New Publication: Canopy structure-corrected retrieval of foliar nitrogen by hyperspectral data

New Publication: Canopy structure-corrected retrieval of foliar nitrogen by hyperspectral data

A new review paper has been recently published by International Journal of Applied Earth Observation and Geoinformation. The paper is amongst the outputs of a PhD thesis by Zhihui Wang from the University of Twente, and focuses on retrieval of forest canopy foliar nitrogen from hyperspectral imagery by additionally correcting for canopy structure effects. Te main research question arose from the fact that the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen, which can be corrected for by using the canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF).

 

Directional area scattering factor (DASF) calculated based on spectral invariant theory for broadleaf, needle leaf, and mixed forest

The results of the research conducted across the Bavarian Forest National Park confirm that %N can be retrieved using the scattering coefficient aftercorrecting for canopy structural effect. With the aid of  upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling ofecosystem processes as well as ecosystem-climate feedbacks.

Further information on this paper can be found here.

Source:

Wang, Z., Skidmore, A. K., Wang, T., Darvishzadeh, R., Heiden, U., Heurich, M., Latifi, H., Hearne, J. 2016. Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects. International Journal of Applied Earth Observation and Geoinformation 54(2017): 84-94.

 

new article: Remote Sensing and New Generation SDMs

new article: Remote Sensing and New Generation SDMs

our new article “Will remote sensing shape the next generation of species distribution models?” in Remote Sensing in Ecology and Conservation is now online. Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future.

Kate S. He, Bethany A. Bradley, Anna F. Cord, Duccio Rocchini, Mao-Ning Tuanmu, Sebastian Schmidtlein, Woody Turner, Martin Wegmann andNathalie Pettorelli (2015) “Will remote sensing shape the next generation of species distribution models?”, Remote Sensing in Ecology and Conservation, DOI: 10.1002/rse2.7

New publication on Hyperspectral Data for Mapping Fractional Cover

New publication on Hyperspectral Data for Mapping Fractional Cover

The outcome of one of our MSc students, Sarah Malec, got published in Remote Sensing titled “Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling”. Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling.

 

Malec, S.; Rogge, D.; Heiden, U.; Sanchez-Azofeifa, A.; Bachmann, M.; Wegmann, M. Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling. Remote Sens. 2015, 7, 11776-11800.

new MSc: Sarah Malec

new MSc: Sarah Malec

LogoGCE_transWe welcome Sarah Sophia Malec as new MSc student. Her thesis in done in close collaboration with DLR-EOC (Dr. Derek Rogge & Dr. Uta Heiden) on “Assessment of Soil degradation in Costa Rica using reflectance hyperspectral and simulated EnMAP imagery”. Sarah will work with hyperspectral imagery from Costa Rica and use spatial statistical models to analyse soil degradation. Sarah is a Global Change Ecology student and trained extensively in remote sensing, GIS, spatial modelling in the context of ecology, global change and its political implications. The first supervisor is Martin Wegmann, the second on Thomas Köllner.