dataesr/scientific-paragraphs-categorization huggingface dataset:
Forecasting carbon dioxide emissions Forecasts of carbon dioxide emissions were obtained by overlaying maps of future deforestation with carbon maps associated with the aboveground forest biomass. Carbon maps (Figs. S3  and S4 ) of the Andapa and Fort-Dauphin (humid and spiny-dry forest) project sites were obtained from two previous studies  (Asner et al. 2012; Vieilledent et al. 2012). Aboveground carbon density (ACD, in Mg. ha À1 ) was estimated for 83 forest plots based on tree diameter inventories  (Asner et al. 2012 ) and biomass allometric models  (Vieilledent et al. 2012). Airborne LiDAR (Light Detection and Ranging) data and remote sensing analysis of Landsat images were used to derive the carbon maps for the Andapa and Fort-Dauphin project sites. Altitude and the fraction of live photosynthetic vegetation (PV) classes were used as explanatory variables to estimate ACD at a resolution of 100 9 100 m  (Asner et al.
2012). In this study, the same classes of altitude and NDVI were used to derive carbon maps for the Ivohibe and Fandriana project sites assuming that PV and NDVI were equivalent indexes (both indexes range between 0 and 1 and increase with an increase in the percentage of green live vegetation). For a forested pixel, data missing on the carbon map due to clouds or shadows on the Landsat image were replaced by an average value of the ACD for the forested area in the corresponding project site. To eliminate unrealistic impulsive noise (also known as "salt and pepper" noise) on the ACD maps, we smoothed the data spatially using a moving average window of 3 9 3 pixels. The carbon maps were obtained for the year 2010  (Asner et al. 2012). We used the year 2010 as the starting date for forecasting deforestation and CO 2 emissions (Figs.
Intensity of the deforestation and population growth rate The mean annual deforestation rates varied considerably between the study areas, that is from 0. 47%. year À1 for the Fort-Dauphin spiny-dry forest up to 2. 45%. year À1 for the Fandriana humid forest. Using the Firaisana population census data for the five study areas and the exponential population growth model (eq. 1), we estimated an annual population growth rate of 3. 39%. year À1 (Fig. 4 ). We found a significant positive effect of population density on the annual deforestation rate (Table  2 ), the latter being relatively homogeneous over the five study areas with values ranging from 0. 010 to 0. 026. The predicted increase in population density should lead to an increase in the annual deforestation rate in the long term. For example, considering the Ivohibe project site, the mean population density should increase from 14.
In this sense, our approach is similar to the one proposed in Dinamica EGO where the weights of evidence can be edited. The incorporation of expert knowledge can corroborate or contradict the purely statistical approach in order to obtain a more realistic model. In our method, the use of scripts for the R  (Ihaka and Gentleman 1996)  and GRASS GIS  (Neteler and Mitasova 2008)  open-source software ensures maximum flexibility (see Appendix S5 and S6). The use of these programs is facilitated by the existence of numerous educational tutorials  (Crawley 2007; Neteler and Mitasova 2008). Moreover, minimal changes should be required to adapt the scripts to the different contexts found in the tropical world. Another advantage of our approach is that deforestation analysis can be performed for large forest areas, that is at the subnational or national scale (whereas CLUE-S software is more appropriate for small regional areas for example) and for a long period of time with repeated land cover observations.
(2008)  showed that the FOM was strongly positively correlated with the observed net change (indeed, the lower the deforestation rate, the more difficult it is to forecast the exact location of the deforested pixels). When the net change was 2. 5%, the FOM was 7. 5% and when the net change was 10%, the FOM was 15%. Kim (2010) also compared the values of the FOM obtained using GEOMOD and LCM software in a deforestation study in Bolivia: the highest value of the FOM he obtained was 8%. In this study, for equivalent net change values, we obtained much higher FOM values. The relatively good performance of our model was confirmed by the values obtained for the TSS (from 0. 16 to 0. 38) and the Kappa statistics (from 0. 16 to 0. 34). In comparison, using GEOMOD software in Mexico,  Guerrero et al.
