Full-Waveform LiDAR Pixel Analysis for Low-Growing Vegetation Mapping of Coastal Foredunes in Western France

Launeau, Patrick; Giraud, Manuel; Ba, Antoine; Moussaoui, Saïd; Robin, Marc; Debaine, Françoise; Lague, Dimitri; Menn, E. Le

doi:10.3390/rs10050669

Cited by 14 publications

(27 citation statements)

References 31 publications

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“…Even though the soil is not sealed, arable land and permanent crops generate negative effects on downwind cooling and air circulation which make LST comparatively higher than those of the other LEAC macroclasses except artificial land. Even the thermal comfort and evapotranspiration provided by areas characterized by intensive agriculture are very low with respect to that of the other LEAC macroclasses, with the exception of the URB macroclass, since in artificial areas trees are almost-totally absent and soils are generally covered by dense low-growing vegetation [57,58]. In general, transitions from natural areas to intensive farming modify some important variables in terms of local climate, such as surface roughness of vegetation, leaf conductance, and soil moisture.…”

Section: Discussionmentioning

confidence: 99%

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

2020

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Land surface temperature (LST) is a key climate variable that has been studied mainly at the urban scale and in the context of urban heat islands. By analyzing the connection between LST and land cover, this study shows the potential of LST to analyze the relation between urbanization and heating phenomena at the regional level. Land cover data, drawn from Copernicus, and LST, retrieved from Landsat 8 satellite images, are analyzed through a methodology that couples GIS and regression analysis. By looking at the Italian island of Sardinia as a case study, this research shows that urbanization and the spatial dynamics of heating phenomena are closely connected, and that intensively farmed areas behave quite similarly to urban areas, whereas forests are the most effective land covers in mitigating LST, followed by areas covered with Mediterranean shrubs. This leads to key policy recommendations that decision-makers could implement to mitigate LST at the regional scale and that can, in principle, be exported to regions with similar climate and land covers. The significance of this study can be summed up in its novel approach to analyzing the relationship between LST and land covers that uses freely available spatial data and, therefore, can easily be replicated in other regional contexts to derive appropriate policy recommendations.Land cover changes, and, in particular, transitions from natural and semi-natural areas to artificial land covers, affect regional and local temperatures [6]. In fact, although urban areas and their surroundings receive the same amount of solar radiation, the local temperatures differ because different surface materials have different heat capacity [7]. From this perspective, land surface temperature (LST) is a significant parameter for investigating the effects of land covers on local temperatures.Hofierka et al.[8] define LST as "the radiative skin temperature of the ground", affected by solar reflectance, thermal emissivity, and heat capacity. In other words, LST combines interactions between land surface and atmosphere with ground-atmosphere energy fluxes. LST, measured through satellite thermal infrared sensors [9], represents a key climate variable, and its study allows researchers to analyze the behavior of the Earth's environmental system [10].Therefore, understanding how land cover changes affect climate represents a key element in international debates [11] that highlight that land cover can significantly affect quality of life, that is human health and safety, through its influence on LST [12,13]. The impacts on climate conditions generated by land-cover change processes can be effectively analyzed by assessing the relations between the spatial distribution of LST and that of land covers. The influence of land use/land cover changes on LST variation has been studied by various authors [6,[14][15][16][17][18]. Feizizadeh et al. [6] analyze the relations between LST and land use/land cover in Maraqeh County (Iran), using a method based on the application of the Surface Energy Bal...

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Section: Discussionmentioning

confidence: 99%

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

2020

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“…The mapping accuracy may be slightly increased (±10%) with other cost‐effective multispectral satellite imagery such as RapidEye or SPOT‐5, which feature additional red edge and SWIR spectral bands, respectively, and which have been shown to be relevant to accurately map wetland natural habitats (Davranche, Lefebvre, & Poulin, ; Stenzel, Feilhauer, Mack, Metz, & Schmidtlein, ). Until now, accuracy greater than 85% could be reached only by using airborne hyperspectral (Burai, Deák, Valkó, & Tomor, ), hyperspatial resolution imagery from an unmanned aerial vehicle (Kaneko & Nohara, ) or full‐waveform LiDAR data (Launeau et al., ) of small sites covering several hectares. However, the new Sentinel time‐series with a high spatial resolution (10 m) seems promising to accurately map natural habitats, using Sentinel‐1 SAR (Schmidt, Fassnacht, Förster, & Schmidtlein, ) or Sentinel‐2 multispectral (Shoko & Mutanga, ) sensors.…”

