Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems

1. Introduction

Forest ecosystems are increasingly threatened by climate change, wildfires, droughts, insect outbreaks, and other natural and anthropogenic disturbances. Effective monitoring and sustainable management of these ecosystems require spatially explicit, accurate, and timely information. In this context, Remote Sensing (RS) and Geographic Information Systems (GIS) have become essential tools for assessing forest dynamics, vegetation health, biodiversity, biomass distribution, and ecosystem resilience across multiple spatial and temporal scales.

Recent advances in satellite observations, UAV technologies, LiDAR systems, machine learning algorithms, and GIS-based spatial analyses have significantly improved our ability to monitor and understand forest ecosystems. The papers included in this Special Issue demonstrate how these modern geospatial approaches contribute to sustainable forest management, ecological conservation, climate adaptation, and environmental decision-making worldwide.

This Special Issue, Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems, was launched to bring together original research reflecting these advances. It invited contributions addressing forest cover and diversity change detection, forest disturbances (fire, drought, insect damage), carbon sequestration, biomass and soil dynamics, dendrochronology integrated with RS data, and forest policy and management applications. All of the techniques and methods included in the Special Issue contribute to a better understanding of forest characteristics, types, management, health, protection, and conservation. Furthermore, the spatial results obtained through these approaches can be effectively used for database development and for improving forest management and decision-making processes. The ten papers included in the Special Issue span a wide geographic range—from the Mediterranean basin and the Western Balkans to Poland, Romania, China, and Chile—and collectively illustrate both the current state of the field and future directions of research.

The study of Durlević et al. [1] conducted in the Šar Mountains concluded that GIS and Remote Sensing techniques are highly effective tools for assessing soil erosion and wildfire susceptibility, providing detailed spatial insights for hazard mapping, forest management, biodiversity protection, and the implementation of preventive environmental measures.

Another study by Mikołajczyk et al. [2] demonstrated that advanced Convolutional Neural Network (CNN) approaches combined with Sentinel-2 multi-seasonal Remote Sensing data can provide highly accurate classification of forest tree species across large geographic regions. Using a novel tabular-to-pseudo-image CNN methodology, the research successfully classified 18 common tree species in Poland with an overall accuracy of 80%, which increased to 93% after post-processing procedures. The study revealed that early vegetation season data (April–May) and spectral bands related to infrared, red-edge, and green wavelengths were the most influential variables for distinguishing tree species. By integrating five years of Sentinel-2 observations, Google Earth Engine processing, and deep learning techniques, the research produced a high-resolution national-scale map of forest species distribution, highlighting the strong potential of AI-based Remote Sensing methods for sustainable forest management, biodiversity monitoring, ecological planning, and large-scale forest inventory applications.

The study of Şenol et al. [3] demonstrated the importance of GIS-based modeling, Remote Sensing, deep learning, and Multi-Criteria Decision Analysis (MCDA) techniques for urban afforestation planning in semi-arid environments. The research integrated Sentinel-2 imagery, Landsat 8 thermal data, SRTM DEM derivatives, AHP weighting methods, and deep learning semantic segmentation models to identify the most suitable afforestation zones in the rapidly urbanizing semi-arid city of Şanlıurfa, Türkiye. The study revealed that ecological and climatic variables such as land surface temperature (LST), slope, solar radiation, aspect, elevation, and flow accumulation are highly important for sustainable afforestation planning in water-scarce urban regions. The results indicated that approximately 192.06 km² of the study area was classified as suitable and 151.33 km² as highly suitable for afforestation, primarily in peri-urban and transitional zones with moderate elevations, stable terrain, and favorable microclimatic conditions. The research also showed that deep learning-based land cover classification achieved high thematic accuracy (83.7% overall accuracy; Kappa = 0.81), confirming the reliability of advanced AI-driven Remote Sensing techniques for ecological planning. Overall, the study highlighted that integrating GIS, artificial intelligence, thermal analysis, and spatial decision-support models can significantly improve climate adaptation strategies, urban heat island mitigation, ecological resilience, and sustainable green infrastructure planning in semi-arid cities.

