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Article

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

1
Department of Geography and Geology, University of Turku (UTU), FI-20014 Turku, Finland
2
Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro Box 3012, Tanzania
3
Arbonaut Ltd., Kaislakatu 2, Joensuu FI-80130, Finland
*
Author to whom correspondence should be addressed.
Forests 2019, 10(3), 279; https://doi.org/10.3390/f10030279
Received: 29 January 2019 / Revised: 18 March 2019 / Accepted: 19 March 2019 / Published: 21 March 2019
Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended. View Full-Text
Keywords: multi-source data; planted forests; forest inventory; linear regression; Sentinel; stratification multi-source data; planted forests; forest inventory; linear regression; Sentinel; stratification
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MDPI and ACS Style

Mauya, E.W.; Koskinen, J.; Tegel, K.; Hämäläinen, J.; Kauranne, T.; Käyhkö, N. Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy. Forests 2019, 10, 279. https://doi.org/10.3390/f10030279

AMA Style

Mauya EW, Koskinen J, Tegel K, Hämäläinen J, Kauranne T, Käyhkö N. Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy. Forests. 2019; 10(3):279. https://doi.org/10.3390/f10030279

Chicago/Turabian Style

Mauya, Ernest W., Joni Koskinen, Katri Tegel, Jarno Hämäläinen, Tuomo Kauranne, and Niina Käyhkö. 2019. "Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy" Forests 10, no. 3: 279. https://doi.org/10.3390/f10030279

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