Alaska FIA Plots Space, Time, Context and Lidar

Transcription

1 Alaska FIA Plots Space, Time, Context and Lidar 11th Biennial USDA FS Remote Sensing Conference Ken Winterberger USDA Forest Service Pacific Northwest Experiment Station Anchorage FSL, FIA

2 Outline The problem An option - Lidar Issues data acquisition correlation with reality space, time, and context Solutions? match data in space, time, and context move observations to match in space? use what we feel confident about & generalize

3 The Problem Large area - limited access High cost and much time to collect data conventionally Dramatic changes are taking place Existing data is old w/ gaps

4 Large area - limited access

5 Forest ~155 million acres

6 All Roads Marine Hwy. in Blue

7 Wildfire ~50 million acres since 1950

8 Insect Damage ~11.5 million acres since 1989

9 FIA Plots since annualized

10 FIA interior AK Plots 1960s through periodic

11 An option - Lidar Lidar data acquisition to supplement ground plot data Study cost of data acquisition Study how well Lidar is able able to measure existing ground plots Compare with existing data reality

12 Lidar Test 120 plots on Kenai Peninsula

13 Lidar Test 120 plots on Kenai Peninsula

14 Lidar - data example Data collected over FIA plot location based on GPS Data collected in swath 300-meters wide 125-meter Lidar plot depicted

15 Lidar and raw ground plot data Lidar data compared with raw ground plot location data FIA plot center from GPS; trees located using tape and compass In best circumstances discrepancies are obvious

16 Lidar and adjusted ground plot data As a test, to compare Lidar with ground plot data - ground plot data was moved to match Lidar Several issues were identified

17 Lidar - in space The most obvious and expected problem was spatial correlation - or lack thereof Ground plot center is only place that GPS is collected (not post processed) Tree locations mapped using tape and compass

18 Lidar - in time Temporal mismatch Ground data collected 2001 Lidar data collected 2004 Kenai Peninsula study area - severe spruce bark beetle infestation

19 Lidar - in context Definition mismatch Ground tree measurement based on species - if not on tree species list, don t measure If tree bole is not in fixed plot, don t measure

20 Tree Cover Estimates Space and time corrected Definition problem Lidar measurement of tree cover Ground sample stem map measurement of tree cover

21 What to do Matching ground plot measurement with Lidar is difficult given need to overcome space, time, definition constraints Probably worthwhile to do over the long run Build a catalog of Lidar/ground measurement correlations in various forest conditions

22 Alternative In the short run don t match ground vs Lidar plot by plot - generalize E.g., use relationship of stand height and cover to biomass (or volume)

23 Betula paperifyra - R2 =.71 ave. stand ht, crown cover v biomass

24 Betula paperifyra - R2 =.71 ave. stand ht, crown cover v biomass

25 Picea glauca - R2 =.69 ave. stand ht, crown cover v biomass

26 Picea glauca - R2 =.69 ave. stand ht, crown cover v biomass

27 Picea mariana - R2 =.58 ave. stand ht, crown cover v biomass

28 Populus balsamifera - R2 =.68 ave. stand ht, crown cover v biomass

29 Populus balsamifera - R2 =.68 ave. stand ht, crown cover v biomass

30 Further Analysis Develop correlation between CC, HT and Biomass (and other measurable variables) using ground data Use Lidar to measure CC and HT and estimate biomass (other variables) Use Lidar to scale-up from ground to larger area via multiphase sample Analyze low-density Lidar (e.g., PSLC and Kenai)