dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017 ), which appears problematic in terms of their damaging effects on the environment (heavy metal pollution, phosphogypsum, radioactivity). In addition, the phosphoric acid consumption in DAP and MAP fertilizer manufacturing was 7. 15 × 10 6 tons per year in China. It is against this background that an LCA study was performed by  Zhang et al. (2017)  to evaluate the environmental impact generated from the DAP and MAP fertilizer industry of China to establish a database on DAP and MAP fertilizer production. Using the ReCiPe method  (Goedkoop et al. , 2009) , they found that among the 18 categories, the key categories were terrestrial acidification, human toxicity, particulate matter formation, and marine toxicity. The most contributive substances were sulfur dioxide, nitrogen oxides, ammonia, particulates, copper, vanadium, nickel, cobalt, lead, mercury, and arsenic (Fig.
Determination of optical properties of ADOM All samples were filtered through 0. 2 mm Whatman acetate cellulose syringe filters and then filled into a 1 cm fluorescence cuvette. The cuvette was placed in a Horiba Aqualog system, which is able to simultaneously measure absorbance and fluorescence. For each absorbance (excitation) wavelength, a fluorescence emission spectrum was recorded at 3. 28 nm intervals at a fixed range between 220. 4 and 596. 9 nm and a 5 s integration time to generate excitation-emission matrix spectra (EEMS). Because DOM always fluoresces at longer wavelengths than it absorbs, only emission data from excitation wavelengths 220e550 nm were considered for generating EEM spectra. Rayleigh and Raman scattering signals were removed from all EEM spectra in Matlab ® using methods described previously  (Zepp et al.
Mass difference network reconstruction Theoretical ion masses of the above assigned CHO molecular formulas were used to generate the mass difference network. Theoretical ion masses (nodes) were connected by edges if their mass differences were equal (±0 ppm) to the theoretical mass differences of DHOCl (51. 971593 amu). Mass difference networks were visualized using the open-source Gephi software (Yifan Hu layout). This technique has been previously described in greater detail  (Tziotis et al. , 2011).
S9 ). Although we cannot determine concentrations of these fatty acids using this approach, the MS-MS spectra of stearidonic, linolenic and linoleic acid matched published spectral records MT000005, MT000072 and MT000114, respectively in the MassBank database  (Horai et al. , 2010)  (Supplementary Material, Fig. S9 ). A diverse group of hydroxylated unsaturated fatty acids also might be present in ADOM, due to oxidative transformations or via other hydroxylated unsaturated fatty acids that are present in metabolic pathways. Proposed putative reactions of these fatty acids to form chlorinated fatty acid DBPs are given in Supplementary Material (Figs. S5eS7). The presence of additional C 18 -DBPs indicated their formation via hydroxylated unsaturated fatty acids in the ADOM. 17hydroxylinolenic acid is known to be involved in a-linolenic acid metabolism.
This research aims to assess the capabilities of Very High Spatial Resolution (VHSR) hyperspectral satellite data in order to discriminate urban tree diversity. Four dimension reduction methods and two classifiers are tested, using two learning methods and applied with four in situ sample datasets. An airborne HySpex image (408 bands/2 m) was acquired in July 2015 from which prototypal spaceborne hyperspectral images (named HYPXIM) at 4 m and 8 m and a multispectral Sentinel2 image at 10 m have been simulated for the purpose of this study. A comparison is made using these methods and datasets. The influence of dimension reduction methods is assessed on hyperspectral (HySpex and HYPXIM) and Sentinel2 datasets. The influence of conventional classifiers (Support Vector Machine-SVM-and Random Forest -RF-) and learning methods is evaluated on all image datasets (reduced and non-reduced hyperspectral and Sentinel2 datasets).
