NEURAL NETWORK APPLICATION FOR ESTIMATING FOREST BIOMASS IN THE BRIX-I FRAMEWORK

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1 NEURAL NETWORK APPLICATION FOR ESTIMATING FOREST BIOMASS IN THE BRIX-I FRAMEWORK Emanele Santi, Simonetta Paloscia, Simone Pettinato Institte of Applied Physics National Research Concil (IFAC CNR)

2 Abstract An algorithm aimed at estimating the forest biomass (t/ha) from airborne SAR data, has been implemented in the framework of BRIX-I exercise, spported by ESA. The retrieval is based on machine learning approach, and in particlar on Artificial Neral Networks (ANN). All the data available on BRIX testbed from the Afrisar, Biosar and Tropisar missions have been considered for implementing, training and testing the proposed algorithm. Several possibilities have been investigated, by developing a general algorithm trained with data representative of the entire BRIX dataset and specific ANN algorithms trained with data of a single campaign.

3 Data Analysis

4 Data analysis The direct sensitivity of s to forest biomass for the available data was investigated By sing the fnctionalities available in Orchestrator, the average backscatter was extracted along with the corresponding grond trth biomass for all the ROIs After filtering non-valid vales, the total dataset reslted in abot 4500 sets of backscattering coefficients (s ) at for polarizations (HH, HV, VH and VV) and corresponding biomass vales. The data covered a biomass range from 0 to 500 t/ha and were representative of several forest types.

5 Data Analysis: entire dataset Ts satrated for biomass vales higher than t/ha. De to different instrmental setps and area characteristics, two different patterns can be identified. a) b) c) d)

6 Data Analysis example: afrisar DLR a) b) c) d)

7 Data Analysis: smmary The analysis of correlation coefficients (R) reflects this behavior: higher correlation for the Afrisar dataset (R from 0.64 to 0.76) and lower vales for the entire dataset (R from 0.06 to 0.23) campaign RVV RVH RHV RHH afrisar_dlr afrisar_onera biosar biosar biosar tropisar all

8 ANN Algorithm

9 Advantages: Why ANN? ANN can be trained to represent arbitrary inpt-otpt relationships (Hornik, 1989; Linden and Kinderman, 1989). ANNs have been sccessflly applied to many remote sensing problems (e.g. Del Frate et al. 2003, Paloscia et al. 2013, Rodrigez-Fernandez et al. 2015, Santi et al. 2016) ANN can easily merge data coming from different sorces into a single retrieval algorithm (e.g different SAR sensors + optical/ir). Training only is time consming: application of a trained ANN to other datasets has a small comptational cost. Training can be pdated with new data (when available) withot modifying the algorithm. Disadventages: ANN are prone to otliersà large errors if testing data have not been properly acconted for in the training The main constraint is represented by the statistical significance of the training set, which mst represent all the observed srface conditions.

optimization for: q Architectre definition q Transfer fnction selection

10 ANN Algorithm implementation Feed-forward mlti-layer perceptron (MLP) ANN Training based on back propagation learning rle (BP) iterative optimization for: q Architectre definition q Transfer fnction selection (linear, hyperbolic tangent, logistic sigmoid) Inpt: SAR acqisitions (4 pol.) Otpt: biomass

from a set of SAR data not considered for the training.

11 Training and validation set definition BRIX dataset divided in two parts sing random sampling. 50% of the data available was considered for training the algorithm The remaining 50% for validation, by predicting the forest biomass from a set of SAR data not considered for the training. The training set is frther sbsampled in 60%, 20% and 20% sbsets: q 60% for iteratively adjsting the ANN weights sing BP q 20% and 20% for a posteriori test at each training iteration. Early stopping rle: the training stops as soon as the three errors are diverging.

inpts) Training repeated 100 times for each architectre, by resetting each time the initial weights.

12 ANN training (Matlab) Optimal ANN architectre (nmber of nerons and hidden layers) is defined iteratively for preventing overfitting and nderfitting q Start: one hidden layer of 4 nerons q Stop: two hidden layers of 12 nerons (3x n. inpts) Training repeated 100 times for each architectre, by resetting each time the initial weights. Training also repeated for each transfer fnction available (linear, tansig and logsig) Otpt is the optimal ANN architectre for the given problem in terms of R, RMSE and BIAS. One «general» ANN + a «specific» ANN for each test site

13 Reslts

Algorithm validation: general ANN Predicted vs in-sit biomass vales

comprised of all the data not involved in training.

retrieval, de to the loss of sensitivity for higher biomass vales.

