Prof. Dr. Tobia Lakes

Profil

Zusammenfassung

Prof. Lakes entwickelt Methoden zur Analyse von Landnutzung, Umweltveränderungen und deren Auswirkungen auf Gesundheit und Gesellschaft. Er kombiniert Fernerkundung, Machine Learning und räumliche Modellierung, um komplexe Wechselwirkungen zwischen Klima, Landwirtschaft, Urbanisierung und menschlichem Wohlbefinden zu verstehen. Seine Expertise ermöglicht es Unternehmen und Behörden, großflächige Umweltveränderungen zu überwachen, Risiken vorherzusagen und evidenzbasierte Strategien für nachhaltiges Landmanagement zu entwickeln.

Skills

Name

Prof. Dr. Tobia Lakes

Titel

Prof. Dr.

Fakultät

Mathematisch-Naturwissenschaftliche Fakultät

Institut

Geographisches Institut

Arbeitsgruppe

Angewandte Geoinformationsverarbeitung

E-Mail

🔒 nur für eingeloggte sichtbarAnmelden

Telefon

🔒 nur für eingeloggte sichtbarAnmelden

HU-FIS-Profil

Quelle ↗

Zuletzt gescrapt

28.6.2026, 01:08:45

• Agricultural Land Markets - Efficiency and Regulation" Teilprojekt SP7 "Quantifizierung der Konzentration an Bodeneigentum und von Zielkonflikten in der Landwirtschaft"

  Quelle ↗

  Förderer: DFG Forschungsgruppe Zeitraum: 03/2021 - 07/2024 Projektleitung: Prof. Dr. Tobia Lakes

• AvH Perez Klimaschutzstipendium

  Quelle ↗

  Förderer: Alexander von Humboldt-Stiftung Zeitraum: 09/2012 - 12/2014 Projektleitung: Prof. Dr. Tobia Lakes

• Building Excellence in Research of Human-Environmental Systems With Geospatial and Earth Observation Technologies (HES-GEO)

  Quelle ↗

  Förderer: Horizon 2020: Coordination and Support Action (CSA) Zeitraum: 01/2021 - 12/2023 Projektleitung: Prof. Dr. Dagmar Haase, Prof. Dr. Tobia Lakes, Prof. Dr. Tobias Kümmerle

🔒 Das System hat 358 mögliche Industrie-Partner gefunden — Firmen, Scores und Begründungen sind nur für eingeloggte Nutzer:innen sichtbar. Anmelden

• Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine

  2021

  Journal of Environmental Planning and Management · 193 Zitationen · DOI

  With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) bring data-driven techniques to the desktops of researchers while changing workflows and making excessive data downloads redundant. We present a study that utilizes machine learning algorithms on the GEE cloud computing platform for land use/land cover (LULC) mapping and change detection analysis using a Landsat satellite image time series. We applied different machine learning techniques to data from an environmentally sensitive area in Northern Iran. We tested their efficiency for LULC mapping and change detection analysis using the support vector machine (SVM), random forest (RF) and classification and regression tree (CART). We obtained LULC maps for the years 2000, 2005, 2010, 2015 and 2020. Training data was collected from field operations and historical datasets, and the respective LULC maps were validated using ground control points. In addition, we validated the reliability of the results through a spatial uncertainty analysis using Dempster-Shafer Theory (DST). The resulting accuracies of the classification outcomes varied significantly. SVM performed best with accuracies of 90.25%, 91.84%, 89.02%, 93.35% and 95.65% for 2000, 2005, 2010, 2015 and 2020, respectively. The spatial uncertainty analysis also validated the efficiency of SVM compared to RF and CART. The results confirm the potential of machine learning techniques for time series LULC mapping on the GEE platform while lowering the barriers to analyzing large amounts of satellite data. The results are also critical for decision-makers and authorities for analyzing the LULC changes and developing the respective environmental protection and polices in Northern Iran.

• Restoration in Urban Spaces

  2015

  Environment and Behavior · 187 Zitationen · DOI

  Despite promising experimental findings, few studies have addressed the potential long-term health benefits of frequent contact with different kinds of urban nature. We examine the cross-sectional relations between two kinds of urban nature (neighborhood vegetation visible from the home, use of public green spaces) and health outcomes (life satisfaction, perceived general health, 2-months hair cortisol levels) in a sample population from Berlin ( N = 32) using a mixed-method approach. Participants whose homes had views of high amounts of diverse kinds of vegetation had significantly lower cortisol levels. Moreover, participants who regularly used a vegetated trail along a canal had significantly lower cortisol levels and reported significantly higher life satisfaction than less frequent users. In addition, vegetated routes or paths played an important role in the restorative activities and daily commutes of participants. We discuss directions for future research and recommend more consideration of greenways in urban development.

• Assessing Weather Effects on Dengue Disease in Malaysia

  2013

  International Journal of Environmental Research and Public Health · 185 Zitationen · DOI

  The number of dengue cases has been increasing on a global level in recent years, and particularly so in Malaysia, yet little is known about the effects of weather for identifying the short-term risk of dengue for the population. The aim of this paper is to estimate the weather effects on dengue disease accounting for non-linear temporal effects in Selangor, Kuala Lumpur and Putrajaya, Malaysia, from 2008 to 2010. We selected the weather parameters with a Poisson generalized additive model, and then assessed the effects of minimum temperature, bi-weekly accumulated rainfall and wind speed on dengue cases using a distributed non-linear lag model while adjusting for trend, day-of-week and week of the year. We found that the relative risk of dengue cases is positively associated with increased minimum temperature at a cumulative percentage change of 11.92% (95% CI: 4.41-32.19), from 25.4 °C to 26.5 °C, with the highest effect delayed by 51 days. Increasing bi-weekly accumulated rainfall had a positively strong effect on dengue cases at a cumulative percentage change of 21.45% (95% CI: 8.96, 51.37), from 215 mm to 302 mm, with the highest effect delayed by 26-28 days. The wind speed is negatively associated with dengue cases. The estimated lagged effects can be adapted in the dengue early warning system to assist in vector control and prevention plan.

• Alice Salomon Hochschule Berlin

  KliBUp - Klimagesundheit in Lebenswelten - Entwicklung von Strategien und Handlungsansätzen zur Förderung von Resilienz durch Bottom-Up-Ansätze - Teilprojekt Urban Health_räumliche Analyse

  other

• Charité – Universitätsmedizin Berlin

  KliBUp - Klimagesundheit in Lebenswelten - Entwicklung von Strategien und Handlungsansätzen zur Förderung von Resilienz durch Bottom-Up-Ansätze - Teilprojekt Urban Health_räumliche Analyse

  university

• Freie Universität Berlin

  Vorbereitungsmodul für ein Einstein Center Climate Change

  university