Refining medium resolution fractional cover for arid Australia to detect vegetation dynamics and wind erosion susceptibility on longitudinal dunes
Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study si...
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| description | Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’ and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous.
A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of fPV and fNPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data.
The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (fBS), 6.5% (fPV) and 15.8% (fNPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as fPV, and 3–9 months later as a peak in fNPV. Components of fPV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of fNPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover |
| doi_str_mv | 10.1016/j.rse.2021.112647 |
| format | Article |
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A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of fPV and fNPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data.
The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (fBS), 6.5% (fPV) and 15.8% (fNPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as fPV, and 3–9 months later as a peak in fNPV. Components of fPV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of fNPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2).
•Arid fractional vegetation cover was unmixed from Landsat and Sentinel-2 data.•Pulse-reserve behaviour was detected as coupled peaks in green then dry vegetation.•Decomposed time series revealed multiple time scales of rainfall response.•30 m resolution or less is required to detect wind erosion risk on vegetated dunes.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2021.112647</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Aeolian ; Arid ; Arid zones ; Aridity ; Dormancy ; Drift ; Dunes ; Ecosystem dynamics ; Estimates ; Fires ; Fractional cover ; Ground cover ; Image processing ; Image resolution ; Landforms ; Landsat ; Landsat satellites ; Photosynthesis ; Rainfall ; Rainfall patterns ; Remote sensing ; Root-mean-square errors ; Sand ; Satellites ; Scale models ; Senescence ; Sentinel-2 ; Spectral unmixing ; Time series ; Vegetation ; Vegetation cover ; Vegetation growth ; Wind erosion</subject><ispartof>Remote sensing of environment, 2021-11, Vol.265, p.112647, Article 112647</ispartof><rights>2021 Elsevier Inc.</rights><rights>Copyright Elsevier BV Nov 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c255t-694bc21579c8142404ac6cf09540170819f7b384d42a07683620dc5c9091038d3</citedby><cites>FETCH-LOGICAL-c255t-694bc21579c8142404ac6cf09540170819f7b384d42a07683620dc5c9091038d3</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.2021.112647$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Shumack, Samuel</creatorcontrib><creatorcontrib>Fisher, Adrian</creatorcontrib><creatorcontrib>Hesse, Paul P.</creatorcontrib><title>Refining medium resolution fractional cover for arid Australia to detect vegetation dynamics and wind erosion susceptibility on longitudinal dunes</title><title>Remote sensing of environment</title><description>Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’ and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous.
A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of fPV and fNPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data.
The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (fBS), 6.5% (fPV) and 15.8% (fNPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as fPV, and 3–9 months later as a peak in fNPV. Components of fPV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of fNPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2).
•Arid fractional vegetation cover was unmixed from Landsat and Sentinel-2 data.•Pulse-reserve behaviour was detected as coupled peaks in green then dry vegetation.•Decomposed time series revealed multiple time scales of rainfall response.•30 m resolution or less is required to detect wind erosion risk on vegetated dunes.