The object of study was the catchment basin of the Klyazma River located in the centre of the East European Plain. It is a 686 km long left tributary to the Oka with a west-east trending catchment basin of 42,500 km² Fig. 1. In this work, the Klyazma River Basin was considered as a single ecosystem, albeit marked by certain internal diversity (Trifonova 2018).

The subject matter of study was the soil and vegetation cover that determines the landscape diversity of the Klyazma River Basin. On its territory, there are different natural areas with varying degrees of anthropogenic impact. The vegetation state was assessed as an integral basin and individually on eight sites located in the natural areas of the identified landscape Provinces (Figs 1, 2). The eight sites were the following:

The Klin-Dmitrov Province is represented by two sites corresponding to natural areas significantly different in the peculiarities of soil and vegetation cover.

Site 1. Klin-Dmitrov Ridge

Sod-weak and medium-podzolic soils predominate. Forest covers about 30%. Ploughing area is about 45%.

Site 2. In the Vladimir High Plain.

The soil cover forms mainly as a combination of grey forest soils on the hilltops and in the upper part of the slopes, dark grey forest soils in the lower part of the slopes. The hill tops and slopes are mostly ploughed or occupied by deposits (over 60% is ploughed), the meadows along the basins bottoms vary in moisture — even swampy.

The Meshchera Province has a uniform landscape and is represented by one site.

Site 3. The Meshchera Site is a flat boggy alluvial Upper Pleistocene-Holocene plain in the Middle Klyazma woodland. Podzolic and sod-podzolic gley soils prevail in the interfluves; peaty-podzolic gley soils, on the slopes of elevations; and peaty bog soils, in the depressions. The Middle Klyazma woodland is covered with forests by about 85%. Pine trees are abundant, occupying 75 to 90% of the woodland.

The Volga-Klyazma Province had two sites.

Site 4. The Nerl-Klyazma Lowland. The soils are sod-podzolic loamy and sabulous on the elevated interfluves, peaty in the lowlands and alluvial argillo-arenaceous within the river terraces and floodplains. The forest cover is unevenly preserved, occupying from 35 to 95% of the territory in different areas. Agricultural development is insignificant.

Site 5. The Lower Lukh Site is the most waterlogged area in the lower reaches of the Luh River, with swamps sprawling out. Under the conditions of surface waters stagnation, the peat-gley soils were formed with sandy sod-podzolic patches, marking the ancient deltas of glacial water flow. There are evergreen coniferous forests with abundant spruce and pine species. Cereal-herbaceous phytocenoses dominate the flood meadows.

The Oka-Tsna Province was represented by three sites due its diverse landscapes.

Site 6. The Oka-Tsna Wall. It is characterised by the wide karst development, deep ground water level and extreme underdevelopment of the hydraulic network. The anthropogenic development of the landscape district is low. The soil cover is represented by sod-weak and medium-podzolic soils. A unified forest cover (pine and birch-pine lichen-dry grass forests) is usually disturbed only by rare open agricultural areas surrounding villages.

Site 7. The karstic phenomena are well developed. The soils are soddy modal podzols, weakly gley soddy podzols and gley soddy podzols. The site is unevenly covered (about 70%, sometimes less than 30%) with boreal forests. Open spaces formed by agriculturally used areas are rather vast, although a significant part of these farmlands have been abandoned and turned into laylands.

Site 8. The Gorokhovets Spur is an intensely erosion-dissected gently undulating Dnieper morainic-glacial plain. The soils cover includes soddy modal podzols and washed-off truncated soddy podzols on the high interfluves, truncated soddy podzols on the slopes and washed-off and alluvial soils at the footslopes and in the gullies and ravines. Forests occupy about 35% of the site and are confined to steep slopes and ravines. Flat tops of the watersheds are small and nearly all ploughed up.

Figs. 1 and 2 show the sites on the map and their satellite images.

The geoinformation analysis of remote sensing data and cartographic information on the soil and vegetation cover was performed by applying the catchment area approach.

The river network vectorisation and the boundary determination were carried out on the basis of the digital elevation model (DEM). The input data were the Shuttle Radar Topography Mission (SRTM) 90 m data. Having prepared the DEM, the river network and the catchment area boundaries were vectorised by automated geo-modelling methods in ESRI ArcGis10.4 using the Hydrology toolset (Ermolayev et al. 2014).

The land types were identified by HDF raster images made on the same four dates in 2001, 2005, 2009 and 2017 according to the MODIS open-source data of Friedl and Sulla-Menashe 2015.

For a more detailed analysis and trends identification in land area changes, we resorted to the Trends. Earth Land Degradation Monitoring Project (Land Cover Dataset, European Space Agency 2015, 300 m spatial resolution) implemented using open-source Quantum GIS 2.18 (Fig. 2) (National Aeronautics and Space Administration 2018).

