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 Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

2013 ◽  
Vol 5 (9) ◽  
pp. 4503-4532 ◽  
Author(s):  
Maurizio Santoro ◽  
Oliver Cartus ◽  
Johan Fransson ◽  
Anatoly Shvidenko ◽  
Ian McCallum ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Backscatter ◽  
Central Siberia ◽  
Growing Stock ◽  
Stock Volume ◽  
Aperture Radar

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.

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.

Changes in ash tree demography associated with emerald ash borer invasion, indicated by regional forest inventory data from the Great Lakes States

2011 ◽  
Vol 41 (11) ◽  
pp. 2165-2175 ◽  
Author(s):  
Scott A. Pugh ◽  
Andrew M. Liebhold ◽  
Randall S. Morin
Keyword(s):  
Great Lakes ◽  
Forest Inventory ◽  
Large Scale ◽  
Emerald Ash Borer ◽  
Agrilus Planipennis ◽  
Department Of Agriculture ◽  
Growing Stock ◽  
Regional Impacts ◽  
Great Lakes States ◽  
Regional Forest

The emerald ash borer (EAB) ( Agrilus planipennis Fairmaire) is a nonnative phloem-feeding beetle that was accidentally introduced near Detroit, Michigan, two to three decades ago. North American ash ( Fraxinus spp.) exhibit little or no resistance, and as this insect species expands its range, extensive mortality results. Previous studies of the impacts of EAB, typical of most insect and disease impact studies, utilized data acquired from sites with known infestations and cannot be used to make regional estimates of change on forest land. By contrast, this study investigated the regional impacts of EAB on the affected resource using information from a large-scale forest inventory (Forest Inventory and Analysis program of the US Department of Agriculture, Forest Service) previously implemented to estimate regional forest resources. Results indicate that since 1980, ash has been increasing throughout many of the Great Lakes States but EAB is reversing this trend in recently invaded areas. Within 50 km of the epicenter of the EAB invasion, a major decline was observed after 2004. For growing stock (trees at least 12.7 cm diameter at breast height), average ash volume decreased from 12.7 to 3.2 m3·ha–1 and mortality increased from 0.1 to 1.4 m3·ha–1·year–1 on timberland between the 2004 and 2009 inventories.

scholarly journals Fluctuations in gross volume increment estimated by the Lithuanian National Forest Inventory compared with annual variations in single tree increment

2019 ◽  
Vol 25 (2) ◽  
pp. 273-280
Author(s):  
Gintaras Kulbokas ◽  
Vaiva Jurevičienė ◽  
Andrius Kuliešis ◽  
Algirdas Augustaitis ◽  
Edmundas Petrauskas ◽  
...  
Keyword(s):  
Land Use ◽  
Volume Change ◽  
Forest Inventory ◽  
Basal Area ◽  
National Forest Inventory ◽  
Reference Level ◽  
National Forest ◽  
Growing Stock ◽  
Volume Increment ◽  
Stock Volume

There are significant inter-annual fluctuations of growing stock volume changes of living trees estimated by the Lithuanian National Forest Inventory (NFI). In the current study, we compared two sources of information on forest productivity: conventional NFI data and dendrochronological data based on tree cores collected in parallel with the measurements of the fourth Lithuanian NFI cycle during 2013–2017 on the same permanent plots (total number of cores was 4967). The main finding is that the dendrochronological basal area increment data confirmed the depression of gross stand volume increment around 2006–2007 (based on Lithuanian NFI measurements in 2008–2009), followed by a steep increase during 2008–2011 (NFI from 2010–2013). The findings explain the differences between projected growing stock volume change, which have been used for forest reference level estimation according to land use, land-use change and forestry sector regulation, and the one recently provided in National Greenhouse Gas Inventory Reports. Key words: Growing stock volume change, basal area increment, forest reference level, greenhouse gas reporting

Multipurpose National Forest Inventory in Mongolia, 2014-2017 -A tool to support sustainable forest management

2019 ◽  
Vol 12 (3) ◽  
pp. 167-183 ◽  
Author(s):  
Dan Altrell
Keyword(s):  
Forest Management ◽  
Boreal Forest ◽  
Forest Inventory ◽  
Dead Wood ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
National Forest ◽  
Green Wood ◽  
Growing Stock ◽  
Stock Volume

Mongolia’s first Multipurpose National Forest Inventory, 2014-2017, was implemented by the Forest Research and Development Centre, in collaboration with international expertise and the country’s main forestry institutions, universities and research organisations.The long-term objective of the multipurpose NFI is to promote sustainable management of forestry resources in Mongolia, to enhance their social, economic and environmental functions.The NFI findings show that there are 11.3 million hectares of Boreal Forest in Mongolia. 9.5 million hectares are Stocked Boreal Forest Area, of which 69 percent is located outside of protected areas, 4 percent are designated for green-wood utilisation through forest enterprise concessions, and another 16 percent designated for fallen dead-wood collection through forest user group concessions. The non-protected stocked forests (i.e. production forest) have an average growing stock volume of 115 m3 per hectare, compared with an optimal growing stock volume of 237 m3 per hectare, and there is an additional 46.5 m3 of dead wood per hectare. The growing stock age distribution shows that 24 m3 per hectare are over 200 years (i.e. economically over-aged). The main tree species in stocked forest are Larix sibirica (81%), Pinus sibirica (7%), Betula platyphylla (6%) and Pinus sylvestris (5%), of which all, except for P. sibirica, are classified as legally harvestable tree species. Wild fire is the current main environmental factor decreasing the forest tree biomass.The NFI helped identifying priority areas for the forestry sector, and to guide the implementation of sustainable forest management at the local level. The main forest management challenges of Mongolia’s boreal forest will be to address that they are a) under-stocked (less than 50% of production potential), b) over-aged (31% of growing stock volume in stocked production forest is above optimal production age), and c) under-utilised (4% of forest area designated to green-wood utilisation). 

Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space

2014 ◽  
Vol 44 (10) ◽  
pp. 1156-1164 ◽  
Author(s):  
Anton Grafström ◽  
Svetlana Saarela ◽  
Liviu Theodor Ene
Keyword(s):  
Forest Inventory ◽  
Auxiliary Information ◽  
National Forest Inventory ◽  
Planning Stage ◽  
Entire Area ◽  
Growing Stock ◽  
Forest Inventories ◽  
Field Samples ◽  
A New Technique ◽  
Stock Volume

By using more sophisticated sampling designs in forest field inventories, it is possible to select more representative field samples. When full cover auxiliary information is available at the planning stage of a forest inventory, an efficient strategy for sampling is formed by making sure that the sample is well spread in the space spanned by the auxiliary variables. We show that by using such a sampling design, we can improve not only design-based estimation, but also estimation based on nearest neighbour techniques. A new technique to select well-spread probability samples, in multidimensional spaces, from larger populations is introduced. As an application, we illustrate how this strategy can be applied to a forest field inventory. We use an artificial dataset corresponding to a full cover forest remote sensing inventory of a 30 000 ha area of Kuortane, western Finland. The target variable (growing stock volume) has been generated for the entire area by a copula technique. The artificial population has been validated by utilizing the Finnish National Forest Inventory.

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