TOWARDS OPERATIVE FOREST INVENTORY BY EXTRACTION OF TREE LEVEL INFORMATION FROM VHR SATELLITE IMAGES

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1 TOWARDS OPERATIVE FOREST INVENTORY BY EXTRACTION OF TREE LEVEL INFORMATION FROM VHR SATELLITE IMAGES Heikki Astola 1, Heikki Ahola 1, Kaj Andersson 1, Tuomas Häme 1, Jorma Kilpi 1, Matthieu Molinier 1, Yrjö Rauste 1, Jussi Rasinmäki 2 1 VTT Technical Research Centre of Finland, Vuorimiehentie 3, Espoo, P.O. Box 1000, FI VTT, Finland, firstname.lastname@vtt.fi 2 Simosol Oy, Asema-aukio 2, FI Riihimäki, Finland, jussi.rasinmaki@simosol.fi ABSTRACT The work related to this paper is part of an on-going study called NewForest - Renewal of Forest Resource Mapping. In this study the methodologies developed for individual tree crown (ITC) recognition and crown width estimation will be combined with forest variable estimates that are produced using features calculated from segmented VHR satellite image. A field visit to Karttula, Eastern Finland, was conducted to collect the class and geo-location information for 1164 ground objects (900 trees and 264 non-tree objects). These data were used for the classifier model and feature selection and for species classification accuracy assessment. For testing the classifier ability to predict tree species proportions, an independent set of 178 forest field inventory plots was used. Seven classes were defined: pine, spruce, deciduous, shadow, open area, bare ground, green vegetation. A modified Local maximum (LM) filtering technique was used for individual tree crown (ITC) detection. The spectral signatures of an ITC were sampled with a radius of r=1.5 m around the ITC brightest pixel (feature set A). Also a set of 9 contextual features were extracted from circular neighbourhood (r=7.25 m) around the ITC (feature set B). A classifier model and feature selection was performed. A 5NN classifier provided the best overall performance in tree species classification in terms of classification accuracy and generalization. The overall classification accuracy for the seven classes was 73.8% with feature set A using 5NN classifier. With feature sets A and B combined the accuracy was 74.1%. The average RMS errors in species proportion prediction were 2.6% with feature set A and 2.5% with feature sets A and B combined. Key words: forestry; tree species classification; individual tree crown, ITC; GeoEye; VHR 1 INTRODUCTION The availability of VHR optical satellite data is constantly increasing with the emergence of new imagers. This facilitates the development of remotely sensed forest inventory methods with improved accuracy. The new methods have also potential to compete with the price paid per hectare when compared with the methods that are presently used, usually requiring costly field work. One of the biggest challenges for remote sensing is to develop data analysis methods that increase the species-wise forest variable estimation accuracy to meet the requirements of operative forest management planning and wood procurement industry. Today, space-borne imagery is available with a resolution comparable to the aerial photographs that are used in forest management planning. The latest advances in the interpretation of the very high resolution (VHR) satellite data and airborne laser scanner (ALS) data can give information at the tree level, which can be utilized in improving the estimation of forest variables. The algorithms and results of research have to be further transformed into a complete chain for the provision of all the key forest variables. The authors studied the separation of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies Karst.) in mature single-species stands from Ikonos-2 images in boreal forest conditions (Astola et al. 2006), and the study was further extended to include also broadleaved class and mixed species stands. The obtained results indicated that the developed method could detect trees, identify their species and determine the species proportions in mixed forest. In the project NewForest Renewal of Forest Resource Mapping the authors will integrate the developed single tree detection and species classification method to existing forest variable estimation method (Häme et al. 2001) expecting an improvement in the present specieswise forest variable estimates.

