USING LIDAR AND RAPIDEYETOPROVIDE ...

USING LIDAR AND RAPIDEYE TO PROVIDEENHANCED AREA AND YIELD DESCRIPTIONSFOR NEW ZEALAND SMALL-SCALEPLANTATIONS

Cong (Vega) XuDr. Bruce ManleyDr. Justin Morgenroth

School of Forestry, University of Canterbury

BACKGROUND AND INTRODUCTION

Small-scale plantation forests (30% of all plantations) are not well understood in net stocked area

Small-scale forests lacks yield information

NZ lacks accurate spatial representation of small-scale plantations

Increasing availability of cost-effective remote sensing data

RESEARCH OBJECTIVES Evaluate different combinations of remote sensing techniques and

datasets in mapping net stocked plantation forests

Evaluate different modelling approaches and remote sensing datasets in modelling height, basal area, volume and stand age

Apply the selected area mapping and modelling approaches to the Wairarapa region

REMOTE SENSING DATASETS

Dataset ResolutionTemporal

CoverageDescription Application

Aerial Photography 0.3 mDec 2012-

Jan 2013

Orthorectified aerial

photography: RGB

Ground truthing for forest

mapping

Airborne LiDAR 3.7 points m-2 Jan-Dec

2013

Wall-to-wall for

Wellington Region

Derived surfaces for forest

mapping, metrics for model

stand variables

RapidEye 5 mNov 2013-

Feb 2014

5-band multispectral

imagery: RGB, RE,

NIR

Derived surfaces for forest

mapping, metrics for model

stand variables

AREASAMPLE SELECTION FOR MAPPING

AREAPROCESS OVERVIEW

NN: Nearest NeighbourCART: Classification and Regression Tree

AREA– RESULTSCLASSIFICATION ACCURACY OF DIFFERENT MAPPING APPROACHES AND DATASETS

80% 82% 81%88%

60%63% 67%

75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NN- RE only NN- RE+LiDAR CART-RE only CART- RE+LiDAR

Cla

ssifi

catio

n A

ccur

acy

Plantation Overall

AREA - RESULTS FOR PLANTATIONALL VALIDATION GRIDS

Total Digitised

(ha)

Total Mapped

(ha)

Difference

(ha)

Difference

%

MAE

(ha)

RMSE

(ha)

All standing trees 6244. 4 5759. 2 -485.2 -7.8% 13.6 42.5

Exclude new

plantings5590. 8 5759. 2 168. 5 3.0% 5.7 9.6

Note: New plantings are generally not visible on satellite imagery

79%89%91% 91%

All Excl. temporal differenceProducer's accuracy User's accuracy

AREA– VISUAL COMPARISON

AREA- PATCH-LEVEL COMPARISON

423 sets of valid patch to patch comparisonsAll patched: Average patch size: 9.5 ha, mean absolute error = 0.8 haLarge areas are more accurately mapped

MODELLING STAND VARIABLES- PLOT SUMMARY

Forest Measurement Approach (FMA) plots Pre-harvest inventory 112 plots

Stand Variables Mean Range

Plot Area (ha) 0.06 0.01 - 0.1

Slope (°) 20 4-38

Age (years) 20 9 - 30

Stocking (stems ha-2) 390 159 - 1212

Diameter at Breast Height (mm) 403 19 - 880

Individual Tree Height (m) 26.40 4.9 - 54.5

Mean Top Height (m) 25.13 9.10 - 42.30

Basal Area (m2 ha-1) 49.69 16.32 - 99.99

Volume (m3 ha-1) 436.70 67.75 - 1134.05

MODELLING STAND VARIABLES- APPROACH

Input predictors: LiDAR (111): height, canopy and intensity metrics RapidEye (68): spectral, textural and vegetation indices

Parametric models Multiple Linear Regression (MLR) Seemingly Unrelated Regression (SUR)

Non-parametric models K Nearest Neighbour (kNN) Random Forests (RF)

10-fold cross-validation

RMSE = Σ − 2n

MD= Σ( − )n

MODELLING STAND VARIABLESMODEL COMPARISON BASED ON 10-FOLD CROSS-VALIDATION

Comparison of Root Mean Square Error as a percentage of predicted mean (RMSE%) for MTH, BA, VOL and age estimated by MLR, SUR, k-NN and RF models.

MODELLING STAND VARIABLES- BEST MODEL

Best model for each stand variable: (lowest RMSE)

Best single model- MLR with LiDAR metrics

Stand Variable Model Input Data RMSE (RMSE%) MD (MD%)

MTH (m) RF LiDAR 1.37 (5.4%) 0.05 (0.19%)

BA (m2 ha-1) MLR LiDAR + RapidEye 9.42 (18.54%) 0.24 (0.47%)

VOL (m3 ha-1) MLR LiDAR + RapidEye 91.18 (19.71%) 1.67 (0.36%)

Age (years) kNN LiDAR + RapidEye 2.05 (10.53%) 0.02 (0.12%)

Stand Variable Input Data RMSE (RMSE%) MD (MD%)

MTH (m) LiDAR 1.81 (6.9%) 0.01 (0.04%)

BA (m2 ha-1) LiDAR 9.92 (19.54%) 0.24 (0.47%)

VOL (m3 ha-1) LiDAR 94.38 (20.46%) 2.95 (0.64%)

Age (years) LiDAR 2.17 (11.17%) 0.07 (0.35%)

APPLICATION TO WAIRARAPA- APPROACH

Area 21 RapidEye scenes and LiDAR surfaces Automated CART classification Manual mapping of young plantation

Stand variables Derive 5 x 5m LiDAR metrics Estimate MTH, BA, VOL and age using MLR Calculate mean for each polygon

PLANTATION AREA

MTH

BA

VOL

AGE

APPLICATION TO WAIRARAPA

Mapped

plantation (ha)

Digitised young

plantation (ha)

Total plantation

(ha)

NEFD

plantation (ha)

LCDB

plantation (ha)

Total 47 168 2 956 50 124 51 871 56 038

Plantation Area

Stand Variable Input Data RMSE (RMSE%) MD (MD%) Stand Variable

MTH (m) 3.86 43.63 21.51 6.85

BA (m2 ha-1) 1.41 89.68 49.51 12.56

VOL (m3 ha-1) 6.09 1175.51 358.03 158.57

Age (years) 5.55 33.33 19.56 4.51

Stand variables

APPLICATION TO WAIRARAPAAGE-CLASS DISTRIBUTION

APPLICATION TO WAIRARAPA- YIELD COMPARISON

CONCLUSION

Best mapping approach: Combined RapidEye and LiDAR with CART

Best modelling approach: MLR using LiDAR metrics

Wairarapa application Fails to detect young plantings (6%) 3.4% lower than NEFD 287 ha higher than UC 2017 Case study (0.6%) 25 m3 ha-1 lower than WAF yield

IMPLICATION

Improve understanding of small-scale forests Identify where they are What the productive areas are How much wood is there

Application to all regions in NZ Develop a national geospatial database of plantation Estimate stand variables for the plantations Allow future update and monitoring of the resources

ACKNOWLEDGEMENT

School of Forestry PhD Scholarship Blackbridge – RapidEye Landcare Research Land Information New Zealand Wellington Regional Council Indufor Asia Pacific Michael Watt (Scion) Jonathan Dash (Scion) Huimin Lin (School of Forestry) Dr Luis Apiolazas and Dr Daniel Gerhard (UC) Alan Bell and forest managers in Wairarapa

THANK YOU