Sustainable Forest Management Tools for the Moscow Region

Abstract

The paper presents an inventory of current forest formations and a map of forest vegetation in the Moscow region. To assess current forest formations, an approach integrating both ground- and remote sensing data was applied. The transformation of forests in the Moscow region was evaluated by the criteria of changing the quality, quantity and spatial configuration of forests, in accordance with the model SLOSS (Single Large or Several Small). The conceptual model "Pressure-State-Response" (hereinafter PRS) was used to develop appropriate tools for sustainable environmental management in the region. The use of this model made it possible not only to assess the state of forests but also to determine the main impacts affecting them, as well as the effectiveness of measures aimed at optimizing environmental management regimes in order to maintain forest biodiversity. Complex assessment of sets of indicators for each group of PRS criteria is performed for the integrated multicriteria assessment of sustainable forest management within the boundaries of urban districts. The average normalized score was calculated for each group of criteria. Correlation between the scores of the groups of criteria evaluated and classification of administrative units according to the ratio of groups of the K-means method criteria performed. As a result of component-by-component evaluation, the values of indicators are presented in form of tables and map сharts. Benchmarking of Specially Protected Natural Area (SPNA) system and reforestation activities is performed regarding to the forest biodiversity conservation in the urban districts. It is shown that single integrated assessment of the ecological value of the territory (the "State" criterion), an integrated assessment of impact factors (the "Load") and appropriate actions to maintain forest biodiversity (the "Response" criterion) can be considered as an expression of generalized information directly used in decision-making and assessment of current trends for a particular region.

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

“…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].…”

“…Table 1 shows the hierarchical structure of formations (in columns) and association groups (in rows) of the Moscow Region, in accordance with the previously published results of the ecological-phytocenotic classification [43]. The cells indicate the number of field sample plots for the association groups.…”

“…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].…”

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

“…The spruce forests spatial structure was estimated by mapping the association groups. The association groups were mapped using the supervised classification approach in two stages: preparation of a training sample [80][81][82][83] and classification. At the first stage, the relevés were filtered and decimated for further use as training sample.…”

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

“…The development of dynamic models is needed as field and remote information on the main patterns of forest cover development. The vegetation cover of the MR is researched well enough [19][20][21][22][23][24][25][26][27], including the map of vegetation cover based on forest inventory data and field studies [28]. The active use of mathematical methods and data analysis allows for a fresh look at the issues of forest's biodiversity assessment and vegetation mapping.…”