Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

Abstract

This study addresses the pressing and pertinent issue of establishing a space-based monitoring system to track forest condition changes. Utilizing geographic information system technologies and the Normalized Difference Vegetation Index (NDVI), it examines the evolution and dynamics of pine forest regeneration in the Semey Ormany State Forest Natural Reserve (Zhanasemey branch), Kazakhstan. Landsat-5 satellite data were employed to map and identify terrestrial vegetation changes within the Zhanasemey branch. Satellite imagery for June 2007 and September 2008 were specifically utilized for this investigation. Post-acquisition, the satellite images were subjected to corrections, followed by radiometric adjustments to minimize atmospheric interference. Subsequently, NDVI classifications, an established method for plant classification, were applied to each image. The study focused on a 15-year timespan (2008 to 2022) to monitor changes in vegetation intensity, classified according to NDVI, and to track the reforestation process in areas affected by fire. Analysis of the regeneration of the pine forest post the 2008 fire, conducted at five-year intervals, revealed an increase in the restored forest area from 6,773.49 ha in 2013 to 10,721.07 ha in 2018, and finally to 14,742.54 ha in 2022. In 2008, the fire-impacted area with reduced vegetation spanned 7,015.41 ha, while by 2022, the restored area encompassed 14,742.54 ha, indicating a significant enhancement in the pine forest restoration process. The findings underscore the critical role of remote sensing in forest fire modelling and identifying areas at risk, suggesting its potential utility in large-scale monitoring and management efforts. The study demonstrates the feasibility and efficiency of using space-based technologies to monitor long-term changes in forest conditions and to track reforestation dynamics in fire-affected regions.

Advancements in Satellite Remote Sensing for Predicting Large-Scale Wildfire Risks: An Image Processing Algorithmic Framework

“…Through the analysis and processing of remote sensing images, vegetation conditions and environmental changes can be monitored in a timely manner. This process effectively identifies areas at risk of wildfires, providing a scientific basis for making decisions related to disaster prevention and mitigation [5,6].…”