Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar
Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of...
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description | Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R2 = 0.81, RMSE = 1.09 kg m−2) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R2 = 0.74, RMSE = 1.26 kg m−2), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m−2) to the G-LiHT SfM point cloud (500–2000 pts. m−2) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
•We map tall shrub biomass in Alaska using structure-from-motion (SfM) and lidar.•UAV and G-LiHT SfM biomass models were similar in precision compared to lidar.•G-LiHT SfM point clouds contain similar information to denser UAV point clouds.•RGB color information helps SfM models retain sensitivity to high shrub biomass.•Accurate maps required distinct models for upper montane and subalpine ecosys |
doi_str_mv | 10.1016/j.rse.2020.111841 |
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•We map tall shrub biomass in Alaska using structure-from-motion (SfM) and lidar.•UAV and G-LiHT SfM biomass models were similar in precision compared to lidar.•G-LiHT SfM point clouds contain similar information to denser UAV point clouds.•RGB color information helps SfM models retain sensitivity to high shrub biomass.•Accurate maps required distinct models for upper montane and subalpine ecosystems.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2020.111841</identifier><language>eng</language><publisher>Goddard Space Flight Center: Elsevier Inc</publisher><subject>Alaska ; Alnus ; Biomass ; Boreal forest ; Calibration ; Density ; Earth Resources And Remote Sensing ; G-LiHT ; High resolution ; Image acquisition ; Image resolution ; Lidar ; Mapping ; Photogrammetry ; Pixels ; Polar environments ; Salix ; Shrub ; Shrubs ; Structure-from-motion ; Subalpine environments ; Terrain models ; Three dimensional models ; UAV ; Unmanned aerial vehicles</subject><ispartof>Remote sensing of environment, 2020-08, Vol.245, p.111841, Article 111841</ispartof><rights>2020 Elsevier Inc.</rights><rights>Copyright Determination: MAY_INCLUDE_COPYRIGHT_MATERIAL</rights><rights>Copyright Elsevier BV Aug 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-84ff47b81a92140db5e869d3f26158e35a425c0cf9076a3bc70fc227cf4e77943</citedby><cites>FETCH-LOGICAL-c389t-84ff47b81a92140db5e869d3f26158e35a425c0cf9076a3bc70fc227cf4e77943</cites><orcidid>0000-0001-9409-2740</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2020.111841$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Alonzo, Michael</creatorcontrib><creatorcontrib>Dial, Roman J.</creatorcontrib><creatorcontrib>Schulz, Bethany K.</creatorcontrib><creatorcontrib>Andersen, Hans-Erik</creatorcontrib><creatorcontrib>Lewis-Clark, Eric</creatorcontrib><creatorcontrib>Cook, Bruce D.</creatorcontrib><creatorcontrib>Morton, Douglas C.</creatorcontrib><title>Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar</title><title>Remote sensing of environment</title><description>Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R2 = 0.81, RMSE = 1.09 kg m−2) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R2 = 0.74, RMSE = 1.26 kg m−2), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m−2) to the G-LiHT SfM point cloud (500–2000 pts. m−2) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
•We map tall shrub biomass in Alaska using structure-from-motion (SfM) and lidar.•UAV and G-LiHT SfM biomass models were similar in precision compared to lidar.•G-LiHT SfM point clouds contain similar information to denser UAV point clouds.•RGB color information helps SfM models retain sensitivity to high shrub biomass.•Accurate maps required distinct models for upper montane and subalpine ecosystems.</description><subject>Alaska</subject><subject>Alnus</subject><subject>Biomass</subject><subject>Boreal forest</subject><subject>Calibration</subject><subject>Density</subject><subject>Earth Resources And Remote Sensing</subject><subject>G-LiHT</subject><subject>High resolution</subject><subject>Image acquisition</subject><subject>Image resolution</subject><subject>Lidar</subject><subject>Mapping</subject><subject>Photogrammetry</subject><subject>Pixels</subject><subject>Polar environments</subject><subject>Salix</subject><subject>Shrub</subject><subject>Shrubs</subject><subject>Structure-from-motion</subject><subject>Subalpine environments</subject><subject>Terrain models</subject><subject>Three dimensional models</subject><subject>UAV</subject><subject>Unmanned aerial vehicles</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>CYI</sourceid><recordid>eNp9kE1r3DAQhkVoINskPyDQg6BnbyRbtmx6CqFpCxt6ac5iLEuJtrblzsiF_ffR4p57mWF4552Ph7E7KfZSyOb-uEdy-1KUuZayVfKC7WSru0JooT6wnRCVKlRZ6yv2kegohKxbLXdsfIZlCfMrTzCOnN5w7Xkf4gREPMz8YQT6DRwSH2EeyMLieI6j4yudXZRwtWlFV3iMUzHFFOLMl7eY4ivCNLmEJ56dfAwD4A279DCSu_2Xr9nL09dfj9-Lw89vPx4fDoWt2i4VrfJe6b6V0JVSiaGvXdt0Q-XLJl_tqhryI1ZY3wndQNVbLbwtS229clp3qrpmn7e5C8Y_q6NkjnHFOa80pVKiqzKfNnfJrctiJELnzYJhAjwZKcwZqjmaDNWcoZoNavZ82jwzEJg5IZ1lmXHmy5ssf9lkl7_7GxwassHN1g0BnU1miOE_w98B5KyImA</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Alonzo, Michael</creator><creator>Dial, Roman J.</creator><creator>Schulz, Bethany K.</creator><creator>Andersen, Hans-Erik</creator><creator>Lewis-Clark, Eric</creator><creator>Cook, Bruce D.</creator><creator>Morton, Douglas C.</creator><general>Elsevier Inc</general><general>Elsevier</general><general>Elsevier BV</general><scope>CYE</scope><scope>CYI</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0001-9409-2740</orcidid></search><sort><creationdate>20200801</creationdate><title>Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar</title><author>Alonzo, Michael ; 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In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R2 = 0.81, RMSE = 1.09 kg m−2) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R2 = 0.74, RMSE = 1.26 kg m−2), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m−2) to the G-LiHT SfM point cloud (500–2000 pts. m−2) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
•We map tall shrub biomass in Alaska using structure-from-motion (SfM) and lidar.•UAV and G-LiHT SfM biomass models were similar in precision compared to lidar.•G-LiHT SfM point clouds contain similar information to denser UAV point clouds.•RGB color information helps SfM models retain sensitivity to high shrub biomass.•Accurate maps required distinct models for upper montane and subalpine ecosystems.</abstract><cop>Goddard Space Flight Center</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2020.111841</doi><orcidid>https://orcid.org/0000-0001-9409-2740</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Alaska Alnus Biomass Boreal forest Calibration Density Earth Resources And Remote Sensing G-LiHT High resolution Image acquisition Image resolution Lidar Mapping Photogrammetry Pixels Polar environments Salix Shrub Shrubs Structure-from-motion Subalpine environments Terrain models Three dimensional models UAV Unmanned aerial vehicles |
title | Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar |
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