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  • 2. Contents  Introduction  Types of Platforms  Applications  Literature Review  Case study  Summary  References 2
  • 3. What is LiDAR ? (Light Detection And Ranging)  Lidar is a remote sensing technology that measures distance by illuminating a target with a laser and analysing the reflected light. Why LiDAR ?  It is used generating precise and directly geo-referenced spatial information.  Airborne LiDAR systems use 1064nm  Lasers produce a coherent light source.  It is Active sensor , do not require sunlight, they can be used either during the day or at night. 3 Figure 1
  • 4.  Lidar is popularly used as a technology used to make high resolution maps, with applications in  Archaeology  Geography  Geology & Geomorphology  Seismology  Forestry  Atmospheric physics  Flood Mapping , and  Contour mapping  Military  Mining  Transport 4 Figure 2
  • 5. PLATFORMS of LiDAR 1.Earth-orbiting satellites 2.Fixed-wing aircraft, manned and unmanned 3.Rotary-wing aircraft (helicopters) 4.Static ground-based systems (tripods) 5.Dynamic ground-based systems (vehicles) 6.Bathymetric mapping system 1 . 2. 3. 4. 5 . 6. 5Figure 3
  • 6. Parameters of a LiDAR sensor  Repetition rate: LiDAR will pulse at 200,000 times per second.  Scan frequency: how fast the scanner is oscillating  Nominal point spacing (NPS)  Scan angle  Flying attitude  Flight line spacing  Across track resolution  Along track resolution  Swath  Overlap 6 Figure 4
  • 7. LiDAR System Components 1.Laser Scanner 2. GPS 3. Inertial measurement unit (IMU) 4. Computer Processing Resources 7Figure 4
  • 8. GPS  LiDAR systems use robust dual-frequency receivers and differential post-processing, utilizing a fixed ground reference station.  Lock on the GPS satellites must be maintained and the LiDAR system must stay within 50 miles of the reference base station.  Typically with not more than 3 to 4 cm of error.  A laser scanner has three sub-components: the opto- mechanical scanner, the ranging unit, and the control processing unit.  Laser scanners are distinguished by high timing (range) accuracy, high sampling density, a high degree of automation. Laser Scanner 8Figure 5
  • 9. Inertial Measurement Unit (IMU):  Lidar systems contain three inertial gyroscopes. The angular rotations of the sensor from vertical can be measured.  Inertial measurement systems also contain accelerometers to measure the velocity Computer Processing Resources:  Reliable computer systems are required to ensure that each individual component is performing correctly.  These computer systems must ultimately integrate the component data streams into usable, accurate elevations on the ground. Data format: LAS format . 9 Figure 6 Figure 7
  • 10. Forestry applications require a precise inventory of individual trees and groups, or "stands" of trees in order to address  Forest management and planning,  Study forest ecology and habitats,  Quantify forest fire fuel, and  Estimate carbon absorption. Application on Forestry 10
  • 11. Direct measurements include: •Stand Density •Tree Height •Crown Width •Crown Length Measuring Forests with LiDAR Estimates include: •Volume •Biomass •Basal Area •DBH (diameter at breast height) 11 Figure 8 Figure 9
  • 12. Characteristics of LiDAR Data Discrete Return LiDAR Visualization of multiple LiDAR returns in a forest canopy, showing first returns from the top of canopy, second returns from forest understory, and third returns near or on the ground Full Waveform LiDAR The load increases at about 30 to 60 times. The opportunities presented by full waveform technology are mostly in the analysis of vegetation density, mapping live versus dead vegetation, forest fuels analysis, and wildlife habitat mapping. 12 Figure 10 Figure 12 Figure 11
