The summer 2018 was the warmest summer in Germany since the start of systematic records in 1881. At the same time, it was as dry as rarely ever before. As a result, the climatic water balance (CWB) was as low as just experienced once in the weather records. This extreme year was followed by a very dry and warm year 2019. Both years in combination are estimated as unprecedented in the last 250 years. In response, almost all important tree species almost everywhere in Central Europe showed severe signs of drought stress in 2018 and started to die in large areas in 2019 and 2020. Although the relation between the calamities and the forest species are rather clearly understood, the relation between hydrological deficits and the forest dieback is not understood fully.
Therefore, this thesis aims for estimating the relation between hydrological drought monitoring platforms and vegetation indices derived by remote sensing using data driven regression algorithms.
Possible questions within the overall aim can be for example:
The topic is related to the research project ‘FirSt 2.0’ which aims to monitor forest damages with remote sensing techniques.
Understand relations between hydrological drought and vegetation drought for forests in GermanyRequirements:
Expertise in optical remote sensing
Expertise in GIS and google earth engine is helpful.
Luftbild Umwelt Planung GmbH, Thünen Institute of Forest Ecosystems, State Forst Administrations of different federal states, Bavarian Forest National Park
Michael Förster (firstname.lastname@example.org)
Vegetation index derived from remote sensing bands is widely used to estimate (empirically) plant traits such as leaf area index (LAI), chlorophyll content and leaf water content. However, in which circumstance an index performs better than modelling individual bands is not entirely clear. This study aims to simulate satellite time-series images based on generated plant traits using Radiative Transfer Model (RTM) such as PROSAIL and SCOPE. These scenarios will vary according to the resolution (radiometric, temporal and spatial) of most common satellites such as Sentinel II and Landsat in different locations (i.e. latitudes). Based on these simulated databases, linear regressions' accuracy to predict plant traits using the most famous index (e.g. NDVI) will be assessed against its separate bands (and interactions). We aim to answer the question, which latitude range, resolutions, type of relationship (linear or non-linear) vegetation indices are better preditors than individual bands?
Basic remote sensing / familiar or open to learning R language
Prof. Dr. Birgit Kleinschmit
Dr. Alby D. Rocha
The availability of images collected by Remote Sensing (RS) devices (e.g. satellites and drones) has rapidly increased in the last years. However, the field measurements to model and validate RS products is still restricted to a very limited number of sampling locations. Soil moisture content (SMC) is an essential variable for ecological and agricultural science that can be retrieved by RS data, but sufficient and well distributed samples with ground truth measurements are needed. The align of field samples with RS image will depend on the spatial resolution and extent, while the representativeness will be affected by the spatial autocorrelation (heterogeneity and patterns) and relations with the modelled covariates. When a timeseries of images is available the temporal patters are also an important factor to take consideration. The sampling strategies such as spatial sampling and space-time designs for monitoring can improve model accuracy while reduce the sample size.
Objectives of this thesis are:
The project is related to an interdisciplinary and transregional research project “Cosmic Sense” that aims to investigate how and where soil moisture changes due to precipitation, evapotranspiration and deep percolation.
Experience in scripting using R (preferred), MATLAB or Python
Interest in working with satellite timeseries and modelling (regressions/machine Learning)
Institute of Environmental Science and Geography, University of Potsdam
email@example.com & firstname.lastname@example.org
The Earth’s boreal forest is subject to transformation linked to global warming. A fire regime change as current visible phenomenon is one of such transformations leading to prolonged and more severe forest fires. 2021 marked a record fire year for the Siberian boreal forest, especially in the eastern Republic of Sakha (Yakutia). An expedition of the Alfred Wegener Institute (AWI) Potsdam together with the TU Berlin acquired a comprehensive LiDAR dataset was using unoccupied aerial vehicles (UAVs) in 2021. Airborne LiDAR scanning is a known technique in forestry, but UAV-based LiDAR and terrestrial LiDAR are recent technological additions that bridge the scale between ground-based forest mensuration and satellite/airborne measurements. However, vast amounts of 3D point data pose a challenge for processing algorithms, while at the same time the often applied conversation into 2D raster for analysis leads to an unfavourable loss of information.
Here, the use of machine learning algorithms from openly accessible code libraries (e.g. PyTorch, Tensorflow) is proposed to be used on selected LiDAR forest plots of Yakutia. This thesis aims to analyse forest structure to detect, annotate and analyse fuel loads (dead wood, snags) and discriminate between boreal tree species.
Objectives of this thesis are:
The project is related to the research project “BioS” that aims to monitor and model the influence of wildfire on boreal forest of Central Yakutia.
Experience in scripting using Python (preferred) or R
Experience working with 3D point cloud data, especially LiDAR and structure-from-motion photogrammetry
Expertise in working with deep learning techniques is helpful
Alfred Wegener Institute Potsdam: Polar Terrestrial Environmental Systems Research Group
Robert.email@example.com & Michael.firstname.lastname@example.org & Stefan.email@example.com & firstname.lastname@example.org
Assessment of fuel loads in Siberian Larch forest using on UAV-based LiDAR data and 3D segmentation
Remote Sensing methods to restore trees and livelihoods in Kenya
Kazi Sajjad Hossain
Time series analysis of the deciduous forest degradation in the Berlin-Brandenburg region based on Sentinel-2 data
Once there was a trail: how we lost the possibility of walking through the territory, the case of Chile.
