RESULTS OF STUDYING NATURAL FORAGE LANDS IN THE STAVROPOL TERRITORY USING EARTH REMOTE SENSING DATA

Abstract

Traditional geobotanical survey methods currently make it impossible to rapidly assess the condition of natural forage lands due to the slow pace and labor-intensive nature of ground-based (continuous) surveys. The primary advantage of modern approaches, such as the use of Earth remote sensing data, lies in their extensive coverage and high temporal resolution. With the advancement of remote sensing technologies, that is, methods conducted without direct physical contact with the Earth's surface, the time required to obtain information regarding the condition of natural pastures has been reduced to a minimum. Between 2023 and 2025, a study was conducted to explore the feasibility of utilizing various types of satellite imagery to assess the condition and map natural forage lands within the hyper-arid and arid zones of the Stavropol Territory. The research methodology involved the preliminary regional assignment and interpretation of natural forage lands based on satellite imagery; the selection of “key” (test) sites, their quantity and planning of optimal routes prior to fieldwork; field studies of natural forage lands followed by final image interpretation and map compilation. The study established that multispectral satellite scanner imagery serves as an excellent cartographic base, enabling a comprehensive assessment of entire landscapes spanning several hundred square kilometers, thereby allowing for a substantial reduction in the time required for fieldwork. Thanks to the highly informative satellite imagery utilized to study pastures in the hyper-arid and arid zones of the Stavropol Territory, the interpretability of all types of natural forage lands (including pastures situated on salt marshes, sandy soils, and eroded terrain) reached an accuracy level of 90–100% within the imagery. The results of this research demonstrate the significant potential for the widespread application of satellite-derived data in the study and mapping of natural pastures, hayfields, and reclaimed land fund.