Light Detection And Ranging (LiDAR) For Improved Mapping .
Light Detection and Ranging (LiDAR) for Improved Mapping of WetlandResources and Assessment of Wetland Conservation PracticesNatural Resources Conservation ServiceConservation Effects Assessment ProjectSummary FindingsLiDAR elevation data can be used tomap the potential, static distribution ofcurrent and historic wetlands and keywetland functional drivers based onphysical controls on water distribution.LiDAR intensity data can be used to mapactual, dynamic variations in wetlandinundation extent which can provideadditional insights concerning keyfunctional drivers.LiDAR intensity data significantlyimproved the mapping of inundationbelow the forest canopy compared withusing aerial photography. The accuracyof the LiDAR intensity based wetlandinundation map was 97% versus 70% forthe aerial photography based map, orabout 30% more accurate.Relief relative to a local elevationmaximum provided a strong indicator ofinundation dynamics (i.e., hydroperiod),but was less useful for mapping wetlandboundaries. Combining local relief andan Enhanced Topographic WetnessIndex produced a map that was wellsuited for mapping wetland extent andhydroperiod. Wetlands mapped usingaerial photographs or LiDAR-deriveddigital elevation models (DEMs)contained a similar amount of inundatedarea, but the LiDAR-derived mapscontained fewer errors of omission. Forthis reason, it was concluded that DEMbased topographic metrics producedenhanced inundation maps relative toaerial photography derived maps.When using LiDAR derived DEMs ourresults support the use of moredistributed flow routing algorithms overalgorithms that force greater flowconvergence for the mapping ofpalustrine wetlands in areas with lowtopographic gradients. Accounting forwater outflow as well as inflow is key todeveloping robust indicators of wateraccumulation potential.A concerted effort is ongoing by NRCSand other federal agencies to hasten thecollection of high quality LiDAR datathroughout the entire United States andfacilitate enhanced analyses of naturalresources and ecosystems.CEAP Science Note, September 2014Remotely sensed data have long been animportant tool for the assessment of landcondition and the effects and effectivenessof land management. The USDA has anextensive history of remotely sensed datause, which has largely focused on aerialphotography. Although the inherentbenefits of aerial photography andestablished operational data processingstructures merit the continued use of thisdata stream, newer types of remotelysensed data, including Light Detectionand Ranging (LiDAR), have been shownto provide robust, synergistic informationon conservation practices when used inconjunction with aerial photography. Thisincludes, but is not limited to, the use ofLiDAR data to improve the mapping andcharacterization of wetlands.Although U.S. wetlands are currentlymapped using aerial photography, thesemaps are often out of date and errors canbe substantial (Stolt and Baker 1995;Kudray and Gale 2000), especially indifficult-to-map areas, which includewetlands with intermittent hydrology andforested wetlands. The Natural ResourcesConservation Service (NRCS) is one ofseveral Federal agencies that haveexpressed the importance of LiDAR datafor improved wetland mapping andcharacterization (Snyder and Lang 2012).Until recently, the spatial resolution ofcommonly available digital topographicdata for the United States (verticalaccuracies of 3.3–32.8 ft [1–10 m]) wasinsufficient to map many geomorphologicfeatures, including most wetlands.However, LiDAR-derived digitalelevation models (DEMs) providesuperior vertical accuracy ( 3.9–5.9 in [ 10–15 cm] and horizontal resolution( 39.4– 78.7 in [ 100–200 cm] [Coren1and Sterzai 2006]), allowing theenhanced mapping and characterizationof existing, former, and restoredwetlands, which can improve theimplementation of wetland conservationpractices. The use of LiDAR data can beespecially vital in areas with lowtopographic variation, particularly whenapplied to mapping or monitoringwetlands that have previously beendifficult to detect, such as forestedwetlands.This Conservation Effects AssessmentProject (CEAP) Science Note brieflyintroduces discrete point return LiDARtechnology, the most readily availabletype of LiDAR; describes multiplestudies that have demonstrated thebenefits of this technology for improvedwetland mapping and characterization;and discusses the