For instance, Pu et al. [15]  obtained an overall accuracy (OA) of 67. 22% with WorldView 2 (eight bands), although the producer's accuracy was not good enough due to this low spectral resolution. Multispectral data such as Sentinel (10-20 m spatial resolution) show also great potential for tree species classification  [22, 23]. Immitzer et al. [22]  obtained an OA of 65% for seven tree species without exploring the full potential of Sentinel2 high repetitiveness. Hyperspectral data also tends to develop for the identification of urban vegetation. While this technology is very spectrally accurate (more than 100 contiguous and narrow bands of about 10 nm each), the spatial resolution of hyperspectral satellite data remains insufficient. Conversely, airborne hyperspectral sensors have demonstrated their capability for tree classification  [16] [17] [18] [19] [20] [21] [22] [23] [24]  (Table  1 ).
When a limited number of training samples is available, the stratified K-folds cross-validation is often used to split the dataset into training and validation samples, taking into account the proportion of individuals from each class. It is assimilated as one of the most rigorous methods to prevent overfitting and to estimate properly the error rate  [47]. But K-folds results depend of the training set and the partition into folds  [47]. Alonzo et al. [48]  and Clark et al. [38]  use stratified 10-fold cross-validation for an unbalanced and limited number of training samples, showing less biased results than with 2-folds while remaining less costly with respect to computation when the number of folds is equal to the number of samples. This state-of-the-art technology shows that the accuracy of tree species classification using VHRS hyperspectral images can be influenced by DR methods, classifiers, learning methods, defined hereafter as hyperspectral techniques.
In this context, evaluating the capabilities of VHSR hyperspectral sensors in order to classify the diversity of tree species in urban area is of prime importance. The aim of this study is to compare and assess which hyperspectral techniques, or combinations, show the greatest performance. Moreover, as spatial and spectral resolutions may influence classification accuracy, another objective consists of assessing which resolution is the most appropriate for urban tree diversity classification. Indeed, most of the listed literature (Table  1 ) used airborne hyperspectral data with a spatial resolution below 4 m width, which might not be achievable for spaceborne hyperspectral sensors. This study would thus contribute to the design of a future spaceborne hyperspectral sensor (HYPXIM) by assessing the optimal spatial resolutions for urban applications.
While limited to tree diversity classification in this study, it has already been assessed for the detection of photovoltaic panels for example  [49]. This paper focuses on commonly-used hyperspectral techniques as their respective influence is rarely assessed. While focusing mainly on hyperspectral data and techniques, the comparison with results obtained with Sentinel2 data may emphasize the added-value of the former. The first part of this article presents the case study of Toulouse, on which this study is focused, the hyperspectral and ground-truth dataset, and all the methods used. The second part describes the results of experiments made to assess the respective and combined influence of hyperspectral techniques. The third part discusses possible perspectives and improvements for tree detection in urban environments, as well as the potential of spaceborne VHRS hyperspectral imaging for the study of urban areas in general.
Remote Sensing Data As shown in the introduction, spaceborne hyperspectral imagery can be very convenient for urban applications. The French National Space Center (CNES) is considering this option by assessing the potential of such a sensor: the HYPXIM hyperspectral sensor is characterized by 192 bands ranging from 0. 4 to 2. 5 µm with a spatial resolution that may vary from 4 m to 8 m (Table  2 ). In order to simulate HYPXIM imagery, an airborne hyperspectral dataset was acquired using the HySpex sensor (Table  2 ) in July 2015. These data have been resampled accordingly to HYPXIM specificities at 4 m and 8 m spatial resolution. Moreover, to highlight the interest of VHSR hyperspectral images, a Sentinel2 multispectral dataset has also been simulated at 10m resolution (for all spectral bands).
In Situ Samples Field work was conducted between May 31st and 2nd June 2017. It is assumed that between the data acquisition (2015) and field survey (2017), the tree composition of the city did not change. Indeed, trees are nowadays part of the climate adaptation and mitigation plan of the municipality, declared as protected contributors to cooling urban heat islands and summer heat waves. However, in the case of disease, some removals may occur, but to our knowledge, there were none during this period. This fieldwork led to the identification and location, using a differential GPS (Trimble Geo7x-with an absolute geometric accuracy below 10 cm), of more than 600 trees in the swath of the hyperspectral dataset. This accuracy allows for the extraction of each sample the corresponding spectral signature.
