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The presence of sea ice leads in the sea ice cover represents a key feature in polar regions by controlling the heat exchange between the relatively warm ocean and cold atmosphere due to increased fluxes of turbulent sensible and latent heat. Sea ice leads contribute to the sea ice production and are sources for the formation of dense water which affects the ocean circulation. Atmospheric and ocean models strongly rely on observational data to describe the respective state of the sea ice since numerical models are not able to produce sea ice leads explicitly. For the Arctic, some lead datasets are available, but for the Antarctic, no such data yet exist. Our study presents a new algorithm with which leads are automatically identified in satellite thermal infrared images. A variety of lead metrics is used to distinguish between true leads and detection artefacts with the use of fuzzy logic. We evaluate the outputs and provide pixel-wise uncertainties. Our data yield daily sea ice lead maps at a resolution of 1 km2 for the winter months November– April 2002/03–2018/19 (Arctic) and April–September 2003–2019 (Antarctic), respectively. The long-term average of the lead frequency distributions show distinct features related to bathymetric structures in both hemispheres.
A satellite-based climatology of wind-induced surface temperature anomalies for the Antarctic
(2019)
It is well-known that katabatic winds can be detected as warm signatures in the surface temperature over the slopes of the Antarctic ice sheets. For appropriate synoptic forcing and/or topographic channeling, katabatic surges occur, which result in warm signatures also over adjacent ice shelves. Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) data are used to detect warm signatures over the Antarctic for the winter periods 2002–2017. In addition, high-resolution (5 km) regional climate model data is used for the years of 2002 to 2016. We present a case study and a climatology of wind-induced IST anomalies for the Ross Ice Shelf and the eastern Weddell Sea. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. Katabatic surges represent a strong climatological signal with a mean warm anomaly of more than 5K on more than 120 days per winter for the Byrd Glacier and the Nimrod Glacier on the Ross Ice Shelf. The mean anomaly for the Brunt Ice Shelf is weaker, and exceeds 5K on about 70 days per winter. Model simulations of the IST are compared to the MODIS IST, and show a very good agreement. The model data show that the near-surface stability is a better measure for the response to the wind than the IST itself.
This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1"5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2"12.5 -µm (instrument NEDT 0.05 K"0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0"10.25 -µm and 10.25"12.5 -µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1"3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.
Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes.rnWe compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources.
Das EU-weite Naturschutznetz Natura 2000 (FFH) umfasst über 11% der terrestrischen Ökosystemfläche. Zur langfristigen Erhaltung dieser Gebiete fehlt ein funktionierendes Monitoringsystem mit geeigneten Indikatoren, Parametern und Datenprodukten, die eine regelmäßig wiederholbare, flächendeckende und vor allem kosteneffiziente Erhebung ermöglichen. Hierfür untersucht diese Dissertation moderne, höchstauflösende Satellitendaten und die Möglichkeiten ihrer Anwendung im Naturschutz, insbesondere als Grundlage zur Indikatorenableitung. Es wurden konkrete Anforderungen von Behörden und NGO bzgl. Daten und Indikatorwerten gesammelt und für zwei Untersuchungsgebiete im Naturpark "Hoher Fläming" in Brandenburg umgesetzt. Dazu wurden zwei Aufnahmen des QuickBird-Satelliten akquiriert und mit vorhandenen GIS-Daten kombiniert. Der praktische Teil der Arbeit beschreibt Eigenschaften und Vorverarbeitung aller Daten, ihre Auswertung nach einem objektbasierten Ansatz und die Ableitung spezifischer quantitativer Parameter. Diese beschreiben den Zustand der Ökosysteme und berücksichtigen die sozio-ökonomischen Belastungen, die auf die Flächen einwirken und Nutzungskonflikte verursachen. Auf der Basis dieser Parameter wurden räumliche Indikatoren erprobt. Zur Anwendung auf der lokalen Ebene in bewaldeten Gebieten und für das Monitoring von Offenland-Flächen werden je zwei Indikatoren vorgeschlagen. Für die regionale Ebene wird ein sozio-ökomischer Indikator empfohlen. Diese fünf Indikatoren sind dazu geeignet, ausgewählte Aspekte der (Bio)Diversität in Schutzgebieten zu beschreiben. Sie analysieren Komposition, Struktur und Funktion der Habitat-Typen sowohl auf der regionalen Landschafts-Ebene, als auch auf der lokalen Ökosystem- bzw. Schutzgebiets-Ebene. Alle Indikatoren besitzen einen Nutzen für das Management von Schutzgebieten und bieten zumindest indirekte Hilfe für die Berichterstattung im Sinne der FFH-Richtlinie. Die vorgeschlagenen Indikatoren sind zwar spezifisch auf die lokalen Untersuchungsgebiete zugeschnitten, doch sind die ökologischen Rahmenbedingungen allgemein gültig. Es ist möglich, diese Indikatoren auch in anderen europäischen Regionen mit den gleichen natürlichen Gegebenheiten und sozio-ökonomischen Strukturproblemen anzuwenden. Für die Anwendung verschiedener Fernerkundungsdaten zur Erfüllung von Monitoringaufgaben sprechen die positiven Ergebnisse der durchgeführten Kosten-Nutzen-Analyse. Vor- und Nachteile von Daten und Auswertungsmethoden werden ausführlich diskutiert.