Shisheng Long, Siqi Zeng, Falin Liu, Guangxing Wang. (2020). Influence of slope, aspect and competition index on the height-diameter relationship of Cyclobalanopsis glauca trees for improving prediction of height in mixed forests. Silva Fennica vol. 54 no. 1 article id 10242. https://doi.org/10.14214/sf.10242

Keywords: secondary forest; effect; improved Hegyi_I; topographic feature; tree height estimation

Highlights: In this study, the effects of slope, aspect and competition index (CI) on the H-DBH relationship were explored and an improved CI was developed and included to improve predictions of Cyclobalanopsis glauca tree height; It was found that the effects were statistically significant and considering slope, aspect and CI for developing the H-DBH models significantly increased the H prediction accuracy.

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Diameter at breast height (DBH) and height (H) of trees are two important variables used in forest management plans. However, collecting the measurements of H is time-consuming and costly. Instead, the H-DBH relationship is modeled and used to estimate H. But, ignoring the effects of slope, aspect and tree competition on the H-DBH relationship often impedes the improvement of H predictions. In this study, to improve predictions of Cyclobalanopsis glauca (Thunb.) Oerst. tree H in mixed forests, we compared eleven H-DBH models and examined the influence of slope and aspect on the H-DBH relationship using 426 trees. We then improved Hegyi competition index and explored its effect on the H predictions by including it in the selected models. Results showed 1) There were statistically significant effects of slope and aspect on the H-DBH relationship; 2) The log transformation and exponential model performed best for sunny- and shady-steep, respectively, and the Gompertz’s model was optimal for both sunny- and shady-gentle; 3) Compared with the whole dataset, the division of the data into the slope and aspect sub-datasets significantly reduced the RMSE of H predictions; 4) Compared with the selected models without competition index, adding the original Hegyi and improved Hegyi_I into the models improved the H predictions but only the models containing the improved Hegyi_I significantly increased the prediction accuracy at the significant level of 0.1. This study implied that modeling the H-DBH relationship under different slopes and aspects and including the improved Hegyi_I provided the great potential to improve the H predictions.

Long, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: shisheng3604@21cn.com
Zeng, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: zengsiqi@21cn.com
Liu, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: liufl680@126.com
Wang, Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA E-mail: gxwang@siu.edu

article id 669, category Research article

Simo Poso, Guangxing Wang, Sakari Tuominen. (1999). Weighting alternative estimates when using multi-source auxiliary data for forest inventory. Silva Fennica vol. 33 no. 1 article id 669. https://doi.org/10.14214/sf.669

Keywords: remote sensing; two-phase sampling; forest inventory methods

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Five auxiliary data sources (Landsat TM, IRS-IC, digitized aerial photographs, visual photo-interpretation and old forest compartment information) applying three study areas and three estimators, two-phase sampling with stratification, the k nearest neighbors and regression estimator, were examined. Auxiliary data were given for a high number of sample plots, which are here called first phase sample plots. The plots were distributed using a systematic grid over the study areas. Some of the plots were then measured in the field for the necessary ground truth. Each auxiliary data source in combination with field sample information was applied to produce a specific estimator for five forest stand characteristics: mean diameter, mean height, age, basal area, and volume of the growing stock. When five auxiliary data sources were used, each stand characteristic and each first phase sample plot were supplied with five alternative estimates with three alternative estimators. Mean square errors were then calculated for each alternative estimator using the cross validation method. The final estimates were produced by weighting alternative estimates inversely according to the mean square errors related to the corresponding estimator. The result was better than the final estimate of any of the single estimators. The improvement over the best single estimate, as measured in mean square error, was 16.9% on average for all five forest stand characteristics. The improvement was fairly equal for all five forest stand characteristics. Only minor differences among the accuracies of the three alternative estimators were recorded.

Poso, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: simo.poso@helsinki.fi
Wang, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: gw@nn.fi
Tuominen, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: st@nn.fi

article id 682, category Research article

Guangxing Wang, Simo Poso, Mark-Leo Waite, Markus Holopainen. (1998). The use of digitized aerial photographs and local operation for classification of stand development classes. Silva Fennica vol. 32 no. 3 article id 682. https://doi.org/10.14214/sf.682

Keywords: calibration; classification; digitized aerial photographs; plot window location; local operation

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The increasing capacity of modern computers has created the opportunity to routinely process the very large data sets derived by digitizing aerial photographs. The very fine resolution of such data sets makes them better suited than satellite imagery for some applications; however, there may be problems in implementation resulting from variation in radial distortion and illumination across an aerial photograph. We investigated the feasibility of using local operators (e.g., non-overlapping moving window means and standard deviations) as auxiliary data for generating stand development classes via three steps: (i) derive 6 local operators intended to represent texture for a 16 by 16 m window corresponding to a forest inventory sampling unit, (ii) apply a calibration process (e.g., accounting for location relative to a photo's principal point and solar position) to these local operators, and (iii) apply the calibrated local operators to classify the forest for stand development. Results indicate that calibrated local operators significantly improve the classification compared to what is possible using uncalibrated local operators and satellite images.

Wang, Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL, USA E-mail: wang12@staff2.cso.uiuc.edu
Poso, Department of Forest Resource Management, P.O. Box 24, FIN-00014 University of Helsinki, Finland E-mail: sp@nn.fi
Waite, Department of Forest Resource Management, P.O. Box 24, FIN-00014 University of Helsinki, Finland E-mail: mlw@nn.fi
Holopainen, Department of Forest Resource Management, P.O. Box 24, FIN-00014 University of Helsinki, Finland E-mail: mh@nn.fi

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