Introduction To Modern Photogrammetry

PROBA-V is a new global vegetation monitoring mission, to be launched in the second quarter of 2013. PROBA-V has been developed to show a consistent performance with SPOT-VEGETATION (SPOT-VGT) data, with similar spectral bands but with an improved spatial resolution of 1/3 km. The innovative mission concept has led to several key research topics related to image quality, which are discussed in this article. To support the existing VEGETATION user community, the data products for PROBA-V continue to provide daily top of canopy synthesis (S1-TOC) and 10 day synthesis products (S10-TOC). In addition, the new top of atmosphere daily synthesis (S1-TOA) products and a radiometrically/geometrically corrected (level 1C) product in raw resolution will also be provided for scientific users.

https://earth.esa.int/documents/700255/1929094/ProbaV+-+Mission+Products+and+Quality.pdf/c7429c73-e1f7-465c-bbae-464fc93eed50

PROBA-V mission for global vegetation monitoring: standard products and image quality

Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS).

This study addressed the classification of multi-temporal satellite data from RapidEye by considering different classifier algorithms and decision fusion. Four non-parametric classifier algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), were applied to map crop types in various irrigated landscapes in Central Asia. A novel decision fusion strategy to combine the outputs of the classifiers was proposed. This approach is based on randomly selecting subsets of the input dataset and aggregating the probabilistic outputs of the base classifiers with another meta-classifier. During the decision fusion, the reliability of each base classifier algorithm was considered to exclude less reliable inputs at the class-basis. The spatial and temporal transferability of the classifiers was evaluated using data sets from four different agricultural landscapes with different spatial extents and from different years. A detailed accuracy assessment showed that none of the stand-alone classifiers was the single best performing. Despite the very good performance of the base classifiers, there was still up to 50% disagreement in the maps produced by the two single best classifiers, RF and SVM. The proposed fusion strategy, however, increased overall accuracies up to 6%. In addition, it was less sensitive to reduced training set sizes and produced more realistic land use maps with less speckle. The proposed fusion approach was better transferable to data sets from other years, i.e. resulted in higher accuracies for the investigated classes. The fusion approach is computationally efficient and appears well suited for mapping diverse crop categories based on sensors with a similar high repetition rate and spatial resolution like RapidEye, for instance the upcoming Sentinel-2 mission.

Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data

We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors.

https://www.mdpi.com/2072-4292/9/11/1161/pdf

Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

With the launch of PROBA-V (Project for On-Board Autonomy – Vegetation) in 2013, the continuity and availability of global land-coverage data in four multispectral bands are ensured for the SPOT (Systeme Pour l’Observation de la Terre)-VEGETATION user community. This community has been served for already more than 14 years with high-quality 1 kilometre-resolution data. To guarantee continuation of this high quality over the full lifetime of PROBA-V, an operational processing platform and in-flight calibration algorithms have to be in place, which fully consider the specific PROBA-V platform and instrument design characteristics. Data quality has to be ensured for all available product levels, i.e. from the radiometrically corrected radiance data to the 10 day global synthesis. In this article we first focus on some specific design characteristics, which impose some challenges for data processing and calibration. Next, a technical description is given for all the processing steps such as mapping, cloud mas...

The PROBA-V mission: image processing and calibration

. This paper describes a dataset consisting of 47 hyperspectral-reflectance measurements of plastic litter samples. The plastic litter samples include
virgin and real samples from the Port of Antwerp. They were measured in dry conditions in the Flemish Institute for Technological Research (VITO) calibration facility, and a selection of the
samples were also measured in wet conditions and submerged in a water tank at Flanders Hydraulics. The construction on top of the tank allowed us to submerge the plastics and keep sediments in suspension. The spectral measurements were performed using an Analytical Spectral Devices (ASD)
FieldSpec 4 and a Spectral Evolution (SEV) spectrometer. The datasets are available on the 4TU.ResearchData open-access repository (ASD dataset:
 https://doi.org/10.4121/12896312.v2 , Knaeps et al., 2020; SEV dataset: https://doi.org/10.4121/uuid:9ee3be54-9132-415a-aaf2-c7fbf32d2199 ; Garaba et al., 2020).

/pdf/hyperspectral-reflectance-dataset-of-dry-wet-and-submerged-13pjez1bbp.pdf

Gert Strackx

Papers

The PROBA-V mission: image processing and calibration

Hyperspectral-reflectance dataset of dry, wet and submerged marine litter