Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Bilous, Andrii; Myroniuk, Viktor; Holiaka, Dmytrii; Білоус, Світлана; See, Linda; Schepaschenko, Dmitry

doi:10.1088/1748-9326/aa8352

Cited by 27 publications

(23 citation statements)

References 35 publications

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“…Logging will lead to spatial spreading of 90 Sr accumulated in the tree stems with wood products during their entire life cycle. Second group includes forests within the Chernobyl Exclusion Zone, both existed before the disaster in 1986 and appeared after due to recovering of abandoned agricultural lands [17]. They accumulate 90 Sr and its redistribution will be dependent on the fate of abovementioned stands.…”

Section: Discussionmentioning

confidence: 99%

“…Forests of Silver birch, Black alder and Common aspen cover one third of forested area within this zone. Such a ratio is slightly increasing due to the natural succession on abandoned agricultural lands [17,18]. Deciduous forests have crucial importance, provisioning ecosystem services such as biomass accumulation, carbon sequestration, oxygen production, biodiversity maintaining, sustaining conditions for hunting and providing other goods for human well-being [19].…”

Section: Forest Growth Modellingmentioning

confidence: 99%

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90Sr Content in the Stemwood of Forests within Ukrainian Polissya

Bilous

Holiaka

Matsala

et al. 2020

Forests

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The consequences of the Chernobyl disaster continue to threaten humans and ecosystems across fallout gradient in Northern Ukraine and nearby. Forest ecosystems contain substantial stocks of long-lived radionuclide 90 Sr which was leached from the fuel matrix during the disaster. Nowadays, there is a lack of information about current transfer factors (TF) of this radionuclide from soil to the stemwood of native tree species. We have estimated 90 Sr content in the forest stemwood of three tree species utilizing models of their growth and yield and collected woody samplings. TFs provided here vary greatly across studied tree species (18.0 × 2.1 ±1 , 8.7 × 2.8 ±1 , and 10.4 × 6.0 ±1 n × 10 −3 m 2 ·kg −1 (geometrical mean (GM) ± geometrical standard deviation, GSD) for the above species, respectively) and together with indicators of soil contamination allow us to reliably assess local stocks in the stemwood. Silver birch stands are estimated to deposit the highest 90 Sr stocks. Herewith, at 25 years old Black alder stands could accumulate higher stocks (up to 35 MBq·ha −1 ) under rich growth conditions. TFs obtained in this study substantially exceed values provided by the International Atomic Energy Agency for studied tree species and thus could entail respective restrictions on use of firewood across large areas in Ukrainian Polissya. Data provided here may be harnessed to support decisions of respective stakeholders to provide credibly safe management of the contaminated forest ecosystems.

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Section: Discussionmentioning

confidence: 99%

Section: Forest Growth Modellingmentioning

confidence: 99%

90Sr Content in the Stemwood of Forests within Ukrainian Polissya

Bilous

Holiaka

Matsala

et al. 2020

Forests

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“…Visual interpretation of VHR images is also used in other forest assessments such as forest structure and biomass estimation. Examples include studies on selective forest logging and its impacts (Read et al 2003;Furusawa et al 2004;Pithon et al 2013), forest species classification (Clark et al 2005;Valérie and Marie-Pierre 2006;Kim et al 2009;Bilous et al 2017), tree crown identification (Garzon-Lopez et al 2013;Karlson et al 2014) and biomass estimation (Hussin et al 2014). In these studies, visual interpretation of VHR images was often used to create training data in the absence of in situ data.…”

Section: Utilization In Forest Monitoringmentioning

confidence: 99%

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

et al. 2019

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The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change.

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“…On one hand, there are a large number of features, vegetation indices and texture measures available. On the other hand, the variables may be interrelated, which leads to information redundancy and affects the improvement of estimation accuracy [15,17,18,30]. A novel method that can provide potential solutions for the challenges is needed.…”

Section: Selection Of Optimal Variable Combinationmentioning

confidence: 99%

Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm

Liu

Lin

et al. 2020

Remote Sensing

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Accurately estimating growing stem volume (GSV) is very important for forest resource management. The GSV estimation is affected by remote sensing images, variable selection methods, and estimation algorithms. Optical images have been widely used for modeling key attributes of forest stands, including GSV and aboveground biomass (AGB), because of their easy availability, large coverage and related mature data processing and analysis technologies. However, the low data saturation level and the difficulty of selecting feature variables from optical images often impede the improvement of estimation accuracy. In this research, two GaoFen-2 (GF-2) images, a Landsat 8 image, and fused images created by integrating GF-2 bands with the Landsat multispectral image using the Gram-Schmidt method were first used to derive various feature variables and obtain various datasets or data scenarios. A DC-FSCK approach that integrates feature variable screening and a combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (kNN) algorithm was proposed and compared with the stepwise regression analysis (SRA) and random forest (RF) for feature variable selection. The DC-FSCK considers the self-correlation and combination effect among feature variables so that the selected variables can improve the accuracy and saturation level of GSV estimation. To validate the proposed approach, six estimation algorithms were examined and compared, including Multiple Linear Regression (MLR), kNN, Support Vector Regression (SVR), RF, eXtreme Gradient Boosting (XGBoost) and Stacking. The results showed that compared with GF-2 and Landsat 8 images, overall, the fused image (Red_Landsat) of GF-2 red band with Landsat 8 multispectral image improved the GSV estimation accuracy of Chinese pine and larch plantations. The Red_Landsat image also performed better than other fused images (Pan_Landsat, Blue_Landsat, Green_Landsat and Nir_Landsat). For most of the combinations of the datasets and estimation models, the proposed variable selection method DC-FSCK led to more accurate GSV estimates compared with SRA and RF. In addition, in most of the combinations obtained by the datasets and variable selection methods, the Stacking algorithm performed better than other estimation models. More importantly, the combination of the fused image Red_Landsat with the DC-FSCK and Stacking algorithm led to the best performance of GSV estimation with the greatest adjusted coefficients of Remote Sens. 2020, 12, 871 2 of 24 determination, 0.8127 and 0.6047, and the smallest relative root mean square errors of 17.1% and 20.7% for Chinese pine and larch, respectively. This study provided new insights on how to choose suitable optical images, variable selection methods and optimal modeling algorithms for the GSV estimation of Chinese pine and larch plantations.

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Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Cited by 27 publications

References 35 publications

90Sr Content in the Stemwood of Forests within Ukrainian Polissya

90Sr Content in the Stemwood of Forests within Ukrainian Polissya

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm

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