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Johannes Reiche

Associate Professor

Wageningen University & Research
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Netherlands

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Johannes Reiche is an Associate Professor at Wageningen University & Research in the Netherlands. His research areas include remote sensing, forest monitoring, and environmental assessment, as evidenced by his recent publications on forest biomass mapping, tropical forest degradation, and the application of satellite data in understanding land use changes. He is actively involved in developing methodologies to enhance forest disturbance detection and improve the timeliness of environmental alerts.

Positions (1)

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Johannes Reiche

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Wageningen University & Research

Postdoc in Long-Wavelength Radar for Forest Disturbance Monitoring at Wageningen University & Research

Postdoctoral opportunity at Wageningen University & Research in long-wavelength radar for forest disturbance monitoring . This 3-year postdoc is hosted by the radar remote sensing team within the Laboratory of Geo-information Science and Remote Sensing (GRS) at Wageningen University & Research, Netherlands. The project focuses on developing and validating radar methods for forest disturbance detection and near-real-time alerting using next-generation long-wavelength satellite missions, including NISAR , BIOMASS , and preparation for ROSE-L . The research is centered on radar remote sensing , Earth observation , forest disturbance monitoring , time-series analysis , and scalable processing of large satellite datasets. The work includes preprocessing multi-frequency radar data from C-, L-, and P-band sensors, studying forest disturbance regimes across Europe, and supporting the operational RADD near-real-time alert system . Target disturbance types include harvest, fire, wind damage, bark beetle infestation, and deforestation. The position is embedded in an international and interdisciplinary environment. You will collaborate with Prof. Cornelius Senf at TU Munich on European forest dynamics and with Dr. Maciej Soja at Wageningen Environmental Research. The team is led by Dr. Johannes Reiche at WUR. Eligibility highlights: a PhD in Remote Sensing, Data Science, or a related field; strong radar remote sensing background; experience with radar satellite data and large Earth observation datasets; strong Python skills; experience with forest or land change dynamics; excellent English (C1). Funding and conditions: this is a salaried postdoc position with a gross monthly salary of €3,546–€5,538 for 38 hours/week, plus standard Dutch university benefits. The initial contract is for one year and may be extended up to the full 3-year project duration. Application deadline: 12 June 2026. Applications must be submitted via the WUR vacancy page.

4 months ago

Articles (17)

Critical Assessment of Cocoa Classification with Limited Reference Data: A Study in Côte d’Ivoire and Ghana Using Sentinel-2 and Random Forest Model

Cocoa is the economic backbone of Côte d’Ivoire and Ghana, making them the leading cocoa-producing countries in the world. However, cocoa farming has been a major driver of deforestation and landscape degradation in West Africa. Various stakeholders are striving for a zero-deforestation cocoa sector by implementing sustainable farming strategies and a more transparent supply chain. In the context of tracking cocoa sources and contributing to cocoa-driven deforestation monitoring, the demand for accurate and up-to-date maps of cocoa plantations is increasing. Yet, access to limited reference data and imperfect data quality can impose challenges in producing reliable maps. This study classified full-sun-cocoa-growing areas using limited reference data relative to the large and heterogeneous study areas in Côte d’Ivoire and Ghana. A Sentinel-2 composite image of 2021 was generated to train a random forest model. We undertook reference data refinement, selection of the most important handcrafted features and data sampling to ensure spatial independence. After refining the quality of the reference data and despite their size reduction, the random forest performance was improved, achieving an overall accuracy of 85.1 ± 2.0% and an F1 score of 84.6 ± 2.4% (mean ± one standard deviation from ten bootstrapping iterations). Emphasis was given to the qualitative visual assessment of the map using very high-resolution images, which revealed cases of strong and weak generalisation capacity of the random forest. Further insight was gained from the comparative analysis of our map with two previous cocoa classification studies. Implications of the use of cocoa maps for reporting were discussed.

Year:

2024

Collaborators (9)

Diego Marcos

Inria Centre de Recherche Sophia Antipolis Méditerranée

FRANCE

Marielos Peña-Claros

Professor at Wageningen University & Research

Wageningen University & Research

NETHERLANDS

R. Valbuena

Bangor University

UNITED KINGDOM

Xiaocheng Shang

University of Birmingham

UNITED KINGDOM

Karimon Nesha

Wageningen University & Research

NETHERLANDS

Jan Verbesselt

Wageningen University & Research

NETHERLANDS

Casey Ryan

Senior Lecturer in Ecosystem Services and Global Change

University of Edinburgh

UNITED KINGDOM

Krzysztof Stereńczak

profressor IBL

Instytut Matki i Dziecka

POLAND

Martin Herold

Professor and section head

Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences

GERMANY
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