Assessing the Replication of Land Degradation Neutrality Indicator Trends to Identify Hotspots and Predict Short-Term Dynamics

Aim . Development and testing of a methodology for assessing and forecasting land degradation based on the concept of land degradation neutral balance (land degradation neutral balance) in terms of identifying hot spots and sustainable trends. Methodology . Using the example of the Tula region, using the Trends.Earth tool, QGIS analysed the dynamics of global indicators of LDN Index – land cover and land productivity – for the period 2001–2022 using the moving average method. The key problem identified in the work is the high spatiotemporal variability of direct estimates of the state of land, which makes it difficult to identify stable negative trends. As a solution, an integrated approach was proposed, including: 1) calculation of the dynamics of land conditions and the integral “LDN Index” (the difference between the shares of improved and degraded land), which clearly reflects the overall balance; 2) per-pixel analysis of the "repeatability" of trends (deterioration, stability, improvement) over several 5-year periods to filter out short-term fluctuations. Results . A steady negative trend in the LDN Index for the region was revealed, especially pronounced in the key area with forest and residential areas. Research implication . Based on the analysis of the repeatability of indicators of the neutral balance of land degradation, a classification of territories (“hot spots,” “risk zones,” “growth points,” “sustainably stable”) was proposed for targeted planning of measures in accordance with the LDN hierarchy of practices modified by the authors (restore, reduce, avoid, replicate). The methodology allows us to move from stating states to analysing the stability of trends and probabilistic forecasting, which is an important tool for adaptive land management in a changing climate.