14 Algorithm validation: general ANN Predicted vs in-sit biomass vales obtained by applying the general ANN to the entire validation set, comprised of all the data not involved in training. The higher biomass vales of Tropisar and Afrisar datasets affect the retrieval, de to the loss of sensitivity for higher biomass vales. Qite good reslt if considering the poor direct relationship s to biomass: R 0.9, RMSE 60 t/ha, Bias negligible

predicted vs. target biomass from R=0.69 to R=0.

15 Algorithm validation: specific ANN Better reslts than the general algorithm (as expected) Correlation coefficient predicted vs. target biomass from R=0.69 to R=0.93, RMSE between 14 t/ha and 58 t/ha a) b) c) d) BRIX Workshop 30 May 2018 e) ESA ESRIN f)

16 Examples of otpt maps (Afrisar-DLR) Previews generated by Orchestrator

17 Examples of validation from Orchestrator Scatterplots as generated by Orchestrator

18 Technical aspects: Matlab vs. Python The algorithm optimization and training was based on the feed-forward mlti-layer perceptron neral networks (MLP- ANNs) available in the Matlab Neral Networks toolbox The licensing isse of was addressed by installing a offline version of Orchestrator on or machines (Windows OS). The general and the specifics ANN have been generated, trained and saved in one configration file each. Then, a standalone exectable that loads the saved ANN configration and applies it to the SAR data has been implemented and integrated in the Orchestrator. To comply with BRIX reqirements, a parallel effort for implementing the ANN algorithm entirely in Python has been carried ot, based on the NeroLab Python library. The Python NN toolbox is less performant than Matlab and training is significantly slower: therefore only the optimal ANN has been trained in Python.

Validation of Python ANN Python specific ANN for Afrisar dataset is reported: q q pper plot: behavior of training error, lower plot: predicted vs. target biomass. The obtained correlation (R=0.

19 Validation of Python ANN Python specific ANN for Afrisar dataset is reported: q q pper plot: behavior of training error, lower plot: predicted vs. target biomass. The obtained correlation (R=0.94), is slightly higher than in Matlab (R=0.92) However, this is not reflected by the matchp scatterplots generated by Orchestrator, which exhibit slightly lower R and regression slopes than in Matlab.

20 Ftre Work Attempt of merging the experimental dataset with data simlated by electromagnetic forward models (e.m.) for overcoming the intrinsic limitation of experimental driven training (site dependency Santi et al. 2017). Backscattering at P band from a forest target simlated by the IFAC implementation of the Water Clod Model (WCM) by Attema and Ulaby (1978) % %! ""#$# =! ""'() +, -.$/ "".$/ 0 3 +, -.$/ 0 where 9 = : "" 2;$<=// q volme scattering coming from the canopy (trnks+branches+leaves) q scattering from soil attenated by the canopy itself. q Doble bonces and mltiple interactions neglected. Backscatter of soil nder vegetation simlated by IEM (negligible for Biomass > 30 t/ha) App and Bpp empirical coefficients obtained by iterative minimization (Nelder-Mead) Still in progress

21 Conclsions An algorithm based on ANN was implemented for estimating the forest biomass from P- band airborne SAR data. The work has been carried ot in the framework of BRIX exercise spported by ESA. The characteristics of the available dataset sggested implementing a general algorithm trained sing a sbset of the entire dataset and specific algorithms for each test area. The validation of the general algorithm reslted in R 0.9 and RMSE 60 t/ha. the validation of the specific algorithms reslted in R from 0.7 to 0.93 and RMSE between 14 and 58 t/ha, depending on the dataset The exportability of the algorithm (withot repeating the training) has to be investigated Work in progress: q training with WCM model data q adding the observation angle to the ANN inpts

22 THANK YOU We wold warmly thank Klas Scipal for involving s in BRIX and Clement Albinet for the helpfl and patient spport and the prompt replies to or nmeros inqiries

23 Data Analysis: entire dataset The analysis of the entire BRIX dataset confirmed the sensitivity to biomass s satrated for biomass vales higher than t/ha. De to different instrmental setps and area characteristics, two different patterns can be identified. a) b) c) d)

24 Data Analysis At low microwave freqencies (i.e. L- and more P- bands) the forest biomass is the main driving parameter, while forest type and tree characteristics have a less significant inflence on s (Ulaby et al ) L-band from IFAC ARCHIVE DATA

MICROWAVE REMOTE SENSING FOR THE MONITORING OF FOREST ECOSYSTEMS

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