Ground-based estimates of foliage cover and LAI: lessons learned, results and implications

Transcription

1 17 th June, 2014 Ground-based estimates of foliage cover and LAI: lessons learned, results and implications William Woodgate

2 Contents Background Part 1 Fieldwork (Completed work) LAI products Instrument comparison Part 2 Modelling Framework (Present work) Part 3 Future work Satellite level Partner input and discussion

3 Background Some of the main factors when sensing vegetation remotely: 1. Clumping (at all scales) 2. Leaf Angle (distribution) 3. Impact of woody components Erectophile leaf angle distribution

4 Background - Clumping Regular Clumped Crown architecture (within-crown clumping) Tree distribution (between-crown clumping)

5 Background Satellite Product Review

6 Part 1: Product Accuracy Requirements Need for accurate ground-based measurements in support of calibration/validation activities OLD LAI accuracy target CEOS/GCOS: NEW Target accuracy ± 0.5 LAI or 20% maximum 5% (products to match within ground based estimates, WMO 2013)

7 Ground Based Methods for LAI Direct Indirect

8 From indirect measurement to LAI estimate 0 90 Classification of gaps -> Gap probability LAI Algorithm (gap probability inversion) Clumping (all scales) LAD Woody components

9 Part 1 Study sites

10 Investigation Are there any significant differences between the ground-based instruments for estimating LAI? Following Best practise guidelines (where they exist) Standard approach

11 Part 1 Instruments Instrument LAI-2200 Model (Manufacturer) Angular resolution (degrees) FOV (degrees) H, V LAI-2200 (Li Cor Inc.) NA 300, 75** <490 Wavelength (nm) HR DHP D90 (Nikon) , LR DHP CI-110 (CID Inc.) , TLS VZ400 (Riegl) ,

12 Part 1 Results

13 Part 1 Results A total of 67 method-to-method pairwise comparisons were conducted across 11 plots. Out of 67 comparisons, 29 had an RMSE 0.5 LAIe. HR-DHP (S) HR-DHP (AT) LR-DHP (S) LR-DHP (AT) LAI-2200 TLS HR-DHP (S) HR-DHP (AT) LR-DHP (S) LR-DHP (AT) LAI TLS NA - Bottom diagonal is the average of plot RMSE s between instruments. Top diagonal is the number of plots where these instruments were compared.

14 Part 1 Results & Lessons learned Large uncertainties exist for these methods for LAI estimates following standard operational protocols for data collection What are the main causes of the differences? DHP = exposure (radiometric sensitivity), lighting and sky background and conditions TLS = combination of beam size, range/power, detection threshold, ranging method (phase vs ToF) & wavelength LAI-2200 = assumption that all elements are black when estimating transmittance

15 Part 2: So how accurate can you get?? (Current research) Test (gap fraction inversion) algorithm accuracy Use tree models as reference or truth Simulate instruments

16 From measurement to LAI estimate 0 90 Gap probability over view zenith angle LAI Algorithm (gap fraction inversion) Woody components Clumping (all scales) LAD

17 Model parameterisation: creating the forest Rushworth Forest 5km

18 Model parameterisation: creating the forest

19 Model parameterisation: creating the trees

20 Model parameterisation: creating the trees Onyx tree Profile view simulation Top-down simulation

21 Model parameterisation: creating the trees Library of 51 trees (5 Eucalypt species)

22 Validation of the 51 tree models

23 Model parameterisation: creating the trees 20x20m plot characteristics Foliage projective cover = 35% Leaf area index = 1.1 Total plant area index = 1.7 Ave tree height = 12.9

24 Part 2 results: Within-crown clumping and LAD Leaf Angle Distribution Background Figure1: Cumulative frequency of five different LADs of de Wit (1965) Figure2: LAD projection functions (G-value) from de Wit (1965)

25 Part 2 results: Within-crown clumping and LAD Within-crown clumping at nadir Foliage only All canopy elements

26 Part 2 results: Within-crown clumping and LAD Crown cover simulations

27 Part 2 results: 1 radian view angle is best for clumping Within crown clumping varying with LAD at nadir but not at 1 radian

28 Part 2 results: Does alpha matter? Within-crown clumping of foliage only vs all elements at nadir

29 Part 2 results: Alpha versus within-crown clumping, which matters more? Errors introduced assuming a constant clumping (left) and constant alpha (right) Constant clumping and alpha Constant clumping Constant alpha

30 Part 2: Up-scaling from tree to plot to stand Ongoing work Tree Plot Stand

31 Model parameterisation: creating the forest Characterising stem distribution (between crown clumping) Stem position map 90m x 90m

32 Exemplar stem distributions Stem Density

33 Part 2: Site scale prelim results

34 Part 2: Example site scale prelim results PAIe = 0.15 Openness = 79% Reference PAI = 0.6 PAIe = 0.28 Openness = 65% Reference PAI = 1.2 PAIe = 0.46 Openness = 51% Reference PAI = 1.8

35 Part 3: Future Work Simulate satellite imagery to compare reflectance of Landsat & Sentinel 2 (future) & investigate sensitivity to the different LAI and stem clumping levels Or Simulate MODIS pixels and use MODIS LAI algorithm to estimate LAI and compare with known LAI value of scene