The productivity indicators were calculated in carbon units, based on the MODIS GPP/NPP data HDF format, scene size 2400*2400, spatial resolution 500 m (Running and Mu 2015). The original space images of gross and net productivity were processed by the reclassification tool according to the created reclassification table, resulting in raster layers with classified pixel values. At the next step, the zonal statistics tool was used to compile attribute tables with productivity indicators in carbon units.

When calculating the carbon balance, we obtained the gross primary production (GPP), net primary production (NPP) and the costs of autotrophs respiration (RE) values (all in gC/m²) for 2000–2015.

The soil organic carbon pool was determined by the UN FAO’s data, based on Trends.Earth and QGIS 2.18 (National Aeronautics and Space Administration 2018). The data were generalised according to the Soil Grids global database of the International Soil Reference and Information Centre with a 250 m spatial resolution at a depth of 0 to 30 cm for 2001–2015 (ISRIC — World Soil Information 2017). The dynamics of soil and vegetation cover for 2001–2015 were calculated in the whole Klyazma River Basin and in each site using Trends.Earth and QGIS 2.18 (National Aeronautics and Space Administration 2018).

The weather statistics were taken from weather website rp5.ru (Anonymous 2019). To assess the climate, the data on temperature and precipitation for the period from 2001 to 2015 were used. The data were mathematically processed using two-factor analysis of variance ANOVA in the software environment STATICTICA 10 and Microsoft Excel.

The variance analysis has been carried out to identify the factors affecting the phytoproductivity indicators of various key sites.

According to the classification of the International Geosphere-Biosphere Program, the Klyazma River Basin includes ten classes of land cover. As of 2017, almost 60% of the basin was covered by mixed forests, about 20% were occupied by grasses, shrubs and open woodlands and about 14% were arable lands.

The conjoint quantitative and graphical analysis of data on the land use structure in 2001–2017 revealed the following changes in the area of different lands in the Basin (Fig. 3):

1. the area of mixed forests that form the main vegetative cover of the basin increased by 9.0%;

2. the area of shrub vegetation and open woodlands decreased by 2.3 and 2.2%, respectively, primarily as a result of succession and their subsequent transition to the forest class;

3. the area of natural meadows, ploughlands/pastures and broad-leaved forests decreased by 2.1, 1.0 and 0.1%, respectively;

4. there was a 1% increase in the area of urbanised territories; water surfaces anProvinces of the studied catchmentd swamps also slightly grew.

The catchment area of the Klyazma River has a complex landscape structure and to make a detailed analysis of the main trends in the different parts of the basin, we took remote data from Trends.Earth with of 300 m spatial resolution for six types of lands.

It was found that the trend has been towards the sprawl of forest vegetation and the reduction of ploughlands and pastures in the entire Basin. The only exception is the interfluve with the Lukh River, where a transition of forest lands into bogs can be observed. The selected sites feature the changes in the land use structure of the main landscape Provinces (Table 1, Fig. 4).

The land structure in the Meshchera Province is the most stable: almost 90% of the Province is occupied by forests and their area have changed insignificantly.

The revegetation of ploughlands with grey wood soils and soddy podzols differs. For example, the Klin-Dmitrov Province has both types of soils. Forests most actively overgrow the pastures and ploughlands with soddy cryptopodzols and mesopodzols, which can be observed on Site 1 (the Klin-Dmitrov Ridge). Here, the revegetation rate is the highest (-3.5%) in the Klyazma River Basin. The grey wood soils, represented on the Vladimir High Plain, are the most fertile and agriculturally developed, hence they become overgrown significantly less (Site 2).

The cartographic analysis revealed areas where various lands transitioned from one category to another (refer to Fig. 4). Such transitions mainly occurred due to revegetation of agricultural land with shrubs and grasses. There was also a significant enlargement of waterlogged areas and bogs in the eastern part of the basin: in the Volga-Klyazma Province (interfluve of the rivers Lukh and Klyazma, Site 5) and in the Oka-Tsna Province (near the Gorokhovets Spur, Site 8).

To quantify the productivity of the vegetation cover in the catchment area, the GPP, NPP and RE values were determined in gC/cm² at the height of vegetation season (mid-July) in 2000–2015, based on the MODIS data.

On average, the GPP was 59.1 gC/cm² and the NPP was 39.5 gC/cm² for the studied period at the height of the vegetation season in the entire territory of the Klyazma River Basin.

