Integration of Multi-Temporal Multi-Source Remote Sensing Data and Field Surveys for Canopy height and Above-Ground Biomass (AGB) monitoringThe main goal of this study is to estimate canopy height and/or Above-Ground Biomass (AGB) and potentially its chan
The topic is open and also suitable for GEM students in track 3 – GEM for Ecosystems & Natural Resources.
Suggested elective courses:
- Forest monitoring and carbon stock estimation with multi-source remote sensing in the context of climate change
The estimation of canopy height, AGB and its changes is typically carried out using active Remote Sensing data such as LIDAR or Interferometric Synthetic Aperture Radars (InSAR) due to their capability of penetrating the tree canopies, thus measuring the main vegetation structural components (i.e., the tree trunks and branches) that are proxies for AGB. However, these data are not publicly available and are not acquired consistently, thus hampering their use for a consistent long-term analysis. Typically bi-temporal change detection analysis are carried out due to prohibitive acquisition costs. In contrast, Sentinel data is able to provide consistent information for a dense time series, which can potentially provide relevant information for the canopy height, AGB and its change estimation. This MSc thesis aims to study the impact of the information provided by the long time series of Sentinel-1 data acquired over the whole study area on the estimation and monitoring of the canopy height and/or AGB.
The main goal of this study is to estimate canopy height and/or Above-Ground Biomass (AGB) and potentially its changes in a forest area located in Indonesia, where Light Detection and Ranging (LIDAR) data were acquired in 2020 and 2022, together with repetitive field surveys that were carried out in 2017, 2019 and 2021. Differently from standard approaches, the AGB (change) estimation will be studied considering the information provided by the Sentinel-1 satellite data to create a continuous observation component of the whole study area. Such information can be used to:
- perform a trend analysis able to track the main land-cover changes for the whole study area on a long time span;
- enhance the interpretation of the canopy height, AGB and its changes obtained using repeated LiDAR acquisitions and field surveys.
- enhance the interpretation and comparability of heterogenous data in terms of temporal and spatial coverage and information content (e.g. field data, airborne LiDAR data, spaceborne Sentinel-1 data).
Michael Schlund, Martyna M Kotowska, Fabian Brambach, Jonas Hein, Birgit Wessel, Nicolo Camarretta, Mangarah Silalahi, I Nengah Surati Jaya, Stefan Erasmi, Christoph Leuschner, et al.,. "Spaceborne height models reveal above ground biomass changes in tropical landscapes." Forest Ecology and Management 497 (2021): 119497.
Yingchang Li, Mingyang Li, Chao Li, and Zhenzhen Liu, “Forest aboveground biomass estimation using landsat 8 and sentinel-1a data with machine learning algorithms,” Scientific reports, vol. 10, no. 1, pp. 1–12, 2020.