Modeling of Effective Leaf Area Index
Mapping of e�ective leaf area index (LAIe) over the Swedish boreal forest test site Krycklan (64°N19°E) was performed using ground-based �eld estimates of LAIe and remote sensing data sources. The LAIe data were collected 2017 and 2018 using the LAI-2200 Plant Canopy Analyzer and its later versio...
| Autor principal: | |
|---|---|
| Formato: | H3 |
| Lenguaje: | Inglés |
| Publicado: |
SLU/Dept. of Forest Resource Management
2018
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| Materias: |
| _version_ | 1855572551733870592 |
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| author | Selin, Lina |
| author_browse | Selin, Lina |
| author_facet | Selin, Lina |
| author_sort | Selin, Lina |
| collection | Epsilon Archive for Student Projects |
| description | Mapping of e�ective leaf area index (LAIe) over the Swedish boreal forest test site
Krycklan (64°N19°E) was performed using ground-based �eld estimates of LAIe and
remote sensing data sources. The LAIe data were collected 2017 and 2018 using
the LAI-2200 Plant Canopy Analyzer and its later version LAI-2200C Plant Canopy
Analyzer. The remote sensing data used were airborne laser scanning (ALS) data,
Interferometric Synthetic Aperture Radar (InSAR) data from TanDEM-X, and stereo
matched drone images. The stereo matched drone images only covered a small subset
of the Krycklan catchment, the ICOS grid area. Point cloud metrics were calculated
from the ALS data and the drone data such as height percentiles, intensity percentiles,
point cloud density and cover metrics. Three metrics from the TanDEM-X data were
evaluated as predictors; interferometric phase height, coherence and backscatter.
Estimations were done by �tting regression models of LAIe and the predicting remote
sensing data sources. The best ALS regression model for predicting LAIe used the
canopy density gap metric, giving an R
2
adj=0.93 for catchment level estimations and
R
2
adj=0.97 for the ICOS grid area. The TanDEM-X metric interferometric phase height
was the single best predictor of the three InSAR metrics, predicting LAIe with a
R
2
adj=0.85 at catchment level and R
2
adj=0.93 at the ICOS grid area. The drone data
model included the variables canopy cover gap and the 99th height percentile, which
resulted in a R
2
adj value of 0.95. The models were used to generate wall-to-wall rasters
and evaluated with the leave-one-out cross validation method. It was concluded
that the ALS model was best suited to predict LAIe as it was able to handle varying
forestation, which both the other methods struggled with. When applied over mature
and homogeneous boreal forest all models performed with similar accuracy. |
| format | H3 |
| id | RepoSLU15280 |
| institution | Swedish University of Agricultural Sciences |
| language | Inglés |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher | SLU/Dept. of Forest Resource Management |
| publisherStr | SLU/Dept. of Forest Resource Management |
| record_format | eprints |
| spelling | RepoSLU152802020-01-21T10:09:35Z Modeling of Effective Leaf Area Index Modellering av "Effective Leaf Area Index" med fjärranalysdata Selin, Lina Sentinel-1 tree species random forest linear discriminant Mapping of e�ective leaf area index (LAIe) over the Swedish boreal forest test site Krycklan (64°N19°E) was performed using ground-based �eld estimates of LAIe and remote sensing data sources. The LAIe data were collected 2017 and 2018 using the LAI-2200 Plant Canopy Analyzer and its later version LAI-2200C Plant Canopy Analyzer. The remote sensing data used were airborne laser scanning (ALS) data, Interferometric Synthetic Aperture Radar (InSAR) data from TanDEM-X, and stereo matched drone images. The stereo matched drone images only covered a small subset of the Krycklan catchment, the ICOS grid area. Point cloud metrics were calculated from the ALS data and the drone data such as height percentiles, intensity percentiles, point cloud density and cover metrics. Three metrics from the TanDEM-X data were evaluated as predictors; interferometric phase height, coherence and backscatter. Estimations were done by �tting regression models of LAIe and the predicting remote sensing data sources. The best ALS regression model for predicting LAIe used the canopy density gap metric, giving an R 2 adj=0.93 for catchment level estimations and R 2 adj=0.97 for the ICOS grid area. The TanDEM-X metric interferometric phase height was the single best predictor of the three InSAR metrics, predicting LAIe with a R 2 adj=0.85 at catchment level and R 2 adj=0.93 at the ICOS grid area. The drone data model included the variables canopy cover gap and the 99th height percentile, which resulted in a R 2 adj value of 0.95. The models were used to generate wall-to-wall rasters and evaluated with the leave-one-out cross validation method. It was concluded that the ALS model was best suited to predict LAIe as it was able to handle varying forestation, which both the other methods struggled with. When applied over mature and homogeneous boreal forest all models performed with similar accuracy. SLU/Dept. of Forest Resource Management 2018 H3 eng https://stud.epsilon.slu.se/15280/ |
| spellingShingle | Sentinel-1 tree species random forest linear discriminant Selin, Lina Modeling of Effective Leaf Area Index |
| title | Modeling of Effective Leaf Area Index |
| title_full | Modeling of Effective Leaf Area Index |
| title_fullStr | Modeling of Effective Leaf Area Index |
| title_full_unstemmed | Modeling of Effective Leaf Area Index |
| title_short | Modeling of Effective Leaf Area Index |
| title_sort | modeling of effective leaf area index |
| topic | Sentinel-1 tree species random forest linear discriminant |