(2008)  found lower Kappa values (from À0. 03 to 0. 29). Finally, we estimated mean CO 2 emissions of 2. 24 and 0. 26 T. ha À1. year À1 , respectively, for the humid and spiny-dry forests of Madagascar. Reducing the uncertainty associated with baseline scenario is essential for financial efficiency given the limited resources dedicated to the REDD+ Programme and also to avert the risk of artificial inflation of avoided deforestation and CO 2 emissions  (Obersteiner et al. 2009). Olander et al. (2008)  presented the essential characteristics of a good method for establishing baseline scenarios including feasibility, accuracy, transparency, comprehensiveness, and flexibility. Here, we present such a method using open-source and free software, while sharing data and computer scripts so that our study can be easily extended to the rest of Madagascar and to other tropical developing countries.
Acknowledgments We thank  Paul G. Ravelomanantsoa ,  Iarivony Randretsa , and  Tovonirina T. Razafimiarantsoa  at the  INSTAT  for providing the population census data needed for the establishment of the demographic models. We also thank  Daniel Pemstein ,  Kevin M. Quinn , and  Andrew D. Martin  for the development of the Scythe C++ library and  Emmanuel Sambale  for sharing the GRASS code to compute the forest fragmentation index. This study is part of the  Holistic Conservation Program  for Forests (HCPF) in Madagascar. The HCPF is a REDD+ pilot project funded by the  GoodPlanet Foundation  with  Air France  as sole sponsor and is jointly implemented on the ground by  WWF  and  GoodPlanet.
stpp is an R package for analysing, simulating and displaying space-time point patterns. It covers many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena. The package also includes estimators of the spacetime inhomogeneous K-function and pair correlation function. stpp is the first dedicated unified computational environment in the area of spatio-temporal point processes. In this paper we describe space-time point processes and introduce the package stpp to new users.
Static and dynamic plotting of spatio-temporal point process data The most effective form of display for a spatio-temporal point process data is an animation, repeated viewing of which may yield insights that are not evident in static displays. Nevertheless, static displays are sometimes useful summaries. The stpp package includes four display functions that we illustrate using data on the locations and times (dates of reporting) of outbreaks of foot-and-mouth disease (FMD), a severe, highly communicable viral disease of farm livestock, during the UK 2001 FMD epidemic. The data-set fmd, included in stpp, contains a three-column matrix of spatial locations and reported days (from 1 February 2001) of FMD outbreaks in the county of Cumbria. Figure  1  shows a static display of the data consisting of locations in the left-hand panel and the cumulative distribution of the times in the right-hand panel.
Application to FMD data The functions STIKhat and PCFhat provide estimates of the space-time inhomogeneous Kfunction and pair correlation function. The following code applies these estimators to the FMD data under the assumption that the spatio-temporal intensity is separable. The spatial intensity is estimated using the function kernel2d of the package splancs. Other R packages capable of this type of estimation include spatstat and spatialkernel  (Zheng and Diggle 2012). In PCFhat the box kernel is used by default. Epanechnikov, Gaussian and biweight kernels are also implemented. Whatever the kernel function, if the bandwidth is missing, a value is obtain from the function dpik of the package KernSmooth  (Wand and Ripley 2012). Note that the bandwidths play an important role in determining the quality of the estimators as they heavily influence the trade-off between bias and variance.
For example, setting g s ="prod" and r ≤ k -1, at the kth step of the algorithm, the probability of acceptance is p s = k-1 j=k-r h s ( s -s j , θ s , δ s ). The functions h s and h t define the nature of the interaction between a pair of points according to their spatial and temporal separations, respectively. In the following, h(•) and δ without subscripts can represent either h s (•) or h t (•) and δ s or δ t. The function h(•) is monotone, increasing, tends to 1 when the separation tends to infinity and satisfies 0 ≤ h(•) ≤ 1. Currently the following functions are implemented in stpp: step: h(x) = 1, if x > δ θ, otherwise , θ ∈ [0, 1]. Gaussian: h(x) =      1, if x > δ + θ/2 exp -(x-δ-θ/2) 2 2(θ/8) 2 , if δ < x ≤ δ + θ/2 0, if x ≤ δ , θ ≥ 0. One distinction between these two functions is that the 'step' function allows points to be generated at distances less or equal to δ if θ > 0, whereas the 'Gaussian' function does not.
