Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation
While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/17/2840 |
| _version_ | 1797554881877245952 |
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| author | Sean P. Healey Zhiqiang Yang Noel Gorelick Simon Ilyushchenko |
| author_facet | Sean P. Healey Zhiqiang Yang Noel Gorelick Simon Ilyushchenko |
| author_sort | Sean P. Healey |
| collection | DOAJ |
| description | While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports. |
| first_indexed | 2024-03-10T16:38:25Z |
| format | Article |
| id | doaj.art-0d133e15a6cd417ba7d74cacd0f105a6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:38:25Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0d133e15a6cd417ba7d74cacd0f105a62023-11-20T12:12:17ZengMDPI AGRemote Sensing2072-42922020-09-011217284010.3390/rs12172840Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal SaturationSean P. Healey0Zhiqiang Yang1Noel Gorelick2Simon Ilyushchenko3US Forest Service Rocky Mountain Research Station, Ogden, UT 84401, USAUS Forest Service Rocky Mountain Research Station, Ogden, UT 84401, USAGoogle Inc., Google Switzerland, 8002 Zurich, SwitzerlandGoogle Inc., Mountain View, CA 94043, USAWhile Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports.https://www.mdpi.com/2072-4292/12/17/2840GEDILandsatGoogle Earth Engineforest structure |
| spellingShingle | Sean P. Healey Zhiqiang Yang Noel Gorelick Simon Ilyushchenko Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation Remote Sensing GEDI Landsat Google Earth Engine forest structure |
| title | Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation |
| title_full | Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation |
| title_fullStr | Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation |
| title_full_unstemmed | Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation |
| title_short | Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation |
| title_sort | highly local model calibration with a new gedi lidar asset on google earth engine reduces landsat forest height signal saturation |
| topic | GEDI Landsat Google Earth Engine forest structure |
| url | https://www.mdpi.com/2072-4292/12/17/2840 |
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