Overall Approach The aim of this study is to assess the influence of hyperspectral techniques and data on their ability to classify tree diversity. Figure  2  sums up the main hyperspectral techniques used in this study. Several methods of DR (Section 2. 4. 2. ) and classification (Section 2. 4. 3. ) are tested to evaluate which combination of techniques offers the best performance according to the considered hyperspectral images. Each classification performance is assessed using various training size datasets (depending on tree Species and Families nomenclatures, themselves divided into two sub-levels according to the number of available individual samples per class inventoried in the field) and various sizes of stratified k-folds (see Section 2. 4. 3). The aim of this study is to assess the influence of hyperspectral techniques and data on their ability to classify tree diversity.
Dimension Reductions Methods Feature extraction is defined as a set of methods for extracting information from an image  [33] , for example, by the use of transformative methods (PCA, MNF). PCA constructs a low-dimensional representation of the data that describes as much of the variance in the data as possible. This is done by finding a linear basis of reduced dimensionality for the data, in which the amount of variance in the data is maximal  [34]. However, it does not take account of the signal-to-noise ratio (SNR) unlike the minimal noise fraction (MNF)  [37]. The MNF is also a component transformation providing ordered noise-reduced components. Feature extraction methods (PCA, MNF) are applied on the four image datasets (HySpex 2 m, HYPXIM 4 m and 8 m, Sentinel2). The selection of the most appropriate components is made empirically, since they explain more than 98% of variance.
For feature selection methods, the literature brings a lot of information on discriminating indices nevertheless none has been applied in urban contexts. In this study, 96 spectral vegetation indices (VI) (listed in  [37] ) have been computed for the three hyperspectral datasets as Sentinel spectral resolution is not sufficient to do so. These VI illustrate various biophysical characteristics such as chlorophyll, leaf area index (LAI), carotenoids, etc. , and the selection of the most uncorrelated VI is based on two methods:.
Training Datasets and Method Two levels of tree nomenclature are considered: Families and Species. Types have been discarded because of their too general level, the unbalanced dataset but also because multispectral data are known to correctly classify this level of nomenclature. Families and species are respectively sub-divided into two training datasets, depending on the number of individuals per class: the first ones are less restrictive with at least four individuals, while the others consider at least 10 individuals per class. The number of tree classes considered depends on the representativeness of training data (Table  3 ).
Classification Methods Two commonly used, but not the only classification methods for hyperspectral data are tested, The training method is based on stratified k-folds  [44, 47] : the original dataset is randomly partitioned into k subsamples where each class is represented with the same proportion of individuals. To assess the influence of the training size, two approaches are considered. The first one uses k-folds = 2 (2-folds): the dataset is divided into 50% training, 50% validation samples and applied for the four training datasets. More precisely, for each class, half of the individuals are used to extract the spectral signature (or assimilated values of reduced datasets) to train the classifier, and the other half is used to validate the resulting classification. In order to optimize the learning capacity of classifiers, each fold is used iteratively as a training and validation sample.
Classification Methods Two commonly used, but not the only classification methods for hyperspectral data are tested, namely the support vector machine (SVM) and random forest (RF). The SVM projects the original feature space into a space with a higher dimensionality to facilitate the discrimination of classes. The classifier is trained using an optimization process associated with a cost function that makes it possible to linearly separate classes to kernel functions (linear, radial basis function-RBF-polynomial, sigmoidal) applied on the original data allowing greater reliability and classification accuracy  [41]. However, it can sometimes lead to an over-fitted model with misclassification  [41]. The RF classifier is an ensemble of many decision trees. This classifier is also known to be appropriate for hyperspectral data  [42, 43, 52, 53]  because it tends to perform a selection of characteristics to build the decision tree during the learning process  [43].
Experiments: Assessing the Respective and Combined Influence of Hyperspectral Techniques and Data The respective influence of hyperspectral techniques and data is evaluated based on their ability to classify tree diversity. Classification accuracy is performed using the overall accuracy (OA) and the Kappa index of agreement (KIA). For the 2-folds learning method and data, each SVM and RF classifier is applied 10 times on each reduced and unreduced image dataset in order to estimate the variance of the OA, leading to the computation of 1120 urban tree classifications. Although these results are appropriate to assess the combined influence of the hyperspectral techniques, their respective influence is difficult to evaluate. We assume that averaged OA and KIA (AOA / AKIA) for each technique computed from these classifications can be useful to evaluate their respective contribution.