Simulation The algorithm used in the stpp package to simulate an infectious processes is as follows. Step 1 1. Set (s 1 , t 1 ). Step k = 2, 3,. 2. Compute h k (t) = h(t|t k-1 , α, β, γ)/{ T h(u) du} and µ k (t) = k j=1 h j (t). 3. Generate u k ∼ U[0, 1]. 4. Generate t k = t k-1 + v and s k = s k-1 + w, where v and w are generated from f t and f s , respectively.
Simulation The simulation of a log-Gaussian Cox process in the stpp package uses the same thinning algorithm as was used for an inhomogeneous Poisson process, but preceded by a simulation of the underlying Gaussian process. For a given covariance function c (s i , t i ), (s j , t j ) and a given mean µ(s, t) for the Gaussian process the algorithm is as follows. 1. Generate a realisation of a Gaussian field, with covariance function c (s i , t i ), (s j , t j ) and mean µ(s, t). 2. Define λ(s, t) = exp {Z(s, t)} and an upper bound λ max for λ(s, t). 3. Simulate a homogeneous Poisson process with intensity λ max ; denote by N the number of points so generated. 4. Thin the simulated process as follows, (a) Compute p = λ(s, t)/λ max for each point (s, t) of the homogeneous Poisson process. (b) Generate a sample u of size N from the uniform distribution on (0, 1).
(c) Retain the n ≤ N locations for which u ≤ p. The stpp package implements an exact simulation of a stationary Gaussian random field on a n 1 × n 2 × n 3 grid, based on circulant embedding of the covariance function  (Chan and Wood 1997). See e. g. , Appendix E in  Møller and Waagepetersen (2003)  for the simulation of Gaussian random fields. In stpp, we have implemented separable, stationary covariance functions of the form c(h, t) = c s (h)c t (t), h ∈ S, t ∈ T, (6) where c s (h) and c t (t) are purely spatial and purely temporal covariance functions, respectively. All models are isotropic c s (h) = c( h ) or c t (t) = c(|t|); they include the Matérn class, the Cauchy class and the wave class (see Table  1 ). Note that the Matérn covariance is defined in terms of the modified Bessel function K ν.
Most groups engaged in 4D flow MR imaging have elaborate inhouse software libraries that require substantial time and effort to create meaningful flow data. Although there are many avenues to explore based on derivatives of the velocity vector fields (eg, streamlines, path lines, particle path traces, relative wall shear stress, pressure gradients), without appropriate software tools, these will remain in the research domain. Combining time-resolved contrast-enhanced MRA (4D tr CE MRA) and 4D flow MR imaging into 1 acquisition is also possible when highly undersampled radial acquisition is used to dramatically reduce the imaging time. 19, 20 This approach, called HYPRFlow, provides time-resolved contrast-enhanced whole-brain serial images that display the vascular anatomy with good temporal and spatial resolution and also generate cardiac-synchronized 3D velocity flow fields.
In volcanic areas exposed to multiple geological hazards, the efficient interpretation of the increasing amount of geographical data is a challenging issue. Within the European MIAVITA project, we investigated to which extent WebGIS can facilitate this interpretation through the visualization of such complex hazard and risk database. This analysis was structured around (1) the development of a Web-based Geographical Information System (WebGIS) and of an application for managing the geographical database, and (2) interactions with a group of hazard and risk analysts and managers, civil security officers, GIS analysts and system developers. We tested the system in the Mount Cameroon volcano, taking advantage of a complex hazard and risk geographical database collected previously. Key requirements here related to the need for a structured and flexible GIS-database and to manage user's privileges differently according to their profile and the status of the volcano.
