I’m glad to share with you my first publication of a chapter with Pauline Perbet.
According to Nicolas Bagdhadi who edited with Clément Mallet and Mehrez Zribi theses new series of books “QGIS in remote sensing”, these 4 volumes “aim to facilitate the appropriation and operational use of the Quantum Geographic Information System (QGIS) software in the field of remote sensing”.
Volume 1 : QGIS and generic tools / QGIS et outils génériques (QGIS, GDAL, GRASS, SAGA, OTB)
Volume 2 : QGIS and applications in agriculture and forest / QGIS et applications en agriculture et en foresterie
Volume 3 : QGIS and applications in territorial planning / QGIS et applications en aménagement du territoire
Volume 4 : QGIS and applications in water and risks / QGIS et applications en eau et risques
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.
My chapter is number 7 in volume 2, “Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park” , and of course it uses dzetsaka plugin :
The Guiana Amazonian Park has satellite imagery available all over its territory. The SPOT-5 sensor offers 10 m imagery resolution that allows for the detection of distinctive vegetation, distinguished from the tropical forest and remote sensing was used to detect those distinct sets of vegetations. SPOT-5 satellite imagery was acquired from the SEAS Guiana project. SEAS offers free French Guiana SPOT-5 archive images to the public organization located in this territory. Procedure and tool location can slightly differ according to the operating system or the quantum geographic information system (QGIS) version used. This chapter discusses the use of Windows 10, QGIS 2.14.14, Grass 6.4.3 and OTB 5.0.0. The dzetsaka plugin was designed to classify images directly from QGIS. To unlock all its capabilities, several dependencies should be installed first to use all the famous classification algorithms like random forest (RF) and support vector machine (SVM). For Windows installation, it is better to use OsGeo.
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.