Adapting transition matrix
The Trend.Earth platform by default uses expert matrices to diagnose
trends in land cover change (degradation, stable, improvement) and
productivity dynamics (improving, stable, stable but stressed, early
decline, declining). The experience of using default matrices for pilot
territories (administrative units or individual farms) revealed that the
greatest challenge was interpretating transitions between the categories
of tree-covered, grasslands, croplands and artificial lands (Ptichnikov
and Martynyuk 2020), (Kust et al. 2019). Tree-covered lands, as a result
of felling in managed forests, often turn into grasslands and then
gradually recover. The recovery successions can continue for dozens and
hundreds of years and can be further complicated by stages of wetlands
and grasslands. For example, as a result of abandonment (Kust, Andreeva,
and Cowie 2017) of agricultural land in Russia in late 80s - 90s (there
are up to 50 million hectares of abandoned farmland) (National Report
2019), significant areas remain covered with forest vegetation, while in
southern regions they turned into unproductive pastures. For these
previously productive lands such transformations are negative from an
economic viewpoint. In contrary, their return to farmland started in
00-s is characterized by a formal decrease in NDVI, but considered as a
positive process from an economic standpoint. Likewise, many of the
depleted peatlands in drained wetlands are hot spots for wildfire risks
despite possible turning to forests or meadows. Therefore, their
artificial re-wetting should be considered a positive change. Finally,
many residential areas, especially in arid regions, are largely covered
with green vegetation and have a higher productive potential in contrast
to the adjacent natural landscapes. Hence, the reconsidering of
evaluation matrices is needed, but it should also be respective
different regions. For example, for individual farm in the Samara region
it was demonstrated that a locally adapted matrix can change the
assessment results from negative to positive (RSF, 2020). For pilot
sites in the Kaliningrad region the opposite situation was observed:
with the positive assessment by default, the matrix adjustment and use
of additional indicators calculated the PDL to be that of 86.6%!
(Makarov et al. 2021). For the test farm in the Penza region, an
increase of the area of degraded lands was also noted after changing the
assessment matrix (Makarov et al. 2021a). In addition to tuning
transitions between major land types, in many cases it is important to
add specific categories (land subtypes). For example, for bushing areas
in the Samara and Kursk regions (forest-steppe subzone) it was important
to distinguish young (up to 10-15 years old) and mature (over 15-20
years old) forests (IGRAS 2021). For sites in the Volgograd region (dry
steppe subzone) the division into irrigated and non-irrigated arable
land was important. For the semi-desert Trans-Volga region, it was
essential to subdivide pastures by the degree of degradation (Slavko,
Kust G. and Andreeva 2022). Ptichnikov et al. 2019) proposed
establishing categories for lands with different types of forest
regeneration dynamics in the Komi forests (for example, overgrown burned
areas and clearings).
Therefore, the reconstruction of
transition matrix is an important element to identify critical land
cover and land productivity trends. It should be carried out selectively
for various locations at local and provincial levels respecting local
conditions and adding land-specific sub-categories.