Measuring the dead component of mixed grassland with Landsat imagery
Detecting non-photosynthetic materials challenges multispectral remote sensing of vegetation. Dead component of grasslands, including litter and standing dead material as the accumulated phytomass of grassland productivity from previous years, is a primary connection in nutrient cycles in grasslands...
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description | Detecting non-photosynthetic materials challenges multispectral remote sensing of vegetation. Dead component of grasslands, including litter and standing dead material as the accumulated phytomass of grassland productivity from previous years, is a primary connection in nutrient cycles in grasslands, and also conserves soil moisture and redistributes the grassland surface temperature. However, unlike green vegetation, it is hard to estimate the dead component with remote sensing approaches because the spectral signal of dead materials is similar to that of bare soil or soil crust (moss and lichen), with the only difference in the shortwave infrared region near 2000nm. In the literature, the Cellulose Absorption Index (CAI), an index based on hyperspectral imagery, is the most popular index for assessing dead cover or dead biomass. However, hyperspectral imagery is still not available for most grasslands worldwide. Therefore, a method to assess dead component using multispectral band imagery (e.g. Landsat imagery in this study) is needed. Grasslands National Park (GNP) in the southern part of Saskatchewan, Canada, has a large amount of accumulated dead vegetation because of long term conservation actions, which makes it a good study area for this project. This study aims to explore the relation between NDVI and dead cover, to investigate how different amounts of dead material change the relation of total biomass and NDVI, and also to test the potential to estimate dead cover using multispectral images. The results show that NDVI and dead cover have positive relationship when dead cover is less than 20%, no correlation when dead cover is between 20 and 80%, and significant negative relation when dead cover is more than 80%; further, the relation of total biomass and NDVI also changes with the same thresholds. The results also indicate that the dead component can be estimated with multispectral images using Normalized Burn Ratio (NBR) or Normalized Difference water index (NDWI), but the relationships are highly influenced by bare soil and soil crust.
•Total biomass and NDVI have positive relationship when dead cover is less than 20%.•The relationship reversed when the dead cover is more than 80%.•Dead cover change affects SWIR and NIR more than other bands.•Landsat TM (TM 4, TM5, and TM7) can be used to estimate dead cover in grassland.•Bare soil and soil crust have high effects on dead cover estimation. |
doi_str_mv | 10.1016/j.rse.2013.11.017 |
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•Total biomass and NDVI have positive relationship when dead cover is less than 20%.•The relationship reversed when the dead cover is more than 80%.•Dead cover change affects SWIR and NIR more than other bands.•Landsat TM (TM 4, TM5, and TM7) can be used to estimate dead cover in grassland.•Bare soil and soil crust have high effects on dead cover estimation.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2013.11.017</identifier><identifier>CODEN: RSEEA7</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Animal, plant and microbial ecology ; Applied geophysics ; Biological and medical sciences ; Biomass ; Dead component ; Earth sciences ; Earth, ocean, space ; Estimates ; Exact sciences and technology ; Fundamental and applied biological sciences. Psychology ; General aspects. Techniques ; Grassland ; Grasslands ; Imagery ; Internal geophysics ; NBR ; NDVI ; NDWI ; Non-photosynthetic vegetation ; Remote sensing ; Satellite imagery ; Soil (material) ; Teledetection and vegetation maps ; Total biomass ; Vegetation</subject><ispartof>Remote sensing of environment, 2014-02, Vol.142, p.33-43</ispartof><rights>2013 Elsevier Inc.