Back Close Full least, a territorial GIS risk repository should therefore contain a multi-hazard map (e. g. Kappes et al. , 2012; Neri et al. , 2013) , an inventory of exposed elements together with attributes such as their physical vulnerability or their importance during disaster crisis or recovery, and the associated risk maps. From the risk management perspective, even more important than the detailled accuracy in a particular dataset is its completeness, that is, the fact that it addresses all hazards and type of assets at risk in a given territory. Within the European MIAVITA project, we examined how WebGIS can be helpful for hazard and risk spatial information dissemination and management. For this purpose, we analysed existing geographical information workflows with a group of stakeholders and designed a Web-based GIS system accordingly (Sect.
Back Close Full Verde, Philippines and Italy. The role of the group was first to formalize requirements for the WebGIS system. At later stages of the project, it was invited to provide comment on two versions of the proof of concept tools. The approach consisted in formalizing progressively requirements through informal discussions, the elaboration of the data flow diagrams, brainstorming meetings and discussions around an evolving prototype. Similarly to the case studies described by  Darke and Shanks (1997) , we did not apply one of the existing methods for user requirements collection in the field of information system science; on the contrary, we set up solutions for the practical problems encountered in an empirical way.
User's requirements As a first requirement, users wished that the database could be managed in a structured but flexible way. For this purpose, we developed a controlled vocabulary describing the content of the hazard and risk geographical database and organised in a hierarchical form (i. e. a taxonomy; see Fig. 4 ). This taxonomy was reviewed by users and provided to the system developer as an example for the first implementation of the system. It covers all geographical datasets presently used in risk management practices in the MIAVITA volcanoes. Secondly, users required a seamless management of the maturity of data, including a clear validation by civil security or by a local volcano observatory. We defined a scheme of information flows between the system, data providers and users (Fig.
5 ). This scheme was designed to ensure that (1) the control of the database remains the sole responsibility of the local volcano observatory (or of the national organization in charge of volcanoes) and (  2 ) the data provided to each type of user remains adapted to its use. Other requirements included the possibility to manage a variety of access rights and privileges for each type of users, particularly according to the different phases of volcanic crisis: during volcanic unrest and the eruption climax, the priority should be given to civil security users or to the organizations in charge of the crisis management. Introduction.
Back Close Full Because the information disseminated through the WebGIS can be sensitive, an authentication of any user willing to access the application was requested. Finally, we identified the need for accessing to the tool using mobile devices, particularly for helping uploading information from the field directly to the system for avoiding timeconsuming copies of notes. Other potential capabilities of the system were discussed. For example, the development of geoprocessing tools (i. e. applications that enables to modify the geometry of geographical objects) was not considered as a priority since the system was designed for helping untrained users to visualize the geographic database. The potential interoperability capabilities were discussed in details: from the user's perspective, they appeared less relevant in a first volcano's hazard and risk dissemination platform than for other environmental data dissemination systems: in fact, we noticed that in many of the volcanoes case-studies of the MIAVITA project, most of the data are owned or managed by a single organization.
System development and functionalities In order to fit those requirements, the GIS architecture has been designed by dividing the main components into four subsystems each one interacting with the rest of the modules, but nevertheless being independently configurable according to the user needs. This subsystems are the following (Fig. 5):  (1) a Web server; (2) an Authentication, Authorization and Audit (AAA) server, which provides security services aimed at applying the access and control rules; (3) a GIS Publisher enabling the final data provisioning and distribution; we enclosed this system into a wrapper to accomplish the security requirements stored in the AAA Server; and (4) repositories that includes applications requested to configure the AAA Server and a file-based or data-base management system (DBMS)-based GIS repository.
Back Close Full can be hosted on High Availability infrastructure that goes from a Redundant Array of Independent Disks (RAID) of type 1 (mirroring without parity or striping) up to a RAID of type 5 (block-level striping with distributed parity) architecture. The GIS database contains both data and metadata based on the taxonomy (Fig. 3 ). However, the system provides the possibility to define other taxonomies as wished by the users. The system administrator can grant different privileges to each user, allowing a very fine configuration tuning, permitting the independent setting of the following privileges on a "per taxonomy node" basis (Table  1 ). The privileges can be applied to a single GIS layer or a whole branch in the GIS Layers' taxonomy and reflect the different actions that a user can perform on a GIS data, starting with the more general ones: -Insert a new GIS Layer into a specific taxonomy location.
