Integration of multi-scale remote sensing data for reindeer lichen fractional cover mapping in Eastern Canada

He, Li; Leblanc, Sylvain G.; Lovitt, Julie; Arsenault, André; Schmelzer, Isabelle; Fraser, Robert; Latifovic, Rasim; Sun, Liang; Prévost, C; White, H. Peter; Pouliot, Darren

doi:10.1016/j.rse.2021.112731

Cited by 20 publications

(14 citation statements)

References 68 publications

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“…On the other hand, the finer spatial resolution of PlanetScope images allowed us to model the variability of FCover values registered by the very high UAVbased map, thus offering a good support for identifying A. saligna patch edges. Similarly, this complementarity between coarse and fine-scale images has been observed in previous studies predicting the fractional cover of tundra vegetation and lichen of taiga using UAV and satellite images with different spatial resolutions (e.g., PlanetScope, Sentinel-2, and Landsat; Riihimäki et al, 2019;He et al, 2021). The analysis of multi-temporal spectral variables derived from visible (blue, green, and red) and NIR bands acquired by Sentinel-2 and PlanetScope platforms effectively depicted the phenological behavior of specific IAS.…”

Section: Figuresupporting

confidence: 78%

“…Other scale issues that might influence the IAS model accuracy are the low number of pixels registering "pure" A. saligna patches in Sentinel-2 images and the relatively limited number of total pixels in the prediction area. In fact, the fuzzy Frontiers in Environmental Science frontiersin.org shape of A. saligna patches as derived by Sentinel-2 models corresponds to better model performance values (Riihimäki et al, 2019;He et al, 2021). On the other hand, the finer spatial resolution of PlanetScope images allowed us to model the variability of FCover values registered by the very high UAVbased map, thus offering a good support for identifying A. saligna patch edges.…”

Section: Figurementioning

confidence: 93%

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Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast

Marzialetti

Febbraro²,

Frate³

et al. 2022

Front. Environ. Sci.

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Invasive alien plants (IAPs) are increasingly threatening biodiversity worldwide; thus, early detection and monitoring tools are needed. Here, we explored the potential of unmanned aerial vehicle (UAV) images in providing intermediate reference data which are able to link IAP field occurrence and satellite information. Specifically, we used very high spatial resolution (VHR) UAV maps of A. saligna as calibration data for satellite-based predictions of its spread in the Mediterranean coastal dunes. Based on two satellite platforms (PlanetScope and Sentinel-2), we developed and tested a dedicated procedure to predict A. saligna spread organized in four steps: 1) setting of calibration data for satellite-based predictions, by aggregating UAV-based VHR IAP maps to satellite spatial resolution (3 and 10 m); 2) selection of monthly multispectral (blue, green, red, and near infra-red bands) cloud-free images for both satellite platforms; 3) calculation of monthly spectral variables depicting leaf and plant characteristics, canopy biomass, soil features, surface water and hue, intensity, and saturation values; 4) prediction of A. saligna distribution and identification of the most important spectral variables discriminating IAP occurrence using a fandom forest (RF) model. RF models calibrated for both satellite platforms showed high predictive performances (R2 > 0.6; RMSE <0.008), with accurate spatially explicit predictions of the invaded areas. While Sentinel-2 performed slightly better, the PlanetScope-based model effectively delineated invaded area edges and small patches. The summer leaf chlorophyll content followed by soil spectral variables was regarded as the most important variables discriminating A. saligna patches from native vegetation. Such variables depicted the characteristic IAP phenology and typically altered leaf litter and soil organic matter of invaded patches. Overall, we presented new evidence of the importance of VHR UAV data to fill the gap between field observation of A. saligna and satellite data, offering new tools for detecting and monitoring non-native tree spread in a cost-effective and timely manner.

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Section: Figuresupporting

confidence: 78%

Section: Figurementioning

confidence: 93%

Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast

Marzialetti

Febbraro²,

Frate³

et al. 2022

Front. Environ. Sci.

