Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery.

À l’ère de l’Anthropocène, la transformation de la nature matérielle par les sociétés est de mieux en mieux assimilée. De nombreuses recherches proposent ainsi d’envisager la nature contemporaine comme le résultat de l’hybridation de la nature et de la culture. Mais que sont les hybrides et quelle place doit-on leur accorder ? L’examen du concept en géographie, et dans quelques disciplines voisines, permet d’effectuer plusieurs constats. La géographie démontre depuis longtemps que l’environnement est constitué d’hybrides possédant une dimension matérielle et dont la compréhension ne peut se faire qu’en les inscrivant dans l’espace et dans le temps. Ces hybrides correspondent à des systèmes (géosystèmes, anthroposystèmes) mais aussi aux objets élémentaires qui les constituent (sédiment, arbre, etc.) qui sont ontologiquement socionaturels. Mais nous n’étudions pas des hybrides de nature et de culture mais des hybrides entre des processus physicochimiques, biologiques et sociaux dont la différenciation relève plus d’une distinction méthodologique qu’ontologique. L’examen montre qu’en géographie, l’hybridation semble soit implicite, soit évidente, et demeure un cadre de référence général plutôt qu’un enjeu scientifique et programmatique. À partir de nos recherches sur les cours d’eau, nous montrons que la matérialité contemporaine est composée d’objets hybrides par construction ou par métabolisme. C’est pourquoi nous proposons une géographie des objets hybrides qui s’appuie sur le renforcement d’un espace intradisciplinaire en géographie, seul à même d’articuler toutes les dimensions nécessaires à leur compréhension.

Riparian zones experience many anthropic pressures and are the subject of European legislation to encourage their monitoring and management, to attenuate these pressures. Assessing the effectiveness of management practices requires producing indicators of ecological functions. Laser Detection and Ranging (LiDAR) data can provide valuable information to assess the ecological status of riparian zones. The objective of this study was to evaluate the potential of LiDAR point clouds to produce indicators of riparian zone status. We used 3D bispectral LiDAR data to produce several indicators of a riparian zone of a dammed river in Normandy (France). The indicators were produced either directly from the 3D point clouds (e.g., biomass overhanging the channel, variation in canopy height) or indirectly, by applying the Random Forest classification algorithm to the point clouds. Results highlight the potential of 3D LiDAR point clouds to produce indicators with sufficient accuracy (ca. 80% for the number of trunks and 68% for species composition). Our results also reveal advantages of using metrics related to the internal structure of trees, such as penetration indexes. However, intensity metrics calculated using bispectral properties of LiDAR did not improve the quality of classifications. Longitudinal analysis of the indicators revealed a difference in attributes between the reservoir and areas downstream from it.