</description><subject>Aeolian</subject><subject>Arid</subject><subject>Arid zones</subject><subject>Aridity</subject><subject>Dormancy</subject><subject>Drift</subject><subject>Dunes</subject><subject>Ecosystem dynamics</subject><subject>Estimates</subject><subject>Fires</subject><subject>Fractional cover</subject><subject>Ground cover</subject><subject>Image processing</subject><subject>Image resolution</subject><subject>Landforms</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Photosynthesis</subject><subject>Rainfall</subject><subject>Rainfall patterns</subject><subject>Remote sensing</subject><subject>Root-mean-square errors</subject><subject>Sand</subject><subject>Satellites</subject><subject>Scale models</subject><subject>Senescence</subject><subject>Sentinel-2</subject><subject>Spectral unmixing</subject><subject>Time series</subject><subject>Vegetation</subject><subject>Vegetation cover</subject><subject>Vegetation growth</subject><subject>Wind erosion</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kM2KFDEQx4MoOO76AHsLeO6xKp10uvG0LLoKC4K455BJqocMPZ0xSY_Ma_jEm3Y8e6kqqupfHz_G7hC2CNh9PGxTpq0AgVtE0Un9im2w10MDGuRrtgFoZSOF0m_Zu5wPAKh6jRv25weNYQ7znh_Jh-XIE-U4LSXEmY_JujWwE3fxTImPMXGbguf3Sy7JTsHyErmnQq7wM-2p2L9Cf5ntMbjM7ez571ANpZjXSl6yo1MJuzCFcuE1M8V5H8riw7rGLzPlW_ZmtFOm9__8DXv-8vnnw9fm6fvjt4f7p8YJpUrTDXLnBCo9uB6lkCCt69wIg5KAGnocRr1re-mlsKC7vu0EeKfcAANC2_v2hn24zj2l-GuhXMwhLqmekY1QfYvYgcLahdcuV1_IiUZzSuFo08UgmBW9OZiK3qzozRV91Xy6aqiefw6UTHaBZlcJp4rK-Bj-o34B1k6OJg</recordid><startdate>202111</startdate><enddate>202111</enddate><creator>Shumack, Samuel</creator><creator>Fisher, Adrian</creator><creator>Hesse, Paul P.</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>202111</creationdate><title>Refining medium resolution fractional cover for arid Australia to detect vegetation dynamics and wind erosion susceptibility on longitudinal dunes</title><author>Shumack, Samuel ; Fisher, Adrian ; Hesse, Paul P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c255t-694bc21579c8142404ac6cf09540170819f7b384d42a07683620dc5c9091038d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aeolian</topic><topic>Arid</topic><topic>Arid zones</topic><topic>Aridity</topic><topic>Dormancy</topic><topic>Drift</topic><topic>Dunes</topic><topic>Ecosystem dynamics</topic><topic>Estimates</topic><topic>Fires</topic><topic>Fractional cover</topic><topic>Ground cover</topic><topic>Image processing</topic><topic>Image resolution</topic><topic>Landforms</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>Photosynthesis</topic><topic>Rainfall</topic><topic>Rainfall patterns</topic><topic>Remote sensing</topic><topic>Root-mean-square errors</topic><topic>Sand</topic><topic>Satellites</topic><topic>Scale models</topic><topic>Senescence</topic><topic>Sentinel-2</topic><topic>Spectral unmixing</topic><topic>Time series</topic><topic>Vegetation</topic><topic>Vegetation cover</topic><topic>Vegetation growth</topic><topic>Wind erosion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shumack, Samuel</creatorcontrib><creatorcontrib>Fisher, Adrian</creatorcontrib><creatorcontrib>Hesse, Paul P.</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>Shumack, Samuel</au><au>Fisher, Adrian</au><au>Hesse, Paul P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Refining medium resolution fractional cover for arid Australia to detect vegetation dynamics and wind erosion susceptibility on longitudinal dunes</atitle><jtitle>Remote sensing of environment</jtitle><date>2021-11</date><risdate>2021</risdate><volume>265</volume><spage>112647</spage><pages>112647-</pages><artnum>112647</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’ and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous.
A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of fPV and fNPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data.
The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (fBS), 6.5% (fPV) and 15.8% (fNPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as fPV, and 3–9 months later as a peak in fNPV. Components of fPV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of fNPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2).
•Arid fractional vegetation cover was unmixed from Landsat and Sentinel-2 data.•Pulse-reserve behaviour was detected as coupled peaks in green then dry vegetation.•Decomposed time series revealed multiple time scales of rainfall response.•30 m resolution or less is required to detect wind erosion risk on vegetated dunes.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2021.112647</doi></addata></record> |
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| subjects | Aeolian Arid Arid zones Aridity Dormancy Drift Dunes Ecosystem dynamics Estimates Fires Fractional cover Ground cover Image processing Image resolution Landforms Landsat Landsat satellites Photosynthesis Rainfall Rainfall patterns Remote sensing Root-mean-square errors Sand Satellites Scale models Senescence Sentinel-2 Spectral unmixing Time series Vegetation Vegetation cover Vegetation growth Wind erosion |
| title | Refining medium resolution fractional cover for arid Australia to detect vegetation dynamics and wind erosion susceptibility on longitudinal dunes |
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