H.J. Abdullah PhD (Haidi)

I am a Senior Researcher specializing in quantitative remote sensing, with a particular focus on remote sensing spectroscopy and optical Earth observation for biodiversity monitoring, vegetation characterization, and environmental modelling. My research focuses on developing and applying remote sensing approaches to understand vegetation and ecosystem properties across multiple spatial scales, particularly in forest and agricultural environments.

A major part of my research involves the use of imaging spectroscopy, hyperspectral and multispectral remote sensing, and high-precision field measurements to estimate biochemical and biophysical vegetation traits. I am particularly interested in linking field observations with airborne and satellite remote sensing data to model vegetation characteristics, biodiversity patterns, forest condition, and ecosystem responses to environmental change. My research also explores the use of remote sensing for biodiversity monitoring, including the integration of spectral, ecological, and environmental information to better characterize spatial patterns of biodiversity in forest ecosystems.

Another important area of my work is soil modelling and Digital Soil Mapping, where I investigate the use of remote sensing, environmental covariates, and data-driven modelling approaches to characterize and predict soil properties. In addition, I apply quantitative remote sensing and image spectroscopy in precision agriculture, including crop monitoring, plant trait estimation, stress detection, and the development of approaches supporting more sustainable agricultural management.

My methodological expertise includes quantitative remote sensing, optical and hyperspectral spectroscopy, vegetation trait retrieval, statistical and machine-learning modelling, field spectroscopy, geospatial analysis, and the integration of multi-source Earth observation and field datasets. The broader objective of my research is to improve our understanding and monitoring of biodiversity, vegetation condition, soil properties, and ecosystem responses to environmental change, while contributing to more effective and sustainable management of forests, agricultural landscapes, and natural resources.

I obtained my MSc in Geographic Information Systems (GIS) and Remote Sensing from the University of Leicester, United Kingdom, in 2012. From 2007 to 2015, I worked as a Lecturer in the Department of Geography, College of Arts, Salahaddin University, where I was involved in teaching and academic activities in geography, GIS, and remote sensing.

Between 2019 and 2023, I worked in the private sector in Sweden as a Research Manager for a company specializing in precision agriculture. In this role, I led and contributed to research and development activities involving remote sensing, field sensing technologies, image spectroscopy, and data-driven approaches for agricultural monitoring and decision support. This experience provided a strong connection between academic research, technological development, and the practical implementation of remote sensing solutions.

My current research brings together experience from academia and industry, with an emphasis on advancing remote sensing methodologies for biodiversity monitoring, forest ecosystem assessment, soil modelling, vegetation trait retrieval, and precision agriculture.

Expertise

• Agricultural and Biological Sciences

  • Bark Beetle
  • beetle infestations

• Earth and Planetary Sciences

  • Temperate Forest
  • Sentinel-2
  • Landsat 8
  • Remote Sensing
  • Land Surface Temperature
  • Chlorophyll

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Research profiles

Courses academic year 2026/2027

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Current projects

Earth Observation for Biodiversity Modelling

Earth Observation for Biodiversity Modelling (EO4DIVERSITY) addresses important biodiversity science gaps, including forecasting ecological degradation in order to define effective actions to reduce terrestrial biodiversity loss, as well as filling data gaps, knowledge gaps, and gaps in the data-policy link, which may lead to a disconnection of biodiversity data that Earth observation can generate and policy strategies including the EU Biodiversity Strategy for 2030, the UN SDGs and the Convention on Biodiversity (CBD) post-2020 targets. The scientific and policy analyses, pilot demonstrations and agenda-setting that will be done during EO4Diversity will serve as a basis for implementing the EC-ESA Biodiversity Flagship Action in 2023. 

BIOSPACE

The overall aim of the BIOSPACE project is to monitor biodiversity by upscaling field observations and genomic (eDNA) information using next generation satellite remote sensing. A further key aim is the deepening of our scientific understanding of how biodiversity is impacted by anthropogenic pressure as well as by natural environmental gradients.To synthesize global biodiversity on a fine granular scale, the first specific objective is to predict biodiversity over large areas using environmental DNA (eDNA) and next-generation hyperspectral and LiDAR satellite remote sensing. As the richness in ecological function remains mostly invisible to remote sensing, the second objective is that global biodiversity may be monitored through ecosystem function by satellite. This would allow ecosystem function, expressed through foliar chemistry (e.g. N:P or C:N ratios) or through plant traits (expressed in Grimes' theoretical Competitor-Stress tolerator-Ruderal [CSR] strategies) to be parameterized and interpolated in next-generation satellite images using the functional genes from eDNA sequences. The third key objective will be to demonstrate and understand how the many available eDNA sequences interpolated by remote sensing for ecosystem function and taxonomy may be affected by environmental gradients and anthropogenic pressure. 

OBSGESSION

OBSGESSION strives to advance the understanding of direct and indirect drivers of biodiversity change through integrating Earth Observation methods, in-situ observations and state-of-the-art ecological modelling. The project addresses science-policy gaps, supports conservation planning, and helps share knowledge for effective engagement of international and EU stakeholders in ecosystem and biodiversity management.

ECO MOSIAC

Ecosystem Monitoring and Scaling for Climate Change Impacts (ECOMOSAIC)

The ECO-MOSAIC (Ecosystem Monitoring and Scaling for Climate Change Impacts) project develops an open, scalable framework to monitor how climate change alters terrestrial ecosystems across Europe. Building on ESA Climate Change Initiative datasets and other satellite Earth Observation products, the project links Essential Biodiversity Variables and Essential Climate Variables with in-situ monitoring networks and advanced AI models to understand the impact of the climate change extreme event on species distribution. ECO-MOSAIC will generate spatially explicit indicators of ecosystem condition, resilience, exposure, and change at multiple spatial and temporal scales, supporting conservation planning and ambitious climate adaptation policies. The project will co-produce methods and open-source tools for users, ensuring interoperability, transparency, and uptake in policy and practice. Ultimately, ECO-MOSAIC aims to deliver transferable workflow and decision-ready information for scientists, land managers, policy makers worldwide, and other users of ecosystem information across Europe and beyond.

Organisations