Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland
Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-...
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description | Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands tn0020 smaller extents. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, have the highest producer's accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.
•Rangeland function is influenced by fine-scale patterns of species & soil |
doi_str_mv | 10.1016/j.rse.2020.112223 |
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•Rangeland function is influenced by fine-scale patterns of species & soil nutrients.•Range plant-soil-nutrient dynamics require very high spatial & spectral resolution.•UAV multispectral-photogrammetry fusion excels at functional cover classification.•UAV hyperspectral-LiDAR fusion excels at species & soil fertility classification.•LiDAR data detect soil fertility changes from ecological disturbance of fire.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2020.112223</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Aerial surveys ; Airborne data ; Airborne sensing ; Carbon ; Carbon cycle ; Carbon sequestration ; Change detection ; Controlled burning ; Creosote ; Degradation ; Deserts ; Digital elevation model (DEM) ; Digital elevation model of difference (DOD) ; Drone ; Ecosystem services ; Encroachment ; Erosion processes ; Fire ; Flowers & plants ; Grass ; Grasses ; Grasslands ; Hyperspectral ; Indigenous species ; Islands of fertility ; Lidar ; Machine learning ; Nutrient ; Nutrient cycles ; Nutrient dynamics ; Nutrients ; Photogrammetry ; Photography ; Plant species ; Prescribed fire ; Primary production ; Rangeland ; Rangelands ; Remote sensing ; Shrub ; Soil ; Soil dynamics ; Soil erosion ; Soil fertility ; Soil nutrients ; Soil surfaces ; Species classification ; Structure from motion (SFM) ; Terrestrial laser scanning ; Unmanned aerial system (UAS) ; Unmanned aerial vehicle (UAV) ; Unmanned aerial vehicles</subject><ispartof>Remote sensing of environment, 2021-02, Vol.253, p.112223, Article 112223</ispartof><rights>2020</rights><rights>Copyright Elsevier BV Feb 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-7ba63c27c6c45a60c0967b7010002d5a2a8276c2ca18497b8ae52498a24855ec3</citedby><cites>FETCH-LOGICAL-c368t-7ba63c27c6c45a60c0967b7010002d5a2a8276c2ca18497b8ae52498a24855ec3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2020.112223$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Sankey, Joel B.</creatorcontrib><creatorcontrib>Sankey, Temuulen T.</creatorcontrib><creatorcontrib>Li, Junran</creatorcontrib><creatorcontrib>Ravi, Sujith</creatorcontrib><creatorcontrib>Wang, Guan</creatorcontrib><creatorcontrib>Caster, Joshua</creatorcontrib><creatorcontrib>Kasprak, Alan</creatorcontrib><title>Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland</title><title>Remote sensing of environment</title><description>Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands tn0020 smaller extents. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, have the highest producer's accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.
•Rangeland function is influenced by fine-scale patterns of species & soil nutrients.•Range plant-soil-nutrient dynamics require very high spatial & spectral resolution.•UAV multispectral-photogrammetry fusion excels at functional cover classification.•UAV hyperspectral-LiDAR fusion excels at species & soil fertility classification.•LiDAR data detect soil fertility changes from ecological disturbance of fire.</description><subject>Aerial surveys</subject><subject>Airborne data</subject><subject>Airborne sensing</subject><subject>Carbon</subject><subject>Carbon cycle</subject><subject>Carbon sequestration</subject><subject>Change detection</subject><subject>Controlled burning</subject><subject>Creosote</subject><subject>Degradation</subject><subject>Deserts</subject><subject>Digital elevation model (DEM)</subject><subject>Digital elevation model of difference (DOD)</subject><subject>Drone</subject><subject>Ecosystem services</subject><subject>Encroachment</subject><subject>Erosion processes</subject><subject>Fire</subject><subject>Flowers & plants</subject><subject>Grass</subject><subject>Grasses</subject><subject>Grasslands</subject><subject>Hyperspectral</subject><subject>Indigenous species</subject><subject>Islands of fertility</subject><subject>Lidar</subject><subject>Machine learning</subject><subject>Nutrient</subject><subject>Nutrient cycles</subject><subject>Nutrient dynamics</subject><subject>Nutrients</subject><subject>Photogrammetry</subject><subject>Photography</subject><subject>Plant species</subject><subject>Prescribed fire</subject><subject>Primary production</subject><subject>Rangeland</subject><subject>Rangelands</subject><subject>Remote sensing</subject><subject>Shrub</subject><subject>Soil</subject><subject>Soil dynamics</subject><subject>Soil erosion</subject><subject>Soil fertility</subject><subject>Soil nutrients</subject><subject>Soil surfaces</subject><subject>Species classification</subject><subject>Structure from motion (SFM)</subject><subject>Terrestrial laser scanning</subject><subject>Unmanned aerial system (UAS)</subject><subject>Unmanned aerial vehicle (UAV)</subject><subject>Unmanned aerial vehicles</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9UcuOEzEQtBBIhMAHcLPEdR1sz8MOnKKFBaRICMRytXpsZ8bRjD3YHqT5Sb4JZwMcOXW3uqq61IXQS0Z3jLL29XkXk91xysvMOOfVI7RhUuwJFbR-jDaUVjWpeSOeomcpnSlljRRsg359WcBnd1qd7_E8lp6k4Ebilxyd9Rmb1cPkdMLO4wi-twVj0ht8tyQXPA4nfH_4jod1tjHNVucIIzm6d4evNw-LaRmz-7eYh5BDH2GabI7rDS5SuI9h8YZ0kKzBD0xiXO8yjPgvfB7Wy3nAaYhLR6zXMYAeCt7YZGMuGpDSxdhz9OQEY7Iv_tQtur97_-32Izl-_vDp9nAkumplJqKDttJc6FbXDbRU030rOkEZpZSbBjhILlrNNTBZ70UnwTa83kvgtWwaq6stenXVnWP4sdiU1Tks0ZeTqkAkr5uqfHyL2BVV_KYU7UnN0U0QV8WousSmzqrEpi6xqWtshfP2yrHF_k9no0q6BKGtcbF8UZng_sP-DbKno-w</recordid><startdate>202102</startdate><enddate>202102</enddate><creator>Sankey, Joel B.