using lidar and rapideyetoprovide
TRANSCRIPT
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
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- 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
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
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