implications of thesestudies and others for improved wetlandmapping and assessment of wetlandconservation practices.Light Detection and Ranging(LiDAR) TechnologyLiDAR sensors provide detailedinformation on the elevation of theEarth’s surface and objects on thelandscape, such as vegetation and human-made structures. LiDAR sensors collectdata through the use of an onboard lasersystem, which sends and receives laserenergy. LiDAR sensors send frequent(hundreds of thousands per second) shortpulses of laser energy, and a portion ofthat energy is reflected back to thesensor where it is recorded. MostLiDAR sensors used for land-basedremote sensing operate in the nearinfrared region of the electro-magneticspectrum (commonly in the 0.90 to 1.55μm wavelength range; Lemmens 2007),
with 1.06 μm (near-infrared) being acommonly used laser wavelength(Goodwin et al. 2006). LiDAR data canbe used to calculate precise x, y, zlocations through the use of a highlyaccurate onboard Global Positioning andInertial Navigation System and bycalculating the distance to an object byrecording the amount of time it takes fora pulse, or a portion of that pulse, totravel from the sensor to the target andback (Goodwin et al. 2006). LiDAR x, y,z points can be used to make DEMsthrough the interpolation of LiDARpoint returns. The resolution of theresultant DEM is based largely upon theoriginal density of LiDAR returns (pointdensity) and user requirements. If onlypoints originating from the Earth’ssurface, as opposed to points originatingfrom above the Earth’s surface (e.g.,trees, grass, and buildings) are used forthe interpolation, then the resultantimage is called a bare earth DEM, and itrepresents topography. While return timeprovides information on location,LiDAR intensity, or the strength of thereturned LiDAR signal relative to theamount of energy transmitted by thesensor per laser pulse (Chust et al.2008), provides information regardingthe identity of target materials which theLiDAR signal reflects from beforereturning to the sensor.Wetland Applications of LiDARLiDAR IntensityLiDAR intensity data are well suited forthe identification of inundation, andpossibly saturation, due to the strongabsorption of near-infrared energy (theenergy detected by most terrestrialLiDAR sensors) by water. Informationderived from LiDAR intensity iscomplementary to LiDAR-basedinformation on x, y, z location, and eachLiDAR point return contains both typesof information. The association ofindividual points of LiDAR intensitywith precise x, y, and z values allows theselection and display of LiDAR intensityoriginating from the Earth’s surfaceexclusively, in this way reducing theimpact of a plant canopy or othervertical structures on the ability todiscriminate inundated versus noninundated areas on the ground. In thisway, LiDAR intensity data can bereadily filtered to remove the influenceof the canopy. On the other hand, aerialphotography cannot be similarly filteredand will contain a mix of informationfrom the plant canopy and the ground.A study was conducted to determine therelative ability of LiDAR intensity andaerial photography to map inundationbeneath the forest canopy in theChoptank River Watershed, anagricultural watershed on the EasternShore of Maryland (McCarty et al.2008). Although inundation does notalways equate with wetland status, datawere collected during maximumhydrologic expression at the beginningof the growing season, March 27, 2007.Therefore, areas that were inundatedduring the study period were very likelyto meet the hydrologic definition of awetland and although areas that were notinundated during the study period couldstill meet this definition they were muchless likely to do so. The mapping offorested wetlands is particularlyimportant because these are the mostcommon type of wetland in the UnitedStates and they are particularly difficultto map using existing technologies, suchas aerial photography. This is especiallytrue in areas of low topographic relief,such as the outer Coastal Plain of theMid-Atlantic. Accurate maps of wetlandextent and character are critical for awide variety of natural resourcemanagement activities. For example,they can be used to assess the effects andeffectiveness of forested wetlandrestoration and compare the level ofecosystem services provided by restoredand less disturbed wetlands.To meet the goal outlined above, LiDARintensity data were collected using anOptech ALTM 3100 LiDAR sensorflown