</creator><creator>Sankey, Temuulen T.</creator><creator>Li, Junran</creator><creator>Ravi, Sujith</creator><creator>Wang, Guan</creator><creator>Caster, Joshua</creator><creator>Kasprak, Alan</creator><general>Elsevier Inc</general><general>Elsevier BV</general><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></search><sort><creationdate>202102</creationdate><title>Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland</title><author>Sankey, Joel B. ; Sankey, Temuulen T. ; Li, Junran ; Ravi, Sujith ; Wang, Guan ; Caster, Joshua ; Kasprak, Alan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-7ba63c27c6c45a60c0967b7010002d5a2a8276c2ca18497b8ae52498a24855ec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aerial surveys</topic><topic>Airborne data</topic><topic>Airborne sensing</topic><topic>Carbon</topic><topic>Carbon cycle</topic><topic>Carbon sequestration</topic><topic>Change detection</topic><topic>Controlled burning</topic><topic>Creosote</topic><topic>Degradation</topic><topic>Deserts</topic><topic>Digital elevation model (DEM)</topic><topic>Digital elevation model of difference (DOD)</topic><topic>Drone</topic><topic>Ecosystem services</topic><topic>Encroachment</topic><topic>Erosion processes</topic><topic>Fire</topic><topic>Flowers & plants</topic><topic>Grass</topic><topic>Grasses</topic><topic>Grasslands</topic><topic>Hyperspectral</topic><topic>Indigenous species</topic><topic>Islands of fertility</topic><topic>Lidar</topic><topic>Machine learning</topic><topic>Nutrient</topic><topic>Nutrient cycles</topic><topic>Nutrient dynamics</topic><topic>Nutrients</topic><topic>Photogrammetry</topic><topic>Photography</topic><topic>Plant species</topic><topic>Prescribed fire</topic><topic>Primary production</topic><topic>Rangeland</topic><topic>Rangelands</topic><topic>Remote sensing</topic><topic>Shrub</topic><topic>Soil</topic><topic>Soil dynamics</topic><topic>Soil erosion</topic><topic>Soil fertility</topic><topic>Soil nutrients</topic><topic>Soil surfaces</topic><topic>Species classification</topic><topic>Structure from motion (SFM)</topic><topic>Terrestrial laser scanning</topic><topic>Unmanned aerial system (UAS)</topic><topic>Unmanned aerial vehicle (UAV)</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sankey, Joel B.</creatorcontrib><creatorcontrib>Sankey, Temuulen T.</creatorcontrib><creatorcontrib>Li, Junran</creatorcontrib><creatorcontrib>Ravi, Sujith</creatorcontrib><creatorcontrib>Wang, Guan</creatorcontrib><creatorcontrib>Caster, Joshua</creatorcontrib><creatorcontrib>Kasprak, Alan</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sankey, Joel B.</au><au>Sankey, Temuulen T.</au><au>Li, Junran</au><au>Ravi, Sujith</au><au>Wang, Guan</au><au>Caster, Joshua</au><au>Kasprak, Alan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland</atitle><jtitle>Remote sensing of environment</jtitle><date>2021-02</date><risdate>2021</risdate><volume>253</volume><spage>112223</spage><pages>112223-</pages><artnum>112223</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands tn0020 smaller extents. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, have the highest producer's accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.
•Rangeland function is influenced by fine-scale patterns of species & soil nutrients.•Range plant-soil-nutrient dynamics require very high spatial & spectral resolution.•UAV multispectral-photogrammetry fusion excels at functional cover classification.•UAV hyperspectral-LiDAR fusion excels at species & soil fertility classification.•LiDAR data detect soil fertility changes from ecological disturbance of fire.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2020.112223</doi><oa>free_for_read</oa></addata></record> |
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subjects | Aerial surveys Airborne data Airborne sensing Carbon Carbon cycle Carbon sequestration Change detection Controlled burning Creosote Degradation Deserts Digital elevation model (DEM) Digital elevation model of difference (DOD) Drone Ecosystem services Encroachment Erosion processes Fire Flowers & plants Grass Grasses Grasslands Hyperspectral Indigenous species Islands of fertility Lidar Machine learning Nutrient Nutrient cycles Nutrient dynamics Nutrients Photogrammetry Photography Plant species Prescribed fire Primary production Rangeland Rangelands Remote sensing Shrub Soil Soil dynamics Soil erosion Soil fertility Soil nutrients Soil surfaces Species classification Structure from motion (SFM) Terrestrial laser scanning Unmanned aerial system (UAS) Unmanned aerial vehicle (UAV) Unmanned aerial vehicles |
title | Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland |
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