Effectiveness of using global and national LDN indicators
This cycle of works represents a significant cluster (about 40%) of the
total number of LDN-related publications, and reflect matters such as:
i. Validating indicators obtained from global datasets against national
data;
iii. Variability factors for LDN global indicators;
iii. Possible alternative indicators for interpreting the dynamics of
LDN global indicators;
iv. The necessity and feasibility of additional LDN indicators (or not
using some global data), depending on local perspectives;
v. Ways of selecting a set of indicators considering regional and local
peculiarities.
In relation to point (i) it was noted that three global LDN indicators
do not much fit national data. At an individual farm level, the
discrepancy between the results obtained using global data, data from
detailed satellite imagery and ground-based observations can be as high
as 20-30% (Belyaeva et al. 2019) for all three LDN indicators, in
particular for SOC. (Lobkovskiy et al. 2022) indicate that the reason is
that global LDN indicators reflect dynamics of certain parameters
over a certain period of time, while the traditional national system is
designed to assess the quality of land suitable for certain
purposes (Table 1). In other words, the national system for land
monitoring and assessing and existing relevant frameworks are based on
sectoral methods of the assessment of land static qualities. This does
not require data on comparable LD parameters obtained with a unified
methodological platform. Moreover, land quality parameters that have the
same names (for example, soil pollution) are often assessed differently
in sectoral systems.
Studies related to point (ii) suggest that beyond physical impacts and
other human activities, the variability of LDN global indicators is
influenced by different combinations of factors such as climate change,
natural fluctuations and successions in vegetation, geological and
geomorphological processes associated with the development of river
basins, and multidirectional trends in the natural development of
landscapes in the postglacial epoch (Andreeva and Kust 2019). It has
been demonstrated, that natural trends and processes are often more
vigorous than anthropogenic ones, and without taking their background
into account it is difficult to assess positive or negative land
dynamics.
In relation to point (iii), it was demonstrated, that the assessment of
land cover dynamics using the traditional national land accounting
system does not fully correspond to the land cover classes adopted for
estimating LDN (Table 1). In the Russian land cadaster land
categories are distinguished according their intended use (forest fund,
agricultural, industrial, etc.) and lands based on actual
economic use. For example, the forest fund convers not only forested
areas, but also includes arable lands, wetlands, roads, and other types
of ‘land’. Statistical accounting for this system is complex and
contradictory. In many cases the state of land cover in terms of ‘land
categories’ remains invariant (especially for agricultural lands and
forest fund), and the main changes (if any) concern ‘lands’, which can
hardly be traced in state land statistics. Moreover, national statistics
records the total actual areas, but do not record the transitions of
‘land categories’ or ‘lands’ from one to another. Therefore, without
using space monitoring data, it is practically impossible to trace land
transitions, and these methods are rarely used at the level of districts
and individual farms.
The land productivity assessment through the use of vegetation indices
obtained from the interpretation of multispectral satellite images is
becoming a common place. However, for these assessment methods, there
are also a number of difficulties associated with both the sectoral and
biophysical features of different regions. For example, for drylands the
NDVI derived from seasonal composites is more important than the annual
average. (Zolotokrylin, Titkova, and Cherenkova 2020) discovered that
moisture changes in early summer affect the state of vegetation more
significantly than those of the full vegetative season. In (RSF 2020)
report it was demonstrated that for farmland the NDVI assessments is of
little use for irregular crop rotations, since the spectral reflectance
of different crops does not repeat in a multi-year cycle. Even for
regular crop rotations individual recognition algorithms are required
for cycles of different terms. Data on the key crops yield can serve as
an alternative, however, they have significant fluctuations depending on
the weather conditions of specific years and on applied agricultural
techniques and crop varieties, making it difficult to trace the patterns
of their dynamics. For boreal forests, it was determined that the
increase in biomass corresponded to the NDVI dynamics, but the growth
rate decreases in long term, when young forests pass into mature ones.