In the study of Pérez-Romero et al. [4], post-forest fire recovery and the effectiveness of emergency restoration treatments were analyzed using advanced Remote Sensing methods and spectral indices across four Iberian ecoregions. The study applied satellite-based indices such as GCI, NBR, NDVI, NDII, MSI, and EVI2 to monitor vegetation regeneration in treated and untreated burned areas. The results demonstrated that Remote Sensing techniques successfully detected significant differences in post-fire vegetation recovery, with treated areas showing faster ecological regeneration and improved vegetation dynamics compared to untreated sites. Among all spectral indices, the Green Chlorophyll Index (GCI) proved to be the most sensitive indicator for assessing early vegetation recovery after wildfire disturbances. Furthermore, the study emphasized that fire severity, soil properties, climate conditions, topography, and the timing of post-fire interventions strongly influence restoration success, highlighting the importance of rapid and site-specific forest management strategies after wildfires.

Basic GIS can represent an exceptionally strong foundation for improving forest management and long-term forest conservation in Romania. The study of Matei et al. [5] demonstrated that integrating Geographic Information Systems (GIS) with traditional Forest Management Plans enables faster, smarter, and more accurate decision-making through spatial analyses and digital geodatabases. The research highlighted that GIS-based forest management allows detailed monitoring of forest composition, regeneration dynamics, silvicultural treatments, forest productivity, topography, slope, elevation, and biodiversity conservation. The study especially emphasized the importance of thematic mapping and spatial visualization for sustainable forest planning, showing that dominant species such as Norway spruce, silver fir, and beech can be monitored more efficiently through GIS technologies. Furthermore, the research revealed that GIS supports ecological protection and economic sustainability simultaneously by improving timber production planning, identifying vulnerable slopes and protected areas, optimizing regeneration strategies, and facilitating rapid forest condition assessments. The authors concluded that GIS-based spatial analysis modernizes Romanian forestry management, supports the implementation of the INSPIRE Directive, improves biodiversity conservation, and provides an indispensable decision-support tool for sustainable forest management and ecosystem protection in the Apuseni Mountains and broader Romanian forest ecosystems.

One interesting study by Trudić et al. [6] analyzed suitable agroforestry and forest management areas in the Western Balkans using Remote Sensing, UAVs, GIS, LiDAR, satellite imagery, and AI-driven geospatial technologies to improve sustainable land-use planning and ecological resilience. The manuscript showed that Remote Sensing methods such as NDVI, NDRE, multispectral UAV imagery, Sentinel-2 satellite data, and GIS-based modeling were highly effective for monitoring forest health, drought stress, wildfire susceptibility, biodiversity, and post-fire vegetation recovery across Serbia, Montenegro, Bosnia and Herzegovina, North Macedonia, Albania, and Kosovo*. The study highlighted that UAVs equipped with RGB and multispectral cameras successfully detected early pest outbreaks in Serbian forests, while satellite-based analyses identified severe drought stress and wildfire risks in mountainous forest ecosystems. In Montenegro and Bosnia and Herzegovina, GIS and satellite technologies were applied to estimate forest biomass, monitor illegal logging, analyze canopy gaps, and evaluate vegetation recovery after fires. The research also demonstrated that agroforestry projects integrated drone technologies, AI systems, and blockchain tools to optimize crop production, biomass management, viticulture, and sustainable land restoration. Furthermore, the manuscript emphasized that combining GIS, Remote Sensing, LiDAR, UAV photogrammetry, and machine learning significantly improved environmental monitoring, climate adaptation strategies, ecological restoration, and precision forestry management. However, the study also identified major limitations, including fragmented UAV legislation, insufficient technical infrastructure, lack of trained personnel, weak institutional cooperation, and dependence on short-term international funding, which currently slow the broader implementation of advanced geospatial technologies in the Western Balkans forestry and agroforestry sector.

