Master

Background:

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:

• Which sensor type (eg. SAR, multispectral) leads to best results?
• Are there different relations between tree species and regions in Germany?
• Are there Phenological seasons when a relation is better to detect?

The topic is related to the research project ‘FirSt 2.0’ which aims to monitor forest damages with remote sensing techniques.
 

Objectives:

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.
 

Partner:

Luftbild Umwelt Planung GmbH, Thünen Institute of Forest Ecosystems, State Forst Administrations of different federal states, Bavarian Forest National Park
 

Contact:

Michael Förster (michael.foerster@tu-berlin.de)

Background:

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?


Objectives:

1. Simulate spectral time-series images based on plant trait landscape scenarios in different locations according to the resolutions of the main current satellites,
2. Modelling the scenarios and predict plant traits using widely accepted vegetation index as predictors against individual bands,
3. Assess which circumstances the use of vegetation indices are indicated to predict plant traits instead of individual bands or wavelengths.

Requirements: 

Basic remote sensing / familiar or open to learning R language
 

Contact:

Prof. Dr. Birgit Kleinschmit
Dr. Alby D. Rocha

Background:

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:

• Using Sentinel 1 and 2 timeseries and drone images previously captured, define optimal sample locations to install SMC sensors in the field.
• Assess the difference in model accuracy (e.g. machine learning regressions) for different spatial sampling and space-time designs using a simulated field.
• Evaluate the use of different remote sensing index (covariate) to define sample locations in the lack of SMC preliminary information.

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.

Requirements:

Experience in scripting using R (preferred), MATLAB or Python
Interest in working with satellite timeseries and modelling (regressions/machine Learning)

Partners:

Institute of Environmental Science and Geography, University of Potsdam

Contact:

a.duarterocha@tu-berlin.de & michael.foerster@tu-berlin.de

Background:

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:

• Identify and evaluate open-access machine learning/deep learning segmentation tools for forest 3D data (e.g. PointNet++, Silvi-Net, Forest Structural Complexity Tool), in the scale from UAV to terrestrial LiDAR
• Train an artificial neural network using a manually segmented and annotated training data set
• Classify billions of LiDAR points to assess fuel loads aiming to evaluate forest fire risk

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.

Requirements:

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

Partners:

Alfred Wegener Institute Potsdam: Polar Terrestrial Environmental Systems Research Group

Contact:

Robert.jackisch@tu-berlin.de & Michael.foerster@tu-berlin.de & Stefan.kruse@awi.de & birgit.heim@awi.de

Background:

Urban Green Infrastructure (UGI) provides ecosystem services such as cooling of temperatures and can provide major benefits for climate change adaptation. Green Volume (GV) describes the 3-D space occupied by vegetation and constitutes an important indicator for the assessment of UGI. Remote sensing-based modelling approaches have been successfully used with Sentinel-2 (S-2) optical data; derived vegetation indices (VIs) and Sentinel-1 (S-1) SAR backscatter images to estimate GV yearly for large areas.

Over the recent decade, estimation of GV at small to medium scales using normalized digital surface models (nDSMs) (derived by Light Detection and Ranging (LiDAR) or stereo matching of aerial imagery) has become more feasible. For such approaches, digital orthophotos are classified to identify vegetated areas and the nDSM is then used, to estimate GV. The necessary data, however, is often only available for small areas or single cities and updated irregularly.

Since 2018, the Global Ecosystem Dynamics Investigation (GEDI) provides medium resolution LiDAR data of Earth’s vegetation and topography from the International Space Station (ISS). The information is provided at 20 m spatial resolution and could be the ideal supplement to optical and SAR data for a large-scale estimation of GV.

Objectives of this thesis are:

• Using available machine learning algorithms to include GEDI data into a satellite-based model to derive Green Volume
• Compare the results to other GV models in exemplary cities and rural areas

The project is related to the research project “UrbanGreenEye” (http://urbangreeneye.de/) that aims to provide annually updated climate adaptation indicators to different municipalities via a cloud-based data portal.