-Update the meta information with a loaded GIS Layer. -Delete a GIS Layer from the system. And continuing with privileges more specific of a GIS publishing server, like: -Publish an already loaded GIS Layer making it available to the users that have granted Web Maps Services (WMS) or Web Feature Services (WFS) privileges; while WMS are standardised protocols to supply georeferenced images (rendered as pictures by the server), WFS enable to request specific features of geographical objects and delivers spatial data in its original vector format. -Validate which affects only the confidence level on the information contained in a GIS Layer as well as its consultation by a broader population of Users (access to Level 1 layers grants access to Level 2 layers but not to Level 0 layers). We is possible to define independent sets of profiles that become active when the relative Alert level is selected.
The WebGIS components have been developed using Open Source libraries such as OpenLayers, GeoExt and ExtJS. Since the latest candidate release of the OpenLayers (2. 11-rc1) library allows the use of the WebGIS also from a mobile device (e. g. tablet PC), it was not necessary to develop an independent Web thin client (Fig. 2 ) for facilitating the use of the application on the field. This makes the MIAVITA WebGIS server a dynamically profiled system wholly based on Open Source products that can be scaled on multiple servers solutions in case of a large GIS database and User's community.
Data and motivation For this study, we used the hazard and risk GIS database collected by  Thierry et al. (2008). This database contains GIS layers describing the exposure and elements at risks as well as their strategic importance, single-hazards and multi-hazard maps, "risk maps" (which consist actually of hazard maps superimposed with the assets vulnerable to these assets), topographic maps, and other background information such as optical satellite data and the geological map. After the completion of the Thierry et al. (  2008 ) study, the hazard and risk GIS database was made available to local and national Cameroonian stakeholders in two formats: a paper atlas and a geographical database accessible through a conventional geographical information system. This GIS was installed on a personal computer in the Ministry of Mines of Cameroon, which is the national entity that coordinates prevention against volcanic risks.
Conversely, the interpretation of the numerous risk maps has been considered too complex (see also  Gehl et al. , 2013). In addition, the GIS data repository is presently accessible through a standard GIS tool, whereas there is still a need for user-training in GIS. If a solution can be found for managing the application locally, the Web application will enable the dissemination of hazard and risk information to many users, enabling them to take appropriate prevention and preparedness measures (for example: relocation of some assets when appropriate, identification of the most suitable sites for new constructions or activities, etc. ). However, it is worth noticing that other members of the stakeholder's group (e. g. from the Philippine's institute of seismology and volcanology) required and received copies of the system.
Back Close Full Further possible developments have been discussed, including for example the possibility to update the database through the WebGIS application (similarly to in  Huang et al. , 2013). However, similarly to previous tools (e. g. Salvi et al. , 1999) , we prefered to limit the scope of our WebGIS to the essential functions of facilitating the visualisation and interpretation of data by untrained users. We also recognize that the range of potential users can be limited, especually in socially and economically disadvantaged volcanic areas. In fact, while there are exemples of WebGIS serving as collaborative decision support tool involving a wide range of users (e. g. Boroushaki and Malczewski, 2010) , our application is primarily targeted to an audience of decision makers, scientists and civil security already familiar with IT, although not necessarily to GIS.
Conclusions In this contribution, we present how we developed and tested a web-based application for managing and displaying a complex geographical database dedicated to hazard and risks in volcanic areas. We formalized key requirements for this application with a group of stakeholders involved in risk management in several volcanic areas. Those showed potential for transportability in other volcanic area, provided the local volcano laboratory owns GIS and IT expertise for sustaining the system. The distribution of the prototype to participants of the stakeholders group indicates that there are opportunities for future operational use of such tools. Introduction Conclusions Thierry, P. , Stieltjes, L. , Kouokam, E. , Ngueya, P. , and Salley, P. M. : Multi-hazard risk mapping and assessment on an active volcano: the GRINP project at Mount Cameroon, Nat.