In 2023, Hydrosat will launch its VanZyl-1 land mapping mission and substantiate accurate and timely thermal infrared (TIR) data from a commercial SmallSat platform. Science and applications communities have made clear the needs and requirements for daily, field-scale land surface temperature and evapotranspiration data. Hydrosat’s eventual SmallSat constellation will significantly advance our monitoring and management capabilities for ecosystems, agriculture, and other applications. VanZyl-1 includes a primary TIR payload with a projected ground sample distance (GSD) of 70 meters, and secondary visible through near-infrared multi-spectral payload with a GSD of 30 meters. The TIR payload incorporates a modern microbolometer Focal Plane Array (FPA) with telescope, thermal control, and calibration subsystems designed for optimal performance within a total payload volume of approximately 16U. The payloads will be hosted on an “ESPA-class” SmallSat in partnership with Loft Orbital, and operated as part of a demonstration mission with up to 5-year planned lifetime.
We are seeing tree mortality increase in western U.S. forests and die-off events around the world caused by serve or interacting disturbances like logging, drought, wildfire and pine beetle infestation. Limiting our knowledge of how forests respond is a lack of data on functional vegetation states at the tree or stand level over long periods of time and broad regions. Moderate resolution satellite imagery can provide changes in percent forest cover but cannot resolve vegetation state changes (e.g. from conifer to deciduous forest). The high resolution of Planet’s Dove imaging technologies may provide an opportunity to capture response at fine scales. We aim to integrate Planet’s constellation of satellites with Landsat imagery to create a multi-scale network for forest monitoring. However, the uncalibrated nature of these systems and the variability of sensor characteristics across the constellation make this problematic. We conducted a limited investigation of radiometric and thematic data methods for linking vegetation properties across spatial scales from 3 to 30 meters. The greatest challenge arises from the variation in Dove sensor radiometric response (roughly +/- 10%) across the constellation and optical cross talk associated with their broad, overlapping Bayer filter response. Applying a spectral band adjustment factor to improve radiometric correlation requires knowledge of the actual spectral response of the sensors which is not readily available. Using a K-means clustering algorithm to bridge scales and minimize sensor differences had mixed results for low reflectance scene components – perhaps again the result of cross-talk between Dove sensor spectral bands.
Research groups at Rochester Institute of Technology and Carnegie Institution for Science are studying savanna
ecosystems and are using data from the Carnegie Airborne Observatory (CAO), which integrates advanced
imaging spectroscopy and waveform light detection and ranging (wLIDAR) data. This component of the larger
ecosystem project has as a goal the fusion of imaging spectroscopy and wLIDAR data in order to improve
per-species structural parameter estimation. Waveform LIDAR has proven useful for extracting high vertical
resolution structural parameters, while imaging spectroscopy is a well-established tool for species classification.
We evaluated data fusion at the feature level, using a stepwise discrimination analysis (SDA) approach with
feature metrics from both hyperspectral imagery (HSI) and wLIDAR data. It was found that fusing data with
the SDA improved classification, although not significantly. The principal component analysis (PCA) provided
many useful bands for the SDA selection, both from HSI and wLIDAR. The overall classification accuracy was
68% for wLIDAR, 59% for HSI, and 72% for the fused data set. The kappa accuracy achieved with wLIDAR
was 0.49, 0.36 for HSI, and 0.56 for both modalities.
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