Section: Discussionmentioning

confidence: 99%

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

Rapinel

Rossignol

Hubert‐Moy

et al. 2018

Applied Vegetation Science

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Aims:The mapping and monitoring of natural vegetation is a challenging but important objective for environmental management. Although remote sensing has been used to map plant communities for several years, the maps produced are not sufficiently accurate to meet management requirements. This can be explained by the cumulative effects of floristic and spectral uncertainty. The objective of this study was to accurately map grassland plant communities using a comprehensive fuzzy approach in order to address floristic and spectral uncertainty.Location: Sub-brackish wet grasslands, Marais Poitevin, France. Methods:We first created a compromise typology-floristically and spectrally consistent-to perform fuzzy noise clustering on a joint PCA matrix derived from vegetation relevés and remote sensing data. This typology had two levels, which corresponded to spectral signatures and plant communities, respectively. Second, we mapped grassland plant communities to predict the fuzzy model from the remote sensing data. We applied this approach using (1) a very high spatial resolution multispectral satellite image and a LiDAR-derived Digital Terrain Model acquired on a 73 km 2 wet grassland site and (2) more than 200 relevés collected in the field. Results:The results show that (1) the compromise typology yields significantly higher mapping accuracy than classic phytosociological typology (62% and 26%, respectively); (2) compared to a crisp approach, the fuzzy approach improves mapping accuracy by 17 percentage points and (3) a single plant community can be defined by several (1-4) distinct spectral signatures. Conclusions:The comprehensive fuzzy procedure successfully mapped herbaceous plant communities at the ecosystem scale using inexpensive remote sensing data.Floristic and spectral uncertainty was considered in a fuzzy approach, resulting in the mapping of nine herbaceous plant communities with acceptable accuracy. As the natural habitats were characterized at the plant community level, correspondence with functional properties of the species or with ecosystem services can be easily inferred. These encouraging results open up new ways to meet the requirements for monitoring the conservation status of natural habitats in the EU Habitats Directive.

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“…By so doing, dark (absorbent) and bright (reflective) medium would give the same function with a distance of between 0 and 1 varying only with the range of diffusion properties. This was tested by [22] to map the low vegetation range where top and bottom did not allow the individualization of separate echoes. Because of signal stretching on the gradient this method works at the smallest possible pixel size without neighborhood contribution.…”

Section: Of 23mentioning

confidence: 99%

Full-Waveform LiDAR Fast Analysis of a Moderately Turbid Bay in Western France

et al. 2019

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In shoreline monitoring, only topo-bathymetric light detection and ranging (LiDAR) can map large corridors from aerial dunes to sandbanks in shallow water. Increasing turbidity masking the formation of 532 nm laser beam echoes on the sea bed makes this challenging. Full-waveform recording all the laser beam damping functions, a turbid water column can be seen as an accumulation of layers forming a single continuum and a distinction can be made between signals ending at the bottom down to a depth of 10 m. In practice full-waveforms are converted by laser beam tracing an image cube with a grid of 1-m-wide pixels and a 0.15 m range resolution storing the mean intensities returned along incident angle. The first derivative of a wide Gaussian filter serves to delineate the full-waveform range limits. Because of turbidity current heterogeneity and complexity of multiple layers radiative transfer model, a drastic simplification is applied by normalizing the cumulative full-waveform to 1, transforming each pixel of the water column into spectrum of intensity ranging from 0 to 1 from bottom to top. A transposition between range and bottom-top information facilitates the water index correction below the sea water level provided by 1064-nm discrete echoes. All echoes remain accessible with maximums of cumulative full-waveform third derivative.

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Full-Waveform LiDAR Pixel Analysis for Low-Growing Vegetation Mapping of Coastal Foredunes in Western France

Cited by 14 publications

References 31 publications

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

Full-Waveform LiDAR Fast Analysis of a Moderately Turbid Bay in Western France

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