Requirements:

Experience in scripting using R or Python or Google Earth Engine.  
Interest in working with satellite timeseries and modelling (regression/machine learning)
Interest in urban planning and urban green

Partners:

Luftbild Umwelt Planung GmbH

Contact:

michael.foerster@tu-berlin.de & annett.frick@lup-umwelt.de & sebastian.lehmler@lup-umwelt.de

Background:

With the amendment of the Renewable Energy Act (EEG), there are ambitious expansion targets for solar energy. By 2050, a total of 400 GW is to be installed, half on roofs and half on open spaces. The previous expansion targets already led to strong regional pressure on land and intensified competition for use.

There are numerous proposals and guidelines from nature conservation or state administrations for the integration of species protection in PV ground-mounted systems. The solar industry is also striving for good professional planning (site selection) and nature-friendly design of solar parks. However, it is unclear whether these efforts are successful and whether mandatory and voluntary measures lead to the desired effects. Although monitoring studies come to the conclusion that solar farms on special sites can certainly be important for nature conservation, there is no reliable evidence of an enhancement of solar farms on previously intensively used arable sites. Individual case-related monitoring studies by operators have not yet led to sufficiently robust statements, not least due to a lack of comparability.

Objectives of this thesis are:

• Is it possible to monitor biotopes created in the solar park (between the rows of modules, at the edge of the solar park) using remote sensing, satellite images or drones?
• The monitoring concept will be implemented for an exemplary solar farm in the federal state of Brandenburg.

The project is related to the feasibility study for the creation of a satellite-based monitoring of solar parks.

Requirements:

Experience in scripting using R (preferred) or Python
Expertise in optical remote sensing and GIS
Interest in working with satellite data and drones
Interest in botanical mapping / biodiversity

Partners:

Luftbild Umwelt Planung GmbH

Contact:

michael.foerster@tu-berlin.de & annett.frick@lup-umwelt.de

Ramona Bauer und Nicola Kist (gemeinsame Arbeit)
Biodiversity and environmental justice in Berlin: Linking the availability of urban biodiversity with socio economic factors

Igor Mishurov
Impacts of recent droughts on plant vitality in Berlin – a satellite time series analysis

Nermin Varol
Multifunctional green infrastructure planning: Revealing trade-offs & synergies and the hotspot areas in Berlin

Audrey Adisi
Assessment of fuel loads in Siberian Larch forest using on UAV-based LiDAR data and 3D segmentation

Qiuandra Taylor
Remote Sensing methods to restore trees and livelihoods in Kenya

Pia Bettancourt
Once there was a trail: how we lost the possibility of walking through the territory, the case of Chile.

Pia Kräft
Estimating the relation between hydrological drought monitoring platforms and vegetation indices derived by remote sensing.

Forestry-UAV-Lidar related

Audrey Adisi
Assessment of fuel loads in Siberian Larch forest using on UAV-based LiDAR data and 3D segmentation

Shirin Akter Mony
Integrating IceSat-2 discrete Lidar canopy information with UAV-based Lidar ground truth for boreal forest systems (working title)

Ramsha Tauqeer
Algorithms, tools and workflows to process and analyse Lidar-based information with focus on forested ecosystems (working title)

2022

Aanjali Fernando
Mapping the invasive species Alianthus altissima in the Urban Environment with satellite Data

Sebastian Lehmler
Modelling green volume using Sentinel-1, -2 and PALSAR-2 satellite data and different machine learning algorithms for urban and semi-urban areas in Germany

Ahuvit Trumper
Evaluation of the temporal development of ash decline with hyperspectral imagery in Demmin, Germany

Kazi Sajjad Hossain
Time series analysis of the deciduous forest degradation in the Berlin-Brandenburg region based on Sentinel-2 data

Frederic Sorbe
Maxent modeling to forecast the future distribution of Acacia dealbata and Ulex europaeus in Chile under changing climate conditions.

Bráullio Nunes de Souza
Mapping expansion of agricultural frontier in Brazil: the case of coffee production in the Cerrado Mineiro Region.

Andrew Rasmussen
Methodische Ansätze für Schwerpunkträume zum Artenschutz in der Windenergieplanung.

2021

Hyunjae Kim
Impacts of land cover on Evapotranspiration in Berlin

Bronwyn L. Dyson
Testing the PlanktoScope: can low-cost flow imaging provide reliable and cost-effective phytoplankton estimates?

