Remote sensing (RS) techniques have been used for mapping forest variables, such as stem volume (important for forest management activities associated with timber production), over large areas which can be updated more frequently than with field inventory (FI) data. In this study, wall-to-wall TanDEM-X synthetic aperture radar images were used as auxiliary RS data for model-based prediction of stand-level volumes for two models, trained using volumes computed from FI (A) and airborne laser scanning estimations (B), respectively. The models were validated with harvester data available for independent stands. It was observed that the performance of model B was slightly better compared to model A based on adjusted R 2 and root mean squared error values. Therefore, it can be concluded that a completely RS based approach for prediction and mapping of stand volumes would be as promising as a method based on FI data along with being cost- and labour-efficient

Ekström, Magnus

Lindberg, Eva

Mukhopadhyay, Ritwika

Nilsson, Mats

Persson, Henrik

English

Epsilon Open Archive

COMPARING TANDEM-X INSAR FOREST STAND VOLUME PREDICTION MODELS TRAINEDUSING FIELD AND ALS DATARitwika Mukhopadhyay1, Mats Nilsson1, Magnus Ekström1,2, Eva Lindberg1, Henrik J. Persson11Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden2Department of Statistics, USBE, Umeå University, SwedenABSTRACTRemote sensing (RS) techniques have been used formapping forest variables, such as stem volume (importantfor forest management activities associated with timberproduction), over large areas which can be updated morefrequently than with field inventory (FI) data. In this study,wall-to-wall TanDEM-X synthetic aperture radar imageswere used as auxiliary RS data for model-based predictionof stand-level volumes for two models, trained usingvolumes computed from FI (A) and airborne laser scanningestimations (B), respectively. The models were validatedwith harvester data available for independent stands. It wasobserved that the performance of model B was slightlybetter compared to model A based on adjusted R2 and rootmean squared error values. Therefore, it can be concludedthat a completely RS based approach for prediction andmapping of stand volumes would be as promising as amethod based on FI data along with being cost- andlabour-efficient.Index Terms— Airborne laser scanning, harvester data,stand volume, synthetic aperture radar, TanDEM-X.1. INTRODUCTIONThe use of auxiliary remote sensing (RS) data has beenincreasing over the past decades. RS data have been pairedwith field reference datasets for estimating forest variablessuch as, height, volume and aboveground biomass (AGB),in, e.g., [1], [2], [3]–[10], [11]–[17]. Stand- and tree-levelvolume estimates have been more extensively used by forestowners for measuring merchantable timber and forestproducts [18]. The collection of field inventory data is morecost and labour intensive when compared to acquiring aerialand spaceborne RS data, especially, in remote andinaccessible terrains.Airborne laser scanning (ALS) data have beenpreviously used for large scale mapping of AGB andvolume [19], [20], but, using a wall-to-wall satellite imagefor large scale mapping of such forest variables would beeven more time efficient [21], [22]. The German syntheticaperture radar (SAR) mission – TanDEM-X constitutes of apair of satellites (launched in 2007 and 2010) carryingX-band SAR sensors, flying as an interferometer with globalcoverage. Several studies have been conducted usingTanDEM-X for model-based inferences of forest AGB,volume and tree heights, in, e.g., [18], [23]–[29]. The use ofTanDEM-X enables extraction of the vegetation heightusing the interferometric phase height (ph) and coherence[22]. The ph contains information about tree heights as wellas forest density, which can be correlated to AGB andvolume estimations [18], [22]. In some previous studies by[18], [30], these interferometric variables have been used toestimate forest volume at the stand level. Until now, manualfield inventory data have been used as reference data formodel-based estimations of volume, except for a handful ofstudies [2]. Still, models entirely based on RS data have notbeen implemented for large-scale mapping of forestvariables.Therefore, the main objective of this study was touse wall-to-wall TanDEM-X SAR data for modelling andmapping of stand-level forest volume across the entire testsite and evaluating the performance of models A and Btrained with stand-level volumes estimated from forestinventory data and volumes estimated based on ALS andnational forest inventory (NFI) data, respectively. Standvolumes for independent forest stands within the same testsite, estimated from harvesters during felling activities, wereused for validating the models.2. MATERIALS AND METHOD2.1. Study areaThe test site located in central Sweden, covers an area of50,000 ha approximately (as shown in Fig 1a-b), majorlydominated by Scots pine (Pinus sylvestris), Norway spruce(Picea abies), Lodgepole pine (Pinus contorta) and otherdeciduous tree species.Figure 1. Represents the test site constituting the foreststands used for training models A and B (marked with‘green’) and forest stands used for validation of the modelsavailable from the harvester data (marked with ‘red’).Pine constituting around 50%, spruce around 44%and deciduous and other tree species constituting about 6%of the total species composition [2].2.2. Field and Remote Sensing dataThe field inventory data were acquired in 2019. Thirtystands were inventoried within the test site (marked with‘green’ in Fig 1b). The inventory was done with an