Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions?
We explore the potential contribution of Sentinel-2 (S2) wavebands and biophysical parameters, i.e. Leaf Area Index (LAI), Chlorophyll content (Cab), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction of Vegetation Cover (FVC) and Canopy Water Content (CWC) in mapping land us...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor and Francis Ltd.
2021
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| Online Access: | https://www.tandfonline.com/doi/abs/10.1080/10106049.2019.1695956 http://hdl.handle.net/11408/4426 |
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| author | Mudereri, Bester Tawona Chitata, Tavengwa Mukanga, Concilia Mupfiga, Elvis Tawanda Gwatirisa, Calisto Dube, Timothy |
| author_facet | Mudereri, Bester Tawona Chitata, Tavengwa Mukanga, Concilia Mupfiga, Elvis Tawanda Gwatirisa, Calisto Dube, Timothy |
| author_sort | Mudereri, Bester Tawona |
| collection | DSpace |
| description | We explore the potential contribution of Sentinel-2 (S2) wavebands and biophysical parameters, i.e. Leaf Area Index (LAI), Chlorophyll content (Cab), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction of Vegetation Cover (FVC) and Canopy Water Content (CWC) in mapping land use and land cover (LULC) in Zimbabwe. Random forest (RF) and naïve Bayes (NB) were used to classify S2 imagery. S2 biophysical variables resulted in LULC overall accuracy (OA) of 96% and 86% for RF and NB respectively, whereas S2 wavebands produced slightly higher accuracies of 97% and 88% for RF and NB respectively. Combining wavebands and biophysical variables enhanced classification results (OA = 98%: RF and 91%: NB). Variable importance analysis showed that FAPAR, red-edge 2, green, red-edge 3, FVC and band 8a, are the most relevant in the classification. Our work shows the strength and capability of biophysical variables in discerning different LULC attributes in semi-arid environments. |
| format | Article |
| id | ir-11408-4426 |
| institution | My University |
| language | English |
| publishDate | 2021 |
| publisher | Taylor and Francis Ltd. |
| record_format | dspace |
| spelling | ir-11408-44262022-06-27T13:49:06Z Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? Mudereri, Bester Tawona Chitata, Tavengwa Mukanga, Concilia Mupfiga, Elvis Tawanda Gwatirisa, Calisto Dube, Timothy Bayesian FAPAR LAI naïve Bayes random forest SNAP® rural Zimbabwe We explore the potential contribution of Sentinel-2 (S2) wavebands and biophysical parameters, i.e. Leaf Area Index (LAI), Chlorophyll content (Cab), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction of Vegetation Cover (FVC) and Canopy Water Content (CWC) in mapping land use and land cover (LULC) in Zimbabwe. Random forest (RF) and naïve Bayes (NB) were used to classify S2 imagery. S2 biophysical variables resulted in LULC overall accuracy (OA) of 96% and 86% for RF and NB respectively, whereas S2 wavebands produced slightly higher accuracies of 97% and 88% for RF and NB respectively. Combining wavebands and biophysical variables enhanced classification results (OA = 98%: RF and 91%: NB). Variable importance analysis showed that FAPAR, red-edge 2, green, red-edge 3, FVC and band 8a, are the most relevant in the classification. Our work shows the strength and capability of biophysical variables in discerning different LULC attributes in semi-arid environments. 2021-06-09T12:32:06Z 2021-06-09T12:32:06Z 2019 Article 1010-6049 https://www.tandfonline.com/doi/abs/10.1080/10106049.2019.1695956 http://hdl.handle.net/11408/4426 en Geocarto International; open Taylor and Francis Ltd. |
| spellingShingle | Bayesian FAPAR LAI naïve Bayes random forest SNAP® rural Zimbabwe Mudereri, Bester Tawona Chitata, Tavengwa Mukanga, Concilia Mupfiga, Elvis Tawanda Gwatirisa, Calisto Dube, Timothy Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| title | Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| title_full | Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| title_fullStr | Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| title_full_unstemmed | Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| title_short | Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| title_sort | can biophysical parameters derived from sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions? |
| topic | Bayesian FAPAR LAI naïve Bayes random forest SNAP® rural Zimbabwe |
| url | https://www.tandfonline.com/doi/abs/10.1080/10106049.2019.1695956 http://hdl.handle.net/11408/4426 |
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