Alberta's LiDAR Experience Lessons Learned Cosmin Tansanu

Transcription

1 Alberta's LiDAR Experience Lessons Learned Cosmin Tansanu Analysis Forester Alberta Environment and Sustainable Resource Development

2 We are mandated to provide environmental leadership. We need to push the envelop in terms of creativity, innovation, risk taking and re-think environmental stewardship and monitoring.

3 Setting the context Anthropogenic disturbance, multiple uses of the landscape Drayton Valley 1950 Drayton Valley 1995 Source: Shane Patterson, Alberta ESRD, Policy Division, Land and Forestry Policy Branch

4 How was LiDAR data acquired in Alberta and why? It was a response to Mountain Pine Beetle infestation that started in It was a response to a specific requirement/crisis access to vulnerable stands The purchase program was developed as a cross-ministry Provincial program involving 8 departments Efforts were made to create a set of standards for the data acquisition

5 Typical survey parameters Discrete return, small footprint Flight Speed: knots Flying height: m Pulse repetition frequency: 71 khz or greater Scan angle: degrees Scan rate: 35 Hz 0.7 to 1.9 points per m 2 (target 1.2 points/m 2 ) 50% overlap Vertical accuracy: 30 cm Horizontal accuracy: 45 cm With steep slopes such as eastern slopes flight lines NW SE to maintain optimum height above canopy

6 How Is LiDAR Data used by the Forest Management Branch? Wet Areas Mapping (WAM) Barry White, Paul Arp, and Jay Ogilvie Ecosite Classification Bev Wilson, Michal Pawlina, and Mike Willoughby LiDAR Derived Growth and Yield Forest Metrics Cosmin Tansanu, Chris Bater, Brad Tyssen, and Bob Sleep

7 Wet Areas Mapping Approach Entirely LiDAR driven Nine separate steps 34 algorithms Time consuming Six summers of field calibration Continual improvement Model remains the IP of UNB GOA solely owns all output

8 1. Obtain the local DEM through use of high resolution bare earth LiDAR data

9 2. Automatically identify and breach road locations where impoundments exist

10 3. Derive LiDAR-based flow accumulation across the landscape

11 4. Predict likely wetland locations using LiDAR point cloud data

12 5. Generate Cartographic depth-to-water index across entire landscape at 1m resolution

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14 Stream Network Depth-to-water Raster, 1 m resolution Flow Direction Raster, 1 m resolution Flow Accumulation Raster, 1 m resolution Symbology (transparent) + = Functional GIS-based datasets User friendly map outputs

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16 Use of LiDAR for Ecosite Classification Bev Wilson, Michal Pawlina, and Mike Willoughby LiDAR processing for Forest Management Unit A10 Bare Earth DEM - Global Mapper, Fusion, LAS Tools Slope / Aspect - ArcGIS Topographic Position Index (TPI) and Slope Position - Land Facet Tools Soil Moisture Regime, Wetness Index, Slope Over Area, Wet Areas - WhiteBox, TauDEM, TAS Vegetation Canopy Height Model - Fusion, Forest Protection Toolbox

17 Soil Moisture Information LiDAR flows and wet areas NDMI Moisture Index Vegetation Canopy Height CHT Soil Moisture Regime (from TauDEM)

18 Slope Position Information Land Facet Tools WB / TAS landscape position TPI index for Slope Position Calculation

19 AVI and Slope Position Initial rules use Slope Position and AVI derived attributes

20 Slope Position, Map Code, Ecosite Class

21 LiDAR Derived Growth and Yield Forest Metrics - A Pilot Study in FMU A15 Cosmin Tansanu, Chris Bater, Brad Tyssen, and Bob Sleep Create predictive models for variables of interest using the LiDAR data for Sw-All and SwAw strata. Variables of interest are: Total Volume (0/0) Merchantable Volumes (15/11) Basal Area Top Height Quadratic Mean Diameter FMU A15 Canopy Bulk Density Height to Live Crown

22 Objectives Objective 1 Grid and process the LiDAR dataset - extract grid metrics Objective 2 Sample the FMU A15 to obtain a field calibration dataset Objective 3 Model the relationship between the two datasets grid metrics and compiled plot data.

23 Objective 1 Employ FUSION software to generate vegetation metrics from lidar point clouds Process first returns > 1.37 m above the ground Approximately seventy heightrelated metrics are generated, including: summary statistics (e.g. mean, standard deviation) height percentiles(e.g. ( 25th, 75th 95th, etc.) Cover estimates (e.g. percentage of first returns above 1.37 m) Also includes a suite of metrics related to vertical biomass distribution, lidar return intensity and terrain. lidar_&_ifsar_tools.htm 23

24 Objective 2 The Fusion processing step into 30 x 30 m grids results in170+ variables per grid cell A Principal Component Analysis was applied to a subset of variables obtained from Fusion processing in Objective 1. Principal Component Analysis (PCA) is a statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is equal to the number of variables used in the analysis. The entire LiDAR dataset was characterized by the first three principal components explaining 90% of the datsaset variation.

25 Plot of principal components calculated from lidar-derived vegetation metrics. Red points indicate selected ground plots.

26 Objective 3 Modeling and Results Dependent Variable Prediction Equation RMSE RMSE % Mean Stratum Sw- All N TOPHT *P % 20.9 BA *P *Perc_FR_above_mean % 32.0 QMD *P % 23.0 VOLTOT *P % VOLMER *P % Stratum AwSw Prediction Equation RMSE RMSE % Mean TOPHT *P % 22.3 BA *P *Perc_FR_above_Mean % 32.9 QMD *P % 23.5 VOLTOT *P % VOLMER *P % 209.5

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28 Going forward... Permanent sample plots n = 1,411 Predominantly located in mature stands Individual tree attributes collected for all stems >= 9.1 cm and include: Species Diameter at breast height Height Height to base of live crown Tree location

29 Alberta Lessons Learned LiDAR targeted sample works provided that we have the processed LiDAR data Operationally better estimates for the variables of interest at the polygon level with limited field sampling Strategically forest growth can be derived from two consecutive measurements, more accurate growth calculated check on the AAC

30 Alberta Lessons Learned LiDAR application in Forestry (Growth and Yield) can be operationalized by any organization Our challenges: Understanding our dataset and knowing what we have Accessing our data in a timely fashion Processing LiDAR Storing and distributing our LiDAR data and our products (i.e. we use LAZ files on external harddrives) Our catalysts: We had full support of our managers: Darren Aitkin, Daryl Price, and Hua Sun We had the know-how in-house

31 Alberta Lessons Learned Interior barriers to full utilization of LiDAR were not anticipated and represented significant constraints to maximizing value. Local innovation community was slow to fully realize the benefits from LiDAR datasets. Institutional desire to bring forth innovative solutions based on LiDAR remains strong. The claim that LiDAR is too expensive of an acquisition for regulators is a myth.

32 Questions?