INTERPRETATION OF REMOTE SENSING DATA BASED ON THE ANALYSIS OF LANDSCAPE FORMING FACTORS

GeoJournal of Tourism and Geosites, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.30892/gtg.62436-1602
Data confidence 70 medium
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

This article discusses the methodology for assessing the natural and recreational potential of landscapes in the Nura River basin. Based on this assessment, it is possible to make a component-by-component evaluation of the natural and recreational potential (relief, climate, water resources, vegetation, and specially protected natural areas). To calculate the potential, it is proposed to apply the balance method. With the help of the considered method, it is possible to conduct a comparative analysis of the prospective possibilities of the territory and natural and recreational resources. The results obtained in the article were ranked by groups. The interpretation of remote sensing data has become a critical tool in landscape analysis, allowing for the identification and evaluation of various landscape-forming factors such as topography, climate, vegetation, and human activities. This study investigates the use of remote sensing imagery to assess and interpret these factors, with a particular focus on their spatial and temporal relationships in shaping the landscape. By integrating high-resolution satellite data, digital elevation models (DEMs), and land use/land cover maps, we identify key patterns of landscape formation across diverse environments. Advanced image processing and machine learning algorithms are employed to analyze the interactions between natural and anthropogenic forces, revealing insights into landscape evolution and dynamics. The findings suggest that remote sensing not only enhances our understanding of landscape morphology but also provides a robust framework for monitoring land change over time. This approach offers valuable implications for land management, environmental monitoring, and conservation planning, contributing to sustainable landscape development and resilience in the face of climate change.