dataesr/scientific-paragraphs-categorization huggingface dataset:
First, we expected that such data would show that some patches become higher, are less affected by overflow sedimentation, and progressively shift from pioneer species to post-pioneer hardwood species along the age gradient. Second, we expected that remote sensing information would be able to show that patches along the hydrological connectivity gradient (considering a similar age-group) become dryer and their vegetation structure becomes less homogeneous and sparser. In both cases, coupling hyperspectral and LiDAR data for the analysis should allow for quantitative estimation of the physical and temporal thresholds associated with important shifts. We explored these two issues on the Ain River, France, where combined airborne and field data exist within a well-established geomorphic framework  [61].
In our analysis, we first took a look at the topographic, structural, and spectral information for the selected forestry plots. Two sites were selected because they have good hydrological connectivity, while also featuring a mosaic of ages with plots ranging from three to forty years old (y. o. ). Other sites were selected based on previous studies featuring mature riparian forest of a similar age, but on a gradient of hydrological connectivity due to channel changes (e. g. , incised vs. stable or slightly aggrading channels). We then expand this analysis over a dataset representing 400 ha of riparian forest for which forestry data is available, to see whether similar trends can be identified. Then, we use random forest classification to try to target different indicators of forest connectivity in a riparian landscape.
Therefore, the riparian forest of the lower Ain River basin can be considered a complex mosaic, where issues concerning species distributions, rejuvenation, and water stress intertwine as a result of the geomorphological changes that have taken place over the last century. Given the extensive datasets available, this carefully selected case study provides a unique opportunity to study the characterization of riparian forests. During the 20th century, a chain of dams was built for hydroelectric purposes upstream of the study reach, with these including the Vouglans dam, which has a reservoir of 600 million cubic meters and induces downstream sediment starvation that propagates 500 m per year on average  [61]. As a result, the incision of the riverbed progresses downstream, and only certain portions of the study reach are still sufficiently geomorphologically active to enable rejuvenation of the riparian forest through channel shifting and maintenance of a groundwater level close to the surface.
Remote Sensing Information The remote sensing data used in this study includes airborne hyperspectral imagery acquired in 2015, two airborne LiDAR surveys (in 2008 and 2015), and a series of aerial photographs that were acquired from eleven campaigns since the 1940s (Table  2 ). 1945, 1954, 1963, 1971, 1980, 1991, 1996, 2000, 2005, 2009  The remote sensing campaign led to the acquisition of twenty hyperspectral flight lines imaged with a Specim AISA Fenix hyperspectral camera sensor flown on a NERC-ARF (Natural Environment Research Council Airborne Research Facility) aircraft. This sensor consisted of two detectors that enabled the coverage of a spectral range between 380 nanometers and 2500 nanometers. The mean full width at half maximum across the 361 spectral bands was 3. 98 nanometers.
LiDAR Data The EUFAR dataset was complemented by a topo-bathymetric LiDAR acquisition made over the study reach in August 2015. This campaign was acquired at the request of Electricité de France (EDF) to study the bathymetry of the river channel  [68]. An Optech Titan sensor was flown on an aircraft 329 m above the ground, resulting in around ten billion data points. The final point density was 18. 6 points/m 2 for each laser, and the vertical accuracy was around 10 cm. A second and older LiDAR dataset was also used in this study. This dataset was acquired on the 6 and 8 March 2008. However, only a post-processed point cloud without the vegetative cover was available; therefore, it was only used in conjunction with the 2015 topo-bathymetric LiDAR data to extract information related to sedimentation or erosion under the canopy cover between the two dates.
Vegetation Survey during the Airborne Campaign in 2015 Following the 2015 acquisition, fieldwork was conducted in September and October by the EVS laboratory staff to survey the riparian forest in thirty plots with a 10 m radius (Figure  3 ). The central position of each plot was acquired with a GeoExplorer 6000 handheld global positioning system. Within a ten-meter radius, each individual tree with a diameter above 30 cm was referenced to a database, and its height was recorded (Table  3 ). In addition, all individual trees with a diameter above 7. 5 cm within a smaller five-meter radius were also referenced to the database. The tree species were assessed according to leaf morphology. Soil-related information, such as organic matter and sediment size, was also assessed. The locations of the plots were decided, so that the surveys included: (1) two series of plots along an age gradient in shifting reaches of the river, and (2) one series of mature forest plots of the same age along a gradient of vertical connectivity.
The two series of plots sampled along the age gradient (Chatillon and Mollon North) benefited from the lateral mobility of the river. The fluvial dynamics of the reach allow for the creation of new gravel bars, and their colonization by new pioneer vegetation. This mobility helps maintain forest rejuvenation and leads to patches differing in age (from 4 to 40 years for the plots sampled along this gradient) and developmental stage within the same geographic area. x FOR PEER REVIEW 8 of 33 The central position of each plot was acquired with a GeoExplorer 6000 handheld global positioning system. Within a ten-meter radius, each individual tree with a diameter above 30 cm was referenced to a database, and its height was recorded (Table  3 ). In addition, all individual trees with a diameter above 7.
