Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information
Three years of summer field studies (2002–2004) were conducted at two sites in the Central Italian Alps. Field spectroradiometer data were acquired on different vegetation typologies where current above ground biomass was measured with traditional agronomic methods. Radiometric measurements were syn...
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| Veröffentlicht in: | Agriculture, ecosystems & environment ecosystems & environment, 2007, Vol.118 (1), p.267-272 |
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| creator | Boschetti, Mirco Bocchi, Stefano Brivio, Pietro Alessandro |
| description | Three years of summer field studies (2002–2004) were conducted at two sites in the Central Italian Alps. Field spectroradiometer data were acquired on different vegetation typologies where current above ground biomass was measured with traditional agronomic methods. Radiometric measurements were synthesised into a set of spectral vegetation indices. Log-transformed regression analysis between fresh biomass and spectral vegetation indices indicated that soil-adjusted vegetation indices (SAVI, MSAVI and OSAVI) show a better correlation (
r
2
>
0.60) than more commonly employed indices as SR and NDVI (
r
2
<
0.50). Cross validation procedure showed that a regressive model based on MSAVI index was the most predictive. Analysis of covariance revealed that data from different years and vegetation types significantly influenced the relation, however no improvement in the regressive model was found when data were used separately. After a suitable calibration procedure, a fresh biomass map of the study area was obtained using Landsat-7 imagery. Results showed that the integrated use of field spectral measurements and satellite derived information can provide a valuable support to the assessment of pasture biomass production in alpine environment. |
| doi_str_mv | 10.1016/j.agee.2006.05.024 |
| format | Article |
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r
2
>
0.60) than more commonly employed indices as SR and NDVI (
r
2
<
0.50). Cross validation procedure showed that a regressive model based on MSAVI index was the most predictive. Analysis of covariance revealed that data from different years and vegetation types significantly influenced the relation, however no improvement in the regressive model was found when data were used separately. After a suitable calibration procedure, a fresh biomass map of the study area was obtained using Landsat-7 imagery. Results showed that the integrated use of field spectral measurements and satellite derived information can provide a valuable support to the assessment of pasture biomass production in alpine environment.</description><identifier>ISSN: 0167-8809</identifier><identifier>EISSN: 1873-2305</identifier><identifier>DOI: 10.1016/j.agee.2006.05.024</identifier><identifier>CODEN: AEENDO</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Agronomy. Soil science and plant productions ; Alpine environment ; Animal, plant and microbial ecology ; Biological and medical sciences ; botanical composition ; calibration ; dry matter accumulation ; field experimentation ; Fundamental and applied biological sciences. Psychology ; General agroecology ; General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping ; General agronomy. Plant production ; General aspects. Techniques ; Generalities. Agricultural and farming systems. Agricultural development ; image analysis ; Landsat ; model validation ; mountains ; pasture plants ; Pasture production ; pastures ; regression analysis ; remote sensing ; Satellite imagery ; spatial variation ; spectral analysis ; Spectroradiometer ; Teledetection and vegetation maps ; vegetation ; Vegetation indices</subject><ispartof>Agriculture, ecosystems & environment, 2007, Vol.118 (1), p.267-272</ispartof><rights>2006 Elsevier B.V.</rights><rights>2007 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-f9014d9986bd27edac245f3b0e082e08ba8f4a2c86b0a0df6b37b608d5677d453</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.agee.2006.05.024$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,4024,27923,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18409387$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Boschetti, Mirco</creatorcontrib><creatorcontrib>Bocchi, Stefano</creatorcontrib><creatorcontrib>Brivio, Pietro Alessandro</creatorcontrib><title>Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information</title><title>Agriculture, ecosystems & environment</title><description>Three years of summer field studies (2002–2004) were conducted at two sites in the Central Italian Alps. Field spectroradiometer data were acquired on different vegetation typologies where current above ground biomass was measured with traditional agronomic methods. Radiometric measurements were synthesised into a set of spectral vegetation indices. Log-transformed regression analysis between fresh biomass and spectral vegetation indices indicated that soil-adjusted vegetation indices (SAVI, MSAVI and OSAVI) show a better correlation (
r
2
>
0.60) than more commonly employed indices as SR and NDVI (
r
2
<
0.50). Cross validation procedure showed that a regressive model based on MSAVI index was the most predictive. Analysis of covariance revealed that data from different years and vegetation types significantly influenced the relation, however no improvement in the regressive model was found when data were used separately. After a suitable calibration procedure, a fresh biomass map of the study area was obtained using Landsat-7 imagery. Results showed that the integrated use of field spectral measurements and satellite derived information can provide a valuable support to the assessment of pasture biomass production in alpine environment.