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Remote Sensing of Environment

by: Mr. Cleveland Friesen

Remote Sensing of Environment FOR 326

Marketplace > West Virginia University > Environmental > FOR 326 > Remote Sensing of Environment
Mr. Cleveland Friesen
GPA 3.78


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Class Notes
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This 5 page Class Notes was uploaded by Mr. Cleveland Friesen on Saturday September 12, 2015. The Class Notes belongs to FOR 326 at West Virginia University taught by Staff in Fall. Since its upload, it has received 21 views. For similar materials see /class/202683/for-326-west-virginia-university in Environmental at West Virginia University.

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Date Created: 09/12/15
Denise Lees Richard Warnick Wendy Goetz Paul aus USDA Forest Servtte Remote Sensing AppllEallO S Center Salt Lake City UT Overview Far derader remarre manager liare trnggled ta ndrnrrety al mlntianrfar mapping farert rtrnrtnre and tree lieigitr Mataring reelinalagy railed lidar g7t detertian and ranging afferr a mean afrallertz ng rim infarmatt39anfrom airrra Lidarpraw de detailed infarmatz39an aaant n01 only vegetation trnrtnre ant alm eartvmrfare feature beneath the vegetarian ranapy Far arannd 1 00 per arre er en lidar afer remarre manager geareferenred infarmatt39an abant tiere vegetarian and eartvmrfare feature arran a large area Lidar Systems Measuring tree heights or mapping small terrain features such as debris slides is difficult even from the ground In the past7 aerial photographs have proved useful for measuring these features but photogrammetry can be expensive to implement and technically challenging Lidar can collect automated measurements of vegetation terrain and structures from aircraft Lidar systems measure distances by determining the travel time of each laser pulse to an object and back This time is divided by two and multiplied by the speed of light to calculate the distance An airborne lidar system scans receives and georeferences multiple pulse returns Figure 39i lllustration ofvisualization potential of lmeter National Agriculture Imagery Program NAIP aerial photography draped over a 3D lidar digital surface model DSM from the ground and tops of surfaces tens of thousands of times per second The system records the location of each lidar return in three dimensions resulting in a realistic surface image of the landscape gure 2 Commercial lidar systems often have a ground sample distance of 025 to 2 meters Normal horizontal accuracy is Within 05 to 075 meters depending on the steepness of the terrain ight height above ground and scan angle Vertical accuracy ranges from 015 to 05 meters As a result lidar can map both the vegetation and terrain to a degree not previously possible Without a large number of manual measurements Lidar Data Products Digital Elevation Models Lidar systems generate millions of recorded data points The point cloud of raw x y and 2 data must be processed and separated into a bareearth digital elevation model DEM and topof surface model depicting features above ground level such as vegetation figure 2 Depending on its density many more energy pulses return from the vegetation canopy than from the ground surface To generate a bareearth DEM returns representing vegetation or other nonground surfaces such as rooftops must be removed from the data cloud LDV V111 USDA Forest Service Facilic Northwest Research Station Figural Visualization in 3D of a lidar point cloud colored by elevation left the same scene with a NAIP color photograph draped overthe lidar point cloud right The ability to generate DEMs for both of the surfaces is the reason that lidar is a useful tool for forestry applications figure 3 The highresolution bare earth DEM that results makes lidar an ideal but not yet commonly used tool in the geosciences figure 4 The ability to map stream channels debris slides and detailed surface features reduces costs for geologic and engineering applications as Well Lidar Applications analyzing these naturalresource tasks Goetz and others 2005 I Forest inventoryiprovides measurements of tree height and density I Wildlife habitat analysisidetermines the forests structural stage I Predictive refuels modelingidetermines understory vegetation density and height I Feature extractionidepicts buildings structures and roads I ThreeD