  • 13. Literature Review Kevin et al.,(2005) studied the horizontal and vertical information of forest from the LiDAR data.  It is concluded that direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume.  The information also offers new opportunities for enhanced forest monitoring, management and planning. Monika et al., (2006) conducted a research with aerial and terrestrial LiDAR to provide detailed forest inventory characteristics such as canopy heights and volumes as well as diameter at breast height.  The estimation of Leaf Area Index (LAI) and forest fuel metrics are also addressed. 13
  • 14. Michael et al.,(2008) studied that the number of forest inventory attributes that may be directly measured with LiDAR is limited.  They present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. Nicholas et al.,(2008) assess the forest structure with airborne LiDAR and the effects of platform altitude .  Including three different platform altitudes (1000, 2000, and 3000 m), two scan angles at 1000 m (10° and 15° half max. angle off nadir), and three footprint sizes (0.2, 0.4, and 0.6 m).  The comparison was undertaken in eucalypt forests at three sites, varying in vegetation structure and topography. Birgit Peterson et al.,(2010) demonstrates how LIDAR data were used to predict canopy bulk density (CBD) and canopy base height (CBH) . The LIDAR data were used to generate maps of canopy fuels for input into a fire behaviour model. 14
  • 15. CASE STUDY-1: Carbon Accounting of Urban Forest in ChennaiCity using LiDAR Data Study Areas: 1. Guindy Reserve Forest (GRF), 2. Indian Institute of Technology Madras (IITM), 3. Central Leather Research Institute (CLRI) 4. Anna University (AU) Total area of the study site is 6.67km2 Objectives: Estimation of carbon stock in the center of the city of chennai. To provide more accurate forest biomass estimation 15
  • 16. Methodology Lidar data Ortho images Regression modelling nDSM=DSM-DTM Object based supervised classification Plot level Biomass Canopy Height Canopy cover Extraction Tree inventory data Percentage MAX & MIN heights Regression Equations TGB Estimation Carbon Stock Estimation 16
  • 17. Field Survey: A Total of 11 square sample plots with the size of 20m were selected on the basis of stratified random sampling.  Each tree coordinates were recorded by using handheld (GPS) unit. Tree height was measured by using a vertex Hypsometer. For each tree, stem diameter was measured at 1.3 m above ground with a diameter tape and the species name was recorded. For Borassus flabellifer height only measured. In each sampling plot of trees, for multi-stemmed trees, bole circumference was measured separately, and summed. The digital ortho image which was preloaded in the GPS instrument was used for verify the location of the trees. 17Figure 13
  • 18. Biomass Estimation in Trees  The diameter at breast height (DBH) and height of trees were measured, then both ends of main trunks and length were measured and volume is calculated.  AGB ( above ground biomass) for trees Y = 1.9724x – 1.0717  For the unavailability of the existing equation for Borassus flabellifer trees. Y = 4.5 + 7.7H Where Y = biomass, kg H= stem height and x =DBH 18 Canopy Cover Extraction Before the LIDAR analysis the canopy cover area should be extracted from the urban features. (1) Generating DTM, DEM, and nDSM from ALTM data, (2) Generating Height thresholding image for masking, (3) Segmentation of ortho image, (4) Supervised Classification for extraction of canopy cover
  • 19. LiDAR Predicted Biomass canopy densities, mean and percentile heights, and second-order height statistics . For this study height percentile parameters are developed for biomass estimation. Regression analysis is the common way to develop the AGB estimation models  The biomass value converted to carbon stock using a conversion factor with the equation Biomass values were multiplied by 0.45 to get carbon storage values of trees . C= TGB.CF 19
  • 20. Biomass (Mg) Trees count Location 1 102.6494 41 IIT 2 130.2500 56 IIT 3 126.0892 33 GRF 4 109.2947 34 GRF 5 17.5677 16 CLRI 6 47.9295 35 IIT 7 63.9100 45 IIT 8 95.8963 28 AU 9 63.8459 43 AU 10 1018.9200 22 CLRI 11 85.6047 27 AU Shows the estimated biomass on the field with site location 20 RESULTS & DISCUSSION Table 1:
  • 21. The relationship between field-measured height and LIDAR-measured maximum height (total 438trees)  The field-measured tree height and LIDAR measured height of 438 trees had an R of 0.957 and RMSE of 0.59 m 2 21 Figure 14
  • 22. S.NO NAME AGB (Mg ha-1) BGB(Mg ha-1) TGB(Mg ha-1) CARBON STOCK(Mg ha-1) 1 Guindy RF 42150.2 10959.05 53109.25 23899.16 2 Anna University 8220.029 2137.2 10357.23 4660.75 3 CLRI 4872.509 1226.85 6099.359 2744.70 4 IITM 31847.4 8280.22 40127.62 18057.42 TOTAL 87090.13 22603.32 109693.45 49362.03 Carbon Stock Estimation of Chennai Urban Forest at the all 4 segments  A very good relationship within the LIDAR 75th percentiles and the field measured AGB. Simple LIDAR metrics such as height percentiles which was derived from canopy heights within plots, gives an impressive capacity to estimate biomass over an urban environment.. 22 Table 2:
  • 23. STUDY AREA: Ahtanum State Forest in Washington State Fusion of LiDAR and imagery for estimating forest canopy fuels CASE STUDY-2: 23 Estimation of canopy fuels by using LiDAR data.  Canopy fuels are defined as all burnable materials, which include live and dead foliage, and redundant stem and branch wood located in the upper forest canopy.  Canopy fuels are important inputs for fire behaviour models that predict crown fire behaviour and spread . Parameters considered as follows 1. Canopy Height (CH), 2. Canopy bulk density (CBD), 3. Canopy base height (CBH), 4. Available Canopy Fuel(ACF) Objective:
  • 24. METHOLODY Field Data Raw LiDAR Raw Imagery Data Processing (Fuel Calculation) Data Processing (FUSION) Data Processing (ENVI,ArcGIS) Independent Variables LiDAR metrics Imagery metrics Regression Analysis LiDAR Models LiDAR/Imagery fusion models Imagery models CH CBH CBD ACF Dependent Variables 24
  • 25. 25 Results The regression models organized by canopy fuel metrics by considering the four parameter of Canopy height ,Canopy base height ,Canopy bulk density ,Available canopy fuel, and calaculated the RMSE for every regression model of Lidar data,Imagery data, and Lidar + Imgaery data and compare the values for these data and observed better results in combination of Lidar and imagery data. Figure 15
  • 26. 26 Figure 16
  • 27. 27 SUMMARY It gives directly geo-referenced spatial information. Highly accurate, high-resolution LiDAR data have particular utility in forest mapping. By using the LiDAR technology ,the characteristics of forest can be acquired in a short period . The first case study shows how to estimate the biomass by using LiDAR technology. The second case study shows the accuracy of estimation of canopy fuels by using LiDAR data.
  • 28. References Birgit Peterson, Ralph Dubayah, Peter Hyde, Michelle Hofton, J. Bryan Blair, and JoAnn Fites- Kaufman,(2010) “Use of LIDAR for Forest Inventory and Forest Management Application” Canadian Journal of Remote Sensing, 29, 650– 657. Kevin Lima, Paul Treitza, Michael Wulderb, Benoît St-Ongec and Martin Flood,(2005) “LiDAR remote sensing of forest structure” Progress in Physical Geography 27,1 (2003) pp. 88–106 Michael A. Wulder Christopher W. Bater, Nicholas C. Coops, Thomas Hilker and Joanne C. White (2008) “The role of LiDAR in sustainable forest management” Remote Sensing of Environment 90: 415–423. Monika Moskal, Todd Erdody, Akira Kato, Jeffery Richardson, Guang Zheng and David Briggs, (2006) “Lidar Applications in Precision Forestry”, Journal of Remote Sensing. 28
  • 29. Muneeswaran Mariappan ,Subbaraj Lingava, Ramalingam Murugaiyan,Vani Krishnan, Srinivasa Raju Kolanuvada ,Rama Subbu Lakshmi Thirumeni,(2012) “Carbon Accounting of Urban Forest in Chennai City using Lidar Data”, European Journal of Scientific Research ISSN 1450-216X Vol.81 No.3 (2012), pp.314-328 Nicholas R. Goodwin , Nicholas C. Coops , Darius S. Culvenor, (2008) “Assessment of forest structure with airborne LiDAR and the effects of platform altitude”, . Todd L. Erdody, L. Monika Moskal (2010) “Fusion of LiDAR and imagery for estimating forest canopy fuels”, Remote Sensing of Environment 114 (2010) 725–737 29
  • 30. THANK YOU 30