See Lab 8, Natural Resource Canada RS Tutorial web pages Tues 3/24 Supervised land cover classification See Lab 9, NR Canada RS Tutorial web pages

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1 SFR 406 Remote Sensing, Image Interpretation and Forest Mapping EXAM # 2 (23 April 2015) REVIEW SHEET (Lecture powerpoint & notes) TOPICS COVERED ON 2 nd EXAM: Reading Assignment Paine & Kiser, 2012 ** In addition to outlines/powerpoint/notes Tues 2/24 Intro to Orono/Old Town forest and land cover Seasonal photography notes mapping project Thurs 2/26 Guest Lecture Carter Stone No notes 3/3-3/13 Spring Break Tues. 3/17 Introduction to non-photographic systems pp , 534, Notes on Landsat TM digital images, Landsat overview and Interpretation of color composite interpretation Landsat 8 OLI imagery Thurs. 3/19 Digital land cover classification methods, Unsupervised classification See Lab 8, Natural Resource Canada RS Tutorial web pages Tues 3/24 Supervised land cover classification See Lab 9, NR Canada RS Tutorial web pages Thurs 3/26 Medium (5-80m) and coarse spatial resolution (250 NR Canada RS Tutorial web pages 1100m) multispectral scanners & sensor comparisons Tues 3/31 Spectral, spatial, temporal resolution; High spatial resolution and hyperspectral sensors NR Canada RS Tutorial web pages, Emerald Ash Borer hyperspectral ash tree ID application Thurs. 4/2 Land cover classification accuracy assessment pp , See Lab 11 Tues 4/7 Lab mapping help session No notes Thurs 4/9 Thermal infrared imaging systems, H.J. Andrews Experiment Forest thermal canopy research, Oregon pp , Thermal applications- forestry notes, handout on H.J.Andrews thermal research Tues 4/14 Radar imaging systems, airborne radar forest canopy structure research, Mississippi. Pg , Radar forest research notes, Radar reflector types & forest canopy research handout. Thurs 4/16 Lidar systems introduction, Rei Hayashi Lidar/forest height research results at Penobscot Experiment Forest (PEF), Elias Ayrey (Guest presentation) PEF- Lidar forest inventory and analysis research Pg , Hayashi Lidar journal article abstract. Tues 4/21 Forest change detection; Exam 2 Review Exam review notes and questions; more sample questions in class Thurs 4/23 Exam 2 All lecture content since Exam 1 INTRODUCTION TO NON-PHOTOGRAPHIC SYSTEMS As remote sensing technology advances, non-photographic systems are increasingly used to compliment and provide alternative data and analysis tools to traditional photographic methods in natural resource applications. In the remaining lectures of this course, we discuss these non-photographic systems, common examples and applications in forestry and natural resources, how they work, and how we interpret the data and create and assess the forest and land cover map products developed from them. Non Photographic Remote Sensing basics: Definition of remote sensing (RS) What is a passive and active RS system? Classification (including photographic vs. non-photographic; active vs. passive) Spectral wavelength regions where passive multispectral scanner systems operate Electronic Imaging Systems and Digital Camera Sources of digital images (scanned photos, digital cameras, multispectral scanners, other non-photographic sensors) Define what a digital image is in simple terms? Photo versus non-photo processing characteristics Photograph vs. image (how does electronic versus photo differ in data capture, storage, manipulation, transmission or communication)?

