Estimating LDN Baseline
(Kust et al. 2018) noted there are three limitations to establishing the
LDN baseline as an average value of 15 years prior to the adoption of
the SDG Target 15.3, namely: irregular land cover and land productivity
dynamics as a result of drastic socio-economic changes and contradictory
land reforms since early 1990s; large areas of forests with a longer
period of restoration or formation of new plant communities, reaching
hundreds of years; long-term ”background” processes of natural evolution
of landscapes and adaptation to climate change. Since this publication,
a large amount of data has been collected, which confirms these
conclusions on the basis of retrospective analysis of space imagery and
scientific archives, studies of evolution and variability of landscapes
and their components in different geographical locations, including
responses to different natural and anthropogenic impacts. For key sites
in forest and agricultural areas an LDN risks assessment against natural
land potential was carried out, including forecasting vulnerability for
existing land management. At a local level, studies were undertaken to
compare results using different basic timelines, as well as using
“timeless” baseline options, such as background landscapes or crop
yield estimations. (Kust et al. 2019) indicated that the irregular
abandonment and subsequent return of farmlands, which is reflected in
the NDVI changes during this period, cannot be interpreted for definite.
The NDVI time-series of agricultural regions in the southern regions of
European Russia, with actively recovered abandoned farmlands in recent
time, formally indicate “negative change” in land productivity between
2000 and 2015 as compared to natural vegetation growth during the same
period. Studies of LDN dynamics of managed forests in the Russian North
(RSF 2020) estimate that despite NDVI changes in forest land becoming
noticeable within 15-20 years after felling and fires, especially in
pine forests, full restoration of forests occurs only after 100-150
years, and restoration of ecosystem and species diversity requires at
least 200-300 years or more (Figures 7, 8).
Even for grasslands a period of 15-20 years is not sufficient for
baseline considering the full restoration of natural capital and
ecosystem services. In steppe natural ecosystems regeneration takes at
least 50-60 years (Kust et al, 2019), and restoration of natural plant
communities and soil cover in disturbed tundra ecosystems takes 40-100
years (Sizov et al. 2020). Regeneration of once disturbed ecosystems is
often inhibited by other constant and prolonged disturbances, such as
forest and grassland fires, pest invasion, etc. In addition, natural
forests are practically not restored to their original state: forested
areas expand at the expense of pine and soft-wooded broadleaf species,
while old areas of native forest consisting of more valuable coniferous
and hardwood species have declined.
Complex processes of rapid depletion and degradation and subsequent
long-term restoration of terrain ecosystems are reflected in the
practice of forensic land use expertise, for which “significant harm”,
“restoration to baseline” or “full restoration” are among the key
concepts, and for which the LDN approach can be useful. (Kutuzova and
Kust 2018) highlight that ”reclamation” actions to restore the full
scope of ecosystem services and social significance of disturbed
ecosystems are almost never sufficient to return an area to the
“initial state”, due to its uncertainty. In fact, we are talking about
diverse, albeit complementary, goals of restoration and qualities of
disturbed and degraded systems by: (i) restoring initial productivity,
(ii) restoring ecological functions, (iii) restoring ecological balance,
(iv) eliminating negative impact , (v) termination of the negative
processes/functions, (vi) elimination of certain negative
qualities/properties, (vii) acquiring certain positive
qualities/processes/regimes relative to the initial state, (viii)
sustainable state. To reduce the uncertainty in expert decisions,
authors propose using the concept of ”optimal state” instead of ”initial
state” when determining the goal of restoration. This can ensure the
implementation of a complex of ecosystem functions that best meet the
criteria of environmental safety in site-specific conditions. This
proposal fits the concept of “compensation for environmental damage”
as stated in Russian legislation, as well as the definition of
“restoration/rehabilitation” provided in (IPBES 2021). Using the term
“optimal state” for LDN estimating purposes helps avoiding
contradictions in setting a baseline for most cases.