Moreover, the influence of training methods (i. e. , the number of k-folds) is assessed differently as the 10-folds is computationally highly time-consuming. It is applied only on the image dataset exhibiting the best classification results, and then compared with the corresponding results obtained with the 2-folds training method. The combined effect of hyperspectral techniques is analyzed more precisely thanks to the OA and KIA, accounting for both the producer's and user's accuracy.
Dimension Reductions: Comparison of Extracted Components The number of selected components from MNF and PCA is respectively of 40 and 30 for all hyperspectral images, and 13 and 9 for Sentinel2. The feature selection method using PCA provides a selection of 19 vegetation index for HySpex 2 m (explaining 86. 16% of the variance), 19 for HYPXIM 4 m (84. 5%) and 17 for HYPXIM 8 m (85. 89%). When using the ANOVA and the correlation matrix, 45 uncorrelated VI are selected for HySpex 2 m, 39 for HYPXIM 4 m and 38 for HYPXIM 8 m. A recurrence of selected VI is observed between datasets. For example the VI Datt (850) is selected for all image datasets. HYPXIM 4 m exhibit the larger F-values interval (between 38. 26 and 1. 96) compared to HySpex (from 34. 26 and 0. 06) and HYPXIM 8 m (27. 96 and 1.
Evaluation of the Respective Influence of Hyperspectral Techniques and Data When using the 2-folds training method, both classifiers offer similar results. Classifications made with RF presents an AOA = 45. 15% and an AOA = 42. 61% with SVM (Table  4 ). The latter has the lower AOA while exhibiting the highest score (AOA = 59% and best AOA = 58. 54% for RF). RF shows the greatest variance with on average σ = 4. 2% against σ = 3. 8% for SVM. 5  and 6 ). Finally, according to the spatial and spectral resolution, HySpex 2m show an AOA = 52. 60% that is greater than any others image datasets: AOA = 42. 38% for HYPXIM 4m, AOA = 40. 51% for HYPXIM 8m and AOA = 41. 93% for Sentinel2 10m (Table  7 ). Table  4. Urban tree diversity classification averaged overall accuracies (AOA) and averaged Kappa index of agreement (AKIA) obtained using the 2-folds learning method and applied on each reduced hyperspectral image, and on the original (hyperspectral and Sentinel2) image datasets with RF and SVM according to the four sub-levels for families and species datasets.
Sub-level training datasets show that those with at least 10 samples (12 Families and 14 Species) help to improve tree classification accuracy compared to those with a minimal number of 4 individual samples. The sub-level "14 species" gives the best results with an AOA = 47. 10% against 45. 62% for the sub-level "12 families". The sub-level "19 families" (n individuals > 4) produces results slightly higher than the sublevel "27 species" with respectively AOA = 42. 23% and 40. 56%. The comparison of the 2-folds and 10-folds method as the training method shows the potential of 10-folds method because it significantly improves AOA. When comparing results obtained with these two training methods applied on MNF image datasets, the 10-folds provides an AOA = 68. 26% although AOA = 58. 42% with the 2-folds one (Tables  5  and 6 ).
significantly improves AOA. When comparing results obtained with these two training methods applied on MNF image datasets, the 10-folds provides an AOA = 68. 26% although AOA = 58. 42% with the 2-folds one (Tables  5  and 6 ). Finally, according to the spatial and spectral resolution, HySpex 2m show an AOA = 52. 60% that is greater than any others image datasets: AOA = 42. 38% for HYPXIM 4m, AOA = 40. 51% for HYPXIM 8m and AOA = 41. 93% for Sentinel2 10m (Table  7 ). Table  4. Urban tree diversity classification averaged overall accuracies (AOA) and averaged Kappa index of agreement (AKIA) obtained using the 2-folds learning method and applied on each reduced hyperspectral image, and on the original (hyperspectral and Sentinel2) image datasets with RF and SVM according to the four sub-levels for families and species datasets.
