LDN assessment at national, subnational and local level
First studies of this cycle were devoted to the results from assessing
individual territories of the Russian Federation using the basic
Trend.Earth Quantum-GIS module (TE, 2018) or author’s analogs using
three global LDN indicators: dynamics of land cover, productivity and
soil organic carbon stock (SOC). The results were quite versatile and
contradictory. (Trifonova et al., 2021, 2020) focus on the importance of
the watershed approach for identifying the boundaries of territories
subject to LDN assessment. The main argument is that the greatest
homogeneity of biophysical and geochemical conditions is conserved
within river basins, which in turn determines the similarity of
ecosystems where the effectiveness of LDN mitigation hierarchy of
avoid-reduce-reverse (Orr et al. 2017) can be considered:. However,
despite the fact that this approach is valid from an environmental
perspective, (Andreeva and Kust 2020), (Kust et al. 2019, Kust et al.
2018b) - demonstrated that for national and subnational reporting
purposes, it is more efficient to use administrative units, due to a
system of relevant statistical data collected by national agencies.
A preliminary assessment for pilot regions of the European part of
Russia (Kust, Andreeva, and Lobkovskiy 2020) demonstrated that the LDN
concept and the Trend.Earth platform can be considered as a basis for
monitoring degradation processes at sub-national level, thus
facilitating effective SLM decisions. At the same time, almost all such
studies noted that specific data obtained from global databases differ
to various extents from ground and statistical data. Also, the
Trend.Earth platform, which uses a relatively low data resolution
(250-300 m per pixel) resulted problematic for local assessment due to
the possible mismatch of boundaries and erroneous interpretation of
satellite imagery (Figure 2). Additional studies displayed that the use
of Landsat satellite imagery and neural networks for image
interpretation, as well as the clarification of land types list, provide
an opportunity to increase the mapping accuracy by at least 70 times and
simultaneously correct the interpretation of positive and negative
dynamics for a specific site (Kust et al. 2021). Although this approach
is relatively laborious, it becomes useful for local LDN estimates
(Figures 3, 4) (RSF 2020).
National level comparative calculations of the total area of degraded
land (SDG indicator 15.3.1) using national statistical data (by summing
an area of ‘deteriorated’ land calculated using sectoral methods for
different types of land (forest, agricultural, industrial land, etc.))
and Trend.Earth, demonstrated that results obtained by the first method
(6.1% of the total land area) are significantly less than obtained
through Trend.Earth (12.3%) (Andreeva and Kust 2020). This clearly
indicates a need for complex harmonizing between the national and global
systems since they use different platforms and calculation methods,
indicators and systems for obtaining and monitoring data.
Nevertheless, the first experience of comparative analysis for all 85
Russian federal subjects (administrative units) using Trends.Earth
(Andreeva and Kust 2020) demonstrates the high efficiency of this method
particularly for comparative studies and the identification of ”hot
spots”. For a country like Russia with a high diversity of biophysical
and climatic conditions and a variety of economic activities, conducting
an overall country assessment of proportion of degraded lands (PDL),
with Trend.Earth as a baseline for further calculations (12.3%) does
not make sense, since for regions it varies from 67% to less than 1%.
In this respect considering the trade-off activities within the whole
country as a compensatory scheme for LD does not correspond the LDN
principle. Eight federal subjects, where the PDL is less than 2% and
the proportion of stable and improved land is high, can be considered as
having achieved LDN. At the same time, in another 8 federal subjects
with a high concentration of agricultural lands the PDL is very high.
From this perspective, a country assessment covering large territories
in general may be quite satisfactory and tends to zero in the long term.
However, one of the key requirements for LDN is not met: “counter
balancing should occur only within individual land types, distinguished
by land potential, to ensure “like-for-like” exchanges” (Orr et al.
2017).
(Kust, Andreeva, and Lobkovskiy 2020) proposed using an “LDN Index”
for the overall comparative assessment of regions. This index indicates
the difference between improved and degraded lands as an intermediate
stage in achieving LDN and demonstrates the effectiveness of land policy
and practice within a certain territory. (Andreeva and Kust 2020)
calculated the Index for all administrative units of the Russian
Federation: for Russia in total the LDN Index averages 25.7%, and for
individual regions it varies from –57% to +96% (Figure 5). According
to this Index, the “hottest” spots in Russia are the territories of
the South and Volga federal districts.