With the increasing pressure on global forests due to deforestation and habitat loss, it is more important than ever to understand our forest ecosystems on a larger scale. Canopy gap fraction is an indicator used for estimating forest biomass and better understanding of ecosystem functioning. For decades there have been research on computing canopy gap fraction using ground measurements as well as Airborne Laser Scanning (ALS). These approaches, however, are limited by the data available. A global coverage of data on canopy gap fraction could be made available by space-based laser altimetry mission Ice, Cloud and land Elevation Satellite (ICESat-2). Although its main scientific objectives are focused on polar areas, it has already proven to facilitate broader scientific disciplines. The ATL08 data product is focused on land-vegetation and already provides global data on canopy heights. However, there is ongoing research for using ATL08 data for canopy gap fraction estimation. Although some approaches have been suggested in the literature, a tested workflow to achieve this goal has not been published.

This thesis tests two methods for estimating canopy gap fraction from ICESat-2 ATL08 data and evaluates the results against openly available ALS data. First, a simple method of using canopy to total photon ratio is used. Then, an alternative method that aims to correct for the surface reflectivity is tested. The results from both methods are similar, therefore the computationally less expensive method is recommended. Although this thesis does not achieve to present a sufficiently accurate approach for using ICESat-2 ATL08 data for canopy structure estimation, it is shown that further research is needed and the results are promising. Furthermore, it is demonstrated that annual trends in canopy gap fraction can be seen in ATL08 data. Considering the global coverage of ICESat-2 data, it is concluded that despite the accuracy not meeting the expectations, using ATL08 for studying canopy gap fraction on a global scale and through time has high value and great potential for environmental research.

The plastic pollution of aquatic environment is undoubtedly an emerging environmental risk, as it negatively affects ecosystems globally to a great extent. To prevent the plastic soup from growing even further, a Delft-based start-up Noria has developed plastic collectors, to remove plastic from rivers and canals before it reaches the ocean. In order for these devices to give maximum positive effect, they need to be installed in areas where plastic is more likely to accumulate - the plastic hotspots. Taking into consideration various natural attributes that affect the movement of the plastic waste in the water, such as wind direction, water flow, canal geometry, vegetation and man made structures in waterways; potential hotspots can be predicted in a model which would allow more efficient coordination of the cleaning process. Thus, this project aims to locate plastic accumulation zones in the city of Delft in a (semi-) automated manner using open spatial data analysed in GIS and a network simulation model.
The methodology developed in this project results in the visualisation of potential plastic hotspots where Noria’s collectors could be placed in order to remove and recycle the plastic. The potential hotspots suggested by the model were compared with ground truth data collected. The final result yielded only 20% accuracy and therefore did not meet the initial expectation. An evaluation of the shortcomings was made with suggestions for future research.