@article{UereyenKuenzer2019,
  author    = {Uereyen, Soner and Kuenzer, Claudia},
  title     = {A review of earth observation-based analyses for major river basins},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs11242951},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193849},
  pages     = {2951},
  year      = {2019},
  abstract  = {Regardless of political boundaries, river basins are a functional unit of the Earth's land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream-downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.},
  language  = {en}
}
@article{SognoTraidlHoffmannKuenzer2020,
  author    = {Sogno, Patrick and Traidl-Hoffmann, Claudia and Kuenzer, Claudia},
  title     = {Earth Observation data supporting non-communicable disease research: a review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {16},
  issn      = {2072-4292},
  doi       = {10.3390/rs12162541},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-211113},
  year      = {2020},
  abstract  = {A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common — environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries' economy due to healthcare costs and missing work force. Additionally, they affect the individual's immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.},
  language  = {en}
}
@article{SognoKleinKuenzer2022,
  author    = {Sogno, Patrick and Klein, Igor and Kuenzer, Claudia},
  title     = {Remote sensing of surface water dynamics in the context of global change — a review},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {10},
  issn      = {2072-4292},
  doi       = {10.3390/rs14102475},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275274},
  year      = {2022},
  abstract  = {Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.},
  language  = {en}
}
@article{RiyasSyedKumaretal.2021,
  author    = {Riyas, Moidu Jameela and Syed, Tajdarul Hassan and Kumar, Hrishikesh and Kuenzer, Claudia},
  title     = {Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield, India using Sentinel-1 SAR data},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {8},
  issn      = {2072-4292},
  doi       = {10.3390/rs13081521},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236703},
  year      = {2021},
  abstract  = {Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017-2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km\(^2\) area of coal fires. Results reveal that nearly 10\% of the coal fire area is newly formed, while 73\% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.},
  language  = {en}
}
@article{ReinersSobrinoKuenzer2023,
  author    = {Reiners, Philipp and Sobrino, Jos{\´e} and Kuenzer, Claudia},
  title     = {Satellite-derived land surface temperature dynamics in the context of global change — a review},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs15071857},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311120},
  year      = {2023},
  abstract  = {Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.},
  language  = {en}
}
@article{ReinermannGessnerAsametal.2022,
  author    = {Reinermann, Sophie and Gessner, Ursula and Asam, Sarah and Ullmann, Tobias and Schucknecht, Anne and Kuenzer, Claudia},
  title     = {Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs14071647},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267164},
  year      = {2022},
  abstract  = {Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3\% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84\%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3\%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.},
  language  = {en}
}
@article{ReinermannAsamGessneretal.2023,
  author    = {Reinermann, Sophie and Asam, Sarah and Gessner, Ursula and Ullmann, Tobias and Kuenzer, Claudia},
  title     = {Multi-annual grassland mowing dynamics in Germany},
  series = {Frontiers in Environmental Science},
  volume    = {11},
  journal   = {Frontiers in Environmental Science},
  issn      = {2296-665X},
  doi       = {10.3389/fenvs.2023.1040551},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-320700},
  year      = {2023},
  abstract  = {Introduction: Grasslands cover one third of the agricultural area in Germany and are mainly used for fodder production. However, grasslands fulfill many other ecosystem functions, like carbon storage, water filtration and the provision of habitats. In Germany, grasslands are mown and/or grazed multiple times during the year. The type and timing of management activities and the use intensity vary strongly, however co-determine grassland functions. Large-scale spatial information on grassland activities and use intensity in Germany is limited and not openly provided. In addition, the cause for patterns of varying mowing intensity are usually not known on a spatial scale as data on the incentives of farmers behind grassland management decisions is not available. Methods: We applied an algorithm based on a thresholding approach utilizing Sentinel-2 time series to detect grassland mowing events to investigate mowing dynamics in Germany in 2018-2021. The detected mowing events were validated with an independent dataset based on the examination of public webcam images. We analyzed spatial and temporal patterns of the mowing dynamics and relationships to climatic, topographic, soil or socio-political conditions. Results: We found that most intensively used grasslands can be found in southern/south-eastern Germany, followed by areas in northern Germany. This pattern stays the same among the investigated years, but we found variations on smaller scales. The mowing event detection shows higher accuracies in 2019 and 2020 (F1 = 0.64 and 0.63) compared to 2018 and 2021 (F1 = 0.52 and 0.50). We found a significant but weak (R2 of 0-0.13) relationship for a spatial correlation of mowing frequency and climate as well as topographic variables for the grassland areas in Germany. Further results indicate a clear value range of topographic and climatic conditions, characteristic for intensive grassland use. Extensive grassland use takes place everywhere in Germany and on the entire spectrum of topographic and climatic conditions in Germany. Natura 2000 grasslands are used less intensive but this pattern is not consistent among all sites. Discussion: Our findings on mowing dynamics and relationships to abiotic and socio-political conditions in Germany reveal important aspects of grassland management, including incentives of farmers.},
