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Themenvorschläge für Masterarbeiten / Proposed topics for master theses

Thesis related to the project FirSt 2.0
Contact: Michael Förster ()
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 Germany
Requirements:
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
Remote Sensing methods to restore trees and livelihoods in Kenya
Contact: Michael Förster ()
Background: Climate change and land degradation are threatening livelihoods, incomes, and food security in Kenya, particularly in natural resource-based subsistence and smallholder farmer communities in Arid and Semi-Arid Land (ASAL) areas, which rely on rain-fed agriculture. Climate resilience and land restoration is driven by healthy ecosystems, which are crucial to support poor rural and remote and vulnerable communities who rely on natural resources for their livelihoods.

Reversing land degradation and achieving sustainable land management is essential to address food insecurity and rural poverty. This will ultimately enhance resilience to the impacts of a changing climate for vulnerable land-based communities across Sub-Saharan Africa. Evergreening systems (which include agroforestry and managed natural regeneration of trees on grazing and communal land) are among the most effective ecosystem-based adaptation techniques for restoring and sustaining land and soil fertility, enhancing climate change adaptation and resilience, and reducing emissions through long-term storage of carbon in landscapes.


The Global Evergreening Alliance (GEA) aims to restore trees, strengthen ecosystems, and increase carbon storage through their upcoming program in the Baringo and Kitui districts of Kenya. The program aims to generate carbon credits to support community livelihoods and the long-term monitoring of program activities. Initial aspects of this part of the program include the generation of a baseline report which involves remote sensing and GIS analysis. This baseline will cover a trend of the 10 years leading up to the program’s inception (i.e., 2011 to 2021).


Within this context, it is proposed that this thesis seeks to undertake a baseline study of landcover and landscape carbon stock trends through a remote sensing 10-year time series analysis.


Within the given demarcated project boundary what have been the baseline 10-year trends in terms of change in landcover and biomass carbon? This question should be answered with reference to the following three trends:


a)    Which areas of land have not been forested for the past 10 years (i.e., eligible carbon project areas must be unforested since 2011)? 
b)    What are the main land cover land use classes and what is the composition (proportions of different land types by % of total and number of hectares at 2011 and at present 2021)?
c)    What have been the ten-year statistical trends in NDVI or other proxy vegetation index (e.g., EVI, SVI) since 2011?


Objectives: To plot 10+ years baseline trends in landcover and vegetation indices monitoring (Landsat sensor)  and 4 years trends (2017-2021) in landcover and vegetation indices monitoring (Sentinel sensor).
Requirements:
Expertise in optical remote sensing
Expertise in GIS and google earth engine is helpful.


Partner: Global EverGreening Alliance (GEA)
Vegetation index vs individual bands/ In which circumstance, predicting plant traits with vegetation indices is more accurate than using a combination of separate remote sensing bands.
Contact:
TU Berlin – Prof. Birgit Kleinschmit (
birgit.kleinschmit@tu-berlin.de)
TU Berlin – Alby D. Rocha

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



Interception loss in urban environment/ The impact of land surfaces on evaporation from intercepted precipitation by the urban canopy.
Contact:
TU Berlin – Prof. Birgit Kleinschmit (
birgit.kleinschmit@tu-berlin.de)
TU Berlin – Alby D. Rocha

Background:

Evapotranspiration (ET) is an essential variable to understand water cycles and heat island effects in urban environments. ET is a measurement of mass (mm of water) evaporated from soil moisture and plant transpiration. However, in a highly fragmented and heterogeneous urban surface, the water intercepted from precipitation (without runoff or percolate) can contribute significantly to ET in specific periods of the year. Little is known about interception loss in the urban environment. Part of the UWI project (Urban Water Interfaces), this study aim to contribute with a method to estimate ET in urban environments based on a study case in Berlin. Urban Land-Surface Models (ULSM) will be used to estimate hourly interception loss with inputs of precipitation and ET time-series measurements and land surface maps derived from GIS/RS data. Two locations in Berlin with different levels of vegetation fraction and impervious surfaces will be compared.