2 Figure 1. Principle of combining individual tree crown information with forest variable estimates obtained from segmented VHR satellite image The study aims in development of operative forest inventory tools and services based on VHR satellite data. The study will also compare the results obtained with VHR satellite images with the results obtained with new ALS methods. The principle of integrating the ITC based information with estimates that are produced using (multi-level) segmented VHR images is depicted in (Figure 1). This paper presents the results related to ITC recognition and tree crown width estimation from VHR satellite image. 2 MATERIALS 2.1 Study Site and satellite data The study area was located in Karttula, Eastern Finland (Figure 2). The test area is a typical eastern Finland managed boreal forest area. The area is dominated by Spruce (Picea abies Karst.), whereas pine (Pinus sylvestris L.) and deciduous trees, mainly birches (Betula pendula and Betula pubescens), usually occur as mixed species. Classified by stand site class, the area consists of mainly fertile forest types. A high-resolution GeoEye image was purchased from the Karttula study site. The image was acquired on at 09:58 GMT. The centre location of the image frame is N, E and its size is W x H = 10.5 km x 11.5 km. Karttula Figure 2. Location of study site in Karttula, Finland. The image has four spectral channels (blue, green, red & NIR) and a pan-chromatic channel. The spatial resolution specifications of the GeoEye instrument are 0.41 m on pan-chromatic, and 1.65 m on multispectral channels at nadir looking angle. The delivered image was rectified and re-sampled to 0.5 m in pan-chromatic channel and 2 m in MS channels using nearest neighbour re-sampling. The off-nadir imaging angle is deg and the instrument azimuth angle deg. The sun azimuth angle is deg, and sun elevation angle deg. The acquired image is with 3% cloud cover, but the clouds do not cover the extents of the intensive forest inventory data. 2.2 Ground Reference Data The Karttula study area is part of large pilot area of Forest Centers (c ha,). Whole pilot area is

3 covered with forest field sample plots and additional plots from intensive data area (covering about ha). The field plots are measured by local Forestry Center and include basic field information related species specific data (volume, d13, h, basal area, age, stem number) and stand information (development class, dominant species, proportion of sawn timber, regeneration situation etc.). 237 of the field plots reside in the GeoEye image area. 25 of these were removed from the data set due to uncertain geo-location, uncertain field data or due to cloud or cloud shadow on top of the field plot. 10 additional zero stem volume field plots were added to represent non-forested areas of the image (clear-cuts, green fields, bare ground etc.). The resulting field plot data set, called the model reference data, thus consisted of 222 field plots (Table 1). University of Eastern Finland has measured an independent ground data set that includes species specific data (volume, d13, basal area, height, age, stem number) from selected forest stands (183 field plots). This data is called test reference data. 178 of the 183 field plots were accepted for the test data set (Table 1). The quantiles of the univariate distributions of forest variables of the model and test data were compared both numerically and visually (QQ-plots). The conclusion of this analysis was that the test data is sufficiently similar to the model data set. The only significant difference was in the larger amount of small pines in the model data. This fact may be taken into account in the interpretation of the final comparison of the methods. Table 1 Stem volume mean and standard deviation of the model and test reference data sets Model ref. ToV PinV SprV DecV data Mean [m3/ha] Stdev [m3/ha] Test ref. ToV PinV SprV DecV data Mean [m3/ha] Stdev [m3/ha] 2.3 Tree map data A field visit to the GeoEye image area was conducted in the autumn 2009 to obtain tree-wise data for the tree species classifier design. A total of 10 field locations were selected randomly from the model reference data set using stratification of the field plots by total stem volume (2 categories: 0 < V < 180 m3/ha, V > 180 m3/ha). All of these locations represent a mixed species forest type. A total of 1164 ground objects were mapped from within 25 m x 25 m squares centered at the field plot coordinates. 900 of these were trees (276 pines, 277 spruces, 347 broadleaved), and 264 non-tree objects (bare ground, road, agricultural fields, green vegetation etc.). At most of the field plots the ground data survey extended beyond the plot square border in order to ensure that all trees (or objects) within the plot square were identified. 2.4 Pure species training data A set of 20 field plots located at pure species forest stands among the model reference field data were selected for extracting additional training samples for the tree species classifier development. All the detected tree candidates from within 9 m radius around these plots were selected and assigned with the corresponding tree species class (291 pines, 183 spruces, 232 broadleaved, 5 non-tree objects). Each of these training samples was also visually checked so as to remove uncertain cases. 2.5 Digital Terrain Model A digital terrain model (DTM) was obtained from Tapio Forest Development Centre. This DTM was derived from ALS data acquired for forest inventory purposes. The vertical resolution of the DTM was 10 cm and horizontal resolution 2 meters. 3 METHODS 3.1 Satellite Data Pre-processing As received, the image was rectified to geographic coordinate system (WGS84, NUTM35). The geocoding accuracy was very good at the average ground altitude of 130 meters. However, since no digital terrain model (DTM) had been used, there was noticeable dislocation at elevated areas. The image geocoding was corrected using the Tapio DTM, resulting in maximum corrections of 20 m, with the mean of 2.65 m. The GeoEye-1 image was delivered without radiometric calibration. First, the pixel values were converted into TOA-reflectances using the band-specific calibration coefficients provided with the metadata. Then the image was atmospherically corrected into surface reflectances by applying the SMAC4-radiation transfer code. 3.2 Individual Tree Crown Data Extraction The tree candidate locations were extracted within the GeoEye image using a modified local maximum filtering technique (Wulder et al. 2000; Astola et al. 2006). The method was applied to the GeoEye panchromatic channel that was smoothed in sub-pixel level (7 x 7 pixel Gaussian kernel of width = 0.8 m).

