Publications

Entropy in Tropical Dry Ecosystems

Published in CRC Press, 2025

The concept of entropy has its roots in the development of thermodynamics, a branch of physics that deals with heat, work, and energy transformations. The journey began in the early 19th century with the work of Sadi Carnot, his work on the efficiency of steam engines laid the foundation for understanding energy transformations and the limitations of converting heat into work. In the mid-19th century, Rudolf Clausius formalized the principles of thermodynamics. In 1850, Clausius introduced the concept of the second law of thermodynamics, stating that the entropy of the universe tends to increase in all natural processes. He coined the term “entropy” in 1865 to describe the measure of disorder or randomness in a system, and formulated the mathematical expression of the second law, which asserts that entropy in an isolated system always increases over time.

Recommended citation: Mashhadi, N., & Sanchez-Azofeifa, A. Entropy in Tropical Dry Ecosystems. In Remote Sensing of Tropical Dry Forests in the Americas (pp. 73-86). CRC Press.
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Evaluating Lorenz entropy for tropical forest discrimination using GEDI and supervised machine learning approach

Published in Ecological Indicators, 2025

Analyzing the vertical structural complexity of tropical forests is essential for understanding their ecological functions and biodiversity. Given this significance, an indicator that quantifies entropy, representing heterogeneity and disorder in structural complexity, plays a significant role in forest ecological studies. This study explored the potential of the Lorenz-entropy (LE) index as an innovative metric for classifying tropical forest types. Using spaceborne LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI) mission from April 2019 to March 2023, we integrated the LE index with supervised machine learning algorithms to evaluate its effectiveness in distinguishing vertical structural complexity across the three tropical forest ecosystems. In addition to the LE index, forest structural variables such as Above Ground Biomass Density (AGBD), Plant Area Index (PAI), and Relative Height 98 (RH98) were …

Recommended citation: Mashhadi, N., & Sanchez-Azofeifa, A. (2025). Evaluating Lorenz entropy for tropical forest discrimination using GEDI and supervised machine learning approach. Ecological Indicators, 173, 113374.
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LiDAR-derived Lorenz-entropy metric for vertical structural complexity: A comparative study of tropical dry and moist forests

Published in Remote Sensing of Environment, 2025

This study introduces an Entropy-based index: the Lorenz-entropy (LE) index, which we have developed by integrating Light Detection And Ranging (LiDAR), econometrics, and forest ecology. The main goal of the LE is to bridge the gap between theoretical entropy concepts and their practical applications in monitoring vertical structural complexity of tropical forest ecosystems. The LE index quantifies entropy by analyzing Relative Height (RH) metrics (representing a one-dimensional (1D) canopy structure metric) distributions from full-waveform LiDAR across successional stages in a tropical dry forest (TDF) and a tropical rainforest. To validate the LE trends derived from LiDAR, we extended the analysis using inventory-based two-dimensional (2D) and three-dimensional (3D) metrics, specifically basal area and biomass. The consistency of trends between the 1D LiDAR-derived LE and the inventory-based 2D and …

Recommended citation: Mashhadi, N., Sanchez-Azofeifa, A., & Valbuena, R. (2025). LiDAR-derived Lorenz-entropy metric for vertical structural complexity: A comparative study of tropical dry and moist forests. Remote Sensing of Environment, 318, 114545.
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Determination of forest burn scar and burn severity from free satellite images: A comparative evaluation of spectral indices and machine learning classifiers

Published in International Journal of Environment and Geoinformatics, 2021

Remote sensing data indicates a considerable ability to map post-forest fire destructed areas and burned severity. In this research, the ability of spectral indices, which are difference Normalized Burned Ratio (dNBR), relative differenced Normalized Burn Ratio (RdNBR), Relativized Burn Ratio (RBR), and difference Normalized Vegetation Index (dNDVI), in mapping burn severity was investigated. The research was conducted with free access moderate to high-resolution Landsat 8 and Sentinel 2 satellite images for two forest fires cases that occurred in Izmir and Antalya provinces of Turkey.

Recommended citation: Mashhadi, N., & Alganci, U. (2021). Determination of forest burn scar and burn severity from free satellite images: A comparative evaluation of spectral indices and machine learning classifiers. International Journal of Environment and Geoinformatics, 8(4), 488-497.
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Modelling Entropy in Tropical Forest Ecosystems

Published in American Geophysical Union (AGU), 2023

Recommended citation: Mashhadi, N., Sanchez-Azofeifa, G. A., & Valbuena, R. (2023). Modelling Entropy in Tropical Forest Ecosystems. AGU23.
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