Role of Silviculture in the Formation of Norway Spruce Forests along the Southern Edge of Their Range in the Central Russian Plain

Chernen’kova, T. V.; Kotlov, Ivan; Belyaeva, N. G.; Суслова, Е. Г.; Морозова, О. В.; Pesterova, Olga; Arkhipova, M. V.

doi:10.3390/f11070778

Cited by 14 publications

(19 citation statements)

References 61 publications

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“…This is especially important for coniferous and broad-leaved coniferous formations. However, the spatial pattern is quite plausible, the results are consistent with the previously developed forest cover models for part of the Moscow Region based on discriminant analysis [43,44], and also with a map of the vegetation cover of the Moscow Region [18].…”

Section: Discussionsupporting

confidence: 86%

“…Formation A: The composition of spruce forests is complex (combinations of spruce with birch, aspen, pine and broad-leaved species) and it is similar to the composition of the zonal primary coniferous broad-leaved forests. The proportion of silviculture is high (mainly monodominant spruce forests) [44]. The species composition of the subordinate stories (grass-dwarf shrub and moss stories) is represented by the full spectrum of transitions from boreal to nemoral types.…”

Section: Pre-processing Of Samplesmentioning

confidence: 99%

“…Landsat 8 and Landsat 5 spectral reflectances and spectral indices as well as Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) and morphometric variables are used as the environmental variables [43,44]. Palsar-2 25 m resolution images are also included: ortho and slope-corrected backscattering coefficient (horizontal-horizontal (HH) and horizontal-vertical (HV) polarization) for 2019 [45].…”

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confidence: 99%

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Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions

Kotlov

Chernen’kova

2020

Forests

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This study tests modern approaches to spatial modeling of forest communities at the regional level based on a supervised classification. The study is conducted by the example of mapping the composition of forest communities in a large urbanized region (the Moscow Region, area 4.69 million hectares). A database of 1684 field descriptions is used as sample plots. As environmental variables, Landsat spectral reflectances, vegetation indices (5 images), digital elevation model and morphometric parameters of the relief, 54 layers in total, are used. Additionally, the Palsar-2 radar dataset is included. The main mapped units are formations and groups of associations identified on the basis of the ecological-phytocoenotic classification. Formations and groups of associations are similar in semantics and principles of allocation to units of forest typology. It is shown that the maximum entropy method has a wide range of applications, in particular, for mapping the typological diversity of forest cover. The method is used in combination with geographically structured spatial jack-knifing, spatial rarefication of occurrence data and independent testing of model feature classes and regularization parameters. Spatial rarefication is a critical technique when points are not evenly distributed in space. The resulting model of the spatial structure of forest cover is based on the integration of the best models of each thematic class of different types of forest cover into a single cartographic layer. It is shown that under conditions of uneven and sparse distribution of points, it is possible to provide an average point matching level of 0.45 for formations and 0.29 for association groups. Herewith, the spatial structure and the ratio of the formation’s composition correspond to the official data of the forest inventory. An attempt is made to identify and evaluate the distribution of more detailed syntaxonomic units: association groups. The necessary requirements for improving the quality of the forest cover model of the study area for 2 hierarchical typological units of forest cover are formulated. These include the additional sampling in order to equalize their spatial density, as well as to achieve equality of samples based on stratification according to the resulting map.

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Section: Discussionsupporting

confidence: 86%

Section: Pre-processing Of Samplesmentioning

confidence: 99%

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confidence: 99%

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Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions

Kotlov

Chernen’kova

2020

Forests

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“…The forest cover in the MR is represented by a succession mosaic of forests of different compositions, ages, and origins. Coniferous and mixed communities account for a significant proportion of artificial forests [38].…”

