Can regional aerial images from orthophoto surveys produce high quality photogrammetric Canopy Height Model? A single tree approach in Western Europe

Michez, Adrien; Huylenbroeck, Léo; Bolyn, Corentin; Latte, Nicolas; Bauwens, Sébastien; Lejeune, Philippe

doi:10.1016/j.jag.2020.102190

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…To quantify shade from riparian vegetation, valley morphology and channel orientation, the ‘analytical hillshading’ method from SAGA ( Tarini, Cignoni & Montani, 2006 ) was used. Based on a digital elevation model (photogrammetric digital surface model derived from regional orthophoto survey from the year 2016 processed as described in Michez et al (2020) , grid resolution: 5 m × 5 m) and sun position, the tool calculates angles at which sunlight impinges on the surface. The values range between 0 and 90°, with 90° corresponding to an area in shadow.…”

Section: Methodsmentioning

confidence: 99%

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Georges

Michez

Piégay

et al. 2021

PeerJ

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Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially. To test these hypotheses, we (i) determined which of the environmental variables reported to be the most influential affected WT and (ii)identified the spatial scales over which those environmental variables influenced WT. To this end, the influence of multi-scale environmental variables, namely land cover, topography (channel slope, elevation), hydromorphology (channel sinuosity, water level, watershed area, baseflow index) and shade conditions, was analyzed on the three model variables (day thermal sensitivity, night thermal sensitivity, and non-convective thermal flux) in the model developed by Georges et al. (2021) of the temporal thermal dynamics of daily maximum WT during extreme events. Values were calculated on six spatial scales (the entire upstream catchment and the associated 1 km and 2 km circular buffer, and 50 m wide corridors on each side of the stream with the associated 1 km and 2 km circular buffer). The period considered was 17 extreme days during the summer identified by Georges et al. (2021) based on WT data measured every 10 min for 7 years (2012–2018) at 92 measurement sites. Sites were located evenly throughout the Wallonia (southern Belgium) hydrological network. Results showed that shade, baseflow index (a proxy of the influence of groundwater), water level and watershed area were the most significant variables influencing thermal sensitivity. Since managers with finite financial and human resources can act on only a few environmental variables, we advocate restoring and preserving the vegetation cover that limits solar radiation on the watercourse as a cost-effective solution to reduce thermal sensitivity. Moreover, management at small spatial scale (50 m riparian buffer) should be strategically promoted (for finance and staffing) as our results show that a larger management scale is not more effective in reducing thermal sensitivity to extreme events.

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Section: Methodsmentioning

confidence: 99%

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Georges

Michez

Piégay

et al. 2021

PeerJ

Self Cite

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“…In contrast, when using point clouds, it may be more difficult to do so with variables related to lower height percentiles, canopy closure or LAI (leaf area index) [76]. A third advantage of CHMs beyond ease of use and robustness is that it can be updated with photogrammetric point clouds once a first Li-DAR survey has been done [77]. The m1 model could thus be better suited to the use of time series or to the comparison of sites covered by different datasets. )…”

Section: Lidar Biomass Estimatesmentioning

confidence: 99%

What Factors Shape Spatial Distribution of Biomass in Riparian Forests? Insights from a LiDAR Survey over a Large Area

Huylenbroeck

Latte

Lejeune

et al. 2021

Forests

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Riparian ecosystems are home to a remarkable biodiversity, but have been degraded in many regions of the world. Vegetation biomass is central to several key functions of riparian systems. It is influenced by multiple factors, such as soil waterlogging, sediment input, flood, and human disturbance. However, knowledge is lacking on how these factors interact to shape spatial distribution of biomass in riparian forests. In this study, LiDAR data were used in an individual tree approach to map the aboveground biomass in riparian forests along 200 km of rivers in the Meuse catchment, in southern Belgium (Western Europe). Two approaches were tested, relying either on a LiDAR Canopy Height Model alone or in conjunction with a LiDAR point cloud. Cross-validated biomass relative mean square error for 0.3 ha plots were, respectively, 27% and 22% for the two approaches. Spatial distribution of biomass patterns were driven by parcel history (and particularly vegetation age), followed by land use and topographical or geomorphological variables. Overall, anthropogenic factors were dominant over natural factors. However, vegetation patches located in the lower parts of the riparian zone exhibited a lower biomass than those in higher locations at the same age, presumably due to a combination of a more intense disturbance regime and more limiting growing conditions in the lower parts of the riparian zone. Similar approaches to ours could be deployed in other regions in order to better understand how biomass distribution patterns vary according to the climatic, geological or cultural contexts.

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“…For example a canopy height model was one of many potential variables, but was not selected for the final classification model. There-fore, the applied classification model differs from remotely sensed based forest inventories, where the canopy height model typically plays a major role regardless of the underlying data sources [10,11].…”

Section: Discussionmentioning

confidence: 99%

“…Many relevant tree attributes vary seasonally in forests and, due to the important role of foliage cover [10,11], studies that primarily focus on such seasonally changing variables, can be considered particularly valuable to classify individual tree species. For example, Gara et al [12] showed that different leaf traits influence the results of tree species classification when using satellite images.…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets

et al. 2021

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For monitoring protected forest landscapes over time it is essential to follow changes in tree species composition and forest dynamics. Data driven remote sensing methods provide valuable options if terrestrial approaches for forest inventories and monitoring activities cannot be applied efficiently due to restrictions or the size of the study area. We demonstrate how species can be detected at a single tree level utilizing a Random Forest (RF) model using the Black Forest National Park as an example of a Central European forest landscape with complex relief. The classes were European silver fir (Abies alba, AA), Norway spruce (Picea abies, PA), Scots pine (Pinus sylvestris, PS), European larch (Larix decidua including Larix kampferii, LD), Douglas fir (Pseudotsuga menziesii, PM), deciduous broadleaved species (DB) and standing dead trees (snags, WD). Based on a multi-temporal (leaf-on and leaf-off phenophase) and multi-spectral mosaic (R-G-B-NIR) with 10 cm spatial resolution, digital elevation models (DTM, DSM, CHM) with 40 cm spatial resolution and a LiDAR dataset with 25 pulses per m2, 126 variables were derived and used to train the RF algorithm with 1130 individual trees. The main objective was to determine a subset of meaningful variables for the RF model classification on four heterogeneous test sites. Using feature selection techniques, mainly passive optical variables from the leaf-off phenophase were considered due to their ability to differentiate between conifers and the two broader classes. An examination of the two phenological phases (using the difference of the respective NDVIs) is important to clearly distinguish deciduous trees from other classes including snags (WD). We also found that the variables of the first derivation of NIR and the tree metrics play a crucial role in discriminating PA und PS. With this unique set of variables some classes can be differentiated more reliably, especially LD and DB but also AA, PA and WD, whereas difficulties exist in identifying PM and PS. Overall, the non-parametric object-based approach has proved to be highly suitable for accurately detecting (OA: 89.5%) of the analyzed classes. Finally, the successful classification of complex 265 km2 study area substantiates our findings.

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Can regional aerial images from orthophoto surveys produce high quality photogrammetric Canopy Height Model? A single tree approach in Western Europe

Cited by 7 publications

References 17 publications

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

What Factors Shape Spatial Distribution of Biomass in Riparian Forests? Insights from a LiDAR Survey over a Large Area

Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets

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