visualizationiportrays the forest or terrain in realistic detail I Watershed analysisidetermines slope aspect and elevation I Geology and Engineeringidetermines transportation corridors geomorphology and subtle terrain features I Landslide hazard assessmentiassesses presence of debris ows slope features and drainages Though lidar has disadvantages there are several reasons to choose lidar for collecting information on vegetation structure and bare earth features Some advantages and Lidar data has proven helpful in disadvantages oflidar are listed on table 1 Tahle1 Advantages and disadvantages of lidar Lidar tor Natural Resource Assessments Advantages Lidar offers detailed elevation information acquired over large areas and at a higher resolution than conventional DElVls Lidar can reduce the costs required to collect field measurements over large areas Lidardata helps pinpOint locations where field data Will be useful The ability of lidar to penetrate dense vegetation allows collection of data over large surface areas thatwould be difficultto survey in any otherway Morphologic features that might be missed altogether by field crews can be captured ata scale thatwould not be possible from a lUrmeter U 8 Geological Survey USES DElVl The interpreted data layers are easy to integrate With otherdata sources in a GlS Forest plans can efficiently incorporate results from lidardata analysis Lidardata can be acquired during day or night underclearweather conditions Disadvantages When used for forest inventory lidardata are currently still more expensive on a perracre basis than aerial photography Processing and analyZing lidar data sets require specialized skills and software Although the potential for using lidar is still grOWing notall the parameters needed to address current forestry issues especially those related to forest inventory can be derived from lidar data and the models are not always understood well enough to generalize findings from local studies Where the vegetation is so dense that lightcannot penetrate the ground such as in tropical forests lidar probably won39t reach the ground either and dense undergrowth may be confused With bare ground Often the vegetation height calculated from lidar data is less than the height obtained through photogrammetry and fieldwork Canloy Height Model ltered for outliers Fi ure 3 A canopy height model Cl a vegetation analysisfrom lidar data is calculated by subtracting the bareearth model generated from the last return data Blfrom the firstreturn topofsurface model A The NAIP photograph B shows the forest clearcuts well butwithout stereo capabilities the heightdifferences in the dense forest are not well defined The study area is located in the Idaho Panhandle National Forest near Emerald Creek Cost It is di cult to set a precise amount for the cost oflidar acquisition but a general rule of thumb in 2001 was between 050 and 100 per acre for project areas larger than 1000 square miles Flood 2001 Generally Forest Example I 1072 I 832 acquiring lidar oVer larger areas is more cost effective than surveying small areas because of mobilization costs which vary depending on the distance from the aircraft location to the project area Other factors in uencing costs include the density of the BareEarth Digital Terrain Model DTM OneMeter NAIP Photography point spacing the available time frame the shape of the project area and the spatial and vertical accuracy required for the project The cost of the analysis increases for each valueadded product requiring additional processing Bare Earth Example to w Lo OneMeter Bare TenMeter DEM Subset oi Clarkia Quadrangle Figure 4 Comparing a hillshade relief obtained from the lidar bareearth data left and a subset of the 10meter US Geological Survey USGS DEM ofthe Clarkia 75minute quadrangle in Idaho right Notice the details visible in the floodplain and along the riverbed Along the south end of the hill in the southwest corner of the image the lidar data show a narrow trail that is not visible at all on the USGS data References and Suggested Reading Floo 2 01 Laseraltlmetry from sclenceto commerclal lldar mapplng Photogrammetrlc Englneerlng and Remote Senslng 67111 120971217 GoetzW Laes D Maus P Lachowskl H 2005 lear mapprng a reference for natural resource managers RSAC70073VRPT1 Salt Lake Clty UT U S Department of Agrlculture Forest Servlce Remote Senslng Appllcatlons Center 22 p Maune D F ed 