Assessing Long-Term Land-Cover Dynamics Along the Presnogorkovskaya–Zhanaesil Railway Corridor (1985–2024), Kazakhstan: A Landsat NDVI Buffer-Gradient Approach for Sustainable Rail Infrastructure

2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/su17209278
Data confidence 85 high
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

The development of railway infrastructure is considered a key driver of vegetation cover transformation, particularly in ecologically sensitive regions. This study aims to quantify the spatio-temporal impact of the Presnogorkovskaya–Zhanaesil railway corridor in Northern Kazakhstan over the period 1985–2024. Using Landsat imagery and a gradient method of comparative analysis with a control area, an innovative coefficient B was developed to assess changes across various vegetation categories. Multiple linear regression was used to determine the influence of natural factors, including precipitation, temperature, and elevation. The results indicate that while some categories (e.g., dense vegetation or wet areas) show consistent degradation near the railway, the observed patterns are also modulated by environmental gradients. Compared to the control area, buffer zones along the railway exhibit an increased presence of degraded land types (≈309 km2 vs. ≈72 km2 in the control) and a reduction in productive vegetation cover (over 100 km2 loss), especially in recent years. The study concludes that the proposed method allows for a differentiated understanding of anthropogenic and natural drivers of vegetation change, offering a replicable approach for assessing the impact of linear infrastructure in other geographical contexts.