Prediction of cattle density and location at the frontier of Brazil and Paraguay using remote sensing

In this paper, we explore the potential of remote sensing to map pastures areas and by this way establish models for predicting cattle density and location. First, an object based classification (OB) was made in Landsat 5 images for three different municipalities to provide a land-cover map. Second, on the basis of Brazilian official livestock database, a statistical model to predict number of cattle in function of declared pasture area by the farmers was produced. Finally, this model was applied to the pasture areas detected by remote sensing to predict cattle density. Coefficient of determination of the model was 0.63. The results indicate that the methodology used for estimating cattle density has a potential to be applied in regions where no information about farm location and cattle density exists. (Résumé d'auteur

Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana

The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS highest elevations and the SRTM elevations and the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest LT (LT12), in some locations, contains trees that lose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season

Turning back the rising sea: Theory performativity in the shift from climate science to popular authority

Action on climate change continues to be hampered by vested interests seeding doubt about science and the need to reduce carbon emissions. Using a qualitative case study of local climate adaptation to sea level rise, we show how climate change science is translated into a self-referential theory focussed on property prices. Our analysis develops two mechanisms – enablement and theorization – to explain the relationship between theory performativity and power within a process of translation. This contributes to i) the performativity debate by showing how the constitution of power relations shapes theory performativity; ii) theories of power, by tracing the ways in which certain actors are able to enrol others and impact the authority of particular theories, and; iii) processes of translation by developing mechanisms for following the ways in which power and theory performativity interact. We conclude by arguing that a performative understanding of how power shapes beliefs is central to combat the failure to address climate change

Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data : application on French Guiana

LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km

A vegetations map of south America.

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Regional scale rain-forest height mapping using regression-kriging of spaceborneand airborne lidar data: application on French Guiana

IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015International audienceLiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset

A Multilevel Analysis of Implicit and Explicit CSR in French and UK Professional Sport

Research question: This paper examines the ways in which French and UK professional sports clubs implement and communicate their CSR policies. In addition to identifying similarities and differences between CSR practices in the two countries, our analysis extends and adapts the implicit-explicit CSR framework to the field of sport. Research methods: We used a mixed methods approach to analyse qualitative and quantitative data on the CSR strategies of 66 professional rugby union (Top 14, Aviva Premiership Rugby) and football (Ligue 1, Premier League) clubs over the 2017-2018 season. Results and findings: We found major differences in CSR communication between France and the UK. Communication by French clubs tends to highlight sport’s values, involve few media channels, whereas communication by UK clubs explicitly vaunts their social responsibility and involves numerous channels. In the case of CSR implementation, there are similarities between French and UK clubs, especially in the fields their CSR initiatives cover (e.g., health, diversity), as well as differences. However, the scope of initiatives varies more between sports than between countries, with football demonstrating a more international outlook than rugby. Implications: This article expands Matten and Moon’s (2008) implicit-explicit CSR framework by identifying the influence of interactions between sectorial/field-level factors and national/macro-level factors on CSR practices, and by distinguishing between CSR communication and CSR implementation. Our results throw light on the shift from implicit to explicit CSR in French professional sport

Encouraging Investors to Enable Corporate Sustainability Transitions: The Case of Responsible Investment in France

This paper studies the case of the socially responsible investment (SRI) industry in France. This case accounts for how the SRI category and practices have successfully moved from the margins of the industry in the late 1990s to become mainstream over two decades. We bring to the forefront the importance of three complementary factors in the process of causing corporations to transition toward more sustainable businesses: the role of investors and, in particular, institutional investors; the importance of the presence of a clear category definition and of intermediary organizations, providing ratings, scores, and other calculative devices; and the role of governments and regulators. With other studies, this case stresses the fundamental influence of investors in how corporations manage sustainability transitions