2 Digital imaging Charged coupled detectors (CCDs) Data capture and storage comparisons between a photo and digital image Spectral and spatial characteristics of a pixel from a digital image Pixels (picture elements), cells, rasters What is a digital number (DN) in a digital image pixel? What sensor/platform system components control the instantaneous field of view (IFOV) or pixel area represented (spatial resolution) on the ground? Comparison of digital imaging vs. photographic film (some advantages/disadvantages) 8 bit data (0 255 (256 values) and brightness levels (black to white) Image Classification for Land Cover Mapping Unsupervised Approach: How does it work? What does the clustering algorithm do in an unsupervised classification? Spectral classes vs. information classes. Describe these in terms of the unsupervised and supervised method What images or tools did you use (in Lab 8) to help guide you in assigning forest and land cover type names to the cluster classes? Why might one of your cluster classes actually represent more than one cover type or have confusion in identification of a specific cover type compared to another Supervised Approach: How does it work? How does your training site selection and knowledge influence accuracy of classification results? Theoretically, with experience, this method should produce better results than unsupervised, however the burden is on the analyst. What are various assumptions and possible sources of error related to this method? Which method would you use if you had very little information about the land cover types (lack of good support orthophotos or aerial photos, large, remote area and field visits difficult)? Some Relevant Questions 1. Briefly describe the difference between an unsupervised and supervised land cover classification in terms of how the image analyst (you) interacts in the process. 2. What is a spectral class in an unsupervised classification and in a supervised classification? 3. What is an information class in a supervised or unsupervised classification 4. In an unsupervised classification (lab 8) you set the number of classes to 45, the convergence threshold parameter to 0.995, and the number of iterations to 150. What is the Erdas-Imagine program doing with this information? 5. Name a possible advantage and disadvantage of each classification method (unsupervised and supervised). 6. List 4 procedures you would use to select good training sites for a supervised classification. 7. When you have all of the training sites selected and statistics stored in Erdas-Imaging signature editor file, what is the computer doing with all of the information to create the output classified image. Medium and Coarse Spatial Resolution Multispectral Scanners and Sensor Comparisons Landsat MSS: (Landsats 1-5): 18 (L1-3) / 16 (L4-5) day repeat visit coverage, approximately 185 x 185 km scene area, 4 bands (green, visible red, 2 near-ir), 80m resolution; land observation, vegetation mapping, historical change detection. Oldest medium resolution data archive. Free data. Landsat TM: (Landsats 4-5): 16 day repeat coverage, approx. 185 x 185 km scene, 7 bands (blue, green, visible red, near-ir, 2 mid-ir, thermal IR), 30m (120m for thermal) resolution; land observation, vegetation and land cover mapping, agriculture / soil mapping, land use/cover change detection, urban applications. Free data Landsat 7 Enhanced TM+ Similar to Landsat 4-5 TM except 60 m thermal and 10 m panchromatic bands, developed a line scan anomaly problem in May 2003 that severely compromised the utility of the data. Landsat 8 Operational Land Imager (OLI) Similar to Landsat Thematic Mapper, except 2 blue bands. Now (in 2014) the best operating Landsat sensor. Free data SPOT HRV: 26 day repeat coverage (with six revisits possible in this time period considering nadir and off nadir view angles), 60km swath at nadir, SPOT 1-3- Panchromatic band (10m) plus 3 multi-spectral bands (green, visible red, 20m; SPOT-4 & 5 and added Mid-IR band and improved panchromatic to 5 and 2.5 m ground resolution; vegetation and land cover mapping, terrain interpretation and mapping (DEMs), urban mapping, agriculture and soils mapping. Gaps in global archive. Relatively expensive data costs per area.

3 NOAA AVHRR: twice-daily coverage, 3,000km swath (very large scene area), 5 bands (visible red, near-ir, 3 thermal IR), 1.1 km resolution; meteorology, global vegetation monitoring, sea surface temperature, snow and ice mapping, fire monitoring. Free data MODIS: Twice daily coverage, 2330 km swath, 2 bands (visible red and near 250m; 7 bands (3-visible, 1-near IR; 3-mid 500m; 36 bands (some narrow 1000m); similar applications to AVHRR but more potential due to better spectral (more bands at 1000m) and better spatial resolution with 2 and several others similar to Landsat TM and OLI Free data What sensor would you use and why for different forest mapping scenarios or applications (large vs small area, costs, spatial, spectral, temporal resolution needed for Level 1, 2, 3 land cover mapping or change detection, # scenes and time to process data and create map output. Some relevant questions related to digital imaging and various multispectral scanners studied 1. Name 3 sensor sources of digital imagery. 