dataesr/scientific-paragraphs-categorization huggingface dataset:
2012; Krisnawati et al. 2012; Birigazzi et al. 2013). The first step is defining the most appropriate approach to biomass and carbon stock assessments (both at local and national levels). The fact that few countries possess a national database for tree models can be explained by various obstacles, access to information and data sharing being probably the most important  (Henry et al. 2011). Existing models can be quality controlled and validated. It is generally necessary to test the applicability of the models (UNFCCC 2011a, b) under a particular situation (i. e. , test whether the described tree allometry changes because of differences in soils, elevation, or climate relative to the site where the equation was originally constructed). The second step is defining the appropriate approach to analyze the forest inventory data taking into account the models available.
Identification of the most suitable decision tree requires, in most cases, access to raw data. If we sum the total number of individuals for the tree models available at GlobAllomeTree  (Henry et al. 2013) , it follows that between 56,937 and 121, 062 individuals were measured across a range of forest ecosystems (Fig. 1 ). 1  This figure is significantly higher than the sample size currently available for creating pantropical models (for moist forests, e. g. , Brown 1997: n= 170;  Chave et al. 2014: n=4004). In addition, while pantropical models were mainly developed for trees in closed and mostly unmanaged forest  (Chave et al. 2014) , the GlobAllomeTree database contains individuals from a wide range of forested landscapes and trees outside forests. The aim of this article is to recommend an approach for assessing biomass and volume at the national level that ensures the lowest possible uncertainty.
e. , some countries do not have university courses in the field of forestry or do not have funds allocated to ensure data collection, storage, and management. Raw data are needed to allow the validation of the estimates and perform accuracy assessments. Although highly desirable, it is not required to have locally developed models; available generic models may be used in early stages of the system. The model selected should be tested at a local level based on a limited number of samples from different ecological environments. Finally, the national forest inventory can provide national-scale coverage of ecosystems and facilitate the field data required to calculate volume and biomass. Cost issues are also relevant but were not addressed during the workshop. The following three options for using tree models as part of a national forest biomass assessment are based on the information available: Option 1 Neither models nor raw data are available.
, and has the advantage of generating weighted model estimates. The weighting can follow the stratification used for inventory or some prior knowledge and improve the estimates to include a greater range of variation in diameters of samples or species. Also, the coefficients used are the result of the weighted average of the information from existing models. This approach is a novel and justifiable method for option 1 and should be a robust (adaptable) alternative to consider when there is adequate information (options 2 and 3). Regarding the calculation of the uncertainty, two options are proposed. The first method is to validate the results using data obtained from destructive measurements. This can be costly and requires additional fieldwork and coordination with local authorities. The second method uses Monte Carlo methods or Bootstrapping  (Molto et al.
2013). These statistical methods are not field intensive but require advanced skills not readily available in all countries. It is clear though that the analysis of error propagation can be achieved when the raw data are available to allow this. 4 Will the establishment of new methodologies have an impact on the emission reduction estimates? Emission reduction estimates in the context of REDD+ will be reported as part of the GHG inventory for the biennial report and the national communication to the UNFCCC (2010). As part of the national communication, it is very likely that developing countries will be requested to provide an improvement plan  (Tulyasuwan et al. 2012 ). An improvement plan is considered as an essential part of a National GHG Inventory System. A well-informed list of possible improvements sorted by priorities (e.
Conclusion This article provides clear advice on how to improve tree and forest volume and biomass assessment at local and national scales. The adequate use and selection of volume and biomass equations and accuracy of estimates contribute to improving national forest inventories. Volume and biomass equations are critical to improve the precision of volume, biomass, and carbon stock and changes. This can be done in different ways: (1) Stratification is valuable, although there will always be the need to sample additional ecosystems or species with particular growth forms. (2) Non-destructive measurements during national forest inventory campaigns can enlarge available datasets, improving models' precision and coverage. (3) Adequate measurement of variables such as wood density, tree diameter, and tree heights.
Acknowledgments UN-REDD, FAO, and the  SilvaCarbon Program  provided funding for the " Regional Technical Workshop on Tree Volume and Biomass Allometric Equations in South and Central America ," where ideas for this paper were first discussed. The BEF unit supported by the  French National Research Agency (Agence Nationale de la Recherche, ANR)  through the  Laboratory of Excellence (Labex) ARBRE  ( ANR-12-LABXARBRE-01 ). This work is part of the  QLSPIMS  project. MRM was supported by two " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-2501 ;  TULIP, ref. ANR-10-LABX-0041 ) and by the  CoForTip  project ( ANR-12-EBID-0002 ). We also acknowledge the input of the 30 participants in this technical workshop. The authors thank  Javier GarciaPerez  and  Emily Donegan  for their advice in the preparation of this article and  Luca Birigazzi  for providing Fig.