4 The features used in the species classification were the signatures of the spectral channels (B, G, R, NIR and PAN) and contextual features extracted from the panchromatic channel of the GeoEye image. The spectral signatures of the observed tree candidates were sampled from the obtained tree candidate locations using 1.5 m radius. The spectral features were defined as the feature set A. Six features describing the pan-chromatic image channel intensity in the spatial context of a tree were calculated: mean (wmean) ratio of mean and median (mean/med), skewness (skew), kurtosis (kurt), contrast (kont), mean and standard deviation of the brightest (pm1, ps1) and the darkest pixels (pm2, ps2). The intensity values around each tree candidate were extracted using a circular mask with radius r = 7.25 m. The contextual features were defined as the feature set B. 3.3 Relating the Data Sets The local maxima that were found from the GeoEye image were matched with the ground data tree map with an automatic adjustment (translation) procedure that minimized the sum of the distances from the local maxima locations to their closest counterparts in the ground reference data. A 2.3 m circular environment around each ground data tree location was investigated in the search for the best match with the GeoEye tree candidates. The best match for every field plot location was found with less or equal to ±0.5 m translations. The resulting mutual matching of the trees and the local maxima on each field plot was also checked visually. 3.4 Tree Crown Width Extraction To extract the tree crown diameter of an ITC classified as a tree, four intensity profiles that cross the brightest pixel were examined. The pan-chromatic image was first smoothed at sub-pixel level with a Gaussian (1 width = 0.3 m) and the horizontal, vertical and two diagonal intensity profiles were extracted from within a circular environment of radius r=15 m around the ITC location and scaled with respect to the maximum intensity. The relative intensity profiles were searched starting from the brightest pixel outwards to locate the intensity minima on both sides of the maximum. An empirical threshold was set to reject too shallow minima as faulty ones. This threshold was set lower for spruce than for pine and broadleaved trees. The crown edge on both sides of the intensity maximum was defined to be the point in the middle of the found minimum and maximum intensity gradient. The crown width was then calculated as the arithmetic mean of the distances between the crown edges along the four intensity profiles. There was no field data to validate the crown width extraction, but the operation was visually verified against the GeoEye pan-chromatic image extracts on ITC locations. Figure 3 shows the principle of tree crown width extraction from the intensity profiles (a) and the obtained edge locations (black dots) connected with blue line on pine (b) and spruce (c) crowns. 3.5 Class Definitions and Test Setup Seven classes were defined for the (bright) objects to be detected from the satellite data: 1) pine, 2) spruce, 3) deciduous trees, 4) shadow, 5) open area (clearance), 6) bare ground, 7) green vegetation. Because there was no individual tree information collected from the pure species plots, they were all assigned as tree species classifier training data. All the objects from the tree map that represented the three tree classes in the mixed species field plots were selected as test data set. 2/3 of the mixed species objects (representing non-tree classes) were randomly assigned to training set and 1/3 to test data set (Table 2). Table 2 Tree species classifier training and test data sets Class Class name Training set Test set Nbr 1 pine spruce deciduous shadow open area bare ground green vegetation Total: Classifier Model and Feature Selection A set of different classifiers were tested, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVM), and knn classifier. The best overall performance was obtained with knn-classifier, with k=5. The training set and test set errors were also more close to each other than with the other classifiers, thus indicating a lower risk of overfitting. The results shown in this paper were obtained using the 5NN classifier. A brute force forward selection method was used to select the best separating features from the two feature sets. The contextual features alone did not produce very good accuracies, and thus the performance was evaluated for the feature set A, and for features obtained when combining the two sets A and B (see chapter 3.2).