As noted above, natural trends often dominate over human impacts,
therefore land dynamics should consider their background for a baseline
setting. Thus, studies of Holocene evolution of landscapes in southern
Russia demonstrate that many LD processes are predetermined by the
history of landscape development: anthropogenic impacts most likely
trigger degradation processes (salinization, alkalinization, deflation)
on sites where their natural analogs took place in the past, despite
these old processes have been completed and their features being no
longer visible. Human attempts to shift the direction of current
degradation processes at a local scale (for example, irrigation against
salinization) can temporarily decrease their intensity, but often are
not able to reverse them with the technologies available (Andreeva and
Kust 2019). Therefore, attempts to achieve LDN in such cases is doomed
to failure with insufficient investment. Achieving LDN targets at a
regional level occurs more rapidly and at a lower cost, especially where
there are “positive” Holocene trends. On the contrary, the “genetic
memory” of landscapes on negative “degradation” trends complicates
the prospect of successfully achieving LDN.
A baseline can be established by selecting natural ”standards” or their
human-supported simulations imitating natural systems while preserving
ecosystem functions and natural capital (e.g., no-till technology,
adaptive farming, pasture rotation, etc.) This hypothesis was tested in
protected areas pilots in the Central Chernozem biosphere reserve and
the Samarskaya Luka National Park. It was determined (Figure 3) that the
PA territories can be considered as observatories for comparative
assessments of LDN indicators for adjacent territories. For the studied
PAs themselves, the baseline period of 15 years is sufficient to assess
their dynamics in the absence of extreme impacts (Kust et al. 2021).
(Lobkovskiy et al. 2020) drew attention to the fact that, in addition to
the temporal and ecosystem factors of baseline setting, the duration of
baseline period also depends on the spatial and scale factors. For
example, if the land quality of individual farmlands can be restored
within several years, then it takes decades to recover a sustainable
system of agricultural landscapes. If forest ecosystem restoration takes
tens of years, then hundreds of years is needed to restore the integrity
of fragmented intact forests. It was also demonstrated that the real
land quality may not be adequately reflected, since the starting point
of the LDN baseline period may not always coincide with the starting
point of recovery. In this case averaging indicators provides an
incorrect interpretation of LD processes. For example, the LDN estimate
for highly degraded landscapes frequently shows stable or positive
dynamics, although the functioning of such landscapes and the
performance of important ecosystem functions could be unbalanced (RSF
2020). Depending on the LDN targets for different land use types it was
proposed to consider a number of baseline states, contained in Russian
regulatory documents: “reference state” as a zero level of loss of
natural and economic land value, “initial state” as an undisturbed and
non-degraded analogue before the start of impact, “basic state”
reflecting the start of monitoring, “background (for local and regional
purposes)” as a state formed within a complex of natural or human
factors typical for given territory.
(RSF 2020) report concluded that in most cases the approaches to
establishing the LDN baseline for practical application in Russia at
regional and local levels should differ from the 15-year period
recommended for global assessment. The selection of baseline parameters
depends on the LDN monitoring objectives. For example, for forest lands,
these parameters should consider the dynamics of forests assessed on the
basis of typology of time series and changes reflecting typical impacts
(felling, fires, etc.). Considering that the peak of the negative
dynamics of the LDN indicators falls on 0-3 years after clearcutting,
and the sustainable recovery of forest stand and biodiversity begins
10-15 years after felling, the periods for establishing LDN baseline
indicators are estimated as follows: for carbon flux 15 years, for
productivity and biomass stock - up to 100 years, for species diversity
and ecosystem >200 years (Figure 6). For tundra vegetation
impacted by wildfires in North-Western Siberia the minimum period for
baseline is determined by a 60-year (Sizov et al. 2020)cycle.
When developing the topic of baseline establishment, both parameters for
reference points and required time interval are proposed (Lobkovskiy et
al. 2022) to distinct the groups of dynamic and target indicators.
Dynamic indicators are useful to define baseline periods for different
land use models (for example, 10-15 years or others). Target indicators
(or State/Quality indicators) are recommended to determine land quality
using the principle of ”maximizing” parameters, to demonstrate a
sustainable/optimal state in terms of the closeness to (attainability)
the “best” state (e.g., achieving a certain soil quality, yield
potential of crops, allowable forest cut, sustainable functioning of PA
ecosystems, etc.). The principle of “minimizing” parameters is also
applicable, for example to demonstrate the rate of desertification as
“remoteness” from the state of natural deserts or badlands.
“Standardization” approach using national environmental norms and
standards also can be applied, e.g., for soil pollution or fertility,
state of pastures, forest lands, etc.