This course is a postgraduate course on "Topics in Vegetation Quantitative Remote Sensing Technology" at Beijing Forestry University.

The course was started in 2013, and it focuses on the research content, main models, inversion methods and forestry application cases of quantitative remote sensing of vegetation, etc. In 2018, we applied for the 13th Five-Year Teaching Materials Project of the Science Publishing House, and systematically organised the scientific research results and teaching insights of the last few years, in order to form a set of reference teaching materials. Against the above background, the textbook Quantitative Remote Sensing for Forestry: Framework, Models and Applications was produced. It is written on the basis of lecture notes and courseware, and includes a summary of the author's thoughts, results and experiences from his long-term work on the front line of forestry remote sensing research.

Quantitative Remote Sensing in Forestry: Frameworks, Models and Applications focuses on the particularities in the forestry industry, including mountainous terrain, tall forest vegetation, cloudy and rainy weather, and biodiversity needs, etc. It builds a bridge between the theory of quantitative remote sensing in a broad sense and forestry applications, proposes a system of frameworks, models, and applications for quantitative remote sensing in forestry, and combines with the authors' experiences in teaching graduate students with forestry backgrounds in an attempt to provide an in-depth introduction of the forestry quantitative remote sensing framework, difficulties, solutions and application frontiers.

The book consists of seven chapters.
Chapter 1 introduces the framework and key points of quantitative remote sensing in forestry;
Chapter 2 introduces the optical remote sensing radiative transfer model;
Chapter 3 introduces the principle methods of remote sensing inversion of forest parameters;
Chapter 4 introduces the LiDAR model;
Chapter 5 describes microwave radar remote sensing models;
Chapter 6 describes thermal infrared remote sensing applications;
Chapter 7 describes recent applications of hyperspectral remote sensing;
Chapter 8 introduces multi-source data simulation, fusion and its monitoring cases in pest and disease remote sensing, forest fire remote sensing, rocky desertification and biodiversity.