Semi-Empirical Models These models are widely used for their analytical nature and use of only a few input parameters. They do not attempt to describe the biophysical parameters and processes that shape BRF, but they provide a mathematical description of observed patterns in BRF datasets. They rely on simplified physical principles of geometrical optical (GO) models and RT theory. For example, linear kernel driven (LiK) models  [2] [3] [4]  calculate BRF as the sum of an isotropic term and anisotropic functions (kernels) that characterize volume and surface scattering. For example, MODIS, POLDER, MSG/SEVIRI, AVHRR, VEGETATION land surface BRF/albedo products are mainly generated using LiK models to invert the BRF parameters of multi-angular bidirectional reflectance in clear skies  [5]. Another example is the Rahman-Pinty-Verstraete (RPV) model  [6] , and its latter inversion accelerating versions: the Modified RPV (MRPV)  [7]  and EMRPV  [8]  models.
The very first RT models used this approach to model general trends such as the evolution of crop BRF/BTF in relation to phenological LAI changes. The approach of homogeneous turbid layers is, however, insufficient for description of complex landscape architectures. The heterogeneous landscapes are being simulated in two following ways (or their combination): (i) discretization of the spatial variable into a 3D set of spatial nodes called voxels  [28, 29]  that contain turbid medium, and/or (ii) representation of each individual landscape element with triangular facets as geometrical primitives. The objective of this paper is to present the latest advances in DART (DART 5 version) modeling of airborne and satellite IS as well as LIDAR data of architecturally complex natural and urban landscapes.
Urban objects (houses, roads, etc. ) contain solid walls and a roof built from facets. Finally, water bodies (rivers, lakes, etc. ) are simulated as facets of appropriate optical properties. Specific 3D transformations and optical properties can be assigned to each landscape object. Additionally, DART can use external libraries (Figure  2 ) to import, and to some extent edit (e. g. , translation, homothetic and rotation transformations) landscape elements, digital elevation models (DEM) and digital surface models (DSM) produced by other software or measured in field. Importantly, the imported and DART-created landscape objects can be combined to simulate Earth scenes of varying complexity. The optical properties of each landscape element and the geometry and optical properties of the atmosphere are specified and stored in SQL databases.
These cells are characterized by their gas and aerosols contents and spectral properties (i. e. , phase functions, vertical profiles, extinction coefficients, spherical albedo, etc. ). These quantities can be predefined manually or taken from an atmospheric database. DART contains a database that stores the properties of major atmospheric gases and aerosol parameters for wavelengths between 0. 3 m and 50 m  [18]. In addition, external databases can be imported, for instance from the AErosol RObotic NETwork (AERONET) or the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric RT modeling includes the Earth-atmosphere radiative coupling (i. e. , radiation that is emitted and/or scattered by the Earth can be backscattered by the atmosphere towards the Earth). It can be simulated for any spectral band within the optical domain from the ultraviolet up to the thermal infrared part of electromagnetic spectrum.
e. , total chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area) and leaf mesophyll structural parameter. -Computation of scene spectra and broadband image data (reflectance, temperature brightness, and radiance), using a sensor specific spectral response function for either a single DART simulation with N spectral bands, or for a sequence of N single spectral band simulations. -Importation of raster land cover maps for creating 3D landscapes that contain land cover units, possibly with 3D turbid media as vegetation or fluid (air pollution, low altitude cloud cover, etc. ). -Importation or creation of Digital Elevation Models (DEM). DEMs can be created as a raster re-sampled to the DART spatial resolution or imported either from external raster image file or as a triangulated irregular network (TIN) object.
-Automatic initiation of a sequence of Q simulations with the Sequence module. Any parameter (LAI, spectral band, date, etc. ) A1, …, AM can take N1, …, NM values, respectively, with any variable grouping (Q ∏ N ). Outcomes are stored in a Look-Up Table (LUT) database for further display and analysis. It is worth noting that a single ray tracking simulation with N bands is much faster than the corresponding N mono-band simulations (e. g. , 50 times faster if N > 10 3 ). -The simulated 3D radiative budget can be extracted and displayed over any modeled 3D object and also as images of vertical and horizontal layers of a given 3D scene. -The transformation from facets to turbid medium objects converts 3D plant objects (trees) composed of many facets (> 10 6 ) into a turbid vegetation medium that keeps the original 3D foliage density and LAD distribution.
This method remediates constraints limiting RT simulations with many vegetation objects (e. g. , forest) that lead to too large computational times and computer memory requirements. -The creation of 3D objects by using volumes with pre-defined shapes that can be filled with various 3D objects (triangles, discs, etc. ). This functionality allows a quick test of simple hypotheses, as for instance the influence of vegetation leaf shape and size in turbid media simulations. -The transformation of LIDAR multi-pulse outputs into industrial Sorted Pulse Data (SPD) format  [55]. Implementation of the SPDlib software allows users to create, display, and analyze their own LIDAR point clouds  [56]. -Display tools for visualization and quick analysis of spectral images and LIDAR waveform and photon counting outputs, etc.
