André Große-Stoltenberg

André Große-Stoltenberg
Justus-Liebig-Universität Gießen | JLU · Division of Landscape Ecology and Landscape Planning

Dr. rer. nat.

About

22
Publications
6,536
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381
Citations
Introduction
Using remote sensing in ecological and ecosystem studies I) mostly to map invasive species and their impacts, II) but also to assess the status of Urban Green Infrastructure and to III) develop indicators of ecosystem health, and IV) just recently to quantify the status of traditional orchards. Started working with airborne hyperspectral and LiDAR data, and now frequently using data from satellites (optical), UAVs (RGB, multispectral) and mobile LiDAR.
Additional affiliations
July 2009 - present
University of Münster
Position
  • Researcher

Publications

Publications (22)
Article
Full-text available
Spatial heterogeneity of ecosystems crucially influences plant performance, while in return plant feedbacks on their environment may increase heterogeneous patterns. This is of particular relevance for exotic plant invaders that transform native ecosystems, yet, approaches integrating geospatial information of environmental heterogeneity and plant-...
Article
The impact of invasive species may depend on the dissimilarity of their functional traits relative to the native community. Therefore, comparing species traits in a multidimensional space can help to better understand invader impacts, but novel methods are needed to effectively measure multiple traits across diverse plant communities. The main aim...
Article
Invasive plant species can have high, self-reinforcing impacts on ecosystem structure and functioning that induce permanent changes of ecosystem properties. Therefore, early detection and timely management is required to alleviate ecosystem consequences of invasion. Integrating airborne hyperspectral imagery with LiDAR data can deliver spatially ex...
Article
Healthy vegetation in cities provides ecosystem services, which contribute to the overall well-being of urban populations, especially in times of climate change and increasing urbanization. More specifically, vegetation monitoring is needed in the context of intensifying and mitigating factors of the Urban Heat Island (UHI) effect. Therefore, fast...
Article
Full-text available
The intensification of food production systems has resulted in landscape simplification, with trees and hedges disappearing from agricultural land, principally in industrialized countries. However, more recently, the potential of agroforestry systems and small woody landscape features (SWFs), e.g., hedgerows, woodlots, and scattered groups of trees...
Article
Full-text available
Context Combining field-based assessments with remote-sensing proxies of landscape patterns provides the opportunity to monitor terrestrial ecosystem health status in support of sustainable development goals (SDG). Objectives Linking qualitative field data with quantitative remote-sensing imagery to map terrestrial ecosystem health (SDG15.3.1 “lan...
Article
Full-text available
Climate change, increasing environmental pollution, continuous loss of biodiversity, and a growing human population with increasing food demand, threaten the functioning of agro-ecosystems and their contribution to people and society. Agroforestry systems promise a number of benefits to enhance nature's contributions to people. There are a wide ran...
Article
The present study aimed to investigate the role of propagule pressure (P), abiotic (A), and biotic (B) factors (collectively indicated as PAB) on the suitability of the Mediterranean island of Sardinia (Italy) to be invaded by the tree Acacia saligna, recently included in the list of invasive alien species of European Union concern. To this aim, a...
Article
Full-text available
Large and comparatively compact European cities such as Bucharest and Leipzig struggle with considerable urban heat island (UHI) effects characterized by heat and drought together with high concentrations of air pollutants (NO2, SO2, O3, CO2). However, a healthy urban green infrastructure is necessary to reduce the impacts of UHI on human health. T...
Chapter
Mapping tree species at the single-tree level is an active field of research linking ecology and remote sensing. However, the discrimination of tree species requires the selection of the relevant spectral features derived from imagery. We can extract an extensive number of image parameters even from images with a low spectral resolution, such as Re...
Article
Full-text available
Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for many high-impact invaders. Furthermore, the identification of the suitable classification algorithms...
Article
Full-text available
Epidermal structures (ES) of leaves are known to affect the functional properties andspectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. Westudied a wider range of different ES and their impact on spectral properties. Additionally, weidentified spectral regions that allow distinguishing different ES. We us...
Article
Full-text available
Linking remote sensing methodology to stable isotope ecology provides a promising approach to study ecological processes from small to large spatial scales. Here, we show that δ1520 N can be detected in fresh leaf reflectance spectra of field samples along a spatial gradient of increasing nitrogen input from an N2-fixing invasive species. However,...
Article
Full-text available
The invasive shrub, Acacia longifolia, native to southeastern Australia, has a negative impact on vegetation and ecosystem functioning in Portuguese dune ecosystems. In order to spectrally discriminate A. longifolia from other non-native and native species, we developed a classification model based on leaf reflectance spectra (350–2500 nm) and cond...
Article
Full-text available
Geoinformationstechnologien gewinnen im Bereich des Tourismus zunehmend an Bedeutung. Durch die rasante Entwicklung innovativer Hard- und Softwarelösungen können ehemals statische und analoge Daten dynamischer und ortsabhängig zur Verfügung gestellt werden. In der Kombination von Naturschutz und Tourismus werden die Potenziale dieser sogenannten or...
Article
Natural Heritage as Adventure - Imparting nature conservation themes with the help of geoinformation technologies Geoinformatics are gaining increasing importance in the tourism sector. The rapid development of innovative hardware and software solutions has caused a shift from analogue and static information to highly dynamic data provision referri...
Article
Full-text available
Acacia spp. are among the most serious plant invaders worldwide, and Acacia longifolia specifically causes problems in Portugal. In this study, we evaluated the impacts of A. longifolia invasion on community structure, light climate, plant diversity and regeneration in pine forests and open stabilized dunes in northern and southern Portugal. Having...
Article
Full-text available
In water-limited ecosystems, where potential evapotranspiration exceeds precipitation, it is often assumed that plant invasions will not increase total ecosystem water use, because all available water is evaporated or transpired regardless of vegetation type. However, invasion by exotic species, with high water use rates, may potentially alter ecos...
Conference Paper
Full-text available
Invasive species can have a high impact on ecosystems services, biodiversity and ecosystem functioning. The fusion of high resolution hyperspectral and structural (LiDAR) data shows promising results in detecting invasive plant species especially when occurring in the understory of forests. Imaging spectroscopy enables to derive a specific spectral...

