The QGIS in Remote Sensing Set aims to facilitate the appropriation and operational use of the Quantum Geographic Information System (QGIS) software in the field of remote sensing. This second volume of the set presents different applications of QGIS and its libraries for agriculture and forestry. A thorough knowledge of agriculture and forest areas is […]
The QGIS in Remote Sensing Set aims to facilitate the appropriation and operational use of the Quantum Geographic Information System (QGIS) software in the field of remote sensing.
This second volume of the set presents different applications of QGIS and its libraries for agriculture and forestry. A thorough knowledge of agriculture and forest areas is fundamental from both an economic and an environmental point of view. These environments are strongly involved in the use of spatial data, which are essential for restoring the spatio-temporal variability of surface conditions. In this context, GIS tools have long been used to support the exploitation of spatial imagery.
This work is carried out by scientists who are proficient to a high level of technicality. The book is targeted at students (Masters, engineering students, PhDs), engineers and researchers who have already adopted geographic information systems. In addition to the text, readers will have access to data and tools allowing the integral realization of the scientific procedures described in each chapter, as well as screenshots of all the windows which illustrate the manipulations necessary for the realization of each application.
1. Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas, Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi and Hassan Bazzi.
2. Disaggregation of Thermal Images, Mar Bisquert and Juan Manuel Sánchez.
3. Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB, Jean-Marc Gilliot, Camille Le Priol, Emmanuelle Vaudour and Philippe Martin.
4. Land Cover Mapping Using Sentinel-2 Images and the Semi-Automatic Classification Plugin: A Northern Burkina Faso Case Study, Louise Leroux, Luca Congedo, Beatriz Bellón, Raffaele Gaetano and Agnès Bégué.
5. Detection and Mapping of Clear-Cuts with Optical Satellite Images, Kenji Ose.
6. Vegetation Cartography from Sentinel-1 Radar Images, Pierre-Louis Frison and Cédric Lardeux.
7. Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park, Nicolas Karasiak and Pauline Perbet.
8. Physiognomic Map of Natural Vegetation, Samuel Alleaume and Sylvio Laventure.
9. Object-Based Classification for Mountainous Vegetation Physiognomy Mapping, Vincent Thierion and Marc Lang.