The productivity of a mature forest, calculated by the NDVI, is usually
less than the same for young forest, although this does not reflect
degradation of forest land. Alternative indicators could be: timber
stock, proportion of forest cover, total growth. However, the biggest
challenge is NDVI interpretation for abandoned farmland overgrown with
weeds, shrubs and trees. The ”greenness” of the surface of such lands is
in the majority of cases higher than on the adjacent farmland, but from
an economic viewpoint these lands are more degraded than the actual
arable land.
Estimation of SOC change is a practically insoluble problem at country
level: global and national datasets do not correspond each other
(National Report 2019). The SOC dynamics in most areas have not been
regularly monitored by field observations since 80-s. For those regions
where it remains possible (only for farmlands), the SOC dynamics rather
than any long-term trends illustrates a short-term perspective of the
economic possibilities of farms for the use of organic fertilizers and
available agricultural techniques (Bezuglova, Nazarenko, and Ilyinskaya
2020).
For the purposes of local assessment of agricultural and forest
enterprises, and in the absence of reliable data, it was demonstrated
that individual software tools cannot yet adequately assess carbon
exchange processes, especially in steppe and forest-steppe landscapes.
However, correctly selected ensembles consisting of simulation models of
C-balance and C-calculators supported by field data, are able to
successfully solve such problems. (Karelin and Tsymbarovich 2022)
determined that if this condition is met, then the net carbon balance of
a certain area can serve as an alternative to SOC. The possibilities of
replacing the SOC layer in the Trend.Earth model with a carbon balance
layer (calculated using Ex-Act tool (FAO, 2021)) for mapping LDN at the
individual farm level have been identified (RSF 2020). However, for
boreal regions, the situation is more complicated: (Ptichnikov et al.
2019) showed that the net carbon balance cannot be applied as an
independent LDN indicator, since it does not consider changes in
biodiversity and primary productivity. The simulation models for natural
forest dynamics through minimization of forest felling at sites with
fireless types of succession accumulating maximum of dead phytomass,
could be more applicable.
Point (iv) is related to using additional and alternative indicators,
which would allow to more accurately indicate degradation trends in
achieving LDN. In this aspect, the soil erosion indicator is the most
important for Russia. It can be interpreted in terms of different
measures. It is asserted that in Russia the total area of eroded lands
and those under the risk of erosion make up more than 50% of
agricultural lands. However, recent studies indicate a decrease in
erosion rate and in the total area of eroded land during the last 30-40
years as a result of abandonment of farmlands and subsequent overgrowth
by natural vegetation. Climate change has resulted in a decrease of the
depth of soil freezing and the flow of spring runoff, which in turn
leads to a decrease in soil erosion. (Tsymbarovich et al. 2020)
demonstrate the possibility of using the soil erosion indicator as an
important complement to the three global LDN indicators both at the
country and local level. At a national and subnational level, soil
erosion neutrality can be interpreted through two indices “Rate of soil
loss” (ton ha-1 yr-1) and “Total soil loss” (ton yr-1) when using a
“one-out-all-out” approach.
(Karelin and Tsymbarovich 2022) demonstrated that soil water erosion
indicators, (in particular length&steepness (LS) factor; the
erodibility potential) can also be used for indirect interpretation of
SOC, nitrogen and water dynamics, as well as in reduced presence and
altered activity of soil microbiota. The authors showed that the
LS-factor calculated on the basis of remote sensing data is applicable
for the evaluation of erosion hazards, as well as for prediction of
carbon content and other related physical, chemical, and biological
indicators of arable Haplic Chernozems on a large spatial scale. The
spectral characteristics of the soil surface obtained from remote
sensing data are less applicable for these purposes.