Regeneration of coniferous forests and better sustainable forest management can be significantly improved by using advanced GIS and Remote Sensing methods. One important study by Kaplan and Özbey [7] on Pinus brutia (Turkish red pine) demonstrated that satellite imagery, UAV observations, climatic datasets, GIS-based spatial analysis, NDVI, land surface temperature (LST), vapor pressure deficit (VPD), and Google Earth Engine were highly effective for identifying areas vulnerable to regeneration failure under increasing climate stress. The research revealed that rising temperatures, prolonged drought, high atmospheric dryness, and increasing water deficits strongly reduced seed germination and early seedling survival in Mediterranean conifer forests. By integrating climatic variables, terrain characteristics, thermal Remote Sensing, and vegetation indices into GIS models, the authors successfully produced environmental screening maps that identified high-risk areas for regeneration failure. These advanced geospatial methods enabled the detection of “analog degradation zones”, where environmental conditions closely resembled already degraded forest sites. The study emphasized that Remote Sensing and GIS technologies provide an effective early-warning system for forest managers because they allow rapid monitoring of drought stress, soil moisture deficits, vegetation recovery, and climate-related forest decline at large spatial scales. Furthermore, the research showed that management practices such as adjusted planting strategies, smaller clear-cut areas, assisted regeneration, shading techniques, and targeted site preparation could substantially improve coniferous forest regeneration when guided by GIS and Remote Sensing analyses. Overall, the study confirmed that combining Remote Sensing, climate datasets, satellite imagery, GIS spatial modeling, and field validation offers a powerful and cost-effective approach for improving regeneration success, climate resilience, and long-term sustainable management of Mediterranean conifer forests under future climate change scenarios.

Topographic analyses of forests using precise Digital Elevation Models (DEM), climatic datasets, and advanced GIS and Remote Sensing techniques may provide highly satisfactory and reliable results for understanding vegetation dynamics, canopy moisture, and forest ecosystem functioning. One important study by Garrido-Leiva et al. [8] conducted in Mediterranean ravine ecosystems of central Chile demonstrated that high-resolution ALOS PALSAR DEM data, combined with Sentinel-2 satellite imagery, NDVI, NDMI, and climatic-topographic indices such as the Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH), successfully identified microenvironmental variations controlling vegetation vigor and moisture conditions. The research showed that concave terrain positions, less rugged areas, and thermally protected slopes exhibited higher vegetation moisture and photosynthetic activity, while exposed and rugged topographic sectors showed increased thermal stress and lower canopy water content. Furthermore, the integration of DEM-derived variables with climatic and spectral datasets enabled the detection of microclimatic refugia, drought-sensitive zones, and areas vulnerable to water stress. The study confirmed that advanced GIS, precise DEM analyses, Sentinel-2 time series, and non-linear statistical models represent powerful tools for forest monitoring, ecological assessment, sustainable forest management, and climate adaptation planning in Mediterranean and mountainous ecosystems.

The very long-term analyses of forest ecosystems and vegetation dynamics can be significantly improved by using advanced spectral analyses, machine learning models, and Remote Sensing techniques. One important study by He et al. [9] demonstrated that the integration of Landsat-derived Enhanced Vegetation Index (EVI), tree-ring chronologies, climatic variables, and advanced machine learning approaches such as Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN) enabled the successful reconstruction of annual vegetation dynamics from 1850 to 1985 in coniferous forest ecosystems of China. The research confirmed that advanced spectral analyses can effectively detect long-term vegetation changes, drought impacts, forest succession, and climate-driven ecosystem shifts. In particular, the Random Forest model achieved the highest accuracy (adjusted R² = 0.90), proving that spectral vegetation indices combined with machine learning provide highly reliable results for analyzing forest health, productivity, and ecological disturbances over long historical periods. Furthermore, the study highlighted that climatic factors, especially temperature and precipitation, strongly influence EVI dynamics, while DEM-derived topographic analyses improved the interpretation of vegetation spatial variability in mountainous landscapes.

These findings demonstrate that advanced spectral analyses, GIS, Remote Sensing, precise DEMs, and machine learning approaches are powerful tools for understanding long-term forest ecosystem evolution, drought sensitivity, climate adaptation, and sustainable forest management [1,2,3,4,5,6,7,8,9].

2. Discussion

The studies included in this Special Issue collectively demonstrate the growing importance of Remote Sensing, Geographic Information Systems (GIS), and artificial intelligence in forest ecosystem monitoring and management. Despite being conducted across different geographic regions and environmental conditions, the published papers consistently highlight the value of geospatial technologies for understanding forest dynamics, biodiversity, ecosystem resilience, and sustainable forest management [1,2,3,4,5,6,7,8,9].