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“…Only the near-infrared band was used from the Sentera camera resampled to 2 cm. Lichen maps were then derived using support vector machine classification that yielded an accuracy greater than 93% (He et al 2021).…”

Section: Pixels Sampled Training Sample Sizementioning

confidence: 99%

“…cover or volume, underpinned by field plot reference data, is becoming a common method for quantifying caribou lichen forage over large spatial extents (Theau and Duguay 2004a, b;Nelson et al 2013;Falldorf et al 2014;Kennedy et al 2020;Macander et al 2020). High-resolution drone mapping has recently been used to provide more comprehensive reference data for training satellite-based lichen retrieval models, assuming that it provides more accurate sampling of satellite pixels compared to conventional field plots (Macander et al 2020;Fraser et al 2021;He et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Using drone mapping to evaluate error of plot-based field surveys and its effects on moderate spatial resolution remote sensing retrieval of lichen cover

et al. 2022

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Effective plot-based field sampling involves a trade-off between implementation efficiency and sample error. Optimal field sampling therefore requires quantifying the sample error under various sampling designs. For remote sensing applications, it is also important to understand how field sample error and training sample size (the number of pixels) affect the retrieval of surface properties. In this research, drone imagery was used to simulate field plots and investigate plot sampling error for forage lichen cover in relation to plot size, number of plots, and sampling strategy. The effect of this error on remote sensing-based lichen cover retrieval was evaluated using varying training sampling sizes in two different study regions in northern Canada. Results showed that cover with high spatial variability increased the number of plots or plot size required to achieve a specified level of error. For lichen cover retrieval at moderate spatial resolution (10–30 m), field sampling (plot size and number of plots) did not have as significant of an effect as regional differences (spectral separability of cover types), sensor, and the number of pixels used for model training. This plot simulation approach using drone images can be applied to other surface properties and regions to provide field sampling guidance.

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“…Techniques from spectral mixture analysis (Théau et al 2005), to spectral indices (Falldorf et al 2014) combined with environmental variables (Nelson et al 2013Silva et al 2019), and machine learning methods (Kennedy et al 2020) have been used. Several recent studies have utilized data from multiple remote sensing sensors; for example, using measurements from sensors mounted on unmanned aerial vehicles (UAVs) as a training data for models employing optical satellite data (Macander et al 2020;He et al 2021), or estimates of forest structure from Light detection and ranging (LiDAR) data combined with optical data from satellite imagery (Hillman and Nielsen 2020). LiDAR data alone has also been utilized for separation of lichens from other ground cover types (Korpela 2008;Moeslund et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Estimation of boreal forest floor lichen cover using hyperspectral airborne and field data

Kuusinen¹,

Hovi²,

Rautiainen³

2023

Silva Fenn

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Lichens are sensitive to competition from vascular plants, intensive silviculture, pollution and reindeer and caribou grazing, and can therefore serve as indicators of environmental changes. Hyperspectral remote sensing data has been proved promising for estimation of plant diversity, but its potential for forest floor lichen cover estimation has not yet been studied. In this study, we investigated the use of hyperspectral data in estimating ground lichen cover in boreal forest stands in Finland. We acquired airborne and in situ hyperspectral data of lichen-covered forest plots, and applied multiple endmember spectral mixture analysis to estimate the fractional cover of ground lichens in these plots. Estimation of lichen cover based on in situ spectral data was very accurate (coefficient of determination (r2) 0.95, root mean square error (RMSE) 6.2). Estimation of lichen cover based on airborne data, on the other hand, was fairly good (r2 0.77, RMSE 11.7), but depended on the choice of spectral bands. When the hyperspectral data were resampled to the spectral resolution of Sentinel-2, slightly weaker results were obtained. Tree canopy cover near the flight plots was weakly related to the difference between estimated and measured lichen cover. The results also implied that the presence of dwarf shrubs could influence the lichen cover estimates.

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Integration of multi-scale remote sensing data for reindeer lichen fractional cover mapping in Eastern Canada

Cited by 20 publications

References 68 publications

Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast

Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast

Using drone mapping to evaluate error of plot-based field surveys and its effects on moderate spatial resolution remote sensing retrieval of lichen cover

Estimation of boreal forest floor lichen cover using hyperspectral airborne and field data

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