Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests
The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estima...
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/16/3325 |
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| author | Jing Liu Longhui Li Markku Akerblom Tiejun Wang Andrew Skidmore Xi Zhu Marco Heurich |
| author_facet | Jing Liu Longhui Li Markku Akerblom Tiejun Wang Andrew Skidmore Xi Zhu Marco Heurich |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R<sup>2</sup> > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R<sup>2</sup> = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP. |
| first_indexed | 2024-03-10T08:25:01Z |
| format | Article |
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| language | English |
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| spelling | doaj.art-0564b175bc5f4e0abfe9967f34dfcfbe2023-11-22T09:35:51ZengMDPI AGRemote Sensing2072-42922021-08-011316332510.3390/rs13163325Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual ForestsJing Liu0Longhui Li1Markku Akerblom2Tiejun Wang3Andrew Skidmore4Xi Zhu5Marco Heurich6Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, ChinaMathematics, Unit of Computing Sciences, Tampere University, 33720 Tampere, FinlandFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The NetherlandsFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The NetherlandsFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The NetherlandsDepartment of Visitor Management and National Park Monitoring, Bavarian Forest National Park, 94481 Grafenau, GermanyThe in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R<sup>2</sup> > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R<sup>2</sup> = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.https://www.mdpi.com/2072-4292/13/16/3325leaf area indexplant area indexclumping indexvirtual forestdigital hemispherical photography |
| spellingShingle | Jing Liu Longhui Li Markku Akerblom Tiejun Wang Andrew Skidmore Xi Zhu Marco Heurich Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests Remote Sensing leaf area index plant area index clumping index virtual forest digital hemispherical photography |
| title | Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests |
| title_full | Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests |
| title_fullStr | Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests |
| title_full_unstemmed | Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests |
| title_short | Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests |
| title_sort | comparative evaluation of algorithms for leaf area index estimation from digital hemispherical photography through virtual forests |
| topic | leaf area index plant area index clumping index virtual forest digital hemispherical photography |
| url | https://www.mdpi.com/2072-4292/13/16/3325 |
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