Center for Remote Sensing and Spatial Analysis, Rutgers University Remote Sensing: Digital Image Analysis

Center for Remote Sensing and Spatial Analysis, Rutgers University Lecture Notes 1: Overview of Remote Sensing A number of these slides were originally produced by Scott Madry and Chuck Colvard with some subsequent modification by Rick Lathrop. Additional slides were produced by Rick Lathrop.

Center for Remote Sensing and Spatial Analysis, Rutgers University The remote sensing cycle

Center for Remote Sensing and Spatial Analysis, Rutgers University Design of A Remote Sensing Effort Clear definition of the problem and information need Evaluation of the overall potential of remote sensing Identification of appropriate remote sensing data & acquisition procedures Determination of the data interpretation & analysis techniques Identification of the criteria by which the quality of information can be evaluated

Center for Remote Sensing and Spatial Analysis, Rutgers University Resolution Four kinds of resolution determined by user needs: Spatial Resolution: How small an object do you need to see (pixel size) and how large an area do you need to cover (swath width) Spectral Resolution: What part of the spectrum do you want to measure Radiometric Resolution: How finely do you need to quantify the data Temporal Resolution: How often do you need to look

Center for Remote Sensing and Spatial Analysis, Rutgers University Detection vs Discrimination vs Identification Detection: spectral signal from object of interest is above background noise - there is some kind of signal there Discrimination: spectral signal from object of interest is detectable and also different from surrounding features - I can discern a distinct feature Identification: spectral signal whose spectral pattern can be discriminated and uniquely attributed to a specific type of biophysical surface material or object - I can positively identify the feature

Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial resolution Instantaneous Field of View (IFOV) determines the dimension, D, of the Ground Resolution Cell (GRC) imaged on the ground IFOV

Center for Remote Sensing and Spatial Analysis, Rutgers University In scanning systems, the Ground Resolution Cell (GRC) is similar to the concept of a Ground Sample Distance (GSD) in digital cameras. While not always strictly true, the GSD and GRC are equated with the pixel size of the image projected onto the ground. No guarantee that you will be able to discriminate objects that are the same size as the GSD – depend where the pixels fall in relation to the object of interest How small a GRC do I need? Object same size as GSD but doesn’t dominate any one pixel

Center for Remote Sensing and Spatial Analysis, Rutgers University General Rule of Thumb: GRC should be less than one half the size of the smallest object of interest (which at a minimum equals to 4 pixels for simple square object). How small a GRC do I need? For identification purposes, will often need to be much smaller, i.e., need multiple pixels within object.

Center for Remote Sensing and Spatial Analysis, Rutgers University meter ground spatial resolution per pixel foot ground spatial resolution per pixel Digital Orthophotography: the new standard

Center for Remote Sensing and Spatial Analysis, Rutgers University Mixed pixels: more than 1 land cover within GRC Landsat TM 30m pixel/GRC boundaries on IKONOS 4m pixel image backdrop How small a GRC do I need? Can you identify the rectangular objects in the IKONOS image?

Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial resolution keeps getting better...

Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial resolution

Center for Remote Sensing and Spatial Analysis, Rutgers University 1, 3, and 10 meters

Center for Remote Sensing and Spatial Analysis, Rutgers University ultra-high spatial resolution 24 inch (60 cm) 6 inches (15 cm)

Center for Remote Sensing and Spatial Analysis, Rutgers University Tradeoffs: Swath width vs. GRC vs. disk storage Landsat GRC:30m SW:185km SPOT GRC:10-20m SW:60 km IKONOS GRC:1-4m SW:11km 80 m = 40 Mb-4 bands (MSS) 30 m = 320 Mb-6 bands (TM) 10 m = Mb-1band 1 m = Tb - 1 band How broad of a region do we need? How much data can we store and process? 185 by 185 km

Center for Remote Sensing and Spatial Analysis, Rutgers University Spectral Resolution: slicing up the EMR

Center for Remote Sensing and Spatial Analysis, Rutgers University The electromagnetic spectrum Comparative Sizes: from subatomic to human scales Atom Nucleus Atom Molecule Bacteria Pinhead Honeybee Human & larger From NY Times graphic 4/8/2003

Center for Remote Sensing and Spatial Analysis, Rutgers University Spectral wavebands of Landsat TM

Center for Remote Sensing and Spatial Analysis, Rutgers University Landsat TM-7 bands-8 bit data Spectral (where we look) Radiometric (how finely can we measure the return) 0-63, 0-255, Landsat TM BAND

