Mapping health status of chestnut forest stands using Sentinel-2 images

In many parts of France, health status of chestnut forest stands is a crucial concern for forest managers. These stands are made vulnerable by numerous diseases and sometimes unadapted forestry practices. Moreover, since last years, they were submitted to several droughts. In Dordogne province, the economic stakes are important. About 2/3 of the chestnut forest area are below the optimal production level. The actual extent of chestnut forest decline remains still unknown. Sentinel-2 time series show an interesting potential to map declining stands over a wide area and to monitor their evolutions. This study aim to propose a method to discriminate healthy chestnut forest stands from the declining ones with several levels of withering intensity over the whole Dordogne province. The proposed method is the development of a statistical model integrating in a parsimonious manner several vegetation indices and biophysical parameters. The statistical approach is based on an ordered polytomous regression to which are applied various technics of models’ selection. We aim to map 3 classes of predictive health status. In this study, Sentinel-2 images (10 bands at 10 and 20 m spatial resolution) acquired during the growing season of 2016 have been processed. Due to insufficient data quality related to atmospheric conditions, only 2 cloud-free images could be analyzed (one in July and one in September). About 36 vegetation indices were calculated from THEIA-MAJA L2A products and 5 biophysical parameters (Cover fraction of brown vegetation, Cover fraction of green vegetation, Fraction of Absorbed Photosynthetically Active Radiation, Green Leaf Area Index, Leaf water content) were processed from ESA level 1C product. These last parameters have been obtained with the Overland software (developed by Airbus DS Geo-Intelligence) by inverting a canopy reflectance model. This software couples the PROSPECT leaf model and the scattering by arbitrary inclined leaves (SAIL) canopy model. Calibration and validation of the predictive model are based on the health status of chestnut forest stands data survey. About 50 plots have been surveyed by foresters describing the chestnut trees health status by using two protocols (ARCHI and expert knowledge). Model stability over time and space will be further analyzed with Sentinel-2 time series during 2017 and 2018 on other different chestnut forest stands

Self-esteem and assertiveness in medical students in Casablanca, Morocco

Abstract Background Self-esteem is a determining factor of good mental health. Medical students need assertiveness in interpersonal interactions with colleagues, patients, and families. This study aims to assess self-esteem and assertiveness in medical students in Casablanca, Morocco; the relationship between these two concepts, on the one hand, and with anxiety and depression on the other hand. Materials and methodology A cross-sectional study was conducted at the Faculty of Medicine in Casablanca, Morocco. The study used an anonymous self-questionnaire that included Rosenberg Scale and Rathus Scale in order to assess self-esteem and assertiveness, respectively, and HADS Scale to assess depression and anxiety. Results A percentage of 54% of students in our study showed low-to-very low-self-esteem and 70% of students were not assertive. We found a strong relationship between self-esteem and assertiveness (p <0.001), self-esteem and depression (p <0.001), self-esteem and anxiety (p <0.001), assertiveness and depression (p <0.001), and assertiveness and anxiety (p <0.001). Conclusions Globally, self-esteem and assertiveness of Moroccan medical students were low in more than half of the students. Moreover, there was a significant relation between these concepts, on the one hand, and between them and anxiety and depression on the other hand. Students need to be valued and their efforts recognized during their studies. Therapeutic strategies should be used when necessary

Mapping health status of chestnut forest stands using Sentinel-2 images

International audienceIn many parts of France, health status of chestnut forest stands is a crucial concern for forest managers. These stands are made vulnerable by numerous diseases and sometimes unadapted forestry practices. Moreover, since last years, they were submitted to several droughts. In Dordogne province, the economic stakes are important. About 2/3 of the chestnut forest area are below the optimal production level. The actual extent of chestnut forest decline remains still unknown. Sentinel-2 time series show an interesting potential to map declining stands over a wide area and to monitor their evolutions. This study aim to propose a method to discriminate healthy chestnut forest stands from the declining ones with several levels of withering intensity over the whole Dordogne province. The proposed method is the development of a statistical model integrating in a parsimonious manner several vegetation indices and biophysical parameters. The statistical approach is based on an ordered polytomous regression to which are applied various technics of models’ selection. We aim to map 3 classes of predictive health status. In this study, Sentinel-2 images (10 bands at 10 and 20 m spatial resolution) acquired during the growing season of 2016 have been processed. Due to insufficient data quality related to atmospheric conditions, only 2 cloud-free images could be analyzed (one in July and one in September). About 36 vegetation indices were calculated from THEIA-MAJA L2A products and 5 biophysical parameters (Cover fraction of brown vegetation, Cover fraction of green vegetation, Fraction of Absorbed Photosynthetically Active Radiation, Green Leaf Area Index, Leaf water content) were processed from ESA level 1C product. These last parameters have been obtained with the Overland software (developed by Airbus DS Geo-Intelligence) by inverting a canopy reflectance model. This software couples the PROSPECT leaf model and the scattering by arbitrary inclined leaves (SAIL) canopy model. Calibration and validation of the predictive model are based on the health status of chestnut forest stands data survey. About 50 plots have been surveyed by foresters describing the chestnut trees health status by using two protocols (ARCHI and expert knowledge). Model stability over time and space will be further analyzed with Sentinel-2 time series during 2017 and 2018 on other different chestnut forest stands