Objectives:

1. Estimate hourly interception loss for two Berlin sites,

2. Assess the contribution of the land cover and land use surfaces for the interception loss and relative to total ET,

3. Analyse the impact of interception loss across the season and during night time.

Requirements: Basic remote sensing and GIS knowledge / familiar or open to learning R language and QGIS/UMEP

Research area: Berlin (two locations)

Related to: UWI project (
Urban Water Interfaces)
Distribution modelling of tree species/ neophytes with volunteered geographical information (VGI) data from the mobile phone app 'Flora Incognita'
Contact:
TU Berlin – Prof. Birgit Kleinschmit ()
TU Berlin - Christian Schulz

Background:

'Flora Incognita' provides highly up-to-date and geo-referenced species distribution data from volunteers all over Germany derived from deep learning image classifiers. The project by the TU Illmenau is highly acknowledged and recommended in the domains of deep learning and ecology. We expect, the species data and its GPS coordinates also provide useful information for the calibration/training of tree/ woodland classifiers in remote sensing. If your are interested in machine learning approaches and earth observation, feel free to write. The area of interest (e.g. Berlin, Brandenburg) and a set of focused species are still open.


Objectives:

1. Data request (if not available, alternative data sets are possible!)

2. Literature review: Volunteered Geographical Information (VGI) in remote sensing

3. Date review: Quality aspects of the used data (spatial preciseness, spatial distribution, class preciseness, up-to-dateness, data size, use restrictions)

4. Development of a prototype: e.g. Tree cover map Berlin, Forest type maps for Brandenburg

Requirements: Basic remote sensing knowledge / attendance of the course “Remote Sensing (MA UP WP 1.4)”, interest in plant ecology

Research area: tba. (e.g. Berlin/Brandenburg for validation data collection)

Related to: TreeSatAI

 
The use of LUCAS data and satellite time series data for forest type mapping
Contact:
TU Berlin – Prof. Birgit Kleinschmit ()
Christian Schulz – (

LUCAS contains up-to-date and highly precise land use and land cover data from in-situ measurements all over Europe which is likely to be useful as calibration/training data in remote sensing. We are interested in the usability of LUCAS data as input data for different aspects, like e.g. forest type mapping, meadow detection, crop type detection etc. If your are interested in machine learning approaches and earth observation data, feel free to write us. The area of interest is still open.

Objectives:

1. Data request

2. Literature review

3. Development of processing chains including machine learning models (e.g. Random Forest, Support Vector Machine or CNNs) for LULC prediction and classification

4. Validation/Testing of the prototype(s) with INVEKOS data or federal forest maps


Requirements: Basic remote sensing knowledge / attendance of the course “Remote Sensing (MA UP WP 1.4)” / Interest in machine learning with the programming language R

Research area: e.g.  Brandenburg or area of your interest

Related to: TreeSatAI
Estimation of soil moisture in a pre-alpine area with drone based TIR Data
Background Drought and soil moisture stress is an increasing threat for ecosystems and agriculture in times of climate change. Therefore, monitoring of ongoing trends at high temporal resolution over larger areas is crucial for adaptation and forecasting. Within the project Cosmic Sense several thermal infrared (TIR) datasets were acquired at an pre-alpine grassland site. These datasets open following questions:
 
  • Which spatial patterns of soil moisture exist?
  • Is it possible to model soil moisture patterns of deeper soil horizons with the thermal data?
  • How do these patterns change in the course of a day?
The topic is related to the research project ‘Cosmic Sense’ which aims to better understand the Cosmic Ray signal in relation to soil moisture at different testsites.
Requirements:
Basic remote sensing knowledge / attendance of the course “Remote Sensing (MA UP WP 1.4)
Research area: Fendt /Bavaria
Related to:
Cosmic Sense

Contact:
Prof. Dr. Birgit Kleinschmit (TUB) –

VeroniKa Döpper (TUB) -

Master thesis in the context of an internship in a non-university research institution or companies

Non-university research institution
 
RPL Agroscience www.agroscience.de

MCC - Mercator Research Institute on Global Commons and Climate Change
www.mcc-berlin.net
 
Naturpark Westhavelland www.westhavelland-naturpark.de

Small and medium-sized companies
Luftbild Umweltplanung www.lup-umwelt.de

Plante Labs www.planet.com

Masterarbeiten (laufend) / Master theses (current)

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

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

Sebastian Lehmler
Analysis of a green volume model for Germany based on Landsat and Sentinel-2 time-series.

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

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

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.

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

Hyunjae Kim
Impacts of land cover on heat and water fluxes in Berlin.

Masterarbeiten (abgeschlossen) / Master theses (completed)

2021

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

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