| Titre | Evaluating ground-truth data collection methods for earth observation of lichen woodlands: error and error propagation when upscaling to higher-level assessments |
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| Auteur | Lovitt, J ; Chen,
W; Wowk, W; Leblanc, S G; He, L; Schmelzer, I; Arsenault, A; Fraser, R; Prévost, C; White, H P; Pouliot, D |
| Source | Canadian Remote Sensing Society, Program, 41st Canadian Symposium on Remote Sensing: landscapes of change; remote sensing for a sustainable future/Société canadienne de télédétection, programme, 41e
Symposium canadien de télédétection : paysages du changement ; télédétection pour un avenir durable; 2020 p. 88 |
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| Année | 2020 |
| Séries alt. | Ressources naturelles Canada, Contribution externe 20200432 |
| Éditeur | Société canadienne de télédétection |
| Réunion | 41st Canadian Symposium on Remote Sensing / 41e Symposium canadien de télédétection; CA; juillet 13-16 juillet, 2020 |
| Document | livre |
| Lang. | anglais |
| Media | en ligne; numérique |
| Formats | pdf |
| Province | Québec; Terre-Neuve-et-Labrador |
| Région | Labrador |
| Sujets | télédétection; photographie; méthodes de prospection; logiciel; Biomasse; Forêt; Méthodologie; Traitement des données; Classification; géophysique; géologie de l'environnement |
| Diffusé | 2020 07 13 |
| Résumé | (disponible en anglais seulement)
Conventional methods of estimating ground lichen abundance (percent cover) include ocular estimates and point-intercept frames. The first can be prone to human
bias depending on the experience and training of the data collector, while the second can be time-consuming and/or introduce significant error depending on the point density used. A third option is to collect digital plot photographs and perform
binary image classification (presence/absence) after returning from the field. Using the three methods described above, we assessed lichen abundance of 93 plots collected in 2019 from 10 study sites across northern Québec and Labrador. Prior to
performing the digital photo classifications (DPC), we segregated plots into three image quality groups (A, B, C). We then extracted cover class samples from Group A photos (highest quality) to train a convolutional neural network in Trimble
eCognition software, and used a combined CNN and OBIA approach to produce a refined binary classification for each photo. We compared the resulting lichen abundance estimates with those derived from the ocular method and multiple densities of digital
photo point intercepts (DPIs). We also performed a theoretical upscaling of each plot to stand-level lichen biomass (kg/ha).
Our initial results (n=53) indicate the greatest discrepancies between methods occurred in plots with more than 25% lichen
cover and a heterogeneous lichen distribution. We found good agreement between DPC and DPI output with high DPI point densities (1000 points; RMSE: 0.09). Ocular estimates of lichen cover were consistently higher, predicting 10-15% more lichen cover
on average than DPI and DPC methods. This translated to higher lichen biomass predictions at the stand-level (+27% mean error). Unsurprisingly, we noted the DPC method is sensitive to image quality. When applied to Group C images the DPC method
produced lichen abundance estimates that were significantly different from ocular estimates of the same plots (-15% mean, RMSE: 0.21). Based on these preliminary results, we consider the DPC method to be especially useful in estimating lichen
abundance for plots with heterogeneous lichen distributions. However, special care must be taken to ensure images are collected with sufficient quality to produce reliable results. |
| GEOSCAN ID | 327276 |
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