Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/6/1/716 |
| _version_ | 1819281678194966528 |
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| author | Beth Cole Julia McMorrow Martin Evans |
| author_facet | Beth Cole Julia McMorrow Martin Evans |
| author_sort | Beth Cole |
| collection | DOAJ |
| description | Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time. |
| first_indexed | 2024-12-24T01:03:30Z |
| format | Article |
| id | doaj.art-d442cfa1ab984837ad32f8594e6001e8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T01:03:30Z |
| publishDate | 2014-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d442cfa1ab984837ad32f8594e6001e82022-12-21T17:23:17ZengMDPI AGRemote Sensing2072-42922014-01-016171673910.3390/rs6010716rs6010716Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration MonitoringBeth Cole0Julia McMorrow1Martin Evans2Centre for Landscape and Climate Research, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UKSchool of Environment Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UKSchool of Environment Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UKPeatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time.http://www.mdpi.com/2072-4292/6/1/716hyperspectralpeatlandrestorationpartial least squares regression (PLSR)plant functional type (PFT)vegetation abundance |
| spellingShingle | Beth Cole Julia McMorrow Martin Evans Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring Remote Sensing hyperspectral peatland restoration partial least squares regression (PLSR) plant functional type (PFT) vegetation abundance |
| title | Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring |
| title_full | Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring |
| title_fullStr | Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring |
| title_full_unstemmed | Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring |
| title_short | Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring |
| title_sort | empirical modelling of vegetation abundance from airborne hyperspectral data for upland peatland restoration monitoring |
| topic | hyperspectral peatland restoration partial least squares regression (PLSR) plant functional type (PFT) vegetation abundance |
| url | http://www.mdpi.com/2072-4292/6/1/716 |
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