Forest Modelling

Transcription

1 Forest Modelling

2 Forest Modelling Satellite Data-based Modelling of Forest Eco-system Services of Forest Areas in China Prof. Xiaoli Zhang Department of Forestry Management Beijing Forestry University China zhang-xl@263.net Prof. Barbara Koch Department of Remote Sensing and Landscape Information Systems University of Freiburg Germany barbara.koch@felis.uni-freiburg.de Presentation and research by: F. Enßle, T. Kattenborn, J. Maack, C. Berger, C. Thiel, M. Zhao, X. Zhang, C. Schmullius & B. Koch

3 Overview Short introduction to German study site ( Karlsruhe ) Classification of forest type with simulated S2 and Landsat 8 data Classification and validation method Biomass modelling with simulated S2, Landsat 8 and WV-2 DEM Random forest and.632 validation Modelling forest ecosystem services (FES) with hyperspectral data Results from Master thesis by Kattenborn T. and Maack J. Biodiversity Micro-Climatic regulation Chinese study site ( Jiangle )

4 Study site Temperate forest. Mixed and pure forest stands. Dominated by Scots Pine. Scots pine (Pinus sylvestris) Red Oak (Quercus rubra) Sessile Oak (Quercus petraea) Beech (Fagus sylvatica) Douglas fir (Pseudotsuga menziesii)

5 303 sample plots of 12m diameter. 3 plots add up to one cluster (radius 35m) Forest inventory Tree species Diameter at Breast Height (DBH) According to DBH different radii (similar to angle count sampling)

6 Forest type classification Calculation of species share in each plot Coniferous share 80% Coniferous (n = 93) Deciduous share 80% Deciduous (n = 109) Others Mixed (n = 101) Classification with Random Forest (R stat. software) 80% training data 20% validation data 100 runs with random samples (without replacement) Final result from frequency of 100 runs 1 map with most frequent forest type 1 map with frequency (0-100) 100 all runs same result Mixed Deciduous Coniferous

7 Remote sensing data Sentinel-2 bands calculated by Hyperion (30m) bands of covered wavelength 10m bands (pan-sharped with spatially degraded WV2) 20m bands (pan-sharped with spatially degraded WV2) Sentinel 10m + 20m bands Landsat 8, OLI bands

8 Landsat 8, OLI Sentinel-2 10m Sentinel-2 20m Sentinel-2 10m + 20m bands

9 Sensor kappa OA pa_mixed pa_con pa_dec ua_mixed ua_con ua_dec S2_10 0,48 0,65 0,53 0,70 0,74 0,50 0,73 0,73 S2_20 0,55 0,70 0,58 0,77 0,77 0,57 0,74 0,78 S2_all 0,56 0,71 0,58 0,81 0,76 0,60 0,72 0,80 OLI 0,55 0,70 0,58 0,82 0,72 0,55 0,79 0,76 Final map of tree type classification Most frequent class (left) Class frequency (right)

10 Synthetic Sentinel-2 10m bands, classification result

11 Synthetic Sentinel-2 20m bands, classification result

12 Synthetic Sentinel-2 all bands*, classification result

13 Landsat 8, OLI classification result

14 Summary of Classification Most confusion in class mixed forest Similar results of Sentinel-2 based and Landsat-8 based classification A frequency map is a valuable tool to judge results at pixel level

15 Biomass modelling with simulated S2, Landsat 8 and WV-2 DEM EO-data: Sentinel-2 and Landsat 8, WorldView-2 stereo imagery 303, 101 sample plots ndsm by DTM (from LiDAR) and DSM from WV2 photogrammetric point cloud DSM calculation with in-house software TreesVIS

16 Biomass modelling Modelling with plots (n = 303) and cluster (n = 101) Feature selection by variable importance (R package Boruta) Example of feature selection

17 Results biomass modelling R² of biomass models RMSE of biomass models

18 Overfitting to data by random forest n = 101, Sentinel-2 features 1000 bootstrapped data sets For each set a random forest is applied Validated against entire data set and hold out. Difference of R² hold out to R² entire set implies overfitting.

