During Dr. Adnan's undergraduate studies in Forestry and his pursuit of a Master of Science degree in Remote Sensing and Geographical Information Systems, he developed a strong interest in research. He pursued a Doctor of Science in Forestry at the University of Eastern Finland, where he focused on exploring new and innovative solutions to enhance forest attribute assessment through the use of remote sensing. His research interest lies in the field of forest remote sensing where he is dedicated to exploring new and innovative solutions to enhance the forest attributes assessment. His work focuses on forest inventory and modelling using 3-dimensional data such as #LiDAR remote sensing to shed light on important questions and generate new knowledge in the field.

Dr. Adnan's doctoral research pertains to enhancing the assessment of forest structural types using airborne laser scanning (ALS). This is a crucial aspect of long-term forest management and planning, given the growing popularity of predicting forest attributes via ALS and its increasing adoption for forest inventory. He collaborated with researchers from several countries, including Finland, Sweden, Spain, the UK, Australia, China, US and Pakistan to develop simple, robust, and efficient methods for assessing the structural heterogeneity of forests and investigating the role of forest structures in aboveground #biomass modeling. Their developed techniques are particularly suitable for large-scale forest mapping and could contribute significantly to the development of essential biodiversity variables, particularly ecosystem structure. One of the most important studies was to develop and provide a mathematical framework for determining maximum entropy in 3-Dimensional remote sensing datasets based on the Gini coefficient of theoretical continuous distributions, intended to replace the foliage height diversity (FHD). #FHD is an entropy measure in one-dimensional LiDAR vertical profiles and has been the gold standard in the determination of structural complexity of forests as it provides insights into the distribution and density of the forest canopy.

During his postdoctoral research projects, he has focused on improving our understanding of the basal area larger than mean (BALM) and its estimation with regards to different plot sizes, stand densities, and point densities of ALS data. The BALM index provides a measure of the skewness of a tree diameter distribution, where high BALM values correspond to closed canopies, such as those found in mature forests, while lower BALM values describe open canopies, such as those observed in seedlings and seed trees. Additionally, he has explored the prediction of tree diameter distribution, which is a crucial stand variable that characterizes the structure of a stand in terms of growth, biomass, volume, economic value, and other biodiversity characteristics. Tree diameter distribution is also essential in simulating forest development and describing the different tree layers. ALS metrics that describe the various tree components have a close association with the tree diameter distribution. However, the vertical distribution of stand canopy elements affects ALS data, which may further impact the prediction of tree diameter distribution. To reduce the uncertainty and obtain accurate and reliable tree diameter distribution, one promising approach is to stratify a forest stand into different forest structural types (FST) directly from ALS data and predict tree diameter distribution in each FST or to incorporate different FST obtained from the direct stratification of ALS data into the tree diameter distribution modeling. In this context, he has worked on developing new methods for stratifying FSTs based on ALS data, including approaches that utilize both vertical and horizontal metrics to improve the accuracy of FST identification. Furthermore, he has explored the potential of using machine learning techniques to develop models that can accurately predict tree diameter distribution based on ALS data and FST information.