averageof 8 circular plots (with 8m radius) distributedsystematically across the stands. The distance between theplots and the number of plots varied for each standdepending on the stand area. The field inventory data wereused as reference to select these 30 stands as training datasince the field inventory data constituted observations overentire range of age-classes in the stands.The ALS data were acquired in 2019 by a LeicaALS80 sensor from a 3000 m flight height and having anaverage point density of 1.5 points/m2 scanning over entireSweden. The mean volumes (in m3ha-1) for the sameindividual 30 stands were extracted from the volumesestimated using the ALS metrics and the corresponding NFIplots over the scanned regions.The TanDEM-X dataset was acquired on 14thNovember 2015 for HH (horizontally transmitted andhorizontally received) polarization in strip-map mode. TheSAR data specifications have been mentioned in Table 1.Table 1. TanDEM-X SAR data specifications.Polariz-ationPixel resolution MultilookfactorSLC* ResampledHH 2.5(slant)×3.3(azimuth) m210×10 m2 5×5*SLC – single look complex.The pre-processing was done as explained in [22]and similar image variables were derived, namelybackscatter, ph and corrected coherence (c_coh). Thecomplex interferogram was obtained asγ~=𝐸[𝑠1𝑠2*]𝐸[ 𝑠1| |2]𝐸[|𝑠2*|2](1)where, is the complex correlation co-efficient, E[.] is theγ~expectation value, * is the complex conjugate and s1 and s2are the Hermitian product of the two complex SAR images[18], [31].A minimum cost flow function was used forunwrapping the phase followed by a phase-to-heightsensitivity raster to obtain the height from theinterferometric phase information [22].The validation dataset consisted of 151 stands withvolumes estimated from the harvester data (marked with‘red’ in Fig 1b) acquired between 2019 and 2022. Theaverage stand-level volume for the entire test site is 172.5m3ha-1 based on the harvester data accounting for onlymatured trees in stands. These 151 stands were checked andcategorised into thinned (28) and clear-felled (123) stands.The thinned stands were discarded from the dataset to avoidrepresenting over-estimated stand-volumes for such stands.2.3. Volume estimation modelsAll the parameters derived from the TanDEM-X data weretested for statistical significance as model co-efficients ofthe explanatory variables for both model A and B. The finalregression models A and B, represented in Eq. 2,𝑣𝑜𝑙 =  β0+ β1𝑝ℎ0.5 + ε (2)where, ‘vol’ represents the response variable (stand volumein this case), β0 and β1 are the model coefficients and ɛ is therandom error.The ph values ranged between [-5, 28]. Models Aand B were compared based on adjusted co-efficient ofdetermination (adj-R2) and root mean squared error (RMSE)values with𝑅𝑀𝑆𝐸 =  1𝑛  Σ𝑖=1𝑛 (𝑦𝑖− 𝑦^𝑖)2 (3)where y is the reference values, is the predicted values,𝑦^and n is the number of stands in the validation dataset.3. RESULTS, DISCUSSION AND CONCLUSIONSThe results show that for both models A and B, expectedvalue of stand volumes had dependency on the ph with apower of 0.5, as represented in Table 2. The power value of0.5 for ph was a slight deviation compared to previousstudies, e.g., [18], [22], [28]. The corresponding modelcoefficients of ph and c_coh were statistically significant inthe models but c_coh did not contribute in improving themodel prediction accuracy, as observed similar to [22], andwas therefore, not included in the models. Also, bothmodels A and B were formulated with interceptsnon-significantly different from 0. The statistical summaryof model validation has been presented in Table 3. Thepredictions of model B were slightly more accurate based onthe adj-R2 and RMSE values when compared to that ofModel A. From Fig 2, there is no distinct differencebetween models A and B in the trend of the two plotsrepresenting the relation between the predicted standvolumes against the observed stand volumes. The predictedvalues in the lower range of volume were over-estimated inboth cases. The overestimation of these stands with lowstand volumes might be due to that thinning activities werecarried out which could not be filtered out during thecategorising of the validation dataset or might be due tomeasurement errors in the harvester volumes. Therelationship between the predicted and the observedvolumes are almost linear for stand volumes rangingbetween 150 m3ha-1 and 400 m3ha-1 . Stand volume maps forthe entire test site were predicted for both the models, aspresented in Fig 3.The approach based on laser scanning estimates(method B) appeared as accurate and promising as the fieldinventory based approach (method A) for mature stands.Therefore, this study indicates that dedicated fieldinventories intended to train models to estimate forestvolume wall-to-wall based on TanDEM-X could beeliminated and replaced by estimates from low-resolutionLiDAR and NFI data. This reduces costs and makes forestplanning and decision making more efficient withoutcompromising the accuracy of the stand volume estimates.The approach needs to be tested further in other sites and thetemporal robustness of the model parameters needs to befurther analysed.Table 2. Summary of model parameters.Model Intercept ph0.5A -82.37• 113.2***B -62.30• 104.3***•= p>0.05 and ***=p≤0.001.Table 3. 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Comparing TanDEM-X InSAR Forest Stand Volume Prediction Models Trained Using Field and ALS Data

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Comparing TanDEM-X InSAR Forest Stand Volume Prediction Models Trained Using Field and ALS Data

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