5 cm within a smaller five-meter radius were also referenced to the database. The tree species were assessed according to leaf morphology. Soil-related information, such as organic matter and sediment size, was also assessed. In comparison, the vertical connectivity gradient includes forest plots that are of the same age (70-80 years) but are spread out across differing levels of lateral mobility and channel incision. These plots were chosen on the basis of previous studies conducted by  Dufour [63]  that highlighted differences in the growth of European ash, depending on the elevation of forest plots, relative to the water level of the river, which affects the distance to groundwater and the frequency of flooding. We consider them to be mature forest plots in the case of the Ain River riparian forest because they belong to the later successional stages identified by the ONF and to some of the oldest sectors of the riparian forest.
The Extensive Vegetation Surveys Performed in 2008 and 2017 by ONF The vegetation surveys (for which remote sensing information was acquired) conducted by the ONF at the request of the local stakeholders in 2008 and 2017 covered c. 400 plots in the study reach, one per hectare of forest (see Figure  3  and Table  2 ). For each vegetation plot, the basal area of each species and the total basal area were measured using a relascope. This basal area measurement corresponds to the superficy (assumed to be circular) of a cross-section of the tree at 1. 3 m from the ground. Additional information, such as grass cover, was collected within a five-meter radius to assess the ecological diversity of the plot. Information on the presence or absence of key invasive species, such as Fallopia japonica and Acer negundo, was added to the survey database.
Species were determined using an identification key focusing on twigs. The soil depth and humidity were also assessed for each plot. The age indications in this dataset came from post-hoc assessments by the ONF team and relied on identifying the year of colonization by looking at the series of historical aerial photographs, described in Section 3. 1. 3. The field operators who performed these surveys were trained forestry professionals and were different from the team of EVS researchers that conducted the survey described in Section 3. 2. 1.
Methods The methodology used in this paper is divided into four different steps, as shown in Figure  4. First, forest indicators were extracted from the LiDAR and hyperspectral data, providing information about the forest structure, spectral reflectance, and the topography of the corresponding forest plot. Then, this expanded dataset was coupled with the vegetation field surveys, in order to explore the characteristics of the riparian forest along an age gradient from early pioneer individual trees that have not yet reached maturity to later successional stages dominated by 50+ year poplars and post-pioneer species. By simultaneously exploring these characteristics along differing geomorphic reaches, we then studied how forest plots of a given age are impacted by reach-scale geomorphic features, such as channel incision and sediment starvation.
an age gradient from early pioneer individual trees that have not yet reached maturity to later successional stages dominated by 50+ year poplars and post-pioneer species. By simultaneously exploring these characteristics along differing geomorphic reaches, we then studied how forest plots of a given age are impacted by reach-scale geomorphic features, such as channel incision and sediment starvation. Finally, random forest classifiers-using remotely sensed indicators generated from LiDAR and hyperspectral data as predictors-were constructed to discriminate between different hydrological connectivity levels according to key target species (for wet and dry environments), identify the transition from pioneer poplar forest towards post-pioneer hardwood forest, and identify growing and mature forest patches.
Data Processing: Extracting Forest Indicators from Hyperspectral and LiDAR Data Both the hyperspectral and LiDAR datasets were processed to facilitate the extraction of metrics at the plot level. The complete list of metrics can be found in Appendix A, and Finally, random forest classifiers-using remotely sensed indicators generated from LiDAR and hyperspectral data as predictors-were constructed to discriminate between different hydrological connectivity levels according to key target species (for wet and dry environments), identify the transition from pioneer poplar forest towards post-pioneer hardwood forest, and identify growing and mature forest patches. The resulting classifications were used to map the key characteristics of the riparian forest along the 20-km reach of the lower Ain River basin and provide a view of the resulting spatial trends at the reach scale.
Data Processing: Extracting Forest Indicators from Hyperspectral and LiDAR Data Both the hyperspectral and LiDAR datasets were processed to facilitate the extraction of metrics at the plot level. The complete list of metrics can be found in Appendix A, and for each metric, the mean, minimum, maximum, variance, and standard deviation were extracted for each plot in the EVS and ONF dataset. The LiDAR data from 2015 were initially processed using the lidR library  [69]  to extract a digital elevation model (DEM), digital height model (DHM), and structural vegetation metrics. The Fluvial Corridor Toolbox  [70]  was then used to detrend the DEM, relative to the water level under low-flow conditions (Q = 16 m 3 /s), to provide the relative elevation of each plot, with respect to the water level.
