s.hillard, r.deo, j.corcoran, d.kepler – mn dnr andrew...

S.Hillard, R.Deo, J.Corcoran, D.Kepler – MN DNRAndrew Brenner & Miles Strain – QSI

• Objective of study• Single Photon

LiDAR (SPL)• Project Area and

Collect Specifications

• MN-DNR LCCMR Forest Inventory Project

• Where SPL plays in the market

Presentation Outline

The Scale of the State’s land portfolio

• The MN-DNR administers 5.4 million acres, 3.7 is forested, and 2.7 is considered in our commercial management pool.

• We classify 47 cover types, 26 are forested cover types, of those, 19 are considered commercially viable, 12 are deciduous, and 7 are conifers.

MN DNR Lands

MN Counties >10% forested

Costly, yet vital – current costs are between $10 & $12 per acre.Develop a New Methodology for Forest Inventory

Stand based data – photo interpreted with multiple plots per stand.Results in an older inventory of 20+ years.Need to address multiple other natural resource values/benefits

Objectives

Three year Pilot Project

July 2017 – June 2019

Acquire high density Single

Photon LiDAR (SPL) Acquired during peak fall color 2017 728,000 acres (~1,135 square miles) >12 pulses per square meter Simultaneous, 4 band 30cm

photography Additional AOI flown with help from

the Superior National forest.

Cass - 628K acres

Lake - 100K acres

Development of Innovative Cost-Saving Methodology for Forest Inventory

Cass County, 432 gridded plots Lake County, 108 Stratified Random plots

1/10 acre fixed radius plots, full stem map, measured >5 in DBH:

Study Area and Field Sampling Design

• Lower-power laser and highly sensitive detection to achieve accurate range measurement

• 100 optical channels respond to each laser shot, achieving 6.0 MHz effective pulse rate

• Originally developed for earth-to-satellite rangefinding

• Became a technology accessible to Leica Geosystems through the acquisition of Sigma Space in 2016

• Lowers cost per acquired data point

Single Photon LiDAR

• Large-area mapping driven by cost per data point

• Dictates high effective pulse rates

• Ability to fly high enough to cover wide swaths, while maintaining high pulse rates

• Current technology has allowed a doubling of pulse rate roughly every two years

• SPL100 breaks pulse rate barriers a 6.0 MHz effective pulse rate

Single Photon LiDAR

Pulse Frequency

Points per second

Number of returns

60 kHz 6 million –100 optical channels

Up to 3 returns per laser shot

• Flew with a Leica RCD 30 – medium format camera– Mn DNR’s program based on peak color

imagery – deciduous species identification– 4 band, 6” gsd

• Flying imagery and LiDAR together– One mobilization– Spectral and structural data together– Need imagery for hydro flattening – Imagery limits LiDAR collection

opportunities– Large format cameras better over large

areas – small weather windows

Imagery System

• Things that went well– High point density– Good ground penetration

• Where there were challenges– Clouds limited acquisition– Hydro flattening a problem– Large amount of data– Use of RCD 30 created processing

challenges – many small frames• Control

– In rural areas – expensive – remote areas– High cost to collect NVA and VVA

accuracy assessment points required for 3DEP compliance

Operational Items

• Grid metrics were produced using FUSION software from the SPL point cloud.

• Grid metrics were used as predictor variables in multiple regression models for estimating a number of response variables measured within our 1/10 acre plots.

Working Towards the inventory

• 356 plots were located within the forested part of our AOI, and 333 plots were used for model fitting.

• Inventory models were predicted across the study area on 20m grid cells.

• Metrics Include:– Standing Volume – Above Ground Biomass – Basal area weighted height– Basal area– Trees per acre– Site index– Max Height

Working Towards the inventory

Working Towards the inventory

Working Towards the inventory

SW+HW

SW HW

FIA-plot: Single photon LiDAR

FIA field observations All trees <5 inch DBH

FIA-plot: Linear LiDAR

An FIA plot in the overlap area of SPL and linear LIDAR acquisitions in the Lake County, MN

FIA subplot-2 No. of trees(> 5”): 4 Maximum Ht.: 13.7-mMax DBH: 16.2-cm

FIA subplot-2No. of trees(> 5”): 4 Maximum Ht.: 13.7-mMax DBH: 16.2-cm

SPL Linear

Linear LiDAR

FIA observed no. of trees: 13; Max ht.: 14.3-m

Linear LiDAR

Working Towards the inventory

• We can use our current stand boundaries to extract the information by domain (stand).

• We are currently working on how to give associated error estimates for each attribute (more on that later)

Working Towards the inventory

Working Towards the inventory

Attribute SRS-SE % MA-SE % Design Effect

Volume 4.16 2.24 0.26

AGB 4.08 2.15 0.26

Basal Area 3.7 2.4 0.37

TPA 3.4 3.0 0.68

• The forested area of the AOI was estimated at 310,000 acres.

• We made estimates of the different attributes across the study area to compare the model assisted (MA) estimate to the design based estimate (SRS).

• The model assisted estimate had improved precision over the design based estimate, the MA estimate of TPA showed the least improvement over the SRS.

Future work and improvements• Comparison of post stratification vs pre-

stratification sampling

• Investigating sample size impacts on model parameter estimation.

• Small area variance estimates ( synthetic domain estimators)

• Mixed effects models with plot level random effects to improve precision ( hardwood/ softwood, topographic, etc).

• Developing models to predict age and classifying cover-type (to some degree)

Attribute SRS MatchMA Error

MA MatchSRS Error

Volume 1213 91

AGB 1247 89

Basal Area 885 125

TPA 488 227

• University of Minnesota investigated data driven stand delineation using object based segmentation.

• Northland Community Technical College also collaborated with us on collecting UAV data across a number of plots within the study area.

Additional Collaborators

• UMN-Remote Sensing lab conducted object based segmentation to produce “stand” objects.

• Stands were based on a large array of inputs.

• Has been the subject of much debate.

UMN-Object based segmentation(OBS)

Chippewa Superior

Band 1: Time ABand 2: Time BBand 3: Time B UMN

RSGAL

Canopy Height Change: 2012 - 2017

Wildfire

Timber Harvests

Wind Blowdown Wind Blowdown

Timber Harvests Wildfire

Ortho-imagery Multi-date CHM

Ortho-imagery Multi-date CHMOrtho-imagery Multi-date CHM

UMN RSGAL

Canopy Height Change: 2012 - 2017

Wind Blowdown

Timber Harvests

Wind Blowdown

Pike Bay

UMN RSGAL

Canopy Height Change: 2012 - 2017

• Red = can only be satisfied by linear-mode systems

• Blue = can be satisfied single-photon technology

• Purple = can be satisfied by either system, but…… may not be the best use of capital for SPL technology

A way to think about how touse different technologies…

10,000 square miles

Acknowledgements

UMN RSGAL

Questions?

Jennifer Corcoran, MN DNRJennifer.Corcoran@state.mn.us

Andrew Brenner, QSIabrenner@quantumspatial.com