Evaluation of the temporal development of ash decline with hyperspectral imagery in Demmin, Germany.
Analysis of a green volume model for Germany based on Landsat and Sentinel-2 time-series.
Estimating the relation between hydrological drought monitoring platforms and vegetation indices derived by remote sensing.
Maxent modeling to forecast the future distribution of Acacia dealbata and Ulex europaeus in Chile under changing climate conditions.
Impacts of land cover on heat and water fluxes in Berlin.
Bráullio Nunes de Souza
Mapping expansion of agricultural frontier in Brazil: the case of coffee production in the Cerrado Mineiro Region.
Methodische Ansätze für Schwerpunkträume zum Artenschutz in der Windenergieplanung.
Bronwyn L. Dyson
Testing the PlanktoScope: can low-cost flow imaging provide reliable and cost-effective phytoplankton estimates?
Analysis of the greenhouse gas emissions from Lake Dagow (Brandenburg, Germany) using the floating chamber method and eddy covariance.
Identification of burn severity after the forest fires in Truenbreitzen using remote sensing techniques.
Object-based classification of individual tree vitality using LiDAR and hyperspectral data.
Rapid Assessment of forest storm damages using PlanetScope and Sentinel-2 satellite imagery.
Investigation on the vitality of selected urban trees in parks in Berlin and Brandenburg
Irina Stockmann (Uni Potsdam)
Biomasseschätzung in einem temperierten Mischwald auf der Basis von Höheninformationen und multitemporalen RapidEye Daten.
Balázs Lajos Dienes
Intra-annual and inter-generic variability of transmissivity of solar radiation through crowns of urban trees and its impact on solar radiation on building facades – a modelling approach
Jill Leonie Wabra (geb. Wagner)
Maximum Entropy Modelling and Classification of Pinus radiata Invasion in Central Chile
Explaining occurrence of different wild bee species groups with terrain indices, climate parameters, land-cover structure, and spectral indices.
How does precipitation influence the backscatter signal of grasslands?
How useful is Twitter data for the delineation of flood extent maps? A comparison of satellite-derived and Twitter-derived flood assessments
Urban blue infrastructure patterns – a review of European cities.
Assessing the quality of urban green spaces in European cities. Case study Berlin.
Modelling of Invasive Plant Species in Central Chile - Spreading patterns of Ulex europaeus and Acacia dealbata.
Estimating biophysical parameters of larix gmelinii using rapideye satellite imagery. The case study of Khatanga, central Siberia, Russia.
„Forest Cover Mapping and Multi-Temporal Analysis of Tugai Forests in Central Asia using Sentinel-2 and Landsat data.
Linear Spectral Unmixing of Sentinel2 time series for detecting invasion of Pinus radiata in the Maule Region in Chile.
Estimation of Soil Moisture in Vegetation Covered-Floodplains with Sentinel-1 SAR Data Using Support Vector Regression
Does urban structure trigger the spread of canine distemper virus in Berlin red foxes?
Assessing drivers of vegetation response in the lower reaches of the Tarim River, China using boosted regression trees.
An Automatic Tool for EUNIS Habitat Classification Using Ontologies and SEaTH.
Assessing desertification processes by remote sensing data in the Safi Region, Marocco.
Change Detection analysis for meadow monitoring using Rapid Eye satellite data.
Nele Steimetz (in Kooperation mit der Uni Potsdam)
Machine learning regression algorithms for biophysical parameter retrieval for Alder and Beech forests in North-East Germany
Sabine Koch (in Kooperation mit dem Institut für Geodäsie)
Storage and Querying of CityGML Models in BaseX - Evaluation of the usage of a native XML database system for 3D city models
Klassifikation von Offenlandvegetation in einem Natura 2000 Gebiet – der Effekt von hoher räumlicher, spektraler und temporaler Auflösung
Analysis of potential distribution and size of photovoltaic systems on rural rooftops - A contribution to an optimized local energy storage system with a remote sensing and GIS-based approach in Swabia, Germany
Agent-based modeling of human decisions on residential land uses in Berlin
A Comparison of Feature Selection Methods for Multitemporal Tree Species Classification
Quantifying land cover and landscape diversity changes at the caatinga (2001-
2012) – Landscape pattern analysis with modis land cover products
Tree species classification using intra-annual Rapid-Eye time series A case study: in Rhineland Palatinate, Germany
Kathrin Ward (in Kooperation mit HU Berlin)
How do urban structure influence the urban heat island effect? - A review of European cities.
Guilherme Henrique Braga Klaussner
Assessing Urban Environmental Justice in two subprefectures of São Paulo, Brazil – a GIS-based synoptic analysis
Marlen Diederitz (in Kooperation mit HU Berlin)
Szenarienbasierte Analyse der Versorgung mit Naherholungsflächen für die Stadtregion Berlin 2030
Investigating the Potential of TerraSAR-X time series for the detection of swath events in NATURA 2000 grassland habitats
When is a city green? - Eine GIS-basierte Methode zur Ermittlung der städtischen Grünflächen-Versorgung für Erholungszwecke
Masterarbeit Garske Zusammenfassung
Potentiale von Geographischen Informationssystemen in der Immobilienwirtschaft