The indicator of aridity is the next most important indicator for LDN
assessment (Kuderina et al. 2020). At a local level, this can be
interpreted through measures of atmospheric precipitation (including
snow accumulation) and soil moisture in different seasons, which
integrally assess the ability to retain moisture during the growing
season and thereby determine the heterogeneity of the soil cover and
landscape dynamics. A series of drought indicators that ultimately
reflect soil degradation through the availability of moisture and
nutrients to plants include characteristics such as soil water holding
capacity (Shcherba et al. 2016) and compaction (Sorokin and Kust 2018),
(Tsymbarovich et al. 2020). Similar approaches are demonstrated in the
works of Belarusian researchers (Yatsukhno and Davydik 2018). Such
drought indicators are primarily relevant for local LDN estimates.
At a subnational level, an increase in aridity in certain regions, the
emergence and growth of “islands of climatic desertification” can also
serve as an indicator of land destabilization and degradation. Such
impacts of overgrazing were demonstrated in Mongolia and in southeast of
European Russia (Zolotokrylin 2019). The area of local lakes can serve
as an indirect integral indicator of aridity, climate change and the
water availability. For example, the dynamics of feeding mechanisms of
lake systems in south-western Siberia and Transcaucasia indicate either
a growing aridity (Shaporenko and Abdurashidov 2021) or very moderate
and decreased anthropogenic impact (Chernykh et al. 2022). The frequency
of forest and steppe fires (Shinkarenko et al. 2022) can also be
considered as an additional LDN indicator at national and subnational
levels. Importance of drought indicators for monitoring LD processes is
reflected in (National report 2021).
Indicators of soil salinity proposed by (Chernousenko 2021),
agro-depletion (decrease in the content of basic nutrients), alkalinity
(Tsvetnov et al. 2020, Tsvetnov et al. 2021a, Makarov et al. 2021a) can
be considered for LDN at different levels. This requires a
well-established monitoring system, which is currently under development
(Unified system 2021). For forest areas additional LDN indicators were
proposed: level of biological diversity (indicated by species diversity
of trees and shrubs) (Ptichnikov and Martynyuk 2020), and the types of
recovery dynamics (also accompanied by changes in species diversity)
(Ptichnikov et al. 2019).
(Zolotov et al. 2020), (Kust et al. 2021) have emphasized the
possibility of using the state of protected areas (PA, area in the
region and internal stability) to characterize changes in land cover.
For example, the total area of PAs in the Altai region due to the
establishment of new PAs and expansion of existing PAs will increase
from 795.55 thousand hectares or 4.74% of the region’s territory to
1616.6 thousand hectares (9.6% of the region’s territory) and preserve
the most significant natural complexes. A twofold increase in the area
of protected areas will undoubtedly contribute to the achievement of
LDN.
The last point (v) is focused on selecting a set of indicators taking
into consideration regional and local characteristics (Andreeva and
Telnova 2018, Kust et al. 2019, 2018a, Lobkovskiy 2020, Lobkovskiy et
al. 2020a, Lobkovskiy et al. 2018). It was observed that in the current
Russian land registration system and statistics there were no indicators
corresponding to LDN indicators used for monitoring LD, except for in
the case of agricultural lands. In order to integrate land quality
indicators used in Russia into the global system, it has been proposed
to streamline various sectoral data through a common consistent
LDN-based “superstructure” for different sectoral systems. A
conceptual scheme of a hierarchical structure for a system of LD
indicators in Russia has been developed (Lobkovskiy et al. 2022),
including, on one side, dynamic indicators characterizing the
achievement of LDN, and on the other side, state indicators
characterizing the quality of land (in terms of risks and degradation
results) in relation to their respective categories (Figure 6).
Using these approaches, a preliminary analysis and selection of
indicators for those operating in different sectors was carried out, in
order to integrate into the LDN-based global assessment system. The
possible indicators are: area of eroded lands, forest cover, average
yield, etc. Further ways of integrating national and international
systems for assessing LD are as follows: (i) development of a unified
national list of indicators and measures for lands of different
categories and types of land use; (ii) use of common qualitative scales
of indicators while varying the quantitative values of their
measurements (by region, industry, land category, etc.); c) use of land
quality scales comparative to either best (maximization of indicators)
and/or worst (minimization of indicators) sites in a certain
locality/region.