19 Modelling forest ecosystem services (FES) with hyperspectral data

20 Biodiversity, Simpsons Index EO-data: EO-1 Hyperion 13 hyperspectral bands R² = 0,69 RMSE = 21,2 % Figure from Kattenborn and Maack 2014

21 Cooling effect of forest EO-data: Landsat 8 thermal bands (10,11) For derivation of temperature. By Kattenborn and Maack 2014

22 Chinese study site Jiangle, material by Xiaoli Zhang and Mingyao Zhao

23 Chinese study site Jiangle The study area is located in Jiangle County, Samming City, Fujian Province, China ( E, N). Main tree species: Cunninghamia lanceolata Pinus massoniana

24 Chinese study site Jiangle DBH, tree height, species, geolocation Forest stand deliniation with WV2 ~ 8km x 8km in size

25 Chinese study site Jiangle EO- data: Landsat8 OLI ALOS PALSAR WorldView-2 Field data: stand mean tree height stand mean breast diameter stand stock volume the measured tree heights in sample plots of the field Objective: Incorporate adjusted entropy (ENTadj) by improved models Computed by tree height variation in each forest stand Landsat-8 band 6 to extrapolate by linear regression to study site Model forest structural paramters by ALOS PALSAR and ENTadj

26 Improved models Original Model Model A 0 σ hv = a ln( x) + b 0 σ hv = a ln( x + adjent ) + b Model B σ = a ln( x) + b adjent 0 hv + c Model C 0 σ hv = a ln( x) + b ln( adjent ) + c Model A 2 σ = a ln( x + 10 adjent ) 0 hv + b

27 Results of mapping timber stock volumes at Jiangle

28 Summary Forest type mapping by most frequent class of 100 runs. Incorporating all Sentinel-2 bands improves classification accuracy (10m + 20m) Biomass modelling achieved moderate results for both sensors. Height is most important predictor. Mapping biodiversity with hyperspectral data Mapping temperature with thermal bands from Landsat-8 Methods can by applied to Chinese test site bidirectional data exchange

29 Outlook Methods can by applied to Chinese test site bidirectional data exchange Biomass modelling needs to be improved Sample size might be to small for RF Other algorithms will be tested Modelling with key parameters (forest type, density, height) Incorporation of real Sentinel-2 data

30 Acknowledgements The authors are grateful to the ESA program: Support for training of European young scientists within the framework of the DRAGON cooperation (Contract No /13/I-BG). This study was partly funded by the German National Space Agency DLR (Deutsches Zentrum für Luft- und Raumfahrt e.v.) on behalf of the German Federal Ministry of Economy and Technology on the basis of a decision by the German Bundestag. Support code: 50EE1265

31 References Efron, B. (1983). Estimating the error rate of a prediction rule: improvement on cross-validation. Journal of American Statistical Association 78, Borra, S. and Ciaccio, A. (2010). Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Computational Statistics and Data Analysis 54 Kattenborn, T. and Maack, J. (2014): Remote Sensing-based Modelling of Forest Ecosystem Services. Master thesis Liaw, A. and Wiener, M. (2002). Classification and Regression by randomforest. R News 2(3), Miron B. Kursa, Witold R. Rudnicki (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11), URL R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL White, J. C., Gomez, C., Wulder, M. A., & Coops, N. C. (2010). Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data. Remote Sensing of Environment, 114(7),

32 Hyperspectral bands for modelling biodiversity (from EO-1 Hyperion) Figure from Kattenborn and Maack 2014

33 ## R code to test.632 bootstrap ## Why bootstrapping has > 63% unique values n <- 100 fx <- function(n_aps, n) { sn <- sample(1:n, n, replace=true) length(unique(sn)) / n } # Test for 1000 runs boots <- c(lapply(1:1000, fx, n=100), recursive=true) # Final result mean(boots)