at 2,000 ft ( 610 m) above theEarth’s surface. Data were collectedwith a laser pulse frequency of 100,000pulses of 1.06 µm wavelength energy2per second at a scan angle of 20o usinga scan frequency of 50 Hz and a 12-bitdynamic range. The resultant data had avertical accuracy of 5.91 in (15 cm)and an average bare earth point densityof 0.23 pt ft-2 (2.5 pts m-2). The sensorwas coupled with a digital camera tocapture coincident 4.72 in (12 cm)spatial resolution aerial photography inthe near-infrared (0.72–0.92 µm), red(0.60–0.72 µm), and green (0.51–0.60µm) bands (Lang and McCarty 2009).The LiDAR intensity data were spatiallyfiltered to reduce noise and a simplethresholding technique was used tocreate a map of inundation below theforest canopy. Prior to analysis, theaerial photograph was resampled to aspatial resolution of 1 m and anunsupervised isodata classificationprocedure was used to create a map ofinundated and non-inundated forestusing all bands of the digital image. Theresultant inundation map was filtered toreduce error. The LiDAR intensity andaerial photography-based maps ofinundation were validated with groundbased information on inundated and noninundated areas collected using a highlyaccurate Trimble GeoXT globalpositioning system (GPS; Lang andMcCarty 2009).The study found that LiDAR intensitydata significantly improved the mappingof inundation below the forest canopyrelative to aerial photography (fig. 1).The LiDAR intensity-based inundationmap was 97 percent versus 70 percentaccurate, respectively or nearly 30percent more accurate than the aerialphotography-based map (Lang andMcCarty 2009). Not unexpectedly,evergreen areas were found to influencethe accuracy of both maps, although theimpact appeared to be much greater onthe aerial photography-based map. Treecanopy reflectance and shadow appearedto cause a large portion of the errorcontained within the aerial photographybased-map. Since water is a strongabsorber of visible and near-infraredenergy, the expected low reflectance of
Figure 1. The original datasets (filtered intensity, top left, and aerial photography, topright) used to produce two different inundation maps (resultant map directly belowparent dataset). Note that inundation patterns are more distinct in the LiDAR intensityimage and resultant inundation map. Adapted from Lang and McCarty 2009.water is easily confused with decreasedreflectance in areas affected by shadow.Conversely, reflectance off of a treecanopy, even during the leaf-off period,is more similar to reflectance from noninundated soils and organic debris (Langand McCarty 2009). These influencesare generally absent from or can beremoved from LiDAR intensity data.Although largely untapped, the potentialof LiDAR intensity data to betterunderstand fundamental ecosystemprocesses and improve land coverclassification is strong. This was the firststudy to examine the ability of LiDARintensity to map inundation below theforest canopy. A later study found thatLiDAR intensity data have the potentialto assist with the relative differentiationof deciduous forests with varyingdegrees of surface wetness and,therefore, wetland status within thecoastal region of North Carolina(Newcomb and Lang 2011), supportingthe conclusions drawn by Lang andMcCarty (2009). Although there areinherent limitations of LiDAR intensitydata, including the fact that the data aretypically uncalibrated (i.e., standardized)between LiDAR collections and thatthey are sensitive to the angle at whichthe laser interacts with the Earth’ssurface, these weaknesses can be greatlyreduced through the interpretation of3LiDAR intensity data within onecollection and the use of these data inareas of relatively low topographicvariability, such as the Coastal Plain.Furthermore, intensity data are oftenincluded with LiDAR elevation data forlow or no cost. Therefore, it makes senseto take advantage of this relativelyuntapped data stream when LiDARintensity data are well suited for projectneeds. This statement is particularlyrelevant given the often limitedavailability of suitable imagery forwetland mapping and characterization.LiDAR-Derived Topographic MetricsDEMs can be used to predict themovement and distribution of water andthus relative wetness across thelandscape. Whereas LiDAR intensitydetects the presence of water, LiDARbased topographic metrics can predictthe potential distribution of wateraccumulation across the landscape.Multiple types of topographic