Akpona Okujeni

• PhD
• GFZ Helmholtz Centre for Geosciences

Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained underst...

This study introduces a novel approach for mapping annual fractional vegetation cover in Sub-Saharan range-lands. We used Sentinel-2 time series data from October 2022 to October 2023 to derive phenological metrics, including the dry season integral and rate of greenness decline after peak season. Phenological metrics effectively separate woody veg...

Croplands are essential for food security but also impact the environment, biodiversity, and climate. Understanding , monitoring, modeling, and managing these impacts require accurate, comprehensive information on cropland vegetation cover. This study aimed to continuously monitor the state and vegetative processes of cropland, focusing on the asse...

Long-term monitoring of grasslands is pivotal for ensuring continuity of many environmental services and for supporting food security and environmental modelling. Remote sensing provides an irreplaceable source of information for studying changes in grasslands. Specifically, Spectral Mixture Analysis (SMA) allows for quantification of physically me...

Croplands are essential for food security but also impact the environment, biodiversity, and climate. Understanding, monitoring, modeling, and managing these impacts require accurate, comprehensive information on cropland vegetation cover. This study aimed to continuously monitor the state and vegetative processes of cropland, focusing on the asses...

Lakes are directly exposed to climate variations as their recharge processes are driven by precipitation and evapotranspiration, and they are also affected by groundwater trends, changing ecosystems and changing water use. In this study, we present a downward model development approach that uses models of increasing complexity to identify and quant...

Savannahs are vital ecosystems whose sustainability is endangered by the spread of woody plants. This research targets the accurate mapping of fractional woody cover (FWC) at the species level in a South African savannah, using EnMAP hyperspectral data. Field annotations were combined with very high-resolution multispectral drone data to produce la...

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be over...

Monitoring the Earth by annually mapping land cover (LC) fractions helps to better understand the ongoing processes and changes of land use and land management. At 10 to 30 m spatial resolution, the combination of time-series data aggregation, specifically spectral-temporal metrics (STM), and regression-based unmixing models has been shown to be hi...

Grasslands provide important ecosystem services to society, including biodiversity, water security, erosion control, and forage production. Grasslands are also vulnerable to droughts, rendering their future vitality under climate change uncertain. Yet, the grassland response to drought is not well understood, especially for heterogeneous Central Eu...

We used imagery from remote sensing (FORCE Time Series Analysis submodule (combining Landsat and Sentinel‐2 imagery) to derive spatially distributed times series (8 years) of NDWI data to infer patterns of floodplain inundation and river‐floodplain connectivity in two contrasting polders in the Lower Oder Valley National Park. The upstream Polder A...

Lakes are directly exposed to climate variations, as their recharge processes are driven by precipitation and evapotranspiration, and indirectly via groundwater trends, changing ecosystems and changing water use. In this study, we present a downward model development approach that uses models of increasing complexity to identify and quantify the de...

Fractional cover time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil from remote sensing provide essential detail to understand how grasslands are affected by recent and future drought periods in the 21st century. In this regard, Sentinel-2A/B offer frequent large-area observations, which have not yet been f...

Mediterranean forests are highly subjected to fire occurrence. Altered fire regimes resulting from changes in land use and climate may jeopardize their resilience to fire and induce changes in forest composition. Disentangling forest cover composition is therefore critical for understanding post-fire forest recovery dynamics. In this study, we demo...

The identification of buildings has become a major research focus of settlement mapping with Earth Observation data. Building area or building footprint data is particularly required in research related to population, such as disaster risk management or urban health. This study examined the suitability of machine learning regression-based unmixing...

Presentation from the EnMAP Session at ESA's Living Planet Symposium

Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring du...

Building libraries of reference spectra for detailed mapping of urban areas at the level of building materials or plant species requires substantial effort. While in the last 15 years many approaches have been proposed to automatically extract pure material spectra from airborne hyperspectral imagery, the labeling of such spectra remains a tedious...

