Changing climatic indicators and mapping of soil temperature using Landsat data in the Yangan-Tau UNESCO global geopark

Bogdan, E. A.; Kamalova, R. G.; Suleymanov, Azamat; Belan, Larisa; Tuktarova, I. О.

doi:10.5510/ogp2022si200768

Cited by 5 publications

(3 citation statements)

References 35 publications

(64 reference statements)

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“…Increasing average annual temperatures and decreasing precipitation have a leading influence on salinization processes in arid and semi-arid regions [59]. Previously, numerous studies have demonstrated changes in climatic indicators in the Ural region [42,[60][61][62], which contribute to soil salinity. As previously mentioned, the key factor driving soil salinization in the research region is groundwater level/table.…”

Section: Discussionmentioning

confidence: 99%

Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

et al. 2023

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The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

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Section: Discussionmentioning

confidence: 99%

Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

et al. 2023

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“…Natural disturbances such as storms, fires, and disease outbreaks are relatively common in the region and in the reserve, which also affect tree growth and mortality [18][19][20]. The above-mentioned negative factors are also exacerbated by climate change, which has been established in the study region [21][22][23]. Changes in temperature, precipitation patterns, and other climatic factors can impact the availability of resources and create new challenges for forest managers [24].…”

Section: Forests Of the Reservementioning

confidence: 99%

Assessing and Mapping Changes in Forest Growing Stock Volume over Time in Bashkiriya Nature Reserve, Russia

Belan,

Suleymanov,

Bogdan

et al. 2023

Forests

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There is growing recognition that forest ecosystems are a key component in the global carbon cycle, and there is a clear demand for their study. This research is a comparative analysis of forest growing stock volume (GSV) and determination of annual growth in Bashkiriya Nature Reserve (Russia) for 1979 and 2015 using 8395 and 8405 observation plots, respectively. Also, we evaluated the spatial distribution and produced digital maps of the species and their GSV for each year. The results showed that pine and birch were the dominant species (60.5 and 24.8% of the area in 2015, respectively) and there were no significant changes in the area of stands during the 36-year period. We found that the GSV in the reserve had increased by an average of 23.2% over the 36-year period. Specifically, the total forest GSV increased from 7,678,960 in 1979 to 10,003,890 m3 in 2015, representing an annual gain of 0–1.5 m3/ha. The increase in GSV was mainly associated with an increase in birch and pine trees. The annual growth of GSV was determined as 1.8–2.1 and 1.4 m3/ha per year for pine and birch forests, respectively. However, these types of trees belong to the age categories of mature and overmature stands, i.e., with reduced intensity of GSV gain. Digital maps produced as part of this study provide a visual representation of the changes in forest spatial patterns and GSV over time, highlighting areas of the reserve where the stock has increased or decreased. This study leveraged a substantial dataset, which provided valuable retrospective insights into the dynamics of pristine forest ecosystems, allowing for the assessment of changes over a 36-year period. Overall, this study highlights the importance of the ongoing monitoring and assessment of GSV levels, especially in the context of rapidly changing environments and climates.

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“…The park is a popular place for recreation and the largest number of visitors to the park arrive during the hot season. As a result, the negative climatic impact in the region is exacerbated by the increased level of recreational load [14][15][16]. The study area is located in the west part of the republic, within the Kandrykul-Usen and Usensko-Ryasky physical-geographical districts of the Belebey upland-level typical forest-steppe district [17].…”

Section: Study Areamentioning

confidence: 99%

Spatio-Temporal Analysis of Forest Growing Stock Volume and Carbon Stocks: A Case Study of Kandry-Kul Natural Park, Russia

Volkov,

Belan,

Bogdan

et al. 2023

Land

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This paper presents the evaluation and digital mapping of forest growing stock volume (GSV) and carbon stocks of the natural park Kandry-Kul (Republic of Bashkortostan, Russia). The field observations were conducted in the process of forest surveys in 1994 and 2018. According to these materials, we identified the predominant tree species in each studied plot. Then, we produced the digital maps of GSV and carbon stocks for each study year and calculated the annual increment. The results showed that birch (Betula pendula) and pine (Pinus sylvestris) were the dominant species in the studied park. The comparison of the two maps showed that the maximum annual GSV between 1994 and 2018 occurred in areas with a fairly small GSV in 1994. These areas were characterized by young trees of different species and pines of different ages, which had not yet reached the age of maturity, when the growth of trees is significantly reduced. We found that young pine crops contributed the greatest potential of carbon sequestration, with an annual GSV of 4.2 m3/ha per year. The birch trees made a minor contribution to the sequestration potential, characterized by relatively average annual growth (1.2 m3/ha per year). The change in carbon stocks for pine stands was on average 2 t/ha per year. For birch stands, the change in carbon stocks was approximately 0.5 t/ha per year, which is 30% of the average values for the forest-steppe region. Our results provide quantitative information on the carbon accumulation rate in secondary forests under conditions of intensive recreational load.

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Changing climatic indicators and mapping of soil temperature using Landsat data in the Yangan-Tau UNESCO global geopark

Cited by 5 publications

References 35 publications

Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

Assessing and Mapping Changes in Forest Growing Stock Volume over Time in Bashkiriya Nature Reserve, Russia

Spatio-Temporal Analysis of Forest Growing Stock Volume and Carbon Stocks: A Case Study of Kandry-Kul Natural Park, Russia

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