metricscan be produced using DEMs and usedto infer relative wetness. These metricsrelate to physical controls on waterdistribution. For example, thetopographic wetness index is acommonly used topographic metricbased on slope and contributing area andis expressed as ln(α/tanβ), where α is theupslope contributing area per unitcontour and tanβ is the local topographicgradient (Beven et al. 1995). Although βhas been calculated using a fairlyconsistent methodology, methods usedto calculate α vary considerably basedon the applied flow-routing algorithm(Lang et al. 2012). Numerous flowrouting algorithms are available,including the commonly used D8(distribution of flow to one neighboringcell); the somewhat more distributed D (distribution of flow to 1 or 2neighboring cells); and FD8, whichdistributes flow to all neighboringpixels. These algorithms proportion flowaccording to slope with greater slopeleading to increased allocations of water.The following section describes a studythat investigated the ability of multipleLiDAR DEM-derived topographic
metrics, including three topographicwetness indices computed usingdifferent flow routing algorithms, to mapwetlands in the Choptank RiverWatershed on Maryland’s Coastal Plain(Lang et al. 2012).Topographic metrics were calculatedusing a DEM derived from LiDAR datathat were collected when very littleflooding was present within study areawetlands. It is critical to collect LiDARdata for topographic analysis whenflooding is not present since floodingoften leads to inaccurate and/orundependable elevation measurements.For this reason data were collected inDecember 2007 during a relatively dryperiod with very little wetlandinundation on the landscape. Theresultant LiDAR data were used togenerate a 9.84 ft (3 m) gridded DEMwhich was subsequently filtered beforeapplying multiple algorithms to producefive different topographic metrics (Langet al. 2012). Topographic wetnessindices were produced using the basicequation detailed above and the D8, D ,Figure 2: Topographic index products including the enhanced topographic wetness index (A), local terrain normalized relief (B), andthe relief enhance topographic wetness index (C), LiDAR intensity during an average (D) and dry spring (E), and false color nearinfrared aerial photograph (F; collected coincident to D) of a forested wetland complex. All images have been overlaid with a wetlandmap produced for the Maryland Department of Natural Resources. On the topographic index products, wetter areas are blue (morelikely to be wetlands) while drier areas are red (less likely to be wetlands). Inundated areas are black on the LiDAR intensity images.Adapted from Lang et al. 2012.4
and FD8 flow-routing algorithms. Alocal terrain normalized relief (LTNR)map was created by subtracting asurface representing maximum elevationper 0.049 acre (200 m2) from theoriginal filtered 9.84 ft (3 m) DEM. Anenhanced topographic wetness index(ETWI) was created by increasing FD8based topographic wetness index valueswithin depressions (i.e., pits or sinks). ARelief Enhanced TWI (RETWI) wascreated by adding the ETWI and LTNRmetrics together after normalizing themetrics. The topographic metric-basedwetland maps were compared withLiDAR intensity derived maps ofinundation created to representmaximum yearly hydrologic expressionduring average weather (March 2007)and drought conditions (March 2009),and a wetland map produced by theMaryland Department of NaturalResources (MD DNR) (fig. 2)The ability of the FD8 TWI to mapinundation status, and therefore wetlandstatus (see above), was superior to theD and especially the D8 TWIs (Langet al. 2012). The utility of the FD8 TWIwas improved by increasing valueswithin areas without a surface wateroutlet to create the ETWI. The outletenhanced FD8 TWI (ETWI) performedwell for wetland mapping but providedlittle information on hydroperiod. Localrelief (LTNR) provided information onhydroperiod but was less capable ofwetland mapping. Combining localrelief and ETWI produced a map thatwas well suited for mapping wetlandextent and hydroperiod. Wetlandsmapped using aerial photographs andLiDAR-derived DEMs contained asimilar amount of inundated area, butthe LiDAR-derived maps containedfewer errors of omission.Our results support the use of moredistributed (FD8) flow routingalgorithms over algorithms thatencourage greater flow convergence(e.g., D8 and D ) for the mapping ofpalustrine wetlands (Lang et al. 2012).This