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Projects

Projects (3)
Project
Various tasks - including research, nature conservation, and economic activities such as forestry, agriculture, or ecosystem service assessments - require accurate information on the geographical distribution of plant species. Due to novel very high spatial resolution satellite missions and Unmanned Aerial Vehicles (UAV), there is a growing availability of Earth observation data revealing both high spatial and temporal detail on vegetation patterns. Consequently, efficient methods are needed to harness this growing source of information for vegetation analysis. In the field of remote sensing of vegetation, Deep Learning methods such as Convolutional Neural Networks (CNN) are currently revolutionizing possibilities for pattern and object recognition. Thus, it is expected that, in tandem with advances in high-resolution sensor technology, CNN will enlarge our capability to determine spatially explicit vegetation patterns. However, CNN commonly require ample reference observations to learn the pivotal image features. A big data approach may provide these reference observations required for training the CNN models. Various initiatives (e.g., CLEF; GBIF, Pl@ntNet) provide a vast amount of labelled image data on plant species, i.e., photographs together with species names. As a result of the constant efforts in the area of Open Data, such image datasets are freely accessible and continue to grow. However, it remains unclear if CNN models trained with such image datasets are directly applicable to very-high-resolution Earth observation data in terms of their spatial resolution, quality, and viewing geometries. Accordingly, in the proposed project, we aim to assess the synergies of big data with high spatial resolution Earth observation data for fully automated vegetation mapping. The proposed approach uses big data in terms of freely available imagery tagged with species names to train CNN models. The trained models are then applied to high-resolution Earth observation data to reveal the spatial distribution of the target species. Thereby, we seek to identify which characteristics of the images used for training affect mapping accuracy (e.g., acquisition geometry, image quality), and we will develop an algorithm for filtering the image datasets according to these characteristics before training. Specifically, our research questions are: 1) How accurately can the spatial distribution of different plant species be identified using the proposed big data approach combined with deep learning and very-high-resolution remote sensing data? 2) What are the critical factors determining the value of Big Data-based image datasets for CNN training, and can these be efficiently filtered using deep learning? 3) How does the spatial resolution of the Earth observation data limit the plant species identification?
Project
Exotic invasive species represent a major threat to earths biodiversity, since they substantially alter biogeochemical cycles of ecosystems with large impacts on ecosystem functioning. However, to date, explicitly quantifying such impacts remains challenging. One reason is the lack of adequate methodology to capture the spatial dimension of ecosystem changes associated with biological invasion. Conventional ecophysiological approaches are largely restricted to comparing neighbouring plant individuals and tend to neglect the spatial dimension. Landscape ecology, although operating on larger spatial scales, is typically limited to measurements of patterns and processes above the organism level, such as species distribution and propagation speed. The central aim of this project is to link ecophysiology with landscape ecology, thus moving towards an integrated understanding of the spatial aspect of plant invasions, enabling the quantification of exotic species impacts across spatial scales. A highly suitable technology to address this purpose is hyperspectral remote sensing, which allows for quantifying alterations in native species biochemistry on the leaf level using spectral measurements while allowing to scale up to the landscape level using aerial images. Utilizing the case study of the N2-fixing leguminous tree Acacia longifolia, a problematic invader in coastal regions of Portugal and many ecosystems worldwide, we will develop new methodology for quantifying changes in ecosystem functioning after plant invasion from the leaf to the landscape scale.