Remote Sensing of Vegetation

• Published in: Remote Sensing and Digital Image Processing with R Vol. 1; pp. 141 - 168
• Main Authors: Alves, Marcelo de Carvalho, Sanches, Luciana
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2023; Taylor & Francis Group
• Edition: 1
• Subjects: Landsat-8 OLI Image; Solar Azimuth; MODIS Sensor Data; Spongy Mesophyll; Digital Image Processing; UTM Zone 23S; Leaf Area Index; Healthy Canopy; Sinusoidal Projection; ESRI Shapefile; R; Phenological Cycle; Plant Canopy; Canopy; MODIS Sensor; vegetation index; Intercellular Air Spaces; phenology; Remote Sensing; Remote Sensing Data; Landsat-8 OLI Data; Landsat-8 OLI; Sensor Spectral Bands; spectral signature; Vegetation Vigor; Vegetation Indices; Thermal Infrared; Visual Image Analysis; Moisture Content; Pest Organisms
• ISBN: 9781032359229; 9781032359823; 103235982X; 1032359226
• DOI: 10.1201/9781003329664-4

The available knowledge about vegetation spatial distribution, phenological cycles, physiological and morphological modifications provide information about the edaphic, climatic, geological and physiographic characteristics of an area on planet Earth over time. The development of sensors, visual image analysis, and digital image processing algorithms makes it possible to obtain biophysical information from different vegetation landscapes through remote sensing. Fundamentals of photosynthesis are addressed to learn about the interaction between electromagnetic radiation and plant structure, the factors affecting the variation of the spectral signature of vegetation, and the factors of relief, shade, planting orientation and cultural treatments that affect the quality of the remotely sensed data, as well as the monitoring of phenological characteristics of vegetation over time by remote sensing. Different vegetation indices are detailed according to their practical applications. A computational practice is performed to remotely assess vegetation vigor based on Landsat-8 OLI data and NDVI time series obtained by MODIS sensor. Exercises on scientific applications of remote sensing image processing of vegetation are presented, suggestion for research in the area, as well as Internet resources to illustrate the subject covered with videos on the subject.