Woodhouse, Iain

Correa, Edgar

2023-11-17T10:15:39Z

2023-11-17T10:15:39Z

2023-11-20

Forests play a crucial role in the pledges made by countries toward meeting Paris Agreement targets (UNFCCC, 2015). Currently, there is a rapid generation of Earth Observation (EO) data that influences decision-making (Guo, Zhang et al. 2015, Filchev, Pashova et al. 2021). In 2018, Belize created its historical baseline Land Use and Land Use Change (LULUCF) Activity Data (AD) following Intergovernmental Panel on Climate Change (IPCC) principles for Greenhouse Gas Inventories (GHGi). Using a sample-based approach, 22,991 plots were visually classified by trained operators with available imagery from 2000 to 2018 (GoB, 2020). Using the tool Collect Earth, this approach provided a vital advantage, enabling a comprehensive classification of land-use sub-categories (Maniatis, Dionisio et al. 2021). However, due to the resource-intensive nature of this process, Belize seeks a more efficient and cost-effective alternative to enable faster reporting cycles for national and international reporting. To achieve this, nine EO products were assessed through a confusion matrix, selecting one product that closely aligns with Belize's Activity Data. The research was divided into three phases, with phases 1 and 2 conducted in the largest district to identify a nationwide suitable product. Out of the nine tested products, the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) algorithm exhibited the highest user accuracy of 96% within stable land use. This finding revealed that between 2000 and 2018, only 24% (5,274) of the 21,991 plots experienced changes or disagreements, while 76% (16,717) of the total plots remained stable throughout this period. Further investigation projected that approximately 961 stable plots would change for 2019-2023. Thus, this discovery could pave the way for a more efficient approach for countries like Belize, which currently utilizes the sample-based approach.

https://hdl.handle.net/1842/41198

http://dx.doi.org/10.7488/era/3934

en

The University of Edinburgh

Change Detection Algorithms

Activity Data

REDD+

Dynamic World

Sample Based Approach

Accuracy Assessment

Remote Sensing

Enhancing national activity data measuring, reporting, and verification in Belize through integration of global land use and change detection earth observation products

Thesis or Dissertation

Masters

MSc Master of Science