scholarly journals The Accuracy of Standwise Forest Inventory in Mature Stands

2014 ◽  
Vol 32 (1) ◽  
pp. 2-8
Author(s):  
Ainārs Grīnvalds
Keyword(s):  
Forest Inventory ◽  
Alnus Glutinosa ◽  
Volume Estimation ◽  
Individual Species ◽  
Mixed Stands ◽  
Black Alder ◽  
Growing Stock ◽  
Study Results ◽  
Stock Volume ◽  
Mature Stands

Abstract Traditionally forest resources are estimated in each compartment or stand with ocular standwise forest inventory. However, this inventory technique has shortages with measurement accuracy. In the study the accuracy of the standwise forest inventory was estimated by comparing the growing stock volume of the standwise inventory with the accurate (instrumental) re-measurements. Comparison was done with 4515 mature stands of pine (Pinus sylvestris L.), spruce (Picea abies (L.) Karst.), birch (Betula spp.), aspen (Populus tremula L.) and black alder (Alnus glutinosa L.). The stands’ measurements by callipers or by harvesters (recalculated to growing stock volume) were used for accurate re-measurements. The study results show that the volume of standwise forest inventory have relative bias of 17.6% (volume is underestimated by 17.6%) and relative root mean square error 27.5 % for the whole data. Spruce stands are more accurately measured and black alder stands - inaccurately. The accuracy of pine, birch and mixed stands was similar to overall trends. Stands with volume 200 - 300 m3 ha-1 are more accurately measured and stands with the volume less than 200 m3 ha-1 - most inaccurately. The accuracy of stands with the volume more than 300 m3 ha-1, decreases by increasing the volume of stands. The volume estimation of individual species has different trends in standwise forest inventory. The volume of pine and birch is overestimated and the volume of spruce, aspen and black alder is underestimated.

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

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

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.

Multitemporal jers repeat-pass coherence for growing-stock volume estimation of siberian forest

2003 ◽  
Vol 41 (7) ◽  
pp. 1561-1570 ◽  
Author(s):  
L.E.B. Eriksson ◽  
M. Santoro ◽  
A. Wiesmann ◽  
C.C. Schmullius
Keyword(s):  
Volume Estimation ◽  
Growing Stock ◽  
Stock Volume

scholarly journals Forest stands volume estimation by using Finnish Multi-Source National Forest Inventory in Stołowe Mountains National Park

2016 ◽  
Vol 58 (1) ◽  
pp. 3-12
Author(s):  
Przemko Pachana
Keyword(s):  
Forest Inventory ◽  
National Park ◽  
Pearson Correlation ◽  
Satellite Image ◽  
National Forest Inventory ◽  
Volume Estimation ◽  
National Forest ◽  
Forest Stands ◽  
Timber Volume ◽  
Growing Stock

Abstract The purpose of the present study was to convey to the reader the method and application of the Finnish Multi-Source National Forest Inventory (MS-NFI) that was devised in the Finnish Forest Research Institute. The study area concerned is Stołowe Mountains National Park, which is located in the south-western Poland, near the border with the Czech Republic. To accomplish the above mentioned aim, the following data have been applied: timber volume derived from field sample plots, satellite image, digital map data and digital elevation model. The Pearson correlation coefficient between independent and dependent variables has been verified. Furthermore, the non-parametric k-nearest neighbours (k-NN) technique and genetic algorithm have been used in order to estimate forest stands biomass at the pixel level. The error estimates have been obtained by leave-one-out cross-validation method. The main computed forest stands features were total and mean timber volume as well as maximum and minimum biomass occurring in the examined area. In the final step, timber volume map of the growing stock has been created.

scholarly journals Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia

Forests ◽  
2014 ◽  
Vol 5 (7) ◽  
pp. 1753-1776 ◽  
Author(s):  
Christian Hüttich ◽  
Mikhail Korets ◽  
Sergey Bartalev ◽  
Vasily Zharko ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Resource Assessment ◽  
Forest Resource ◽  
Central Siberia ◽  
Growing Stock ◽  
Stock Volume

scholarly journals Polarimetric Parameters for Growing Stock Volume Estimation Using ALOS PALSAR L-Band Data over Siberian Forests

2013 ◽  
Vol 5 (11) ◽  
pp. 5725-5756 ◽  
Author(s):  
Tanvir Chowdhury ◽  
Christian Thiel ◽  
Christiane Schmullius ◽  
Martyna Stelmaszczuk-Górska
Keyword(s):  
Volume Estimation ◽  
Alos Palsar ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume ◽  
Siberian Forests

Correlations, distributions, and trends in forest inventory errors and their effects on forest planning

2010 ◽  
Vol 40 (7) ◽  
pp. 1386-1396 ◽  
Author(s):  
Antti Mäkinen ◽  
Annika Kangas ◽  
Lauri Mehtätalo
Keyword(s):  
Forest Inventory ◽  
Simulation Method ◽  
Error Distribution ◽  
Economic Losses ◽  
Forest Planning ◽  
Simulation Methods ◽  
Growing Stock ◽  
Constant Bias ◽  
Mature Stands ◽  
Error Simulation

Errors in forest planning data are known to have various undesired effects, which have been examined previously by simulating their impact on forest planning systems. In most cases, the simulation of forest inventory errors has been simplified by assuming the error distribution to be Gaussian, possibly with a constant bias, and neglecting possible correlations between the errors in various attributes. The first aim here was to examine the distributions, correlations, and trends in errors when using alternative forest inventory methods, and the second was to analyse how different error simulation methods affect the estimated economic losses caused by suboptimal harvest timing on account of errors. We found that the errors were not normally distributed, had notable trends, and showed significant correlations between the errors for the various attributes. The most important factor affecting the inoptimality losses was the powerful tendency to underestimate the growing stock properties of mature stands. The error simulation method clearly makes a difference when analysing the effects of errors, and it is therefore important to use a simulation method that generates realistic errors.

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