2. Compare photographic and non photographic systems (digital system) for: Data capture Data manipulation Data transmission Photo Digital 3. What two advantages of non photographic sensors, and one possible disadvantage, compared to aerial photography (with film)? 4. What is an active versus passive remote sensing system? Give a sensor example of each. 5. What does spatial resolution and spectral resolution mean in reference to a pixel on a digital image? 6. Non photographic sensors can record energy in the following wavelength regions: (a) visible, (b) near infrared, (c) mid- infrared, (d) thermal, (e) microwave, (f) all of the above. 7. The ground pixel resolution of Landsat 8 OLI reflective wavebands are: (a) 1 x 1 m, (b) 5 x 5m, (c) 10 x 10m, (d) 30 x 30m, (e) 50 x 50m 8. The ground pixel resolution of Landsat 8 OLI in acres is (a) ¼ acre, (b) ½ acre, (c) 1 acre, (d) 2 acre 9. The Landsat 8 OLI scene area (such as scene P11/R28 that includes Orono) is approximately (a) 60 x 60km, (b) 100 x 100km, (c) 185 x 185km, (d) 360 x 360km. 10. Explain 2 difference between a digital image and an aerial photograph 11. Name two major factors that will influence the ground area covered when flying a digital camera in aircraft. 12. If you double the # of rows and columns available in your digital camera (say from 5 to 10 megapixel), the data (# pixel) increase will be: (a) 2 times, (b) 3 times, (c) 4 times, (d) 6 times, (e) 8 times. 13. The digital number (DN) range of an 8 bit image is: (a) 0-63, (b) 0-127, (c) 0-255, (d) A digital number of 60 representing an Landsat reflective band for a pixel on the computer screen would be: (a) black, (b) dark gray, (c) medium gray, (d) light gray, (e) white 15. For a Landsat visible and infrared reflected wavebands, the digital number will increase with an increase in ground object: (a) absorption, (b) refraction, (c) transmission, (d) reflectance, (e) emittance 16. The 3 primary colors (color guns or write function) in the computer display are: (a) red, yellow green, (b) red, magenta, blue, (c) red, green, blue, (d) green, magenta, cyan. 17. What does spectral resolution mean? 18. What does spatial resolution mean? 19. What does temporal resolution mean? 20. Compare AVHRR and MODIS, advantages/disadvantages 21. Compare Landsat and SPOT, advantages/disadvantages to sensor spatial and spectral resolution. 22. Pick a sensor above and explain your choice: You have $1000 and two weeks to produce a Level 1 forest map of the Pacific Northwest from Alaska to Mexican border with California. 23. Pick a sensor above and explain your choice: You have $5000 and one month to produce a Level 2 (S, SH, HS, H) forest map of the State of Maine. 24. Compare Landsat and SPOT advantage/disadvantage with regard to cost/per area scene coverage.

4 Types of High Resolution Multispectral and Hyperspectral Scanner Sensors Ikonos: 2-3 day repeat coverage depending on latitude and off nadir viewing, 11 km swath (scene coverage), 4 bands (blue, green red and near 4m, 1m, similar applications to aerial photography or other very high resolution imagery (Quickbird). Hyperion 16 day repeat coverage (like Landsat), 7.6 km swath coverage, 220 narrow bands between 0.4 (blue) to 2.4 (Mid-ir or SWIR) 30m spatial (like Landsat). Experimental sensor for specific applications (see notes). Geologist have found certain minerals that have a unique combination of spectral peaks and troughs in certain narrow bands? Perhaps with an aircraft high resolution/hyperspectral sensor, we can identify certain hardwood or softwood species by spectral reflectance peaks in some narrow bands or find the best combination of wavebands for detecting ash vulnerable to Emerald Ash Borer? What is the relative tradeoffs of spectral (number of bands and where they are located in EMS), spatial (pixel ground resolution), and temporal resolution (revisit frequency to same location on earth) between satellite hyperspectral (Hyperion) and high resolution sensors, like Ikonos? See notes on this. Some relevant questions on high spatial resolution and hyperspectral imagery 1. How many wavebands does the IKONOS multispectral scanner have? What is the ground pixel resolution in meters? 2. What is one possible advantage and disadvantage of IKONOS or Quickbird compared to 1:15,840 color infrared aerial photos? 3. Is IKONOS primarily a pushbroom (along track) or a whiskbroom (across track) scanner? 4. What is the ground pixel resolution of the IKONOS panchromatic band? 5. What IKONOS bands coupled with the red, green, blue (RGB) color guns or write functions in the computer monitor would render a true or natural color composite? 6. What is a disadvantage of using IKONOS imagery to map forest types of the entire state of Maine? 7. What does hyperspectral mean in regard to a hyperspectral sensor like Hyperion? 8. Is IKONOS a hyperspectral scanner? Why or why not? 9. What is an advantage and disadvantage of Hyperion compared to IKONOS? 10. How might a hyperspectral sensor flown in an aircraft at high spatial resolution be used for a forest canopy mapping or ecological experiment related to vegetation? 11. How might a geologist use a hyperspectral sensor (specifically what type of study or application)? 12. Usually a satellite sensor with very high spectral resolution (hyperspectral) usually has (a) high or (b) low to medium spatial resolution. 13. Usually a satellite sensor with high spatial resolution usually has (a) large ground area coverage or (b) small ground area coverage? 