Section: Study Areamentioning

confidence: 99%

Environmental Performance of Regional Protected Area Network: Typological Diversity and Fragmentation of Forests

et al. 2023

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Protected areas (PAs) are among the main tools for preserving biodiversity and creating an environment for the natural course of ecological processes. The identification of forest biodiversity is especially important for large metropolitan areas. An obvious problem in assessing the efficiency of the PAs network is the lack of up-to-date cartographic materials representing the typological diversity of vegetation. The aim of the paper is to identify forest biodiversity and fragmentation in the example of the Moscow region (MR)—the largest metropolis in Eastern Europe. The typological classification was carried out at a detailed hierarchical level—33 association groups (ass.gr.) considering the diversity of the land cover. A random forest algorithm was used for cаrtographic mapping (overall accuracy 0.59). Remote sensing (RS) data included Sentinel-2A, DEM SRTM, and PALSAR radar images. Six fragmentation metrics were calculated based on the raster map of forest typological diversity. A significant correlation between the forest diversity and PAs forest patch fragmentation metrics was noted. It has been established that the PAs proportion of the territory accounts for almost 20% only within the northernmost district and noticeably decreases to the south to 1–2%. At the same time, fragmentation noticeably increases from Northeast to Southwest. The category of PAs does not affect the state of the forest cover. Additionally, there was no direct influence of the anthropogenic factor from both local sources and a large regional source, i.e., the city of Moscow. It is shown that the average area of PAs, supporting 75% of the typological diversity of regional communities, was about 1000 ha. The results of the study suggest that there is a general lack of environmental protection measures in the region. It is recommended to increase the area of PAs, primarily for less fragmented forest patches, including indigenous forest-steppe and forest types of communities.

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“…The identification of syntaxa at the level of association group was performed basing on the prevailing ecological and morphological groups of plants in subordinate layers. Each spruce and spruce-small-leaved forest formation is represented by four or six association groups [72].…”

Section: Field Data and Classificationmentioning

confidence: 99%

Spatiotemporal Modeling of Coniferous Forests Dynamics along the Southern Edge of Their Range in the Central Russian Plain

et al. 2021

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Forests with predominance of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) within the hemiboreal zone are considered as secondary communities formed under long-term human activity (logging, plowing, fires and silviculture). This study raises the question—how stable is current state of coniferous forests on the southern border of their natural distribution in the center of Eastern Europe using the example of the Moscow region (MR)? The object of the study are spruce and pine forests in different periods of Soviet and post-Soviet history within the Moscow Region (MR). The current proportion of spruce forests is 21.7%, and the proportion of pine forests is 18.5% from total forest area according to our estimates. The direction and rate of forest succession were analyzed based on current composition of populations of the main forest-forming species (spruce, pine, birch, aspen, oak, linden, and ash) based on ground-based research materials collected in 2006–2019. This allowed to develop the dynamic model (DM) of forest communities with the participation of Norway spruce and Scots pine for several decades. Assessment of the spatial distribution of coniferous communities is based on field data and spatial modeling using remote sensing data—Landsat 8 mosaic for 2020. In parallel, a retrospective model (RM) of the spatial-temporal organization of spruce and pine forests for a 30-year period was developed using two Landsat 5 mosaics. For this, nine different algorithms were tested and the best one for this task was found—random forest. Geobotanical relevés were used as a training sample combined with the 2006–2012 mosaic; the obtained spectral signatures were used for modeling based on the 1984–1990 mosaic. Thus, two multi-temporal spatial models of coniferous formations have been developed. Detailed analysis of the structure of spruce and pine forests based on field data made it possible to track trends of successional dynamics for the first time, considering the origin of communities and the ecological conditions of habitats. As a result, ideas about the viability of spruce and pine cenopopulations in different types of communities were formulated, which made possible to develop a dynamic model (DM) of changes in forest communities for future. Comparison of the areas and nature of changes in the spatial structure of coniferous formations made possible to develop the RM. Comparison of two different-time models of succession dynamics (DM and RM) makes possible to correct the main trends in the transformation of coniferous forests of natural and artificial origin under the existing regime of forestry. A set of features was identified that indicates risk factors for coniferous forests in the region. A further decrease of the spruce and pine plantations and increase of the spruce-small-leaved and deciduous formations are expected in the study area. The proportion of pine-spruce forests does not exceed 3% of the area and can be considered as the most vulnerable type of forest.

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Role of Silviculture in the Formation of Norway Spruce Forests along the Southern Edge of Their Range in the Central Russian Plain

Cited by 14 publications

References 61 publications

Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions

Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions

Environmental Performance of Regional Protected Area Network: Typological Diversity and Fragmentation of Forests

Spatiotemporal Modeling of Coniferous Forests Dynamics along the Southern Edge of Their Range in the Central Russian Plain

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