Scots pine (Pinus sylvestris L.) forests are one of the main vegetation types in the Asian forest-steppe zone. However, over-harvesting currently threatens the natural regeneration and sustainability of these forests. In this study, we examine the long-term effects of different logging intensities on soil properties and natural regeneration in a natural Scots pine forest in the West Khentii Mountains (Mongolia), 19 years after selective logging. Our experimental design included five treatments: clear cut (CC), treatments with high (HI), medium (MI), low (LI) intensities, and a reference parcel with no logging impact at all (RE). We described and quantified the harvest events and applied ANOVA and LMM modeling to analyze and explain the long-term impacts of the logging intensities on soil properties and natural regeneration. We found that logging has a significant negative influence on the physical and chemical properties of the soil because it increases soil compaction and reduces soil nutrients. The most critical impacts of logging were on soil bulk density, total porosity, organic matter, and total nitrogen and phosphorus. The LMM modeling showed that organic matter (OgM), total nitrogen (TN), available K (AK) and pH values are especially impacted by logging. Our study revealed that the values for all of these variables show a linear decrease with increasing selective logging intensity and have a level of significance of p < 0.05. Another finding of this study is that selective logging with low and medium intensities can promote natural regeneration of Scots pine to numbers above those of the reference site (RE). High intensity logging and clear-cuts, however, limit the regeneration of Scots pine, reduce overall seedling numbers (p < 0.05), and create conditions that are suitable only for the regeneration of deciduous tree species. This underlines the risk of Scots pine forest degradation, either by replacement by broad-leaf trees or by conversion into non-forest ecosystems.
Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.
Promoting the recovery of forest management has been identified as a key priority by the Government of Mongolia. The objective of this paper is to define land cover classification and land cover change in Khandgait valley between 2000 and 2019. The study area is located in the North central part of Mongolia in Bulgan province. Landsat satellite images with 30m resolution were applied. For the validation, we used ground truth measurements. Maximum-likelihood method was applied in this study. The output map of land cover classification was analyzed and compared with the ground truth measurements. The results showed an overall accuracy of 86.5% and 89.0% for the 2000 and 2019 images, respectively. Land cover changes were quantitatively presented with the results of accuracy assessments between 2000 and 2019. In the future, we need to improve forest monitoring and analyze forest management using satellite images.
Abstract. This paper aims to apply Forest Index (FI) and to determine forest coverage in the study area. The study area (49° 15ʹ to 49° 10ʹ N and 104° 05ʹ to 104° 15ʹ E) is located in the northern region of Mongolia and consist of mixed forest. Larch forest (86.12%) is dominating in the study area. The Sentinel-2 satellite data for the years 2015–2019 were used in the research. The land surface temperature (LST) was produced from Landsat-8 OL. FI methodology was applied for the Sentinel data in order to estimate larch forest coverage. The output map of forest coverage was compared with ground truth measurements and thematic map. The agreement between FI map and ground measurement was 85%. LST from Landsat and FI from Sentinel were sampled in to same size. The relationship between LST (Landsat-8) and FI (Sentinel-2) was reasonable (R = 0.5). FI index and LST is applicable for different forest type in the region.
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