Mapping health status of chestnut forest stands using Sentinel-2 images

International audienceIn many parts of France, health status of chestnut forest stands is a crucial concern for forest managers. These stands are made vulnerable by numerous diseases and sometimes unadapted forestry practices. Moreover, since last years, they were submitted to several droughts. In Dordogne province, the economic stakes are important. About 2/3 of the chestnut forest area are below the optimal production level. The actual extent of chestnut forest decline remains still unknown. Sentinel-2 time series show an interesting potential to map declining stands over a wide area and to monitor their evolutions. This study aim to propose a method to discriminate healthy chestnut forest stands from the declining ones with several levels of withering intensity over the whole Dordogne province. The proposed method is the development of a statistical model integrating in a parsimonious manner several vegetation indices and biophysical parameters. The statistical approach is based on an ordered polytomous regression to which are applied various technics of models’ selection. We aim to map 3 classes of predictive health status. In this study, Sentinel-2 images (10 bands at 10 and 20 m spatial resolution) acquired during the growing season of 2016 have been processed. Due to insufficient data quality related to atmospheric conditions, only 2 cloud-free images could be analyzed (one in July and one in September). About 36 vegetation indices were calculated from THEIA-MAJA L2A products and 5 biophysical parameters (Cover fraction of brown vegetation, Cover fraction of green vegetation, Fraction of Absorbed Photosynthetically Active Radiation, Green Leaf Area Index, Leaf water content) were processed from ESA level 1C product. These last parameters have been obtained with the Overland software (developed by Airbus DS Geo-Intelligence) by inverting a canopy reflectance model. This software couples the PROSPECT leaf model and the scattering by arbitrary inclined leaves (SAIL) canopy model. Calibration and validation of the predictive model are based on the health status of chestnut forest stands data survey. About 50 plots have been surveyed by foresters describing the chestnut trees health status by using two protocols (ARCHI and expert knowledge). Model stability over time and space will be further analyzed with Sentinel-2 time series during 2017 and 2018 on other different chestnut forest stands

Mapping health status of chestnut forest stands using Sentinel-2 images

In many parts of France, health status of chestnut forest stands is a crucial concern for forest managers. These stands are made vulnerable by numerous diseases and sometimes unadapted forestry practices. Moreover, since last years, they were submitted to several droughts. In Dordogne province, the economic stakes are important. About 2/3 of the chestnut forest area are below the optimal production level. The actual extent of chestnut forest decline remains still unknown. Sentinel-2 time series show an interesting potential to map declining stands over a wide area and to monitor their evolutions. This study aim to propose a method to discriminate healthy chestnut forest stands from the declining ones with several levels of withering intensity over the whole Dordogne province. The proposed method is the development of a statistical model integrating in a parsimonious manner several vegetation indices and biophysical parameters. The statistical approach is based on an ordered polytomous regression to which are applied various technics of models’ selection. We aim to map 3 classes of predictive health status. In this study, Sentinel-2 images (10 bands at 10 and 20 m spatial resolution) acquired during the growing season of 2016 have been processed. Due to insufficient data quality related to atmospheric conditions, only 2 cloud-free images could be analyzed (one in July and one in September). About 36 vegetation indices were calculated from THEIA-MAJA L2A products and 5 biophysical parameters (Cover fraction of brown vegetation, Cover fraction of green vegetation, Fraction of Absorbed Photosynthetically Active Radiation, Green Leaf Area Index, Leaf water content) were processed from ESA level 1C product. These last parameters have been obtained with the Overland software (developed by Airbus DS Geo-Intelligence) by inverting a canopy reflectance model. This software couples the PROSPECT leaf model and the scattering by arbitrary inclined leaves (SAIL) canopy model. Calibration and validation of the predictive model are based on the health status of chestnut forest stands data survey. About 50 plots have been surveyed by foresters describing the chestnut trees health status by using two protocols (ARCHI and expert knowledge). Model stability over time and space will be further analyzed with Sentinel-2 time series during 2017 and 2018 on other different chestnut forest stands