Invasive aquatic plants are a serious global ecological and socio-economic problem because they can cause local extinction of native species and alter navigation and fishing. Eichhornia crassipes (water hyacinth) is a dangerous invasive floating plant that is widely distributed throughout the world. In Lebanon, it has spread since 2006 in the Al Kabir River. Remote sensing techniques have been widely developed to detect and monitor dynamics and extents of invasive plants such as water hyacinth over large areas. However, they become challenging to use in narrow areas such as the Al Kabir River and we developed a new image-analysis method to extract water hyacinth areas on the river. The method is based on a time series of a biophysical variable obtained from Sentinel-2 images. After defining a reference period between two growing cycles, we used the fractional vegetation cover (FVC) to estimate the water hyacinth surface area in the river. This method makes it possible to monitor water hyacinth development and estimate the total area it colonizes in the river corridor. This method can help ecologists and other stakeholders to map invasive plants in rivers and improve their control.

Salinization is a major soil degradation threat in irrigated systems worldwide. Irrigated systems in the Niger River basin are also affected by salinity, but its spatial distribution and intensity are not currently known. The aim of this study was to develop a method to detect salt-affected soils in irrigated systems. Two complementary approaches were tested: salinity assessment of bare soils using a salinity index (SI) and monitoring of indirect effects of salinity on rice growth using temporal series of a vegetation index (NDVI). The study area was located south of Niamey (Niger) in two irrigated systems of rice paddy fields that cover 6.5 km². We used remote-sensing and ground-truth data to relate vegetation behavior and reflectance to soil characteristics. We explored all existing Sentinel-2 images from January 2016 to December 2019 and selected cloud-free images on 157 dates that covered eight successive rice-growing seasons. In the dry season of 2019, we also sampled 44 rice fields, collecting 147 biomass samples and 180 topsoil samples from January to June. For each field and growing season, time-integrated NDVI (TI-NDVI) was estimated, and the SI was calculated for dates on which bare soil conditions (NDVI < 0.21) prevailed. Results showed that since there were few periods of bare soil, SI could not differentiate salinity classes. In contrast, the high temporal resolution of Sentinel-2 images enabled us to describe rice-growing conditions over time. In 2019, TI-NDVI and crop yields were strongly correlated (r = 0.77 with total biomass yield and 0.82 with grain yield), while soil electrical conductivity was negatively correlated with both TI-NDVI (r = −0.38) and crop yield (r = −0.23 with total biomass and r = −0.29 with grain yield). Considering the TI-NDVI data from 2016–2019, principal component analysis followed by ascending hierarchical classification identified a typology of five clusters with different patterns of TI-NDVI during the eight growing seasons. When applied to the entire study area, this classification clearly identified the extreme classes (i.e., areas with high or no salinity). Other classes with low TI-NDVI (i.e., during dry seasons) may be related to areas with moderate or seasonal soil salinity. Finally, the high temporal resolution of Sentinel-2 images enabled us to detect stresses on vegetation that occurred repeatedly over the growing seasons, which may be good indicators of soil constraints due to salinity in the context of the irrigated paddy systems of Niger. Further research will validate the ability of the method developed to detect moderate soil salinity constraints over large areas.

Le projet Paytal (Paysages et étalement urbain) est un travail pluridisciplinaire qui propose une méthodologie reproductible d’analyse des liens entre paysages et urbanisation. Ces liens sont ambivalents puisque en habitant des paysages qu’elles valorisent, les populations les modifient. Les politiques publiques visant à améliorer les paysages et le cadre de vie auront donc des effets pervers si elles ne contrôlent pas également le développement urbain. Pour mesurer et caractériser ces liens, nous développons une méthodologie en trois étapes, appliquée à l’aire urbaine d’Angers. D’abord, nous caractérisons finement l’urbanisation par télédétection. Ensuite, pour prendre en compte les aspects sensibles des paysages, nous générons une information spatialisée sur la perception des paysages à partir d’une ontologie. Celle-ci est basée sur l’information contenue dans les atlas des paysages tout en pouvant être généralisée à d’autres sources d’information. Enfin, nous expliquons l’urbanisation par un modèle de conversion des terres tenant compte des grands déterminants de l’urbanisation (proximité aux centres d’emploi, etc.), des caractéristiques physiques des paysages et de leurs dimensions sensibles. Nous montrons que ces dimensions jouent un rôle dans les phénomènes de périurbanisation.

Salinization and alkalinization are worldwide among the soil degradation threats in irrigated schemes affecting soil productivity. Niger River basin irrigated schemes in the Sahel arid zone are no exception (ONAHA, 2011). The use of remote sensing for identifying and evaluating the level of these phenomena is an interesting tool. The launching of the Sentinel2 satellite constellation (2015) brings new perspectives with high spectral and temporal resolutions images. The aim of this study was to develop a methodology for detection of salt-affected soils in this climatic condition.To achieve our goal, we used two types of data: remote sensing and ground truth data.Two complementary approaches were used: one by observing salinity on bare soil by the use of salinity index (SI) and the other by observing the indirect effects of salinity on the vegetation during eight (8) rice growth phases  using vegetation index NDVI.Remote sensing data were acquired from multi temporal sentinel2 images over 4 years (from 11/12/2015 to 30/11/2019). One hundred and fifty seven (157) images were downloaded (one image each 5 days) and corrected from atmospheric effects and some bands resampled to 5 m using python software. The salinity and vegetation indices were calculated. NDVI index was calculated and NDVI integral between NDVI curve and the threshold of 0.21 NDVI calculated for the eight growing cycles.Ground truth data were collected in 2019 during the dry growing season (January – may 2019) from 24 calibration plots and 40 validation plots. One hundred and twenty (120) soil samples collected and analyzed for pH and electrical conductivity and finally forty six (46) biomass samples were collected, air dried and weighed for biomass yield and 46 grains samples collected for grain yield.NDVI integral proved to be good indicator for yield variations and could distinguish crops behavior according to the growing period. It also makes it possible to distinguish plots which were not cultivated or with weak growth due to strong constraints of which the main one is salinity. It showed also that the effect of salinity on growth differs according to the growing season and the possibility of managing irrigation. Bare soil analysis distinguishes fields with different salinity indexes despite the low number of dates for which bare soil can be observed.Ascending Hierarchical Classification (AHC) enabled to identify four classes of NDVI dynamics over time and bare soil salinity index. High saline soils according to direct soil measurements were related to the class characterized by high frequency of no-cultivation during the dry season and low NDVI integral during the wet season. Multi-temporal Sentinel2 images analysis enabled therefore to detect rice crop fields affected by salinity through its influence on crop behavior. This approach will be tested over the whole paddy schemes of the Niger River valley.