Further processing included using SAGA GIS  [71]  to extract a wide range of plotlevel topographic indexes found to be relevant for riparian grassland classification in a previous study  [56]. The LiDAR data from 2008 were also processed using the lidR library to extract a DEM. Only this information was extracted from the data because only ground points were present in the available post-processed point cloud. This digital elevation model was then subtracted from the 2015 model to assess sedimentation processes, according to the difference in plot elevation between the two dates. The accuracy of this process was assessed by choosing 12 ground control points along the study reach in areas that we assumed to have a stable elevation (such as roads and fields) and looking at the difference in vertical elevation between the two LiDAR datasets for these control points.
This resulted in a mean error of 9. 6 cm and a median error of 9. 5 cm. Since the spatial coverage of the second LiDAR dataset was also limited, it was not used as a predictor for the random forest classification in step 3 of the methodology, but only to help characterize the vegetation plots in step 2 of the analysis. For the hyperspectral imagery, a minimum noise fraction was produced, in order to denoise the initial image into meaningful bands holding the majority of the spectral information. The minimum noise fraction was calculated for only the vegetated area of the hyperspectral image by applying a vegetation mask based on the LiDAR-derived canopy height model. In addition, a set of hyperspectral vegetation indexes were calculated, with these targeting key vegetation features, such as pigmentation, greenness, and canopy water content (listed in Appendix A).
Random Forest Classifications of Forest Connectivity and Resulting Maps Following the analysis of the forest characteristics, random forest classifiers were constructed for the four different classification targets of age group, forest type, presence of Fallopia japonica, and presence of Tilia cordata (Table  4 ). All the indexes listed in Appendix A were used as inputs to the classifiers, and a randomly sampled dataset of 50 plots was used for each class. The classification targeting age groups attempted to distinguish between the growing forest and mature forest patches. Growing forest patches are likely to be located in areas where lateral mobility occurs and leads to forest rejuvenation, whereas mature patches are likely to be located on either well-connected areas of the river that have not yet been eroded or along reaches impacted by sediment starvation.
Exploring Forest Characteristics and The elevation relative to the low-flow water level was lowest for the youngest forest plots of the EVS dataset, which are located on recently-formed gravel bars (Figure  5a ). After these, the elevation rose quickly because most forest plots above 8 years old were located two meters above the low-flow water level. Because of the free meandering mechanics of the Ain River within this reach, some of the older plots (17 y. o. , 20 y. o. , and 22 y. o. ) were located near to topographic depressions, such as paleo-channels and cut-off reaches, and featured a lower elevation (<2 m). This suggests that local fluvial dynamics can induce variability in plot elevation within a given reach and for a given age; for example, low elevation due to the presence of paleo-channels that are closer to the water table.
Both overbank sedimentation and the elevation of plots, relative to the water level of the river, provide information about flooding frequency, and therefore, hydrological connectivity, whereas plot elevation also provides information about the distance of the vegetation plot to groundwater resources. Hence, lower overbank sedimentation and higher plot elevation are representative of dryer physical conditions in the corresponding vegetation plots. The elevation relative to the low-flow water level was lowest for the youngest forest plots of the EVS dataset, which are located on recently-formed gravel bars (Figure  5a ). After these, the elevation rose quickly because most forest plots above 8 years old were located two meters above the low-flow water level. Because of the free meandering mechanics of the Ain River within this reach, some of the older plots (17 y.
This is also the case for the ONF dataset, which provides a more extensive view of the riparian forest of the Ain River, but one in which plots are less homogeneous in age. Indeed, in the ONF plots, the elevation relative to low-flow water level (Figure  5b ) was, on average, lower than two meters for plots up to 10 years old and for the few 19-year-old plots, but was two meters or higher when riparian vegetation was older, reaching values above four meters for some of the oldest plots. The mean overbank sedimentation of plots younger than ten years was above 20 cm, reaching as high as 60 cm on one plot, but this value dropped sharply afterwards, with sedimentation reaching values closer to 10 cm on average (which was also the mean error for the vertical difference between the two LiDAR studies in locations with stable altitude) (Figure  5d ).
However, age is not the only driver of change evident on Figure  5 , as there are differences between the river reaches with lower lateral mobility (the incised reach and the sediment-starved one, where lateral mobility is constrained by morainic deposits) and the shifting reach of the river where lateral mobility is high. Plots of the same age located in the incised or sediment-starved reaches, as opposed to the shifting reach of the river, feature a higher elevation relative to the water level of the river (averages for different ages varying between 50 and 100 cm) and lower sedimentation (averages for different ages varying by up to 10 cm). Two field measurements focusing on the soil at the center of each of the forest plots were explored for the ONF dataset: (1) soil depth up to one meter measured by an auger (Figure  6a ) and (  2 ) the available water capacity of the soil (Figure  6b ).