Several contributions emphasized the importance of satellite imagery, UAV observations, and advanced Remote Sensing techniques for monitoring forest structure, vegetation dynamics, species distribution, and post-disturbance recovery. Sentinel-2, Landsat, LiDAR, and other geospatial datasets provided highly detailed information that improved the assessment of forest conditions and ecological processes across multiple spatial scales.

Artificial intelligence and machine learning approaches also emerged as important methodological advances within the Special Issue. Deep learning models, Convolutional Neural Networks (CNN), Random Forest (RF), and other classification techniques demonstrated strong capabilities for tree-species mapping, vegetation analysis, ecological assessment, and environmental monitoring. These studies confirmed that AI-driven approaches can significantly improve the accuracy and efficiency of forest ecosystem analyses and support evidence-based management strategies.

Several studies highlighted the value of GIS-based spatial modeling and decision-support frameworks for addressing practical forestry challenges. Multi-Criteria Decision Analysis (MCDA), suitability modeling, spatial assessment, and topographic analyses were successfully applied to afforestation planning, forest vulnerability assessment, wildfire susceptibility evaluation, and sustainable resource management. These approaches enable the integration of environmental, climatic, and landscape variables into comprehensive analytical frameworks that support forest planning and decision-making processes [10,11,12,13,14,15,16,17].

Climate change and ecosystem resilience represent another major theme emerging from the papers included in this Special Issue. The published studies demonstrated that geospatial technologies can effectively identify drought-sensitive areas, wildfire-prone regions, vegetation degradation patterns, and post-fire recovery processes. Such analyses provide valuable information for climate adaptation planning, ecological restoration, and long-term sustainable forest management [1,4,7,8,9,13,15,18].

Topographic and environmental analyses also played an important role in several contributions. The integration of Digital Elevation Models (DEM), climatic datasets, and spectral vegetation indices improved the understanding of vegetation vigor, moisture conditions, biomass distribution, and ecological variability. These findings further demonstrate the importance of combining topographic, climatic, and Remote Sensing data in forest ecosystem assessments [4,8,9].

Overall, the studies published in this Special Issue confirm that the integration of GIS, Remote Sensing, UAV technologies, LiDAR datasets, machine learning algorithms, and spatial modeling approaches provides a powerful scientific framework for modern forest monitoring and sustainable forest management. The collective findings of these contributions illustrate how advanced geospatial technologies can improve biodiversity conservation, ecological resilience, afforestation planning, post-disturbance recovery assessment, and climate adaptation strategies. Future research should continue to explore the integration of hyperspectral data, advanced artificial intelligence models, cloud-computing platforms, and climate prediction systems to further enhance forest ecosystem monitoring and environmental sustainability.

3. Conclusions

The contributions included in this Special Issue demonstrate that advanced GIS, Remote Sensing, machine learning, and artificial intelligence techniques represent highly effective tools for modern forest ecosystem monitoring and sustainable forest management. Collectively, the published papers show that the integration of satellite imagery, UAV observations, LiDAR datasets, Digital Elevation Models (DEM), vegetation indices, and spatial modeling approaches significantly improves the assessment of forest health, biodiversity, biomass distribution, wildfire susceptibility, drought stress, and long-term vegetation dynamics.

The studies published in this Special Issue further highlight the importance of machine learning and artificial intelligence methods, including Random Forest, Convolutional Neural Networks, and other advanced classification approaches, for forest species mapping, afforestation planning, vegetation monitoring, and ecological forecasting. Several contributions also demonstrate the effectiveness of GIS-based spatial analyses and multi-criteria decision-support frameworks for addressing practical challenges related to forest conservation, restoration, and sustainable resource management.

In addition, the papers emphasize the growing role of geospatial technologies in understanding climate-related impacts on forest ecosystems, including drought stress, wildfire risk, regeneration processes, and post-disturbance recovery. The integration of Remote Sensing observations with environmental, climatic, and topographic datasets provides valuable information for improving ecological resilience and supporting adaptive forest management strategies.

Overall, the articles included in this Special Issue confirm that the combination of GIS, Remote Sensing, artificial intelligence, and spatial modeling offers a powerful framework for advancing forest ecosystem research and management. Future developments should focus on the integration of hyperspectral data, LiDAR technologies, advanced AI algorithms, cloud-computing platforms, and climate prediction models to further enhance forest monitoring capabilities and support environmental sustainability at regional and global scales.