2. b): Linden are over estimated although only London plane species is present. These misclassifications are less present when classification is applied with reduced HYPXIM image at 4m (Figure  6 ). Species are better detected with less errors of neighboring pixels (Figure  6. 1. b and 6. 2. b). However, the classification with 2-folds method (Figure  6. 1. b) evinces a bigger misclassification than 10-folds method as for example on the Grand-Rond roundabout where Silver linden are over predicted. Related confusion matrices (Supplementary Table  S2 ) and producer's and user's accuracies (respectively PA and UA -Supplementary Tables  S3 ) highlight the influence of the unbalanced in situ dataset. Table  8  illustrates the PA and UA for the classifications shown in figure  5  and 6. Table  8.
PA and UA for the MNF-reduced HySpex and HYPXIM 4m hyperspectral images, classified with SVM and trained with the 14 species training dataset with 2-folds and 10-folds. These PA and UA correspond to those obtained for classifications shown in figures 5 and 6, exhibiting the greatest OA. Although the 10-folds always provides higher results compared to 2-folds learning method (with MNF DR only), it reinforces the interest of HYPXIM imageries. Indeed, with the 10-folds learning method, a significant improvement of urban tree diversity classification accuracy is observed: HySpex 2 m and HYPXIM 4 m reduced with MNF both exhibit an OA of 78. 38% and 73. 81% for 12 families and 14 species classes, respectively (Figures  3  and 4 ). In the meanwhile, improvements for HYPXIM 8 m and Sentinel2 due to 10-folds are much lower, or exhibit an OA below 65%.
2. b): Linden are over estimated although only London plane species is present. These misclassifications are less present when classification is applied with reduced HYPXIM image at 4 m (Figure  6 ). Species are better detected with less errors of neighboring pixels (Figure  6 (1. b,2. b)). However, the classification with 2-folds method (Figure  6. 1. b) evinces a bigger misclassification than 10-folds method as for example on the Grand-Rond roundabout where Silver linden are over predicted. Related confusion matrices (Supplementary Table  S2 ) and producer's and user's accuracies (respectively PA and UA -Supplementary Table  S3 ) highlight the influence of the unbalanced in situ dataset. Table  8  illustrates the PA and UA for the classifications shown in Figures  5  and 6. First, the greater the number of individuals per tree species, the better the PA and UA.
Third, the number of k-folds significantly improves accuracies for under-represented tree species, particularly for MNF-reduced HySpex images. Finally, the number of missed tree species (0% of PA or UA) is slightly greater with RF than SVM for both 14 species' and 12 families' nomenclatures. Table  8. PA and UA for the MNF-reduced HySpex and HYPXIM 4 m hyperspectral images, classified with SVM and trained with the 14 species training dataset with 2-folds and 10-folds. These PA and UA correspond to those obtained for classifications shown in Figures  5  and 6 , exhibiting the greatest OA.
Evaluating the Respective Influence of Hyperspectral Techniques First of all, comparing the respective influence of the hyperspectral techniques considered remains a difficult task since the influence of one technique on another is not linear. Although the use of averaged OA values per technique can be criticized, this provides interesting information on the respective contribution of the methods that have been tested for the classification of tree diversity. The method that allows the greatest gain in overall accuracy depends on the learning process and the data. In our case, the gain can be greater than 15% for some image datasets (HySpex 2 m, HYPXIM 4 m) as soon as the number of k-folds with a minimum of 10 individuals per class increases. Second, dimensional reduction methods also improve the classification's overall accuracy than other classification methods.
However, depending on the considered image dataset or even the DR methods for instance, some may be punctually more effective than others, fuelling the debate on the justification for choosing one or the other method. Nevertheless, it can be assumed that the very high spatial resolution of hyperspectral data has an inherent limitation: it increases the dimensionality of the data and makes any classification method ineffective if dimensional reduction methods are not previously applied. Indeed, in some few cases, the Sentinel2 image gives similar or even slightly better results than hyperspectral images even though the overall accuracy remains quite low. While we have focused on dimensional reduction methods, our results are in line with the scientific literature. The most effective method is MNF.