While the basic DART modules are programmed in C++ language (~400,000 lines of code), most external tools are written in Python language. In addition, a Graphic User Interface (GUI), programmed in Java language, allows users to manage model inputs (RT approach, scene geometry, view direction, etc. ), to specify required output products (BRF, radiative budget, etc. ), display results, and run the external scripts. A strong feature of DART is acceleration of RT modeling using multithreaded computation, allowing use of a specified number of processor cores simultaneously, which results in a near linear scaling of the processing time.
The aim was to design a tool that calculates the time interval, for any date and for any region on the Earth, during which the useful radiance Lu,TOA that originates from the Earth surface is reliable, while considering the expected sensor relative accuracy (~3%), sensor signal-to-noise ratio, atmosphere, local topography, etc. Lu,TOA is the difference between TOA radiance and radiance Latm due to the atmosphere only. Four typical African landscapes (grass savannah, tree savannah, tropical forest, desert), with varying parameters such as spatial resolution, signal-to-noise ratio and elevation were considered. Simulations used local atmosphere conditions from the AERONET network and ECMWF database. Three specific DART features were used: (i) RT modeling through a spherical atmosphere, (ii) automatic computation of satellite view direction for each Earth coordinates, and (iii) automatic calculation of sun direction for any date, satellite and scene coordinates, etc.
That is why the next ICESat-2 mission  [64]  will carry a photon counting instrument called the Advanced Topographic Laser Altimeter System (ATLAS). DART simulates photon counting data using the statistical information derived from one or several simulated waveforms. Figure  14  shows an example using a simple bare ground with a vegetation plot and signal of a photon counting LIDAR acquired at  = 1064 nm along a horizontal sensor path, perpendicular to the vegetation plot. Because, the sun direction was set as oblique (s = 45°), part of the bare ground is in the shade of the vegetation cover. Figure  14b  shows the radiance image demonstrating the sun illumination, which is used to compute the solar noise of LIDAR signals. Figure  14c  illustrates the photon counting simulation along the sensor path (horizontal axis) and Figure  14d  is a subset enlargement of Figure  14c.
Modeling IS Data with the Perspective Projection RT simulations of BRF are usually based on the assumption that the whole landscape is observed along the same viewing direction. This assumption is acceptable when a relatively small landscape is observed from an altitude ensuring that the divergence of the FOV over the landscape can be neglected. However, this disqualifies direct comparison of RT modeled images with actual observations that do not meet this assumption. DART was, therefore, improved to properly consider landscape dimensions together with sensor altitude and consequent sensor FOV  [65]. This new functionality provides DART images simulated at large scales with realistic geometries, which allows their per-pixel comparability with actual RS acquisitions. Two types of sensor geometries are available: a pinhole camera and an imaging scanner.
Since DART can apply the ray tracking method to simulate IS and the Ray-Carlo method to simulate LIDAR multi-pulse waveforms of the same landscape in a single run, it can also directly facilitate the in-flight fusion of both simulated datasets. During the first step of this procedure, the position of the LIDAR FOV center at the minimum altitude is recorded per pulse together with corresponding pulse identification number (ID). Then, the imaging scanner image at the minimum altitude is simulated with the parallel-perspective projection. This image is automatically referenced in DART scene coordinate system, which allows the radiance value to be computed in accordance with the FOV center of each recorded LIDAR pulse through a cubic-spline interpolation of the scanner image. The LIDAR output is then converted into the SPD format and processed with the SPDlib software to produce the LIDAR point clouds.
Conclusions During more than 20 years of development, DART has reached the stage of a reference RT model in the field of optical RS. Free licenses are provided for research and teaching activities by the Paul Sabatier University (UPS) in Toulouse in cooperation with Centre National d'Etudes Spatiales (CNES, France). More than 100 research organizations and universities use it for designing future satellite sensors, for developing new RS applications for forestry, agriculture, and urbanism, and also for educational purposes, especially for lecturing the physical bases of RS and radiative budget. This paper demonstrates new DART modeling and functionalities for simulating data of satellite and airborne LIDAR waveform and photon counting sensors, as well as images produced by IS from visible to thermal infrared wavelengths, both for an identical scene containing any vegetation and urban objects.