  language  = {en}
}
@article{MayrKuenzerGessneretal.2019,
  author    = {Mayr, Stefan and Kuenzer, Claudia and Gessner, Ursula and Klein, Igor and Rutzinger, Martin},
  title     = {Validation of earth observation time-series: a review for large-area and temporally dense land surface products},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {22},
  issn      = {2072-4292},
  doi       = {10.3390/rs11222616},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193202},
  year      = {2019},
  abstract  = {Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.},
  language  = {en}
}
@article{MayrKleinRutzingeretal.2021,
  author    = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia},
  title     = {Systematic water fraction estimation for a global and daily surface water time-series},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {14},
  issn      = {2072-4292},
  doi       = {10.3390/rs13142675},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242586},
  year      = {2021},
  abstract  = {Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6\% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.},
  language  = {en}
}
@article{KnauerGessnerFensholtetal.2017,
  author    = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Forkuor, Gerald and Kuenzer, Claudia},
  title     = {Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment},
  series = {Remote Sensing},
  volume    = {9},
  journal   = {Remote Sensing},
  number    = {2},
  doi       = {10.3390/rs9020132},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-171905},
  year      = {2017},
  abstract  = {Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92\% (2001), 91\% (2007), and 91\% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91\% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future.},
  language  = {en}
}
@article{KleinOppeltKuenzer2021,
  author    = {Klein, Igor and Oppelt, Natascha and Kuenzer, Claudia},
  title     = {Application of remote sensing data for locust research and management — a review},
  series = {Insects},
  volume    = {12},
  journal   = {Insects},
  number    = {3},
  issn      = {2075-4450},
  doi       = {10.3390/insects12030233},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-234090},
  year      = {2021},
  abstract  = {Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species — the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera) — and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l'Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.},
  language  = {en}
}
@article{KleinCoccoUereyenetal.2022,
  author    = {Klein, Igor and Cocco, Arturo and Uereyen, Soner and Mannu, Roberto and Floris, Ignazio and Oppelt, Natascha and Kuenzer, Claudia},
  title     = {Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {23},
  issn      = {2072-4292},
  doi       = {10.3390/rs14236050},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297232},
  year      = {2022},
  abstract  = {The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43\% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23\% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97\%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.},
  language  = {en}
}
@article{KacicKuenzer2022,
  author    = {Kacic, Patrick and Kuenzer, Claudia},
  title     = {Forest biodiversity monitoring based on remotely sensed spectral diversity — a review},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs14215363},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290535},
  year      = {2022},
  abstract  = {Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts "vegetation indices", "spectral information content", and "spectral species" for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.},
  language  = {en}
}
@article{HolzwarthThonfeldAbdullahietal.2020,
  author    = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia},
  title     = {Earth Observation based monitoring of forests in Germany: a review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs12213570},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216334},
  year      = {2020},
  abstract  = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.},
  language  = {en}
}
@article{HaHuthBachoferetal.2022,
  author    = {Ha, Tuyen V. and Huth, Juliane and Bachofer, Felix and Kuenzer, Claudia},
  title     = {A review of Earth observation-based drought studies in Southeast Asia},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {15},
  issn      = {2072-4292},
  doi       = {10.3390/rs14153763},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286258},
  year      = {2022},
  abstract  = {Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70\%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97\% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21\%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81\% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region.},
  language  = {en}
}
@article{DirscherlDietzKneiseletal.2020,
  author    = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia},
  title     = {Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs12071203},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203735},
  year      = {2020},
  abstract  = {Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017-2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.},
  language  = {en}
}
@article{DirscherlDietzKneiseletal.2021,
  author    = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia},
  title     = {A novel method for automated supraglacial lake mapping in Antarctica using Sentinel-1 SAR imagery and deep learning},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {2},
  issn      = {2072-4292},
  doi       = {10.3390/rs13020197},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-222998},
  year      = {2021},
  abstract  = {Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0\% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.},
  language  = {en}
}