Table 1. The number of plots by tree species and tending need class in the training data.
Main species Need for tending Total
Immediately 1–5 years No need
Scots pine 12 37 55 104
Norway spruce 19 21 37 77
Deciduous trees 6 8 13 27
Total 37 66 105 208
1

Fig. 1. The tending need classes in the training data as a function of stem density and mean height.

2

Fig. 2. An example of a seedling plot in ALS data, in aerial image with 25 cm resolution, and in the field. This plot needed tending immediately to free more growing space for the spruce seedlings. However, it is difficult to observe it from the ALS data (red dots are vegetation hits above 0.5 m, blue dots ground hits) or from the aerial image.

3

Fig. 3. Left: Four seedling stands delineated on an aerial image. Middle: Probability of tending need predicted by the logistic model. Right: Tending need predicted by the support vector machine. The red color indicates a need for tending and blue indicates no need. Note that some cells are missing because of the 10 meter height limit.

Table 2. The number of stands by tree species and tending need class in the validation data.
Tree species Need for tending Total
Immediately 1–5 years No need
Scots pine 2 18 9 29
Norway spruce 7 14 13 34
Deciduous trees 3 1 1 5
Total 12 33 23 68
Table 3. The accuracies and predictive variables of the best models for tending need in the training data. Prefix f_ denotes first and l_ last echo. Savg is the “sum average” textural feature.
Model Predictors Overall accuracy Kappa
Logit f_h50, f_p40, log(savg) 77% 0.55
SVM l_h70, l_p40, l_p5, l_i50, f_h50, l_h30, l_h50 86% 0.71
Table 4. Confusion matrix of the tending need classification using the logistic model. OA = 77%, kappa = 0.55.
Logistic model Field value  
Tending No tending Total User’s accuracy
Tending 80 24 104 77%
No tending 23 81 104 78%
Total 103 105 208  
Producer’s accuracy 78% 77%    
Table 5. Confusion matrix of the tending need classification using the support vector machine. OA = 86%, kappa = 0.71.
SVM Field value  
Tending No tending Total User’s accuracy
Tending 90 17 107 84%
No tending 13 88 101 87%
Total 103 105 208  
Producer’s accuracy 87% 84%    
Table 6. Error rates in different stand parameter classes for the modeling and validation plots. Note that height was not estimated in the field for the validation stands, so ALS-based f_h80 was used instead*.
  Model data Validation data
n Logistic error % SVM error % n Logistic error % SVM error %
Tending need No need 105 22.9 16.2 23 30.4 43.5
1–5 years 66 28.8 18.2 33 36.3 21.2
Immediate 37 10.8 2.7 12 8.3 16.7
Species Pine 104 26.0 14.4 29 41.4 37.9
Spruce 77 20.8 15.6 34 20.6 20.6
Deciduous 27 14.8 11.1 5 20.0 20.0
Site type Very fertile 20 20.0 20.0 6 16.7 16.7
Fertile 134 20.9 14.9 53 28.3 30.2
Rather poor 46 28.3 8.7 9 44.4 22.2
Poor 8 25.0 25.0 0 - -
Height (*f_h80) 0–2 28 25.0 17.9 6 0.0 0.0
2–4 106 25.5 13.2 28 25.0 25.0
4–6 57 19.3 17.5 29 37.9 34.5
6+ 17 11.8 5.9 5 40.0 40.0
Density 0–2500 32 9.4 6.2 16 31.3 50.0
2500–5000 61 26.2 19.7 25 24.0 20.0
5000–10000 66 33.3 21.2 23 34.8 26.1
>10000 49 12.2 4.1 4 25.0 0.0
Table 7. Confusion matrix of the logistic classifier in the validation stands. OA = 71%, kappa = 0.38.
Logistic model Field value  
Tending No tending Total User’s accuracy
Tending 32 7 39 82%
No tending 13 16 29 55%
Total 45 23 68  
Producer’s accuracy 71% 70%    
Table 8. Confusion matrix of the SVM classifier in the validation stands. OA = 72%, kappa = 0.37.
SVM Field value  
Tending No tending Total User’s accuracy
Tending 36 10 46 78%
No tending 9 13 22 59%
Total 45 23 68  
Producer’s accuracy 80% 57%    
Table 9. Confusion matrix of the logistic classifier for the omission of 25% of the validation stands having the highest reliability. OA = 100%, kappa = 1.00.
Logistic model Field value Total
Tending No tending
Tending 14 0 14
No tending 0 3 3
Total 14 3 27
Table 10. Confusion matrix of the SVM for the omission of 25% of the validation stands having the highest reliability. Overall accuracy = 81%, kappa = 0.43.
SVM Field value  
Tending No tending Total User’s accuracy
Tending 16 1 17 94%
No tending 3 1 4 25%
Total 19 2 21  
Producer’s accuracy 84% 50%    
Table 11. Confusion matrix of the logistic classifier for the omission of 50% of the validation stands having the highest reliability. Overall accuracy = 91%, kappa = 0.80.
Logistic model Field value  
Tending No tending Total User’s accuracy
Tending 21 0 21 100%
No tending 3 10 13 77%
Total 24 10 34  
Producer’s accuracy 88% 100%    
Table 12. Confusion matrix of the SVM for the omission of 50% of the validation stands having the highest reliability. Overall accuracy = 79%, kappa = 0.40.
SVM Field value  
Tending No tending Total User’s accuracy
Tending 23 3 26 88%
No tending 4 4 8 50%
Total 27 7 34  
Producer’s accuracy 85% 57%    
4

Fig. 4. Overall accuracy and kappa of the logistic model in the validation stands with the most reliable predictions. The percentage at the x-axis describes the proportion of stands that would be left without field check, and the y-axis shows the consequent model errors.