In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature.

Arkansas’ subtropical climate nurtures extensive forested regions, particularly within the Ozark-St. Francis and Ouachita National Forests. Despite this, the state lacks an up-to-date high-resolution map detailing the distribution of tree species within its forests. This study harnesses the power of machine learning, specifically the Random Forest (RF), Gradient Tree Boosting (GTB), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) classifiers, within the Google Earth Engine (GEE) framework. These classifiers are applied to classify tree species distribution in Arkansas’ forests by integrating data from various sources, including Sentinel-1/-2, Landsat-8, and the National Agriculture Imagery Program (NAIP). The study evaluates the classification accuracy of single-sensor images against fused composites, revealing that the fused Landsat-8 and Sentinel-1 data achieve the highest validation accuracy at 0.8875, followed by single-sensor Sentinel-1 and Landsat-8, which yield validation accuracies of 0.8863 and 0.8859, respectively. Among the classifiers, RF demonstrates the highest accuracy, followed by GTB, K-NN, and SVM when applied to fused Landsat-8 and Sentinel-1 images. This study incorporates the Shapley Additive Explanations (SHAP) to elucidate feature importance, and results show that elevation is the most influential variable across all forest classes, followed by vegetation indices. The study introduces a weighted ensemble method, resulting in a remarkably accurate tree species distribution map with an accuracy score of 0.9772. This research demonstrates the effectiveness of combining machine learning and satellite image fusion with explainable AI (XAI) and GEE to enhance tree species classification and produce a precise, high-resolution regional tree cover map.

Multi-source remote sensing fusion and machine learning are effective tools for forest monitoring. This study aimed to analyze various fusion techniques, their application with machine learning algorithms, and their assessment in estimating forest type and aboveground biomass (AGB). A keyword search across Web of Science, Science Direct, and Google Scholar yielded 920 articles. After rigorous screening, 72 relevant articles were analyzed. Results showed a growing trend in optical and radar fusion, with notable use of hyperspectral images, LiDAR, and field measurements in fusion-based forest monitoring. Machine learning algorithms, particularly Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), leverage features from fused sources, with proper variable selection enhancing accuracy. Standard evaluation metrics include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Overall Accuracy (OA), User’s Accuracy (UA), Producer’s Accuracy (PA), confusion matrix, and Kappa coefficient. This review provides a comprehensive overview of prevalent techniques, data sources, and evaluation metrics by synthesizing current research and highlighting data fusion’s potential to improve forest monitoring accuracy. The study underscores the importance of spectral, topographic, textural, and environmental variables, sensor frequency, and key research gaps for standardized evaluation protocols and exploration of multi-temporal fusion for dynamic forest change monitoring.

Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the “natural state”. About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981–2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained R-squared values are significant, with 0.99 for training the model and 0.92 for the validation. The results show that the Ouachita National Forest and the Ozark Forest, in west-central and west Arkansas, respectively, have the highest susceptibility to wildfires. The southern part of Arkansas has low-to-moderate fire susceptibility, while the eastern part of the state has the lowest fire susceptibility. These new results for Arkansas demonstrate the potency of remote-sensing-based random forest in predicting fire susceptibility at the state level that can be adapted to study fires in other states and help with fire preparedness to reduce loss and save the precious environment.

Satellite images have been used historically to measure and monitor fluctuations in the surface water reservoirs. This study integrates remote sensing and Geographic Information System (GIS) technologies to investigate the impact of drought on 10 selected surface water reservoirs in San Angelo and Dallas, Texas. Oscillations in summer and winter months throughout the 2005–2016 period were assessed using multispectral images from Landsat-5, −7, and −8, and changes in the reservoirs were characterized and correlated against local climate data of each reservoir. For quantitative comparisons of the time-series measurements, a robust density slicing approach was employed to classify the range of values of the raster cells in the near-infrared band of Landsat images for each lake into three desired classes (deep water, shallow water, and dry area) based on the natural breaks inherent in the dataset. Statistical analysis shows that the overall accuracy of the classification is about 94%, which demonstrates the efficiency of the density slicer to accurately estimate surface water area changes from an individual Landsat band. Shrinkage in the surface water area over the study period reveals the concrete impact that the drought along with other factors have on the 10 selected lakes. The San Angelo lakes located in west central Texas experienced a nearly consistent pattern of change during most of the study period; whereas the Dallas lakes in northeast Texas followed the oscillating pattern of drought and correlated closely to the local conditions. Shockingly, the extreme drought caused complete vanishing of several lakes, and consequently Texas had to remove them from its recreational plans. Our new findings can certainly help with the water resource management in Texas and our study approach can be adapted for monitoring lake oscillations in other areas across the world. This geospatial study demonstrates the societal benefits from incorporating remote sensing and GIS in investigating geo-environmental problems associated with severe climate changes.

Land use and land cover (LULC) changes significantly shape urban environments and directly impact ecological and socioeconomic systems. This study aims to explore these interconnections by employing the Cellular Automata–Markov (CA–Markov) model to assess and predict LULC dynamics in Arkansas. Historical LULC datasets from 2001 to 2021, obtained from the National Land Cover Database, were simplified from 11 into 5 classes to facilitate analysis and effectively map transitions. The model was validated by predicting LULC for 2016 and 2021 and comparing the predictions with the real maps, achieving an overall accuracy of approximately 91.9%, using model validation metrics, including precision, recall, F1-score, and Kappa Coefficient, and highlighting the strength of the predictions. Predictions for 2026 and 2031 reveal a continuous increase in built-up areas at the expense of vegetation cover, underscoring ongoing urbanization trends. Specifically, built-up areas are projected to increase from 28.39% in 2021 to 30.15% in 2031, while vegetation cover is expected to decline from 49.30% to 47.48%. This research demonstrates the utility of the CA–Markov model in simulating urban growth patterns and provides actionable insights into sustainable urban planning and land management strategies.