</description><subject>Agronomy. Soil science and plant productions</subject><subject>Alpine environment</subject><subject>Animal, plant and microbial ecology</subject><subject>Biological and medical sciences</subject><subject>botanical composition</subject><subject>calibration</subject><subject>dry matter accumulation</subject><subject>field experimentation</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agroecology</subject><subject>General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping</subject><subject>General agronomy. Plant production</subject><subject>General aspects. Techniques</subject><subject>Generalities. Agricultural and farming systems. Agricultural development</subject><subject>image analysis</subject><subject>Landsat</subject><subject>model validation</subject><subject>mountains</subject><subject>pasture plants</subject><subject>Pasture production</subject><subject>pastures</subject><subject>regression analysis</subject><subject>remote sensing</subject><subject>Satellite imagery</subject><subject>spatial variation</subject><subject>spectral analysis</subject><subject>Spectroradiometer</subject><subject>Teledetection and vegetation maps</subject><subject>vegetation</subject><subject>Vegetation indices</subject><issn>0167-8809</issn><issn>1873-2305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNp9kEGP1CAYhonRxHH0D3iRi946flDa0sTLZKPuJpt40D0TCh8jkxYqUBP_vYyzibclIRx43jff9xDylsGBAes_ng_6hHjgAP0BugNw8YzsmBzahrfQPSe7Cg2NlDC-JK9yPkM9vJU7cjrmjDkvGAqNjq46ly0hXVO0myk-BuoDLT-R3hU9ex3ocV4z3bIPJ5pXNCXFBUvyhupgacIlFqQZwz_ABxfToi81r8kLp-eMbx7fPXn48vnHzW1z_-3r3c3xvjGiY6VxIzBhx1H2k-UDWm246Fw7AYLk9U5aOqG5qf-gwbp-aoepB2m7fhis6No9-XDtrRv82jAXtfhscJ51wLhlxUYxyF5ABfkVNCnmnNCpNflFpz-Kgbo4VWd1caouThV0qjqtofeP7TobPbukg_H5f1IKGNtqfU_eXTmnY21JlXn4zoG1AIMQvGOV-HQlsMr47TGpbDwGg9analXZ6J8a5C8BiJfe</recordid><startdate>2007</startdate><enddate>2007</enddate><creator>Boschetti, Mirco</creator><creator>Bocchi, Stefano</creator><creator>Brivio, Pietro Alessandro</creator><general>Elsevier B.V</general><general>Elsevier Science</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope></search><sort><creationdate>2007</creationdate><title>Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information</title><author>Boschetti, Mirco ; Bocchi, Stefano ; Brivio, Pietro Alessandro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-f9014d9986bd27edac245f3b0e082e08ba8f4a2c86b0a0df6b37b608d5677d453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Agronomy. Soil science and plant productions</topic><topic>Alpine environment</topic><topic>Animal, plant and microbial ecology</topic><topic>Biological and medical sciences</topic><topic>botanical composition</topic><topic>calibration</topic><topic>dry matter accumulation</topic><topic>field experimentation</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agroecology</topic><topic>General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping</topic><topic>General agronomy. Plant production</topic><topic>General aspects. Techniques</topic><topic>Generalities. Agricultural and farming systems. Agricultural development</topic><topic>image analysis</topic><topic>Landsat</topic><topic>model validation</topic><topic>mountains</topic><topic>pasture plants</topic><topic>Pasture production</topic><topic>pastures</topic><topic>regression analysis</topic><topic>remote sensing</topic><topic>Satellite imagery</topic><topic>spatial variation</topic><topic>spectral analysis</topic><topic>Spectroradiometer</topic><topic>Teledetection and vegetation maps</topic><topic>vegetation</topic><topic>Vegetation indices</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boschetti, Mirco</creatorcontrib><creatorcontrib>Bocchi, Stefano</creatorcontrib><creatorcontrib>Brivio, Pietro Alessandro</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Agriculture, ecosystems & environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boschetti, Mirco</au><au>Bocchi, Stefano</au><au>Brivio, Pietro Alessandro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information</atitle><jtitle>Agriculture, ecosystems & environment</jtitle><date>2007</date><risdate>2007</risdate><volume>118</volume><issue>1</issue><spage>267</spage><epage>272</epage><pages>267-272</pages><issn>0167-8809</issn><eissn>1873-2305</eissn><coden>AEENDO</coden><abstract>Three years of summer field studies (2002–2004) were conducted at two sites in the Central Italian Alps. Field spectroradiometer data were acquired on different vegetation typologies where current above ground biomass was measured with traditional agronomic methods. Radiometric measurements were synthesised into a set of spectral vegetation indices. Log-transformed regression analysis between fresh biomass and spectral vegetation indices indicated that soil-adjusted vegetation indices (SAVI, MSAVI and OSAVI) show a better correlation (
r
2
>
0.60) than more commonly employed indices as SR and NDVI (
r
2
<
0.50). Cross validation procedure showed that a regressive model based on MSAVI index was the most predictive. Analysis of covariance revealed that data from different years and vegetation types significantly influenced the relation, however no improvement in the regressive model was found when data were used separately. After a suitable calibration procedure, a fresh biomass map of the study area was obtained using Landsat-7 imagery. Results showed that the integrated use of field spectral measurements and satellite derived information can provide a valuable support to the assessment of pasture biomass production in alpine environment.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.agee.2006.05.024</doi><tpages>6</tpages></addata></record> |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Agronomy. Soil science and plant productions Alpine environment Animal, plant and microbial ecology Biological and medical sciences botanical composition calibration dry matter accumulation field experimentation Fundamental and applied biological sciences. Psychology General agroecology General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping General agronomy. Plant production General aspects. Techniques Generalities. Agricultural and farming systems. Agricultural development image analysis Landsat model validation mountains pasture plants Pasture production pastures regression analysis remote sensing Satellite imagery spatial variation spectral analysis Spectroradiometer Teledetection and vegetation maps vegetation Vegetation indices |
| title | Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information |
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