1. Chapter 4. Remote Sensing Information Process

2. n Remote sensing can provide fundamental biophysical information, including x,y location, z elevation or depth, biomass, temperature, and moisture content. Introduction

3. n Remote sensing–derived information is now critical to the successful modeling of numerous natural (e.g., drought) and cultural (e.g., land-use conversion at the urban fringe; population estimation) processes.

4. n The remote sensing data-collection and analysis procedures used for applications in resources and environment are often implemented in a systematic fashion referred to as the remote sensing process.

5. Information process in remote sensing n Radiant energy is collected by remote sensors and processed using analog and/or digital image processing techniques to extract information. n Such information is usually valuable only when used in conjunction with other information in a well-conceived application. n

7. Data collection n In situ measurement n Collateral data n Remote sensing

8. n In situ measurement  Field GPS Biomass spectroradiometer  Laboratory Reflectance Leaf area index (LAI)

10. n Collateral data  DEMs  Soil  Population density

11. n Remote sensing  Passive Frame photography Scanners (multispectral, hyperspectral) Linear and area arrays (multispectral, hyperspectral)  Active Microwave (RADAR) Laser (LIDAR)

12. The amount of electromagnetic radiance, L (watts m -2 sr -1 ; watts per meter squared per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a picture element in a digital image) is a function of: where, = wavelength (spectral response measured in various bands or at specific frequencies). = wavelength (spectral response measured in various bands or at specific frequencies). The amount of electromagnetic radiance, L (watts m -2 sr -1 ; watts per meter squared per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a picture element in a digital image) is a function of: where, = wavelength (spectral response measured in various bands or at specific frequencies). = wavelength (spectral response measured in various bands or at specific frequencies). Remote Sensing Data Collection

13. s x,y,z = x, y, z location of the picture element and its size (x, y) t = temporal information, i.e., when and how often the information was acquired  = set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing system P = polarization of back-scattered energy recorded by the sensor  = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system. s x,y,z = x, y, z location of the picture element and its size (x, y) t = temporal information, i.e., when and how often the information was acquired  = set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing system P = polarization of back-scattered energy recorded by the sensor  = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.

14. Remote Sensor Resolution Spatial - the size of the field-of-view, e.g. 10 x 10 m. Spatial - the size of the field-of-view, e.g. 10 x 10 m. Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave (radar). Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave (radar). Temporal - how often the sensor acquires data, e.g. every 30 days. Temporal - how often the sensor acquires data, e.g. every 30 days. Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. Spatial - the size of the field-of-view, e.g. 10 x 10 m. Spatial - the size of the field-of-view, e.g. 10 x 10 m. Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave (radar). Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave (radar). Temporal - how often the sensor acquires data, e.g. every 30 days. Temporal - how often the sensor acquires data, e.g. every 30 days. Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. 10 m BGRNIR Jan15Feb 15 15 10 m

15. Phenological Cycle of Hard Red Winter Wheat in the Great Plains

16. Phenological Cycles of San Joaquin and Imperial Valley, California Crops and Landsat MSS Images of One Field During A Growing Season

17. Data-to-information conversion n Analog (visual) image processing Using the elements of photo-interpretation n Digital image processing

18.  Pre-processing  Enhancement  Digital photogrammetry  Thematic mapping  Hyperspectral analysis  Change detection  Modeling  Scientific geovisualization

19. n Pre-processing  Radiometric correction  Geometric correction

21. n Thematic mapping  Parametric Maximum likelihood classifier  Non-parametric Artificial neural networks  Non-metric Expert systems Decision-tree classifiers Machine learning

22. n Modeling  Spatial modeling using GIS data  Scene modeling based on physics of energy/matter interactions

24. Information presentation n Analog and digital output n Graphs n Statistics n Metadata n Accuracy assessment

25. n Image/map ouput  Orthoimages  Thematic maps  GIS databases  Animations  Simulations

26. Landsat Thematic Mapper Imagery of the Imperial Valley, California Obtained on December 10, 1982

27. Landsat Thematic Mapper Color Composites and Classification Map of a Portion of the Imperial Valley, California

28. Integrated analysis using RS and GIS n Geospatial data sources n Improved DIP n This merging creates a synergy in which the GIS improves the ability to extract information from remotely sensed data, and this in turn keeps the GIS up-to-date with actual environmental conditions.

29. Summary n The EMR reflected, emitted, or back-scattered from an object or geographic area is used as a surrogate for the actual property under investigation. n Metadata, processing lineage, and the accuracy of the information are provided along with information products n Integration of remote sensing and GIS is the key

30. Questions 1. Can we formulate remote sensing of land use and land cover change quantitatively?