f6ttingen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1046\/j.1466-822X.2003.00010.x","article-title":"Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: Factors affecting the generality of relationships","volume":"12","author":"Drake","year":"2003","journal-title":"Glob. 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