14. Why is the specific level of forest mapping and the size of the study area an important consideration about which satellite sensor the user might chose? 15. Will a high resolution or hyperspectral sensor work well for forestry application when operated at night? In a rain event? Some questions on Land Cover Classification Accuracy Assessment 1. Why should we conduct an accuracy assessment of a land cover map? 2. Procedure for conducting an accuracy assessment (collect reference data, sampling design, construct error matrix) 3. Developing and interpreting the Error Matrix (overall, user s, and producer s accuracy) - see lab 11, and powerpoint. 4. How are producer s and user s accuracy related to commission and omission errors? 5. If given an error matrix, compute the overall accuracy, user s and producer s accuracy. Thermal Scanners Across-track scanning operation (whiskbroom), like Landsat MSS or TM or aircraft thermal scanner Where is the thermal wavelength regions (where are the windows )? The U.S. Forest Service thermal scanner flown in the western states for big project fires beginning in the early 1970s was a bi-spectral thermal scanner. What does this mean in terms of the wavelengths where it operates? What liquids are used to cool the detectors? Why do the detectors need to be kept cool?

5 Emitted IR (heat) wavelengths and BW tones (what tone and relative 8 bit DN for fire?) Internal and external sources of distortion on thermal images. Thermal interpretation elements (6) what are they? What is thermal diurnal cross-over? What are some applications of thermal imagery in natural resources, environmental monitoring or engineering studies?. NASA Forest canopy thermal research example-oregon. What ground data and GIS variables used? What were the statistically significant results related to stand conditions and topography? Why are tree canopies cooler than grasslands on mid day mid summer imagery? Is thermal imagery good for forest type mapping? Why or why not? Some relevant questions related to Thermal Sensors and Data 1. What general wavelength ranges (in microns) do thermal (heat) infrared sensors operate in? 2. Thermal infrared can be recorded directed on on color or color infrared film (true or false)? Why or why not? 3. What is the Dewar inside the thermal sensor and what does it do? 4. What are five thermal image interpretation features or principles? 5. If you have a black group of pixels (DN = near zero) on a mid day, summer, black and white thermal infrared image on the computer screen. What type of land cover feature does it likely represent and why? 6. Part of the roof of your house has a band of white pixels on the thermal image taken at night in the winter, but the rest of the roof is dark. What do you think this means? 7. What is the relevance of diurnal cycle in terms of timing in thermal image acquisition and interpretation if you wanted to conduct a forest canopy research study, for example? 8. If you flew a thermal sensor over a young, sparse forest versus an older dense forest, both on a south facing slope in the North American summer (early afternoon), which would likely be a darker tone? Why? 9. Name one source of an internal thermal distortion and one source of an external distortion that can affect the thermal image quality. Radar Systems and Applications RaDAR (radio detection and ranging) Operation of active radar system Relationship of waveband size and relation to military codes (X, C, L, etc.)? Two types of side-looking airborne radar -Real aperture (RAR) synthetic aperture (SAR) Which of two SLAR is also called brute force? Ground resolution controlled by antenna length and flying height (RAR) What is polarization? What polarizations appear to be best correlated to forest structure or height? Advantage and operation of SAR (How can SAR have relatively high resolution from space)? Types of ground reflector targets (3) and how they affect the strength of the radar signal RADAR shadow - what situations create this? What tone on BW image? Displacement of tall objects radar layover effect (inward toward nadir). Why? Interpretation of radar images (What tones do you expect for forest and other features and how do these relate to reflector types or targets?) See handout. Application in natural resources; forest research example in Mississippi forests What forest canopy measurements were correlated? What polarization produced the best correlations with canopy measurements? Also see literature review notes on this. What is an advantage and disadvantage of RADAR for mapping forests? Some relevant questions on RaDAR Sensors and Data 1. What does RaDAR mean? 2. Is Radar an active or passive remote sensing system 3. Are the wavelengths sensed by Radar longer or shorter than visible wavelengths? Thermal wavelengths? 4. What are the two types of Side Looking Airborne Radar (SLAR)? 5. Which type of SLAR depends on a fixed antenna length to define its ground resolution. 6. Why is Synthetic Aperture Radar (SAR) considered more advanced and higher resolution, especially at satellite altitudes? 7. The letters used to describe the wavelength or waveband of the Radar system (C, L, P, X) is a system that conforms alphabetically to the size of the wavelength (for example, X band is longer than L band). True or False?