5 a) No: 18 crown width = m No: 19 crown width = m b) c) Figure 3 Tree crown width extraction. a) intensity profiles of a pine crown with the crown width and edge locations indicated with red line and dots, b) the edge locations of a) shown on top of the sub-pixel smoothed GeoEye panchromatic image centered on a pine crown, c) edge locations for a spruce crown (intensity profiles not shown) 3.7 Evaluation of Species Classification Performance The tree species classifier classification accuracy was evaluated as the overall accuracy calculated as the sum of correct classifications (obtained from the classifier s confusion matrix diagonal) divided by the number of all test samples 1 Ac N C x jj j1 (1) where x ij is the number from confusion matrix cell ij (i=predicted class number, j=true class number; if i=j, then the cell is on the diagonal), C is the number of classes and N is the total number of test samples. The performance for tree species proportion prediction was evaluated with the residual mean square error E RMSE : E RMSE n i1 p t i n i 2 (2) where p is the predicted species class proportion as calculated from the species-wise stem number, t is the true species class proportion obtained from the field plot test data set, n is the number of tree classes (= 3). 4 RESULTS The confusion matrix for tree map test set classification with feature set A is shown in Table 3. The confusion matrix for the test set A+B was almost identical and is not shown here. The overall classification accuracies for classifiers using test sets A and A+B were 73.8% and 74.1% correspondingly. The results relate to the training/test set division described in chapter 3.5. Also several other training and test set divisions were used where the tree map data of some of the mixed species field plots were included in the training set, but the

6 results did not differ significantly from the results shown. Table 3 Confusion matrix for test set classification (feature set A) Predicted class Class pine spruce decid shadow open area bare ground green vegetation Producer's accuracy N % True class pine spruce deciduous shadow open area bare ground green vegetation N User's accuracy [%] A typical result of a feature selection run is shown in Figure 4. The blue line indicates the overall test set classification accuracy with a classifier that uses the feature set A. The magenta line shows the accuracy for the classifier with feature sets A+B. In both cases the maximum accuracy is reached with the first 2-4 features. In most of the feature selection runs with the feature set A+B the best separating features were 2 or 3 spectral features, and the added value of the contextual features to the classification accuracy was marginal. Classification accuracy [%] Classification accuracy vs. number of features / knn, K = 5 Max Accuracy = 74.0% Feat ranking: NIR RED PANKRO BLUE GREEN Test set size = 1018 test setup: D Max Accuracy = 74.2% Feat ranking: NIR RED pm2_29 skew29 wmean29 ps1_29 kurt29 kont29 ps2_29 mean/med29 pm1_29 PANKRO BLUE GREEN Figure 5 shows the tree species proportions predicted with the 5NN classifier using feature set A (blue bars) and the combined feature set A+B (magenta bars). The RMS errors corresponding to this chart are E RMSE-A = 2.6% and E RMSE-A+B = 2.5%. In general, when using alternative training and test set splits, the average species proportion prediction accuracy was slightly better with the feature set A than with feature set A+B (E RMSE-A = 3.0%, E RMSE-A+B = 4.8%) Number of features included in classifier Figure 4. Overall classification accuracy plotted against number of included features (feature set A = blue line, feature set A+B = magenta line) Tree species proportions averaged over test data field plots [%] True Pred/A Pred/A+B Pine Spruce Deciduous Species classes Figure 5. Predicted tree species proportions and tree species proportions from test set data plotted for pine, spruce and deciduous trees.