2001 Dlgltal elevatlon model technologles and appllcatlons the DEM users manual Bethesda MD Amerlcan Socletyfor Photogrammetry and Remote Senslng 539 M us P Jarvls B JohnsonV Laes D 2003 A checkllst for lldar users RSAC700397RPT1 Salt Lake CIV UT U ment ongrlculture Forest Servlce Remote Senslng Appllcatlons Center Remote Senslng Appllcatlons Center 2005 lear the workshop May17e19 2005 Salt La RSACV4021VDM1 Salt Lake Clty UT U S Department of Agrlculture Forest Servlce Remote Senslng Appllcatlons Center CDrROM DTM colored by tree height For addrtiohal rhformatroh Contact Henry Lachowski Remote Senslng Appllcatlons Center 2222West 2300 Sout Salt Lake Cty UT 84119 phone 801797573750 ermall hlachowsklfs led us Thls publlcatlon can be downloaded from ourWeb slte httpfswebrsacfsfedus The Forest Servrce Uhrted States Department of Agrrculture USD has developed thrs rhformatroh product or servrce to the eXclusroh of others that may be sortable The u 5 Department or Agrlculture USDA prohlblts dlscrlmlhatloh m all lts programs and acuvltles on the has mm ersohs wl dlsabllltles who requlre alternatlve means ror commumcatloh 3 i Q i 6 3 E 6 L8 0 u 3 c 9 9 U o 2 o o USDA Dlrector Of ce of Clvll nghts 1400 lndependehce e Ue SW Washl C 20250794 0 orcall an equal opportuhlty provlder and employer Uhted States Department of Agrlculture Forest Servlce Engrneerlng Remote Sensln Appllcatlons Center Sponsored bythe Remote Senslng Steerlng Commttee RSACV73rTlP1 e g Ffanklinel al 1981 Stone and Porter 1998 While composition k on taining the x Y and z coordinates in the lsefrspeci ed coordinate system figure 1 combining all returns from a particular area results w t r Characteristics othe forestquot Stone and Porter 1998 Ecologij furl it I a w r r t I A mte that canopy penetration is quite low and that there are very few returns t a 2 Treuhalt et al 2004 Knowledge of canopy vertical structure is pare rnrrnzllnn hop area as one might for example tally only trees ofa certain species easy to obtan using basic eld skills and tools for more demanding investigations and analyses however canopy is us is r 111g in situ and remotely sersed data Remotely sensed data sources large footprint lidar sensors Lefsky et al 1999 Means et al 1999 00 Nasset and Bjeflmes 2001 McCombs et al 2003 Popescu et al 2003 Of these possibilities only digital photogramnetry and small p bothi1 4 39 N 39 and 2 suitable for structural analyses at the scale of management which tablishnent fertilization thinning release and harvest are made it n ost Common Small Footprint Sensors An Overview ork of How They W An aircraltemounted laser fange ndef emits tens of thousands of laser pulses per second As a pulse is reflected back to the sersor ime and often retum intensity is recorded Given that the speed of light is known and corstant elapsed time can easily be converted to distance slant range Many sensors now record multiple returis from a single pulse Rotating or scanning mirrors are used to collect data perpendicular to the line of flight The posie 70705996414539296 0 l 7071876641452679Z 1899 Flgure 1 Exampie of pomol39l of a rst return ASCH mass point Me Flgure 2 Example oi lidar pom doud over pll le plantation Courtesy PM Sforzcl Analysis Units The lidar point cloud shown in Figure 2 represents all returns that fell in a 100 m2 gnd Grid cells ofvafious sizes have been lsed as system GPS and an iiertial neasurement unit IMU provides the orientation of the aircraft The scan angle slant range GPS position and lMu orientation are combined in postprocessing to create very by Meats eta 2000 and Nasset 2002 The other primary unit of tree clusters as shown by Boriolot in this issue used to particular Rmndlhnro I 00 intersity for each pulseeretum combination Just how accurate are and Popescu etal 2004 Anewef unit of analysis at least for lidar d accuracy of the elevation data cf 15720 cm or less with horizontal position being on the order of 10s of centineters N ot an I ma 9 e Ore common misconceptim about lidar data is that they are raster data sets Thisis untrue the data as delivered are typically no more ata eg Mason etal 2003 choosing the optimum analysis unit for a particular application is a function of 1 lidar spot size and poste spacing 2 forest type 3 required information and 4 whether a on t pc last point as it also tends to cause some confusion onllrlued on page 1312


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