TY - JOUR A1 - Riyas, Moidu Jameela A1 - Syed, Tajdarul Hassan A1 - Kumar, Hrishikesh A1 - Kuenzer, Claudia T1 - Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield, India using Sentinel-1 SAR data JF - Remote Sensing N2 - 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. KW - coal fire KW - InSAR KW - subsidence KW - remote sensing KW - coal KW - interferometry KW - SBAS Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236703 SN - 2072-4292 VL - 13 IS - 8 ER - TY - JOUR A1 - Rokhafrouz, Mohammad A1 - Latifi, Hooman A1 - Abkar, Ali A. A1 - Wojciechowski, Tomasz A1 - Czechlowski, Mirosław A1 - Naieni, Ali Sadeghi A1 - Maghsoudi, Yasser A1 - Niedbała, Gniewko T1 - Simplified and hybrid remote sensing-based delineation of management zones for nitrogen variable rate application in wheat JF - Agriculture N2 - Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times. KW - precision agriculture KW - management zones KW - remote sensing KW - Sentinel-2 KW - clustering KW - winter wheat KW - drought KW - digital agriculture Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-250033 SN - 2077-0472 VL - 11 IS - 11 ER - TY - JOUR A1 - Rösch, Moritz A1 - Plank, Simon T1 - Detailed mapping of lava and ash deposits at Indonesian volcanoes by means of VHR PlanetScope change detection JF - Remote Sensing N2 - Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis). KW - lava KW - volcanoes KW - PlanetScope KW - change detection KW - object-based image analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262232 SN - 2072-4292 VL - 14 IS - 5 ER - TY - JOUR A1 - Rösch, Moritz A1 - Sonnenschein, Ruth A1 - Buchelt, Sebastian A1 - Ullmann, Tobias T1 - Comparing PlanetScope and Sentinel-2 imagery for mapping mountain pines in the Sarntal Alps, Italy JF - Remote Sensing N2 - The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand and quantify the impacts of increasing logging activities and climatic changes in the European Alps, accurate information on the occurrence and distribution of mountain pine stands is needed. While Earth observation provides up-to-date information on land cover, space-borne mapping of mountain pines is challenging as different coniferous species are spectrally similar, and small-structured patches may remain undetected due to the sensor’s spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) and Sentinel-2 (10 m) and combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, spatial mean and spatial variance) from gray level co-occurrence matrix (GLCM), topographic features (elevation, slope and aspect) and canopy height information) to overcome the present challenges in mapping mountain pine stands. Specifically, we assessed the influence of spatial resolution and feature space composition including the GLCM window size for textural features. The study site is covering the Sarntal Alps, Italy, a region known for large stands of mountain pine. Our results show that mountain pines can be accurately mapped (PlanetScope (90.96%) and Sentinel-2 (90.65%)) by combining all features. In general, Sentinel-2 can achieve comparable results to PlanetScope independent of the feature set composition, despite the lower spatial resolution. In particular, the inclusion of textural features improved the accuracy by +8% (PlanetScope) and +3% (Sentinel-2), whereas accuracy improvements of topographic features and canopy height were low. The derived map of mountain pines in the Sarntal Alps supports local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline. Furthermore, our study highlights the importance of freely available Sentinel-2 data and image-derived textural features to accurately map mountain pines in Alpine environments. KW - mountain pines KW - PlanetScope KW - Sentinel-2 KW - gray level co-occurrence matrix Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281945 SN - 2072-4292 VL - 14 IS - 13 ER - TY - JOUR A1 - Schwindt, Daniel A1 - Kneisel, Christof T1 - Optimisation of quasi-3D electrical resistivity imaging – application and inversion for investigating heterogeneous mountain permafrost JF - The Cryosphere Discuss N2 - This study aimed to optimise the application, efficiency and interpretability of quasi-3D resistivity imaging for investigating the heterogeneous permafrost distribution at mountain sites by a systematic forward modelling approach. A three dimensional geocryologic model, representative for most mountain permafrost settings, was developed. Based on this geocryologic model