Research interest & output

Research interests

Maps and statistics
Spatial Analyses
Agriculture
Biochar
Environmental impacts of coal mining
Environmental Systems Analysis

Summary of latest research

Mapping urban green structures using object based image analysis of satellite imagery: A review

Review highlights

Advancements in Object-Based Image Analysis (OBIA): The review emphasizes the superiority of OBIA in mapping urban green structures (UGS) over traditional pixel-based methods. OBIA effectively incorporates spectral and spatial information, offering better delineation of complex urban environments and improving classification accuracy.
Segmentation and Classification Techniques: Popular segmentation techniques like multi-resolution segmentation (MRS) and watershed segmentation are explored, alongside classification methods such as random forests, support vector machines (SVM), and convolutional neural networks (CNN). Each method's advantages and limitations are discussed, with examples of their successful application in urban contexts.
Challenges in UGS Mapping: The review identifies key challenges in UGS mapping, including the lack of standardized taxonomies, difficulties in handling heterogeneous urban landscapes, and limitations in data availability, such as high-resolution imagery and seasonal variability.
Future Directions: Integrating OBIA with advanced deep learning methods, leveraging emerging satellite technologies, and developing standardized classification frameworks are highlighted as crucial for enhancing UGS mapping's accuracy and scalability.

Read the complete article here.

Scaling Residual Biomass: Ensuring Future Bioeconomy Outperforms Current Baseline

Research highlights

The study evaluates the environmental impact of utilizing residual biomass to support low-fossil carbon economies.
A single framework integrating spatially-explicit resource flow analysis, consequential life cycle assessment (LCA), and uncertainty analysis is proposed.
The framework is applied to France, setting an environmental threshold for future large-scale bioeconomy strategies.
France's residual biomass baseline is estimated to generate significant climate change, marine eutrophication, and particulate matter formation impacts.
Current use of crop residues and livestock effluents on arable lands represents over 90% of total environmental impacts and uncertainties.
The study suggests comparing future bioeconomy trajectories to a projected baseline that accounts for ongoing mitigation efforts, rather than the current situation.

Data

The modelling was performed in ArcGIS, SimaPro and Python. The output data generated in this study is available online on Mendeley Data. Read the full study here.

Potential of Biochar from Crop Residues as a Climate Solution.

Research highlights

As a carbon dioxide removal (CDR) pathway, biochar from crop residues (CR) offers a promising solution for removing CO2 from the atmosphere. When biochar is added to soils, the carbon can be stored in the soil for decades or even centuries.
By developing a comprehensive high spatial resolution global dataset of CR production, we show that, globally, CRs generate around 2.4 Pg C annually. If 100% of these residues were utilized for making biochar, the maximum theoretical technical potential for biochar production from CRs amounts to 1.0 Pg C per annum (3.7 Pg CO2e per annum).
Even considering limitations on sustainable residue harvesting or competing uses such as livestock feed, the global biochar production potential still amounts to 510 million metric tons per year, with 360 million metric tons of carbon per year remaining sequestered after a century.
The permanence of biochar refers to how long the carbon can be stored in the soil. This varies by region, with a range of 60% to nearly 100% of the initial carbon remaining after 100 years.
Twelve countries have the potential to sequester more than one-fifth of their current emissions as biochar from crop residues, with Bhutan (68%) and India (53%) having the largest potential.
The high-resolution maps of crop residue production and biochar sequestration potential will offer valuable insights and support decision-making related to biochar production and investment in biochar production capacity.

Data

The modelling was performed in Python and R. The output data generated in this study is available online on Harvard Dataverse. Read the full study here.

Why and where to apply biochar in Sweden: exploring different narratives. What was our motivation?

Biochar is known to have positive effects on agronomy and various soil properties (e.g. pH, structure, nutrient, water). Here, we wanted to identify where in Sweden biochar application could be beneficial, and lead to e.g. improvements in soil quality, reduction in nitrogen leaching, or improvements in resilience to drought. Read full study here.

Research highlights

We have designed multiple narratives of biochar use in agricultural soils, reflecting either 1) improving soil quality, 2) improving crop resilience, and 3) reducing nitrogen leaching.
We have compiled spatial data (maps) of Swedish soil and climate such as: soil pH, soil organic matter, soil texture, nitrogen leaching estimates from SMED and low soil moisture estimates from SMHI.
Coupling multi-criteria analysis with expert knowledge on biochar-soil interactions, we provide indications on where to prioritize biochar use for each narrative.
For all the narratives, we found that a significant fraction of the Swedish arable land could potentially benefit from biochar application. Furthermore, arable land that scored high for a given narrative did not necessarily score high in the others, thus indicating that biochar application schemes can be adjusted to various objectives and local needs.
Overall, the results of this study indicate that a priority-based framework for biochar application could help identify the magnitude and location of areas where biochar application could be beneficial.

Data

The modelling was performed in ArcGIS and Microsoft Excel. The output data generated in this study is available online on Zenodo. The uploaded data contains the following:

Raster files for three different biochar prioritization narratives.
High-resolution biochar use indication maps (in JPEG) for different prioritization narratives.