6 8. For forest canopy or woody biomass estimation studies, the published literature suggests that the longer waveband (for example L & P) are better correlated with canopy height and basal area than a shorter band (like C band). True or False 9. What does polarization relate to in terms of the operation of the Radar signal? 10. The published literature suggests that having cross-polarization (HV,VH) data may produce better results with canopy height and structure relationships than when using like-polarization data (HH, VV). True or False? 11. What is a possible advantage of Radar compared to other remote sensing systems? 12. What is a possible disadvantage of Radar compared to aerial photos or multispectral scanner data for forest mapping? 13. For what purpose was Radar first developed during World War II 14. Why are buildings in an urban environment usually a whitish tone on Radar images? Why is water dark or nearly black? 15. What is a Radar shadow and what orientation of objects causes it to occur? 16. There are specific terms for generic types radar reflector surfaces. Name the type of Radar reflector surface that a forest or shrubby target represents when the radar pulse hits it. 17. Name a specific natural resource application area where Radar might be used, other than in forestry. 18. In the NASA Airborne SAR study of forest canopy characteristics, the correlation of Radar data was better in pine plantations compared to mature mixed hardwood forest. Why? Lidar Systems and Applications LIDAR- Light detection and ranging) Lidar sensor active operation Three components of a modern airborne Lidar system Older nadir pointing pulsed Lidar versus continuous wave scanning Lidar What wavelength region used to penetrate water surface? What wavelength region usually used for forest measurements? Questions on Lidar Sensors and Data 1. What does LiDAR mean? 2. How does a Lidar system differ from Radar, and what is one similarity. 3. What general wavelength size pulses are used by a Lidar system? 4. Can Lidar be flown successfully at night? In the rain? 5. How did the older type of Lidar differ from the newer Lidar systems, in regard to the sensor operations and data recording characteristics? 6. What forestry measurements or applications might be addressed with a Lidar system? 7. What type of applications or studies, other than forestry might be addressed with Lidar? 8. Are conifer tree heights usually over or underestimated? Why? 9. What specific problem might occur when using a low density Lidar for meausurements in a very dense forest canopy 10. What are not ideal weather or seasonality conditions for operating Lidar for forest measurements? 11. What forest tree measurements can be retrieve directly from Lidar data? Indirectly? 12. Maine recently collected Lidar data for parts of southern Maine to create Digital Elevation Models (DEM). What was the spatial resolution of the DEMs in meters? Comparison of camera, thermal, RADAR, LIDAR systems use of each given weather, smoke, or day/nighttime and relative advantages under different forest mapping, measurement or other natural resource applications see handout. Remote Sensing for Mapping Forest Change Questions 1. Why use remote sensing for detecting and mapping forest damage or changes? 2. How does the NDVI digital value change when a forest canopy is undisturbed at the first image date, but a harvest occurs by the second image date? For example, NDVI(1) - NDVI(2) equals a high or low value for a pixel representing this change?

7 3. Noone et al. (Forest Science) used RGB-NDMI to detect statewide forest change rates in Maine, among forest landowner groups. Land owned by the public (state land) and land owned by conservation organizations had (a) higher or (b) lower harvest rates, compared investment organizations (TIMO/REIT)? Circle a or b. 4. According to Noone et al., state-owned lands in Maine had (a) higher percentage of coniferous forest or (b) lower percentage, compared to privately owned forests? Circle a or b. 5. What is aerial sketch mapping as used by the U.S Forest Service? 6. What is a limitation of aerial sketch mapping for large area inventories of insect defoliation or damage? 7. What is a possible advantage of MODIS over Landsat TM when mapping insect defoliation, such as the Gypsy Moth? 8. What is a disadvantage of MODIS, compared to Landsat or aerial sketch mapping in detecting and mapping the extent of Gypsy Moth s defoliation? >> See Exam 2 sample questions handout in Tuesday April 21, 2015