quasi-3D models were generated by collating synthetic orthogonal 2D arrays, demonstrating the effects of array types and electrode spacing on resolution and interpretability of the inversion results. The effects of minimising the number of 2D arrays per quasi-3D grid were tested by enlarging the spacing between adjacent lines and by reducing the number of perpendicular tie lines with regard to model resolution and loss of information value. Synthetic and measured quasi-3D models were investigated with regard to the lateral and vertical resolution, reliability of inverted resistivity values, the possibility of a quantitative interpretation of resistivities and the response of the inversion process on the validity of quasi-3D models. Results show that setups using orthogonal 2D arrays with electrode spacings of 2 m and 3 m are capable of delineating lateral heterogeneity with high accuracy and also deliver reliable data on active layer thickness. Detection of permafrost thickness, especially if the permafrost base is close to the penetration depth of the setups, and the reliability of absolute resistivity values emerged to be a weakness of the method. Quasi-3D imaging has proven to be a promising tool for investigating permafrost in mountain environments especially for delineating the often small-scale permafrost heterogeneity, and therefore provides an enhanced possibility for aligning permafrost distribution with site specific surface properties and morphological settings. KW - permafrost KW - permaforst mountain KW - electrical resistivity imaging KW - ERI KW - optimisation Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-138017 VL - 5 ER - TY - JOUR A1 - Schönbrodt-Stitt, Sarah A1 - Ahmadian, Nima A1 - Kurtenbach, Markus A1 - Conrad, Christopher A1 - Romano, Nunzio A1 - Bogena, Heye R. A1 - Vereecken, Harry A1 - Nasta, Paolo T1 - Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem JF - Frontiers in Water N2 - Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{−3}\), and 89% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{−3}\, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon. KW - near-surface soil moisture KW - Sentinel-1 single-look complex data KW - SAR backscatters KW - terrain parameters KW - Alento hydrological observatory KW - Mediterranean environment Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259062 VL - 3 ER - TY - JOUR A1 - Sogno, Patrick A1 - Klein, Igor A1 - Kuenzer, Claudia T1 - Remote sensing of surface water dynamics in the context of global change — a review JF - Remote Sensing N2 - 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. KW - remote sensing KW - surface water KW - dynamics KW - global change KW - earth observation KW - hydrology KW - biosphere KW - anthroposphere KW - review Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275274 SN - 2072-4292 VL - 14 IS - 10 ER - TY - JOUR A1 - Sogno, Patrick A1 - Traidl-Hoffmann, Claudia A1 - Kuenzer, Claudia T1 - Earth Observation data supporting non-communicable disease research: a review JF - Remote Sensing N2 - 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. KW - Earth Observation KW - land surface dynamics KW - atmosphere KW - exposure KW - geoanalysis KW - non-communicable disease KW - public health KW - remote sensing KW - review Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-211113 SN - 2072-4292 VL - 12 IS - 16 ER - TY - JOUR A1 - Stereńczak, Krzysztof A1 - Laurin, Gaia Vaglio A1 - Chirici, Gherardo A1 - Coomes, David A. A1 - Dalponte, Michele A1 - Latifi, Hooman A1 - Puletti, Nicola T1 - Global Airborne Laser Scanning Data Providers Database (GlobALS) — a new tool for monitoring ecosystems and biodiversity JF - Remote Sensing N2 - Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS. KW - LiDAR KW - forest KW - database KW - networking KW - GlobALS Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207819 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Thonfeld, Frank A1 - Gessner, Ursula A1 - Holzwarth, Stefanie A1 - Kriese, Jennifer A1 - da Ponte, Emmanuel A1 - Huth, Juliane A1 - Kuenzer, Claudia T1 - A first assessment of canopy cover loss in Germany's forests after the 2018–2020 drought years JF - Remote Sensing N2 - Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time. KW - forest KW - canopy cover loss KW - drought KW - Sentinel-2 KW - Landsat-8 KW - disturbance index KW - time series Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-255306 SN - 2072-4292 VL - 14 IS - 3 ER -