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c434t-ae5e59ee7111ae0132f6e125be91b67f3d221d95f80350289f259c205dbe93833</citedby><cites>FETCH-LOGICAL-c434t-ae5e59ee7111ae0132f6e125be91b67f3d221d95f80350289f259c205dbe93833</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.2013.11.017$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,782,786,3554,27933,27934,46004</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28268590$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Xu, Dandan</creatorcontrib><creatorcontrib>Guo, Xulin</creatorcontrib><creatorcontrib>Li, Zhaoqin</creatorcontrib><creatorcontrib>Yang, Xiaohui</creatorcontrib><creatorcontrib>Yin, Han</creatorcontrib><title>Measuring the dead component of mixed grassland with Landsat imagery</title><title>Remote sensing of environment</title><description>Detecting non-photosynthetic materials challenges multispectral remote sensing of vegetation. Dead component of grasslands, including litter and standing dead material as the accumulated phytomass of grassland productivity from previous years, is a primary connection in nutrient cycles in grasslands, and also conserves soil moisture and redistributes the grassland surface temperature. However, unlike green vegetation, it is hard to estimate the dead component with remote sensing approaches because the spectral signal of dead materials is similar to that of bare soil or soil crust (moss and lichen), with the only difference in the shortwave infrared region near 2000nm. In the literature, the Cellulose Absorption Index (CAI), an index based on hyperspectral imagery, is the most popular index for assessing dead cover or dead biomass. However, hyperspectral imagery is still not available for most grasslands worldwide. Therefore, a method to assess dead component using multispectral band imagery (e.g. Landsat imagery in this study) is needed. Grasslands National Park (GNP) in the southern part of Saskatchewan, Canada, has a large amount of accumulated dead vegetation because of long term conservation actions, which makes it a good study area for this project. This study aims to explore the relation between NDVI and dead cover, to investigate how different amounts of dead material change the relation of total biomass and NDVI, and also to test the potential to estimate dead cover using multispectral images. The results show that NDVI and dead cover have positive relationship when dead cover is less than 20%, no correlation when dead cover is between 20 and 80%, and significant negative relation when dead cover is more than 80%; further, the relation of total biomass and NDVI also changes with the same thresholds. The results also indicate that the dead component can be estimated with multispectral images using Normalized Burn Ratio (NBR) or Normalized Difference water index (NDWI), but the relationships are highly influenced by bare soil and soil crust.
•Total biomass and NDVI have positive relationship when dead cover is less than 20%.•The relationship reversed when the dead cover is more than 80%.•Dead cover change affects SWIR and NIR more than other bands.•Landsat TM (TM 4, TM5, and TM7) can be used to estimate dead cover in grassland.•Bare soil and soil crust have high effects on dead cover estimation.</description><subject>Animal, plant and microbial ecology</subject><subject>Applied geophysics</subject><subject>Biological and medical sciences</subject><subject>Biomass</subject><subject>Dead component</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>Grassland</subject><subject>Grasslands</subject><subject>Imagery</subject><subject>Internal geophysics</subject><subject>NBR</subject><subject>NDVI</subject><subject>NDWI</subject><subject>Non-photosynthetic vegetation</subject><subject>Remote sensing</subject><subject>Satellite imagery</subject><subject>Soil (material)</subject><subject>Teledetection and vegetation maps</subject><subject>Total biomass</subject><subject>Vegetation</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqFkEtPwzAQhC0EEqXwA7j5gsQlwevESSxOiLdUxAXOlmtvWld5FDsF-u9x1IojnHYPs7MzHyHnwFJgUFytUh8w5QyyFCBlUB6QCVSlTFjJ8kMyYSzLk5yL8pichLBiDERVwoTcvaAOG--6BR2WSC1qS03frvsOu4H2NW3dN1q68DqERneWfrlhSWdxC3qgrtUL9NtTclTrJuDZfk7J-8P92-1TMnt9fL69mSUmz_Ih0ShQSMQSADTGqLwuELiYo4R5UdaZ5RysFHXFMsF4JWsupOFM2KjIqiybksud79r3HxsMg2pdMNjEYNhvgoIijw6SxbL_S3kpiwKK0RV2UuP7EDzWau1jMb9VwNQIV61UhKtGuApARbjx5mJvr4PRTe11Z1z4PeQVLyohxxjXOx1GLJ8OvQrGYWfQOo9mULZ3f3z5AZCWjV0</recordid><startdate>20140225</startdate><enddate>20140225</enddate><creator>Xu, Dandan</creator><creator>Guo, Xulin</creator><creator>Li, Zhaoqin</creator><creator>Yang, Xiaohui</creator><creator>Yin, Han</creator><general>Elsevier Inc</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>SOI</scope><scope>7SU</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20140225</creationdate><title>Measuring the dead component of mixed grassland with Landsat imagery</title><author>Xu, Dandan ; Guo, Xulin ; Li, Zhaoqin ; Yang, Xiaohui ; Yin, Han</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-ae5e59ee7111ae0132f6e125be91b67f3d221d95f80350289f259c205dbe93833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Animal, plant