Remote Sensing Methods for high-latitude Vegetation

Our aim is mapping high-latitude vegetation, it's bio-physical quantities and actual changes by applying Remote Sensing at a wide range of spatial scales (Backpack LiDAR, drone-based multispectral, hyperspectral and LiDAR sensors, as well as airborne multispectral and LiDAR and satellite remote sensing). 

Data and methods:

• ‘close range’ remote sensing: 

  drone-based instrumentation: LiDAR Yellowscan mapper with RGB modul; multispectral Micasense Altum Image Scanner with NIR and thermal; hyperspectral HAIP camera (VIS and NIR)

  airborne-based instrumentation on the AWI airplane platforms Polar-5 and Polar-6: multispectral Stereo Modular Aerial Camera System (MACS) (DLR Adlershof in house development, Dep. Optical Sensors): RIEGL NIR LiDAR LMS-Q6801; hyperspectral Image Scanner AISA

  supported by in-situ instrumentation LiDAR Backpack DGC50, LICOR LAI, PAR; field spectrometry: Buchhorn & Petereit developped a transportable field goniometer for bidirectional measurements (BRDF) in difficult terrain (AWI patent DE10-2011111117713.A1)

• ‘in situ' to 'close range’ to satellite remote sensing:

  We do have thematic and technical expertise in remote sensing applications for high-latitude vegetation with focus on the Taiga-Tundra Ecotone (TTE) and high-latitude surface waters. By this, we are currently involved in the generation of standardised remote sensing training data sets for machine learning in the circum-boreal and the Northern treeline: 

• BorFIT: Individual tree detection based on 3-D drone LiDAR point clouds (DataHub Information Infrastructure Funds)
• AI-vergreens: Forest type and forest structure based on superspectral Sentinel-2 satellite data (BMWi funding)

    and Validation: ESA CCI Permafrost Validation team for the Permafrost_cci Products circumarctic Ground Temperature, Active Layer Thickness, Permafrost Probability 

our main activities: qualitative (land cover, forest type, tundra type and surface water qualitative classifications) and quantitative (terrestrial: vegetation biomass, aquatic: Dissolved Organic Carbon, turbidity) applications for vegetation and surface waters of the high latitudes. Based on optical satellite data from Landsat, RapidEye, CHRIS-PROBA, SENTINEL-2 satellite missions; Scientific preparation and applications of hyperspectral EnMAP data; SAR satellite data of the TDX and Sentinel-1 satellite missions by cooperation with DLR and b.geos (AT), PI Annett Bartsch; Evaluation of operational satellite products from NASA, ESA, Copernicus Land Cover and Fluorescence remote sensing products.