Md Saifuzzaman, PhD

Department of Biology, Faculty of Science, McGill University.

Dr. Saifuzzaman (Saif) is a Research Associate in the Department of Biology at McGill University, where he develops Earth observation, GeoAI, and modelling approaches for biodiversity monitoring and ecosystem service assessment within the Canadian Biodiversity Observation Network (CAN BON). His research integrates satellite and UAV observations, field and ecological data, big-data analytics, and machine and deep learning to investigate soil and water dynamics, ecosystem structure and function, biodiversity, and responses to environmental and global-change drivers. His research interests span soil and ecosystem science, natural resource assessment, GIScience, environmental remote sensing, and biodiversity conservation. He also teaches at McGill and Concordia universities.
He earned a PhD specializing in GIScience and Earth Observation from McGill University, where he integrated machine learning and AI with proximal and remote sensing to investigate soil dynamics in Canadian production systems, followed by a postdoctoral fellowship in McGill’s Faculty of Agricultural and Environmental Sciences. Previously, he held faculty positions in Physical Geography and Environmental Remote Sensing at Jahangirnagar University, Bangladesh. He has supervised and mentored students and contributes to the scientific community through professional societies and peer review.

RESEARCH HIGHLIGHTS

Biodiversity from Space

The Hudson–James Bay Lowlands (HJBL) are among the world’s largest peatland complexes, providing critical ecosystem services and culturally significant landscapes for the Cree Nation. Despite their ecological importance, biodiversity patterns and ecosystem dynamics remain poorly understood due to limited monitoring and fragmented data. My research integrates ecological observations, Earth observation data, biodiversity records, citizen science, and field knowledge to improve understanding of species distributions and ecosystem processes in Canadian peatlands. I am developing a GeoAI-enabled monitoring framework that combines optical and microwave remote sensing with biodiversity data to derive ecosystem structure and function indicators, assess ecosystem condition, and track ecosystem-state transitions for operational biodiversity monitoring and ecosystem service assessment, supporting Canadian northern ecosystem monitoring priorities and global biodiversity targets.🌿🌎

Ecosystem Processes and Carbon Uptake using remote sensing and climate data

This research examines the spatiotemporal dynamics of carbon, water, and energy fluxes to better understand ecosystem processes. By integrating remote sensing observations and climate data, it evaluates ecosystem productivity, evapotranspiration, and radiation balance, revealing how climate variability influences ecosystem functioning across landscapes. 🌍🛰️🌱

Soil Sensing and Terrain Analysis for Field Variability

This research integrates proximal soil sensing and high-resolution topographic derivatives to characterize spatial variability in agricultural fields. Apparent soil electrical conductivity data combined with terrain metrics (e.g., slope and topographic wetness index) are used in predictive models to estimate key soil properties, including organic matter, phosphorus, and cation exchange capacity, improving soil assessment and management of agricultural ecosystems. 🌱🛰️

Probabilistic Models for Vegetation Winter Persistence

This research develops probabilistic models to assess vegetation winter persistence by integrating weather conditions, soil characteristics, and management factors. Using field observations and environmental data, the approach evaluates risks of winter damage and variability in plant survival, supporting improved monitoring and management decisions for resilient agricultural and ecological systems. 🌱📊