Unmanned aerial systems in remotely sensed biomass estimates : how they improve the quality of existing satellite based approaches

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesForests of the world provide an important ecosystem service in the fight against climate change by sequestering carbon from the atmosphere and storing them as biomass. However, cloud cover and terrain inaccessibility hamper studies of forest biomass using satellites, especially in the dense jungles of the tropics. This study investigated the use of UAS to complement existing satellite based approaches by exploring what information can be derived from UAS sensors and how their biomass estimates can be applied to satellite sensors to improve their accuracies. A biomass estimation model was built using on the ground measurements while GIS was used to generate biomass maps. The results from the model show that NDVI and tree heights were statistically significant explanatory variables for biomass in the Mixed Oak Forests of Davert, Germany. Estimates from UAS were the most accurate in terms of R2, compared to other sensor estimates from Sentinel 2, World View 3 and Orthophotos. Hence, two adjustment factors were proposed to improve the accuracy of World View 3 and Sentinel 2 estimates. UAS are thus a versatile sensor platform for biomass studies that complements satellite sensors to improve studies of global biomass of forests

A High-Resolution Map of Singapore’s Terrestrial Ecosystems

The natural and semi-natural areas within cities provide important refuges for biodiversity, as well as many benefits to people. To study urban ecology and quantify the benefits of urban ecosystems, we need to understand the spatial extent and configuration of different types of vegetated cover within a city. It is challenging to map urban ecosystems because they are typically small and highly fragmented; thus requiring high resolution satellite images. This article describes a new high-resolution map of land cover for the tropical city-state of Singapore. We used images from WorldView and QuickBird satellites, and classified these images using random forest machine learning and supplementary datasets into 12 terrestrial land classes. Close to 50 % of Singapore’s land cover is vegetated while freshwater fills about 6 %, and the rest is bare or built up. The overall accuracy of the map was 79 % and the class-specific errors are described in detail. Tropical regions such as Singapore have a lot of cloud cover year-round, complicating the process of mapping using satellite imagery. The land cover map provided here will have applications for urban biodiversity studies, ecosystem service quantification, and natural capital assessment

The State of Mangroves in Tanintharyi (Myanmar) from 1989-2014, and its Implications for Coastal Management

Bachelor'sBachelor of Social Sciences (Honours

Identifying spatial patterns and interactions among multiple ecosystem services in an urban mangrove landscape

10.1016/j.ecolind.2020.107042ECOLOGICAL INDICATORS12

Spatial Methods for Inferring Extremes in Dengue Outbreak Risk in Singapore

10.3390/v14112450VIRUSES-BASEL141

Fine-scale estimation of effective reproduction numbers for dengue surveillance

10.1371/journal.pcbi.1009791PLOS COMPUTATIONAL BIOLOGY18