Associated Changes in Species Composition According to Field Surveys The plots sampled by the EVS survey mainly comprised pioneer riparian species such as Salix sp. and Populus nigra, whose colonizing ability relies on frequent flooding (Figure  7a ). Post-pioneer species, such as Fraxinus excelsior, only become more abundant in the older plots that were sampled on the vertical connectivity gradient (70 y. o. ) across both the shifting and incised reaches of the river. This shift towards more post-pioneer compositions was more advanced in the plots sampled in the incised reach of the river, with only 20% of pioneer species remaining in a given plot, while 50% remained in the plots located in the shifting reach. The same trend can be observed for the extensive ONF dataset, which shows the colonization of forest plots by Fraxinus excelsior after 30 years (Figure  7b ).
Those plots with lower connectivity are also favored by shrub species, such as Corylus avellana and Crataegus monogyna. The same trend can be observed for the extensive ONF dataset, which shows the colonization of forest plots by Fraxinus excelsior after 30 years (Figure  7b ). Once again, this colonization is more advanced for plots in either the incised or sediment-starved reaches of the river, compared with plots located in the shifting reach. This suggests that the hydrological disconnection caused by the incision of the riverbed creates an environment that is more favorable to Fraxinus excelsior than to the typical pioneer species of the Ain River. In the case of the survey conducted by EVS, this variation in hydrological connectivity results in the presence of an abundance of species that prefer to colonize dryer soils (Table  5 ).
Channel incision also leads to the implantation of species preferring dryer conditions. Although the forest grows with age, the heights of forest patches in the incised reach were found to be lower than those of patches in other reaches. In mature forest plots, the canopy structure correlated with hyperspectral indexes of greenness and canopy water content, suggesting that this difference could be due to higher water stress in the communities growing along the incised reach of the Ain River. In addition to characterizing the impact of age and vertical connectivity on the riparian forest and its health, the datasets were used to attempt to predict forest connectivity from remotely sensed indicators generated from LiDAR and hyperspectral data, with random forest classification being used to target key species, the shift between the poplar forest and post-pioneer hardwood forest, and whether forest plots were still growing or mature.
The resulting forest maps highlighted the downstream-upstream gradient of hydrological connectivity along the studied section of the Ain River. Forest rejuvenation does not occur in the incised reach, and channel incision led to a more post-pioneer type hardwood forest, which is favorable to species liking dry conditions. In contrast, the shifting reach of the river, where fluvial dynamics occur, features a forest where rejuvenation processes are still ongoing and where older and mature patches can still feature the pioneer poplar forest typical of the Ain River, although they are more likely to also be favorable for the development of the invasive Japanese knotweed. Our study highlights the potential of hyperspectral and LiDAR data for studying riparian forests and understanding the co-occurring processes of forest growth and impacts from anthropic changes, such as channel incision.
The techniques could provide diagnostic tools to help differentiate healthy riparian forest from dryer patches that could warrant restoration actions. In addition, the possibility of using reflectance data alone for mapping the connectivity of riparian forests is promising for monitoring our forests and their responses to climate change and restoration actions. However, fully exploring the changes occurring in riparian forests along the two gradients of age and connectivity required good field data and information derived from LiDAR data and was a crucial first step for understanding the processes at play in our study site. Nevertheless, further research is needed to assess the reproducibility of our results and whether they could be obtained using low-cost and readily available multispectral data from satellites.
Acknowledgments: The authors would like to thank both EDF and the  Université de Rennes  ( UMR 6118 ) for providing the LiDAR data used in this work, the  Airborne Research and Survey Facilities of the Natural Environment Research Council  ( NERC-ARSF ) for carrying out the hyperspectral data acquisition, and the  French National Forests Office (ONF)  and the  Syndicat d'Ain Aval et de ses Affluents  ( SR3A ) for one of the vegetation surveys used in this paper. The authors would also like to thank  Cui Dandan ,  Zhi Zhang ,  Bianca Räpple ,  Fanny Arnaud ,  Christine Mouquet-Noppe ,  Vincent Warwrzyniak ,  Franck Perret , and  Charles-Robin Gruel  for their contributions to the fieldwork.
Funding: This research was funded by the  Graduate School   H2O ' Lyon  ( ANR-17-EURE-0018 ) of the  Université de Lyon (UdL) , which is part of the program " Investissements d'Avenir " run by  Agence Nationale de la Recherche (ANR). This research was also funded by the  Rhône-Méditerranée-Corse Water Agency  and partially funded by the  "National Science Centre, Poland ", under contract agreement  UMO-2017/25/B/ST10/0967. The hyperspectral data acquisition was funded by the  European Facilities for Airborne Research (EUFAR).