Spaceborne imaging spectrometers are expected to facilitate regional-scale vegetation analyses with multi-season hyperspectral imagery. However, we still lack a better understanding on both whether multi-season hyperspectral approaches are favorable over single-season approaches, as well as on the benefits of hyperspectral compared to multispectral...

Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environm...

The EnMAP-Box is a free and open source QGIS plugin. It integrates the strength of Python-based image processing and machine learning with graphical interfaces for handling hyperspectral images and spectral libraries in a GIS environment.

Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on...

The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) and land use (LU) processes related to settlements. The heterogeneity of built-up areas and infrastructures as well as the importance of not only mapping, but also characterizing anthropogenic structures suggests using...

The next generation of spaceborne imaging spectrometers will enable hyperspectral analysis of vegetation cover across large spatial extents. Spectral unmixing provides a means to assess subpixel vegetation composition in such imagery. Here we implement a regression-based unmixing approach to generate fractional vegetation cover on a regional scale...

This dataset is composed of simulated EnMAP mosaics for the San Francisco Bay Area, USA. Hyperspectral imagery used for the EnMAP simulation was collected across three time periods (Spring, Summer, and Fall) in 2013 with the AVIRIS-Classic sensor flown as part of the HyspIRI Preparatory Campaign. Flight lines were simulated to EnMAP-like data using...

This dataset is composed of simulated EnMAP mosaics for the San Francisco Bay Area, USA. Hyperspectral imagery used for the EnMAP simulation was collected across three time periods (Spring, Summer, and Fall) in 2013 with the AVIRIS-Classic sensor flown as part of the HyspIRI Preparatory Campaign. Flight lines were simulated to EnMAP-like data using...

The Landsat archive offers great potential for monitoring forest cover change, and new approaches moving from categorical towards continuous change products emerge rapidly. Most approaches, however, require vast amounts of high-quality reference data, limiting their applicability across space and time. We here propose the use of a generalized regre...

We evaluated the effectiveness of different approaches to compensate for across-track brightness gradients within a hyperspectral image mosaic comprised of multiple flight lines in the San Francisco Bay Area. We calculated the spectral consistency of adjacent flight lines and conducted regression-based unmixing of woody- and non-woody vegetation fr...

An improved trade-off between resolution, coverage and revisit time, makes Sentinel-2 multispectral imagery an interesting data source for mapping the composition and spatial-temporal dynamics of urban land cover. To fully realize the potential of Sentinel-2′s high amount of available data, efficient urban mapping workflows are required. Machine le...

The EnMAP-Box 3 is a toolbox for visualising and processing imaging spectroscopy data and spectral libraries, and is particularly developed to handle data from the upcoming EnMAP (Environmental Mapping and Analysis Program) mission. The integration as a python-based plug-in into the free and open source geographic information system QGIS 3 makes th...

Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for env...

The regression-based unmixing approach using synthetically mixed training data from spectral libraries is now implemented as application in the EnMAP-Box. Check-out our tutorial to learn more .... https://enmap-box.readthedocs.io/en/latest/tutorials/tutorial_1.html#

The Landsat archive presents a unique source for mapping and monitoring of shrublands. Still, efficient and accurate mapping approaches are needed that provide shrub cover fraction estimates over space and time. The spectral signal of shrubs is composed of green vegetation and non-photosynthetic vegetation as well as varying fractions of soil, gras...

Forthcoming spaceborne imaging spectrometers will provide novel opportunities for mapping urban composition globally. To move from case studies for single cities towards comparative and more operational analyses, generalized models that may be transferred throughout space are desired. In this study, we investigated how single regression models can...

Rapid urban population growth in Sub-Saharan Western Africa has important environmental, infrastructural and social impacts. Due to the low availability of reliable urbanization data, remote sensing techniques become increasingly popular for monitoring land use change processes in that region. This study aims to quantify land cover for the Ouagadou...

Remotely sensed observations of built environments are controlled by the physical properties of the materials and the structural form of the objects used to construct the environment. When using remote sensing to map and monitor built environments, it is important to understand how different sensors respond to the objects and materials. We give a b...