The GPP was unevenly distributed across the Basin (Table 2). Fertile grey wood soils of the Vladimir High Plain do not have high gross productivity, which, in that zone, is even slightly lower than the average, due to the predominance of agrocenoses there. The Gorokhovets Spur area (Oksko-Tsninsk Province) is characterised by the highest gross productivity, it is higher than average for the Klyazma River Basin. In this area, in comparison with others, there are more grasses and shrubbery that are more productive than forests. Phytoproductivity of the other landscapes corresponded to the average values in the Basin.

The productivity indicators differed significantly over the years both generally in the Basin and separately by the sites. The coefficient of variation characterising the samples was rather significant (Fig. 5, Table 2).

We compared the phytoproductivity dynamics of the whole River Basin and individual landscape Provinces, represented by the sites (Fig. 6).

The variance analysis was used to describe the impact of the temporal factor and landscape belonging of eight key sites regarding gross primary productivity (Table 3).

The data in Tables 2 and 3 indicate the change in carbon content in the soil in 2001–2015. In general, the Klyazma River Basin ecosystem increased carbon reserves by 0.6%, but the data on the sites showed that the rate and the direction of this process varied in the different landscape Provinces of the studied catchment area.

From 2000 to 2015, the GPP, NPP and RE values in the ecosystem of the Klyazma River Basin were fluctuating both up and down as compared to the respective average values. No sustainable trends in growth or decline in productivity were observed. The distribution histograms showed periods of growth, decline and steady state of productivity, which can be mainly associated with the weather conditions.

Both GPP and NPP were stable from 2007–2010 when the gross productivity was in the range from 70 to 71 gC/m² and the net productivity was 44 gC/m² on average. Despite the hot and arid summer of 2010, when the average temperature in July reached 24.8°C, the total precipitation in June and July was 54 mm, the number of days with weather elements was only 22 and the relative humidity did not exceed 60%. The considered indicators remained at a relatively high level: GPP was 71 gC/m² and NPP was gC/m². On the other hand, the consumption for autotrophic respiration was also rather high: RE was 27 gC/m². It could have been caused by adverse weather conditions and large-scale summer fires.

The GPP and NPP maximums occurred during the humid summer of 2014 and reached 75 and 51 gC/m², respectively. In July 2014, the average temperature in July 2014 was 19.3°C, the total precipitation in June and July was 156 mm, the number of days with adverse weather elements was 30 and the relative humidity did not exceed 66%.

The worst period for the land cover was year 2006 when GPP dropped to 38 gC/m² and NPP was 18 gC/m², the average temperature in July was below normal and amounted only to 16.6°C and the total precipitation in June and July was 77 mm. A relatively small amount of precipitation in May and June, amounting to 62 mm, could also have had a negative impact on productivity, which apparently contributed to a decrease in groundwater levels by July and formed the conditions of water deficit.

It is shown that, although, on the one hand, the studied landscapes differ from each other in absolute indicators of gross and net biological productivity, however, their dynamics do not depend on the initial indicators (as evidenced by the Fisher criterion (F = 0.02) below critical F (0.97) at p < 0.05) (Table 3).

The crucial role here belongs to the temporal factor, determining the annual change in productivity indicators in all the studied areas, which is confirmed by the value of F actual 4.8 with F critical 0.0001 (p < 0.05). As a result, the course of the curves that reflect the change in gross and net productivity over the years for 15 years in different areas coincide, although their absolute values differ. As a result, the curved lines reflecting the annual change in gross and net productivity during 15 years in different areas coincide, although their absolute values differ (Fig. 6).

The periods of increased and decreased productivity in different provinces generally coincided; however, the magnitude of these fluctuations was different.

The most significant changes in the productivity indicators from year to year were observed in the Klin-Dmitrov Ridge, which can be confirmed by the maximum coefficients of variation for GPP (23.3%), NPP (26.3%) and RE (33.1%). The smallest productivity fluctuations occurred in the Oka-Tsna Wall: here, they are below the Basin average.

The sites were grouped by productivity, based on visual analysis and subsequent comparison of histograms. The sites having similar productivity parameters, but confined to different landscapes, generally had a similar distribution of lands. Thus, the Meshchera Site (Site 3) and the Oka-Tsna Wall (Site 6), both covered with mixed forest by 86–88%, were alike in terms of productivity distribution. The landscapes of the Volga-Klyazma Province (Site 4) and the Kovrov-Kasimov Plateau (Site 7), both covered with mixed forest by 71–76%, were also alike in terms of productivity distribution and differed from the others by the largest variation of indicators.

The most dynamic site was the interfluve of the rivers Lukh and Klyazma (Site 5), where the structure of land use had taken significant changes. Thus, the forested areas decreased by 18% due to bogging, but the productivity indicators in the studied period were the lowest. On the other hand, the Gorokhovets Spur (Site 8) and the Klin-Dmitrov Ridge (Site 1), both having 50% of mixed forests and 35–38% of arable lands, were grouped according to the ratio of NPP to RE.