Center for Remote Sensing and Spatial Analysis, Rutgers University An example-plant leaves Chlorophyll absorbs large % of red and blue for photosynthesis- and strongly reflects in green (.55um) um=micrometers or microns=1 millionth of a meter Peak reflectance in leaves in near infrared (.7-1.2um) up to 60% of infrared energy per leaf is scattered up or down due to cell wall size, shape, leaf condition (age, stress, disease), etc. Reflectance in Mid IR (2-4um) influenced by water content-water absorbs IR energy, so live leaves reduce mid IR return

Center for Remote Sensing and Spatial Analysis, Rutgers University Landsat TM: each waveband provides different information about earth surface features

Center for Remote Sensing and Spatial Analysis, Rutgers University Hyperspectral Data: contiguous spectral channels of narrow bandwidth

Center for Remote Sensing and Spatial Analysis, Rutgers University AVIRIS image of Goldfield, NV Hyperspectral sensing To detect narrow absorption features of specific chemical or mineral composition in rock, soils or vegetation

Center for Remote Sensing and Spatial Analysis, Rutgers University 4 m multi-spectral 1 m panchromatic Space Imaging IKONOS Imagery Sample: Bound Brook NJ Tradeoffs: Higher spectral resolution generally has lower spatial resolution

Center for Remote Sensing and Spatial Analysis, Rutgers University Radiometric resolution Dark Bright Determined by the A-to-D quantization 6 bit = 0-63, 8 bit = 0-255, 10 bit = Sensitivity of the detector to differences in EMR signal strength determines the smallest difference in brightness value that can be distinguished

Center for Remote Sensing and Spatial Analysis, Rutgers University Radiometric resolution Higher radiometric resolution is especially important for quantitative applications such as sea- surface temperature mapping where the user wants to distinguish small differences in temperature

Center for Remote Sensing and Spatial Analysis, Rutgers University Satellite remote sensing orbits give repeat coverage Geostationary Polar Sun-synchronous Constant view of hemisphere Covers entire Earth 35,800 km km

Center for Remote Sensing and Spatial Analysis, Rutgers University Hurricane Isabel hits the Outer Banks gov/stories/s2091.htm Sept 18, 2003 from NOAA satellite image

Center for Remote Sensing and Spatial Analysis, Rutgers University SPOT has steerable mirror to increase overpass frequency

Center for Remote Sensing and Spatial Analysis, Rutgers University Change Detection The ability to monitor change is one of the benefits of remote sensing We can monitor human and natural changes in the landscape

Center for Remote Sensing and Spatial Analysis, Rutgers University Many different systems. Which to choose?

Center for Remote Sensing and Spatial Analysis, Rutgers University Different sensors and resolutions sensor spatial spectral radiometric temporal AVHRR 1.1 and 4 KM 4 or 5 bands 10 bit 12 hours 2400 Km , , (0-1023) (1 day, 1 night) , (um) Landsat TM 30 meters 7 bands 8 bit 16 days 185 Km ,.52-.6, ,.76-.9, , , um SPOT 10m P / 20m X P -1 band X- 3 bands 8 bit 26 days 60 Km P um (0-255) (2 out of 5) X , , um IRS15.8 meters1 band6 bit 22 days 70 km.5-.75(0-63) IKONOS1m P/ 4m XP -1 band bit 1-2 days 11 kmX-4 bands , , , um (0-1023) (1.5 out of 3) Quickbird.6-1m P/ 2.5-4m XP -1 band bit 1-2 days kmX-4 bands , , , um GeoVantage.1-1.1m4 bands m , , um 8 bit airborne Digital Camera km

Center for Remote Sensing and Spatial Analysis, Rutgers University Image Interpretation & Analysis Strong trend towards GIS-ready digital output products Computerized image analysis can help to enhance and extract information content of imagery in a time- efficient, cost-effective manner for direct input to GIS Computers can not replace the human image analyst; visual interpretation is still a valued technique Many recent advances in image analysis software

Center for Remote Sensing and Spatial Analysis, Rutgers University Digital Image Analysis software

Center for Remote Sensing and Spatial Analysis, Rutgers University Design of a remote sensing effort must clearly define information needs, analysis procedures and consider the 4 types of remote sensing resolution spatial spectral radiometric temporal when considering the types of imagery to use Remote Sensing - Summary