Uta Heiden

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Automated differentiation of urban surfaces based on airborne hyperspectral imagery

The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces.

Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data

Abstract The urban environment is characterized by an intense multifunctional use of available spaces, where the preservation of open green spaces is of special importance. For this purpose, area-wide urban biotope mapping based on CIR aerial photographs has been carried out for the large cities in Germany during the last 10 years. Because of dynamic urban development and high mapping costs, the municipal authorities are interested in effective methods for mapping urban surface cover types, which can be used for evaluation of ecological conditions in urban structures and supporting updates of biotope maps. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for a test site in the city of Dresden (Germany) with regard to their potential for automated material-oriented identification of urban surface cover types. Previous investigations have shown that the high spectral and spatial variabilities of these data require the development of special methods, which are capable of dealing with the resulting mixed-pixel problem in its specific characteristics in urban areas. Earlier, methodological developments led to an approach based on a combination of spectral classification and pixel-oriented unmixing techniques to facilitate sensible endmember selection based on the reflective bands of the DAIS instrument. This approach is now extended by a shape-based classification technique including the thermal bands of the DAIS instrument to improve the detection of buildings during the process of identifying seedling pixels, which represent the starting points for linear spectral unmixing. This new approach increases the reliability of differentiation between buildings and open spaces, leading to more accurate results for the spatial distribution of surface cover types. Thus, the new approach significantly enhances the exploitation of the information potential of the hyperspectral DAIS 7915 data for an area-wide identification of urban surface cover types.

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP’s spatial coverage and revisit capability. However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest

Assessments of ecosystem functioning rely heavily on quantification of vegetation properties. The search is on for methods that produce reliable and accurate baseline information on plant functional traits. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate two functional leaf traits: leaf dry matter content (LDMC) and specific leaf area (SLA). Inversion of PROSPECT usually aims at quantifying its direct input parameters. This is the first time the technique has been used to indirectly model LDMC and SLA. Biophysical parameters of 137 leaf samples were measured in July 2013 in the Bavarian Forest National Park, Germany. Spectra of the leaf samples were measured using an ASD FieldSpec3 equipped with an integrating sphere. PROSPECT was inverted using a look-up table (LUT) approach. The LUTs were generated with and without using prior information. The effect of incorporating prior information on the retrieval accuracy was studied before and after stratifying the samples into broadleaf and conifer categories. The estimated values were evaluated using R2 and normalized root mean square error (nRMSE). Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered in the pooled samples. The use of prior information improved accuracy of the retrieved traits. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy level by using remotely sensed data.

Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived From the PROSPECT Model

Leaf nitrogen content has so far been quantified through empirical techniques using hyperspectral remote sensing. However, it remains a challenge to estimate the nitrogen content in fresh leaves through inversion of physically based models. Leaf nitrogen has been found to correlate with leaf traits (e.g., leaf chlorophyll, dry matter, and water) well through links to the photosynthetic process, which provides potential to estimate nitrogen indirectly. We therefore set out to estimate leaf nitrogen content by using its links to leaf traits that could be retrieved from a physically based model (PROSPECT) inversion. Leaf optical (directional-hemispherical reflectance and transmittance between 350 and 2500 nm) and leaf biochemical (nitrogen, chlorophyll, dry matter, and water) properties were measured. Correlation analysis showed that the area-based nitrogen correlations with leaf traits were higher than mass-based correlations. Hence, simple and multiple linear regression models were established for area-based nitrogen using three leaf traits (leaf chlorophyll content, leaf mass per area, and equivalent water thickness). In addition, the traits were retrieved by the inversion of PROSPECT using an iterative optimization algorithm. The established empirical models and the leaf traits retrieved from PROSPECT were used to estimate leaf nitrogen content. A simple linear regression model using only retrieved equivalent water thickness as a predictor produced the most accurate estimation of nitrogen (R2 = 0.58, normalized RMSE = 0.11). The combination of empirical and physically based models provides a moderately accurate estimation of leaf nitrogen content, which can be transferred to other datasets in a robust and upscalable manner.

Analysis of Surface Thermal Patterns in Relation to Urban Structure Types: A Case Study for the City of Munich

Scientists have reached to a large extent agreement on climate warming for the coming decades. This will especially have immense impact on cities which show in general a significantly higher temperature compared to rural surroundings, e.g. due to high percentage of impervious surfaces. This study shows capabilities of airborne and spaceborne thermal remotely sensed data to derive and analyze land surface temperatures (LST). Dependencies of LST to urban structure types (UST) with respect to their location within the city are analyzed. Results prove distinct correlations between LST and vegetation fraction as well as percentage of impervious surfaces. Beyond this, different USTs prove influences on LST. Last but not least, a general decrease of LST with increasing distance to the city center is confirmed for the city of Munich. However, the USTs superimpose this trend and have a significant influence on the local LST.

Differentiation of urban surfaces based on hyperspectral image data and a multi-technique approach

Airborne hyperspectral data yield a new potential for spectrally-based identification, but also raise new challenges in image analysis caused by a high spatial and spectral variability of the urban environment. The algorithms have to analyze spectrally mixed and non-mixed-pixels of various classes which often show spectrally similar characteristics. In this context the authors developed a multi-technique approach which combines linear spectral unmixing and spectral classification for a complete inventory of main urban surface cover types. Despite the good results, problems remained in differentiation of spectrally similar surfaces, such as buildings and sealed open surfaces. The authors present an improved approach including a new algorithm for shape-based detection of buildings and new rules for an optimized pixel-oriented endmember selection. The approach was developed using DAIS hyperspectral image data of the reflective and thermal wavelength ranges covering a study area in the city of Dresden (Germany). In the result a much improved identification of urban surfaces was achieved due to the incorporation of shape-based techniques.

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.

Overview of terrestrial imaging spectroscopy missions

This paper provides a brief overview of current civilian imaging spectroscopy (hyperspectral) missions currently operating in space or ready for launch for imaging the Earth. This overview is followed by a list of missions currently under development, and the paper concludes with a survey of missions in a planning stage. The latter is probably not a complete list of missions, but provides a good cross-section of sensors, which might be in space around the 2020 time frame.