LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS
The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimate...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W4/299/2015/isprsarchives-XL-1-W4-299-2015.pdf |
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| author | I. Kalisperakis Ch. Stentoumis L. Grammatikopoulos K. Karantzalos |
| author_facet | I. Kalisperakis Ch. Stentoumis L. Grammatikopoulos K. Karantzalos |
| author_sort | I. Kalisperakis |
| collection | DOAJ |
| description | The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications.
To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular,
canopy levels were estimated from i.e., (<i>i</i>) hyperspectral data, (<i>ii</i>) 2D RGB orthophotomosaics and (<i>iii</i>) 3D crop surface models. The
computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea,
Greece. The overall evaluation indicated that the estimated canopy levels were correlated (<i>r</i><sup>2</sup> > 73%) with the in-situ, ground truth LAI
measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics.
The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later
the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate
LAI in cases with sparse, weak, unhealthy plants and canopy. |
| first_indexed | 2024-12-22T12:04:09Z |
| format | Article |
| id | doaj.art-6f0a1c4b70dc414e9a76c38327b093e6 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-22T12:04:09Z |
| publishDate | 2015-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-6f0a1c4b70dc414e9a76c38327b093e62022-12-21T18:26:29ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342015-08-01XL-1-W429930310.5194/isprsarchives-XL-1-W4-299-2015LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELSI. Kalisperakis0Ch. Stentoumis1L. Grammatikopoulos2K. Karantzalos3up2metric, Athens, Greeceup2metric, Athens, GreeceLaboratory of Photogrammetry, Technological Educational Institute of Athens, Athens, GreeceRemote Sensing Lab., National Technical University of Athens, Athens, GreeceThe indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimated from i.e., (<i>i</i>) hyperspectral data, (<i>ii</i>) 2D RGB orthophotomosaics and (<i>iii</i>) 3D crop surface models. The computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea, Greece. The overall evaluation indicated that the estimated canopy levels were correlated (<i>r</i><sup>2</sup> > 73%) with the in-situ, ground truth LAI measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics. The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate LAI in cases with sparse, weak, unhealthy plants and canopy.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W4/299/2015/isprsarchives-XL-1-W4-299-2015.pdf |
| spellingShingle | I. Kalisperakis Ch. Stentoumis L. Grammatikopoulos K. Karantzalos LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS |
| title_full | LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS |
| title_fullStr | LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS |
| title_full_unstemmed | LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS |
| title_short | LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS |
| title_sort | leaf area index estimation in vineyards from uav hyperspectral data 2d image mosaics and 3d canopy surface models |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W4/299/2015/isprsarchives-XL-1-W4-299-2015.pdf |
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