Florencia Arias
Analysis of the greenhouse gas emissions from Lake Dagow (Brandenburg, Germany) using the floating chamber method and eddy covariance.

Kayynat Shafiq
Identification of burn severity after the forest fires in Truenbreitzen using remote sensing techniques.

Leon-Friedrich Thomas
Object-based classification of individual tree vitality using LiDAR and hyperspectral data.

2020

Anjes Bloch
Rapid Assessment of forest storm damages using PlanetScope and Sentinel-2 satellite imagery.

Natalie Arnold
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.

2019

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

Kathrin Frank
Explaining occurrence of different wild bee species groups with terrain indices, climate parameters, land-cover structure, and spectral indices.

Isabel Blanke
How does precipitation influence the backscatter signal of grasslands?

Gina Maskell
How useful is Twitter data for the delineation of flood extent maps? A comparison of satellite-derived and Twitter-derived flood assessments
 

2018

Sophie Renner
Urban blue infrastructure patterns – a review of European cities.

Sara Muznik
Assessing the quality of urban green spaces in European cities. Case study Berlin.

Alexandra Rios
Modelling of Invasive Plant Species in Central Chile -  Spreading patterns of Ulex europaeus and Acacia dealbata.

Liubov Shirotova
Estimating biophysical parameters of larix gmelinii using rapideye satellite imagery. The case study of Khatanga, central Siberia, Russia.

Rayk Albrecht
„Forest Cover Mapping and Multi-Temporal Analysis of Tugai Forests in Central Asia using Sentinel-2 and Landsat data.

2017

Roman Wolf
Linear Spectral Unmixing of Sentinel2 time series for detecting invasion of Pinus radiata in the Maule Region in Chile.

Ann-Kathrin Holtgrave
Estimation of Soil Moisture in Vegetation Covered-Floodplains with Sentinel-1 SAR Data Using Support Vector Regression

Sara Knuth
Does urban structure trigger the spread of canine distemper virus in Berlin red foxes?

2016

Alexandra Singleton
Assessing drivers of vegetation response in the  lower reaches of the Tarim River, China using boosted regression trees.

Niklas Moran
An Automatic Tool for EUNIS Habitat Classification Using Ontologies and SEaTH.

Nicolas Specklin
Assessing desertification processes by remote sensing data in the Safi Region, Marocco.

2015

Cornelius Jopke
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

Christine Schwarzer
Klassifikation von Offenlandvegetation in einem Natura 2000 Gebiet – der Effekt von hoher räumlicher, spektraler und temporaler Auflösung

Ana Gonzales
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

Lisa Heinsch
Agent-based modeling of human decisions on residential land uses in Berlin

Kyle Pipkins
A Comparison of Feature Selection Methods for Multitemporal Tree Species Classification

Christian Schulz
Quantifying land cover and landscape diversity changes at the caatinga (2001-
2012) – Landscape pattern analysis with modis land cover products

Eva Tsimeka
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.
 

2014

Pierre-Adrien Dugord
Assessing the influence of land use Patterns on urban climate and citydwellers vulnerability toward heat stress

Guilherme Henrique Braga Klaussner
Assessing Urban Environmental Justice in two subprefectures of São Paulo, Brazil – a GIS-based synoptic analysis 

2013

Adina Tillack
Estimation of Seasonal Leaf Area Index  in an Alluvial Forest Using High Resolution Satellite-based Vegetation Indices

2012

Marlen Diederitz (in Kooperation mit HU Berlin)
Szenarienbasierte Analyse der Versorgung mit Naherholungsflächen für die Stadtregion Berlin 2030

2011

Iftikhar Ali
Investigating the Potential of TerraSAR-X time series for the detection of swath events in NATURA 2000 grassland habitats

Theresa Garske
When is a city green? - Eine GIS-basierte Methode zur Ermittlung der städtischen Grünflächen-Versorgung für Erholungszwecke
Masterarbeit Garske Zusammenfassung

Rolf Breitschaft
Potentiale von Geographischen Informationssystemen in der Immobilienwirtschaft