may be especially true in areas oflow topographic relief. It ishypothesized that the ETWI map morecompletely represented the presence ofsurface water outlets from a given areato complement the input of surfacewater (i.e., specific catchment area).The ability of the local relief index(LTNR) to indicate temporal trends inflooding could support the use of thisindex to map hydroperiod and indicatecritical zones associated with climatechange. We hypothesize that LTNR andRETWI are dependent on two differentphysical drivers, surface expression ofgroundwater and lateral inflows andoutflows, respectively (Lang et al.2012). The metrics discussed aboveprovide some degree of flexibility tobest represent wetland distribution andboundaries within different study sites.Furthermore, topographic metricsillustrate gradual changes through space,which more accurately depict naturalecologic gradients, instead of the abruptboundaries present on classified maps.This study demonstrated that thepredictive power and efficiency ofwetland mapping efforts could beimproved through the incorporation ofLiDAR-derived DEMs (Lang et al.2012). The use of LiDAR data will beespecially vital in areas with lowtopographic variation or when applied tomapping wetlands that have previouslybeen difficult to detect, such as forestedwetlands. Optical (e.g., aerialphotography) and LiDAR data aredistinct remotely sensed datasets whichoffer unique benefits and limitations.The synergistic combination of thesedatasets has the potential to significantlyimprove not only the mapping offorested wetlands but also the mappingof historic wetlands (e.g., priorconverted croplands) within agriculturalwatersheds. These historic wetlands arecritical agricultural management zonesthat can exert substantial control on cropproductivity via nutrient processing (i.e.,N and P) and water availability,especially during years of drought orflood.5Current and Future Availability ofLiDAR Data and SpecificationsAvailability of LiDAR data hasincreased rapidly over the past 2decades, but these data are not currentlyavailable for the entire United States.Although airborne LiDAR data arecurrently available for only abo
Light Detection and Ranging (LiDAR) Technology LiDAR sensors provide detailed information on the elevation of the Earth’s surface and objects on the -made structures. LiDAR sensors collect data through the use of an onboard laser system, which sends and receives laser energy. LiDAR sensors send frequent (hundreds of thousands per second) short
-LIDAR Light detection and ranging-RADAR Radio detection and ranging-SODAR Sound detection and ranging. Basic components Emitted signal (pulsed) Radio waves, light, sound Reflection (scattering) at different distances Scattering, Fluorescence Detection of signal strength as function of time.
Illinois Airborne Light Detection and Ranging (LiDAR) Data Acquisition Plan September 2019 DRAFT PLAN Sheena K. Beaverson State Champion and Data Management Staff Sheena Beaverson serves as the state liaison for airborne Light Detection and Ranging (LiDAR) data projects within Illinois. Ms.
Light Detection and Ranging: Applications for transportation Light Detection and Ranging (LIDAR) is a surveying tool that utilizes an optical remote sensing technology to measure properties of scattered light to determine range and other information about a target. LIDAR allows highly accurate 3D (x, y and z) measurements to be taken.
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3.2 Laser imaging Detection and Ranging (LiDAR) Laser Imaging Detection and Ranging (LiDAR) is an optical remote sensing technology that can measure the distance to, or other properties of, targets by illuminating the target with laser light and analyzing the backscattered light (Figure 2). Lidar was used to provide contour
RRP 5-16 - Light Detection And Ranging (LiDAR) Asset Management PJ1400.docx Page 9 of 16 As already noted, the principal benefit of the LiDAR survey is to enhance UE's ability to improve its safety performance and reduce bushfire risks.
Lidar, which is commonly spelled LiDAR and also known as LADAR or laser altimetry, is an acronym for light detection and ranging. It refers to a remote sensing technology that emits intense, focused beams of . light . and measures the time it takes for the reflections to be . detected . by the sensor. This information is used to compute . ranges
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