7 Figure 6 shows the predicted species-wise stem numbers plotted against the stem numbers obtained from the test reference data set. A clear systematic under-estimate can be seen with spruce and deciduous classes. The results indicate the situation, where the trees of a secondary layer (especially spruces) often remain undetected from above. Young deciduous trees also grow often in tight groups, and may thus easily be taken as a single crown when observed from above. The noise apparent in all of the three scatter plots is partly due to the test reference data small collecting radius (r=8 m), and due to location differences between satellite and ground data. Predicted number of pines/field plot y=0.85*x R2 = 0.34 An example of ITC detection and classification for a GeoEye image extract of approximate size of 1 km x 1 km is shown in Figure 7. The tree crowns that are classified as trees are indicated with colour code (green = pine, blue = spruce, red = deciduous). The non-tree classes are not shown in the image. On the agricultural field in the middle of the image frame there is considerable amount of confusion with non-tree objects classified as deciduous trees. Fortunately the fields, clear-cuts and roads can easily be masked out with a forest masks obtained from the forest variable estimation process. 5 DISCUSSION Methods for individual tree crown (ITC) recognition and tree crown width extraction were successfully developed. The ITC recognition could separate three species into three classes: pine, spruce and deciduous trees. The species could be separated also in mixed stands. The method locates the crowns of individual trees and analyzes the spectral characteristics of each tree. Of the detected trees the proportion of correct identification average accuracy was 78.0% for pine, 68.7% for spruce and 81.7% for the deciduous trees. A somewhat surprising result was that the spruce and pine trees were separated from each other approximately as well as conifers from the broadleaved trees. At canopy level the pure pine and broadleaved tree forests can be easily separated due to the lower near-infrared reflectance of pine. The original idea was to include information from the target ITC spatial neighbourhood in order to increase species classification accuracy. Anyway, the results showed little or no increase in tree species classification or tree species proportions prediction accuracies with the inclusion of the contextual image features. Predicted number of spruces/field plot Predicted number of broadleaved/field plot True number of pines/field plot a) y=0.98*x y=0.33*x R2 = 0.24 y=0.98*x True number of spruces/field plot b) y=0.56*x R2 = True number of broadleaved/field plot c) Figure 6. Predicted species-wise stem numbers plotted against the test reference data set (N = 178) a) pine, b) spruce, c) deciduous

8 a) b) Figure 7.a) A true colour extract of GeoEye image from Karttula, b) result of ITC detection and classification, green = pine, blue = spruce, red = deciduous The results obtained for the ITC detection indicated that the trees in the lower part of the crown layer remained often undetected, especially with spruce and deciduous stands. This caused a systematic underestimation of stem number, and consequently the correlation of the total stem number or the species-wise stem number was not as good as expected. The presented results show, that the tree species and their proportions for major species/species categories in mixed boreal forest can be predicted with moderately good accuracy from GeoEye satellite image. The reflectance signatures sampled with small radius from ITC location are a good choice for tree species classifier predictors. The inclusion of contextual features from the local neighbourhood of the detected tree may still increase the species classification accuracy, especially in limited conditions (Astola et al. 2006), but then the features do not characterize only the target tree, but the surrounding trees, the site type, canopy structure, sun elevation angle etc. This imposes strong requirements for the collection of representative training data, and may, in fact, prove unfeasible. When using the spectral signatures only, pure species data for classifier training is sufficient. The integration of the ITC information with the forest variable estimation process will be accomplished in the last phase of project NewForest. The validation of the tree crown width estimation method will be subjected to future research. ACKNOWLEDGEMENTS This work was carried out in project NewForest, which was funded by the Finnish Funding Agency for Technology and Innovation (TEKES), and the companies/parties Stora Enso Oyj, Tornator Oy, Metsähallitus, FM-International Oy FINNMAP and Oy Arbonaut Ltd. The model reference data set and the digital elevation model were provided by Tapio, and the test reference data set by the University of Eastern Finland (UEF). REFERENCES Astola, H. ; Sirro, L. ; Ahola, J. ; Häme, T. ; Molinier M. (2006), Separation of Coniferous Species in Boreal Forest Using Spectral and Contextual Features from Ikonos Imagery, Proceedings of IGARSS, Denver, USA, August Häme, T.; Stenberg, P.; Andersson, K.; Rauste, Y.; Kennedy, P.; Folving, S.; Sarkeala, J. (2001), AVHRRbased forest proportion map of the Pan-European area. - Remote Sensing of Environment. 2001, 77, s Wulder, M., Niemann, K.O., and Goodenough. D.G. (2000). Local maximum filtering for the extraction of tree locations and basal area from high resolution imagery. Remote Sensing of Environment, 73(1):