Recent improvements enhanced the capacity of DART to simulate any Earth landscape with any atmosphere conditions from common databases (e. g. , AERONET network and ECMWF database). Natural and urban landscapes can be now simulated with geometrically explicit surfaces (facets) and turbid vegetation/fluid volumes. It addition, DART can import and manage 3D objects (tree, house, etc. ) in the conventional *. obj format. Moreover, it can transform part or all of these objects into 3D turbid voxels. Finally, three major improvements in radiation modeling were introduced. (1) Modeling of satellite and aircraft LIDAR waveform and photon counting signals using the specifically designed Box and Ray-Carlo methods. (2) Image simulation of spectroradiometers mounted on aircraft or unmanned aerial vehicles in the perspective projection.
This simulation is useful to bridge the scaling gap between in situ radiometric measurements and satellite observations. The possibility to model LIDAR and spectral image data of the same landscape is highly appealing for RS data fusion techniques. (3) Simulation of data acquired by an IS aboard a geostationary satellite, for any Earth region, and at any date from sunrise to sunset. Apart from new scientific functionalities, many technical improvements took place in DART, such as parallelizing computations with the multithread approach or, management of DART outputs as SQL databases. Improvement of DART physics, optimization of its computational performance, and development of new functionalities still continue. The DART developing team is currently working on the following five improvements.
Acknowledgments This work was supported by the  ANR  in the frame of the  FOLI3D  project and by the  Centre National d'Etudes Spatiales (CNES)  in the frame of the TOSCA projects " Stem-Leaf " and "Geostationary satellite". Part of the work was conducted in the frame of the  NASA  project " DART-LIDAR ". Contribution of J. -B. Féret was supported by a  CNES  post-doctoral research grant and contribution of Z. Malenovský was supported by the  ARC Discovery  project  DP140101488  " AirLIFT ". The authors are grateful for the Toulouse city geo-database provided by Toulouse town hall and thankful to all scientists who contributed to DART development since its first steps in 1992.
Data collection Ecosystem services supply. We identified the key ecosystem services provided by floodplains following Harrison et al. [47]  at European scale, and Vidal-Abarca and Suárez  [48]  at national scale, as well as from prior knowledge of the functioning and the ecosystem services of the study area  [29]. We gathered available data of 12 ecosystem services that were relevant to maintain the flow of services in the area. The selection included two supporting ecosystem properties (soil conditions, composed of soil formation and soil stability, and habitat quality), four regulating services (nutrient regulation, carbon sequestration, biological control, and water quality), three provisioning services (freshwater supply, food production, and raw materials), and three cultural services (aesthetic, recreation, and environmental education).
Data analysis Ecosystem services supply. To model the flow of ecosystem services, we built an initial path model (Fig  3 ) on the basis of the classification of ecosystem services as intermediate or final. To build the path model, we convened an expert panel in May 2014 composed of four experts from the fields of ecosystem service science, conservation ecology, and limnology. The experts independently modelled the flow of ecosystem services in the study area. To assess the performance of the model we used structural equation modelling (SEM), a statistical technique to model complex multivariable relationships. SEM includes two models: the relations among all latent variables (i. e. the ecosystem services), and the relations between the manifest (observed) variables and their own latent variable (i.
7  [67]  and ii) Dillon-Goldstein's rho greater or equal to 0. 7  [68]. Manifest variables not meeting these criteria were dropped from the initial model. The quality of the final model was assessed using: i) the Relative Goodness of Fit index  [69] ; ii) the adjusted R 2 of the latent variables; and iii) the average communalities  [70]. All statistical analyses were performed with the software XLSTAT (2014. 3. 01). Ecosystem services benefits. The ecosystem services mentioned by each stakeholder group during the interviews (see Table B in S2 File) provided evidence of their role in relation to ecosystem services. According to this information, we linked each stakeholder group to the services they used, contributed to produce, or impaired. Additionally, we classified the ecosystem services identified within a gradient from rival to non-rival, and from excludable to non-excludable through an expert panel.
Acknowledgments We thank botanists, technicians, and students at  Instituto Pirenaico de Ecología -CSIC  for assistance in field and lab work. We also acknowledge all participants in the interviews and others who provided us with meaningful information to understand the social-ecological system of the River Piedra basin,  Emilie Crouzat  and  Adeline Bierry  for useful discussions about ecosystem services classification, and two reviewers whose comments significantly improved this work.
Detailed records of the positions of floating objects only became available with the emergence of GPS-equipped, satellite-linked buoys in the late 1990s which were coupled to a GIS software system onboard the vessels to monitor dFAD positions in near real-time. However, despite the intensification of dFAD fishing, information on buoy positions has remained highly confidential until recently. Under an agreement with the French purse seine fleet, we have obtained detailed dFAD tracking information for the period 2007-2011 from the 3 French purse seine fishing companies operating in the Atlantic and Indian Oceans. Our objectives here are  (1)  to develop the baseline methodology for treatment and analysis of dFAD GPS positions and (2) to carry out an initial examination of dFAD spatio-temporal use and potential impacts.