Peer-reviewed publications in chronological order

2025

Karan, S.K., Borchsenius, B.T., Debella-Gilo, M., and Rizzi, J., 2025. Mapping urban green structures using object-based analysis of satellite imagery: A review. Ecological Indicators, 170, p.113027. DOI: https://doi.org/10.1016/j.ecolind.2024.113027

2024

Javourez, U., Karan, S.K., and Hamelin, L., 2024. Residual biomasses at scale: ensuring future bioeconomy uses outperform current baseline. Science of the total environment, 949, p.174481. DOI: https://doi.org/j.scitotenv.2024.174481

2023

Karan, S.K., Woolf, D., Azzi, E.S., Sundberg, C., and Wood, S.A., 2023. Potential for biochar carbon sequestration from crop residues: a global spatially explicit assessment. Global Change Biology Bioenergy, 15(12), 1424-1436. DOI: https://doi.org/10.1111/gcbb.13102
Karan, S.K., Osslund, F., Azzi, E.S., Karltun, E., and Sundberg, C., 2023. A spatial framework for prioritizing biochar application to arable land: A case study for Sweden. Resources, Conservation and Recycling, 189, p.106769. DOI: https://doi.org/10.1016/j.resconrec.2022.106769
Singh, V., Karan, S.K., Singh, C. and Samadder, S.R., 2023. Assessment of the capability of SWAT model to predict surface runoff in open cast coal mining areas. Environmental Science and Pollution Research, 30, 40073 - 40083. DOI: https://doi.org/10.1007/s11356-022-25032-y

2022

Singh, C., Karan, S.K., Sardar, P., and Samadder, S.R., 2022. Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308, p.114639. DOI: https://doi.org/10.1016/j.jenvman.2022.114639
Shen, Z., Tiruta-Barna, L., Karan, S.K., and Hamelin, L., 2022. Simultaneous carbon storage in arable land and anthropogenic products (CSAAP): Demonstrating an integrated concept towards well below 2° C. Resources, Conservation and Recycling, 182, p.106293. DOI: https://doi.org/10.1016/j.resconrec.2022.106293

2021

Karan, S.K., and Hamelin, L., 2021. Crop residues may be a key feedstock to bioeconomy but how reliable are current estimation methods?. Resources, Conservation and Recycling, 164, p.105211. DOI: https://doi.org/10.1016/j.resconrec.2020.105211

2020

Karan, S.K., and Hamelin, L., 2020. Towards local bioeconomy: A stepwise framework for high-resolution spatial quantification of forestry residues. Renewable and Sustainable Energy Reviews, 134, p.110350. DOI: https://doi.org/10.1016/j.rser.2020.110350

2019

Karan, S.K., Ghosh, S. and Samadder, S.R., 2019. Identification of spatially distributed hotspots for soil loss and erosion potential in mining areas of Upper Damodar Basin–India. Catena, 182, p.104144. DOI: https://doi.org/10.1016/j.catena.2019.104144

2018

Karan, S.K., and Samadder, S.R., 2018. Improving accuracy of long-term land-use change in coal mining areas using wavelets and Support Vector Machines. International Journal of Remote Sensing, 39(1), pp.84-100. DOI: https://doi.org/10.1080/01431161.2017.1381355
Karan, S.K., Samadder, S.R. and Singh, V., 2018. Groundwater vulnerability assessment in degraded coal mining areas using the AHP–Modified DRASTIC model. Land degradation & development, 29(8), pp.2351-2365. DOI: https://doi.org/10.1002/ldr.2990
Karan, S.K., and Samadder, S.R., 2018. A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas. Environmental Earth Sciences, 77(20), p.713. DOI: https://doi.org/10.1007/s12665-018-7893-5
Karan, S.K., and Samadder, S.R., 2018. Dual-tree complex wavelet transform-based image enhancement for accurate long-term change assessment in coal mining areas. Geocarto International, 33(10), pp.1084-1094. DOI: https://doi.org/10.1080/10106049.2017.1333534

2017

Karan, S.K., Kumar, A. and Samadder, S.R., 2017. Evaluation of geotechnical properties of overburden dump for better reclamation success in mining areas. Environmental Earth Sciences, 76(22), p.770. DOI: https://doi.org/10.1007/s12665-017-7116-5

2016

Karan, S.K., Samadder, S.R. and Maiti, S.K., 2016. Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. Journal of environmental management, 182, pp.272-283. DOI: https://doi.org/10.1016/j.jenvman.2016.07.070
Karan, S.K., and Samadder, S.R., 2016. Reduction of spatial distribution of risk factors for transportation of contaminants released by coal mining activities. Journal of environmental management, 180, pp.280-290. DOI: https://doi.org/10.1016/j.jenvman.2016.05.042
Karan, S.K., and Samadder, S.R., 2016. Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas. Environmental monitoring and assessment, 188, pp.1-13. DOI: https://doi.org/10.1007/s10661-016-5494-x

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