Local climate zone (LCZ) mapping is an emerging field in urban climate research. LCZs potentially provide an objective framework to assess urban form and function worldwide. The scheme is currently being used to globally map LCZs as a part of the World Urban Database and Access Portal Tools (WUDAPT) initiative. So far, most of the LCZ maps lack pro...

Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size...

Global warming and the increasing world population will only put more pressure on the living conditions in urban environments. From a thermal comfort point of view, it is clear that there is a need for sustainable urban planning in which the thermal behavior of new developments can be accounted for. Mapping the city into local climate zones (LCZs),...

Hyperspectral remote sensing data offer the opportunity to map urban characteristics in detail. Though, adequate algorithms need to cope with increasing data dimensionality, high redundancy between individual bands, and often spectrally complex urban landscapes. The study focuses on subpixel quantification of urban land cover compositions using sim...

Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training sam...

Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variabil...

Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference...

In times of global environmental change, the sustainability of human–environment systems is only possible through a better understanding of ecosystem processes. An assessment of anthropogenic environmental impacts depends upon monitoring natural ecosystems. These systems are intrinsically complex and dynamic, and are characterized by ecological gra...

The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing ac...

Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis Program (EnMAP), frequent and area-wide mapping of na...

Global environmental change is occurring at unprecedented rates. Triggered by climate change impacts, population growth, changes in life style and, thus, increasing demands for food, feed, fiber and fuel, rapid changes in global land use can be observed. A better understanding of change processes on the land surface, e.g. land degradation and aband...

The upcoming hyperspectral satellite mission Environmental Mapping and Analysis Program (EnMAP) will provide timely and globally sampled imaging spectrometer data on a frequent basis. This will create unprecedented opportunities for a variety of environmental research fields and lead to manifold novel applications. These opportunities specifically...

Das weltweite Ausmaß der Urbanisierung zählt zu den großen ökologischen Herausforderungen des 21. Jahrhunderts. Die Fernerkundung bietet die Möglichkeit das Verständnis dieses Prozesses und seiner Auswirkungen zu erweitern. Der Fokus dieser Arbeit lag in der Quantifizierung der städtischen Landbedeckung mittels Maschinellen Lernens und räumlich unt...

Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution....

Hyperspectral unmixing is a complex process in which several steps are consecutively executed to derive the desired results: the image endmembers and their corresponding fractional abundance maps. Each of these unmixing stages benefits nowadays from a plethora of algorithms, continuously developed and improved. In this paper, we analyze three of th...

Hyperspectral images pose many challenges in endmember extraction applied to urban environments. The heterogeneity of the scenes and the relative high spatial resolution of the acquisition sensors lead to difficulties in the inference of the correct number of endmembers in the scene and possibly to the existence of a large number of pure pixels for...

In this letter we explore probabilities derived from an import vector machines (IVM) classifier as quantitative measures of class proportion. We have developed a parameter selection strategy that improves the description of class proportions. This strategy incorporates the use of spectral mixtures, which represent gradual class transitions, into th...

Exploiting imaging spectrometer data with machine learning algorithms has been demonstrated to be an excellent choice for mapping ecologically meaningful land cover categories in spectrally complex urban environments. However, the potential of kernel-based regression techniques for quantitatively analyzing urban composition has not yet been fully e...

The light use efficiency of photosynthesis dynamically adapts to environmental factors and is one major factor determining crop yield. Optical remote sensing techniques have the potential to detect physiological and biochemical changes in plant ecosystems, and non-invasive detection of changes in photosynthetic energy conversion may be of great pot...

Remote sensing of sun-induced chlorophyll fluorescence is a promising approach to track photosynthetic activity at various scales. So far, knowledge on the spatial patterns of the signal is very limited. In this study, we investigate the possibility to derive sun-induced chlorophyll fluorescence over an intensive agricultural area by means of airbo...

The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Ex...