The Vladimir High Plain (Site 2) can be isolated as unique in terms of productivity distribution for it bore no similarity to any other site, but had a similar productivity distribution as the River Basin taken as a whole. This plot differs from the others by the degree of ploughing(60% of arable lands, 30% of forests).

On average, in the Klyazma River Basin, the coefficient of variation of GPP was 18.5%, while RE varied more significantly, the coefficient of variation being 27.9%. It should be noted that the dynamics of landscape productivity on the selected sites, referring to various landscapes, corresponded to the processes in the whole River Basin in general.

The coefficients of correlation between the variables GPP, NPP and RE, as a whole for the basin landscapes and the vegetation of these areas, were maximum. However, the dynamics of plant community productivity on the sites of the Vladimir High Plain and the Klin-Dmitrov Ridge most closely corresponded to the dynamics of productivity of the whole River Basin. Since these sites and the River Basin appear to have matching patterns of phytoproductivity dynamics, they can be used as monitoring sites for the Klyazma River Basin. On that subject, the Klin-Dmitrov Ridge is even more preferable since it is more consistent with the whole River Basin in terms of land use.

The analysis of productivity, land use and carbon accumulation in the soil in the whole Klyazma River Basin and the individual sites associated with different landscapes identified a representative site for environmental monitoring in the studied catchment area, that is the Klin-Dmitrov Ridge. It matches to the whole River Basin by several parameters (productivity and its dynamics, land use structure, carbon accumulation) and hence Site 1 can serve as a model of the Klyazma River Basin.

It should be noted that although the catchment area of the Klyazma is rather large, it functions stably enough in the regime of a single ecosystem. This is facilitated by a favourable correlation of climatic parameters, the location of various land areas and a relatively stable anthropogenic load.

We compared the data on the carbon balance in the soil cover of the Klyazma River Basin and the change in land use structure (Tables 1, 4, 3).

Variance analysis ANOVA demonstrated that the temporal factor does not affect soil organic carbon amount, because F actual 0.001 as the criterion is less than F critical 1.00 (p < 0.05). It might depend on the fact that the organic carbon amount in soil is changing slowly over time and has remained relatively stable in the studied areas for 15 years.

On the other hand, the effect of the land use structure on organic carbon amount was found in all landscapes, as indicated by F actual 17.7 (F critical = 0.0001, p < 0.05). The land sites ratio significantly affects changes in soil organic carbon, which is especially evident in succession processes, ploughing and waterlogging.

Parametric analysis proved that forests make the greatest contribution into the soil organic carbon dynamics (t = 5.7 at Tcr = 0.0001, p< 0.05). Grassy vegetation and wetlands are slightly less affected. In arable land, carbon amount in soil does not change significantly (t = -0.1 at tcr = 0.9, p < 0.05).

The carbon balance in the soil cover of the whole River Basin was positive, with the exception of the Meshchera Site and the Vladimir High Plain where there were insignificant changes in the dynamics of soil carbon over the period from 2000 to 2015 and, therefore, the carbon balance can be assumed to be zero. The Meshchera Site was marked by a small negative dynamics of soil carbon content that decreased by 0.1%, while the Vladimir High Plain had a 0.1% increase in soil carbon content.

The land use structure of the sites with a zero balance is notably different. On the Meshchera Site, most of the land is occupied by forests (89%) and on the Vladimir High Plain, it is arable land (65%). A common feature of these site is the stability of land. On the Meshchera Site, the area of forest vegetation changed insignificantly and, on the Vladimir High Plain, forested territories grew only slightly and farmland areas remained almost unchanged.

In the other landscapes, the farmlands were actively overgrown with forest vegetation and, at the same time, the rate of carbon accumulation in the soil increased. For the analysed period, this accumulation amounted to 0.6% in the Klyazma River Basin.

On the Klin-Dmitrov Ridge, the rate of carbon deposition in the soil corresponded to the River Basin average (0.7%), which must be taken into account when choosing key sites for environmental monitoring.

The highest rate of carbon accumulation in the soil was observed on the Gorokhovets Spur (1.5%).

Summing up, in the Klyazma River Basin, overgrowing of agricultural land with forest vegetation is accompanied by increasing carbon accumulation in the soil. In the landscapes with a stable land use structure, the carbon balance is zero or slightly negative.

Due to the reduction in the area of arable land and pastures (alienation for urban development, waterlogging), the mass of organic carbon in the soil decreased. This is especially evident in the Meshchera Province that showed a negative trend to accumulate organic carbon in the soil.

As a whole, in the Klyazma River Basin ecosystem, GPP is gC/m², NPP is about 40 gC/m² and the carbon content in the soil has grown by 0.6%.