and microbial ecology</topic><topic>Applied geophysics</topic><topic>Biological and medical sciences</topic><topic>Biomass</topic><topic>Dead component</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Estimates</topic><topic>Exact sciences and technology</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>Grassland</topic><topic>Grasslands</topic><topic>Imagery</topic><topic>Internal geophysics</topic><topic>NBR</topic><topic>NDVI</topic><topic>NDWI</topic><topic>Non-photosynthetic vegetation</topic><topic>Remote sensing</topic><topic>Satellite imagery</topic><topic>Soil (material)</topic><topic>Teledetection and vegetation maps</topic><topic>Total biomass</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Dandan</creatorcontrib><creatorcontrib>Guo, Xulin</creatorcontrib><creatorcontrib>Li, Zhaoqin</creatorcontrib><creatorcontrib>Yang, Xiaohui</creatorcontrib><creatorcontrib>Yin, Han</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Dandan</au><au>Guo, Xulin</au><au>Li, Zhaoqin</au><au>Yang, Xiaohui</au><au>Yin, Han</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Measuring the dead component of mixed grassland with Landsat imagery</atitle><jtitle>Remote sensing of environment</jtitle><date>2014-02-25</date><risdate>2014</risdate><volume>142</volume><spage>33</spage><epage>43</epage><pages>33-43</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><coden>RSEEA7</coden><abstract>Detecting non-photosynthetic materials challenges multispectral remote sensing of vegetation. Dead component of grasslands, including litter and standing dead material as the accumulated phytomass of grassland productivity from previous years, is a primary connection in nutrient cycles in grasslands, and also conserves soil moisture and redistributes the grassland surface temperature. However, unlike green vegetation, it is hard to estimate the dead component with remote sensing approaches because the spectral signal of dead materials is similar to that of bare soil or soil crust (moss and lichen), with the only difference in the shortwave infrared region near 2000nm. In the literature, the Cellulose Absorption Index (CAI), an index based on hyperspectral imagery, is the most popular index for assessing dead cover or dead biomass. However, hyperspectral imagery is still not available for most grasslands worldwide. Therefore, a method to assess dead component using multispectral band imagery (e.g. Landsat imagery in this study) is needed. Grasslands National Park (GNP) in the southern part of Saskatchewan, Canada, has a large amount of accumulated dead vegetation because of long term conservation actions, which makes it a good study area for this project. This study aims to explore the relation between NDVI and dead cover, to investigate how different amounts of dead material change the relation of total biomass and NDVI, and also to test the potential to estimate dead cover using multispectral images. The results show that NDVI and dead cover have positive relationship when dead cover is less than 20%, no correlation when dead cover is between 20 and 80%, and significant negative relation when dead cover is more than 80%; further, the relation of total biomass and NDVI also changes with the same thresholds. The results also indicate that the dead component can be estimated with multispectral images using Normalized Burn Ratio (NBR) or Normalized Difference water index (NDWI), but the relationships are highly influenced by bare soil and soil crust.
•Total biomass and NDVI have positive relationship when dead cover is less than 20%.•The relationship reversed when the dead cover is more than 80%.•Dead cover change affects SWIR and NIR more than other bands.•Landsat TM (TM 4, TM5, and TM7) can be used to estimate dead cover in grassland.•Bare soil and soil crust have high effects on dead cover estimation.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2013.11.017</doi><tpages>11</tpages></addata></record> |
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subjects | Animal, plant and microbial ecology Applied geophysics Biological and medical sciences Biomass Dead component Earth sciences Earth, ocean, space Estimates Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Grassland Grasslands Imagery Internal geophysics NBR NDVI NDWI Non-photosynthetic vegetation Remote sensing Satellite imagery Soil (material) Teledetection and vegetation maps Total biomass Vegetation |
title | Measuring the dead component of mixed grassland with Landsat imagery |
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