Jason's GIS project

  I have hands-on experience conducting precise land surveys using a total station, a tool crucial for measuring angles and distances in various terrains. After gathering the necessary field data, I utilize CAD software to create detailed and accurate maps, ensuring the representation of survey results meets professional standards. This combination of skills allows for efficient and reliable geospatial analysis and mapping.

This study investigated the determinants of flood risks in the Lake District, UK, by using Sentinel-2A remote sensing satellite data integrated into Google Earth Engine (GEE) to extract monthly water covered areas. A further regression analysis was performed to assess the influence of precipitation and snowfall on variations in water covered area. While the findings did not quite match the early predictions, they still indicate that more precipitation and snowfall might lead to an increase in the overall area covered by water, thereby increasing the possibility of flooding. The main period of flood danger was identified as occurring from November to January, coinciding with the season characterised by the greatest amounts of precipitation and snowfall. This research is limited by cloud coverage of the study area during precipitation, which affected the examination of remote sensing photographs. Furthermore, other variables like as vegetation might also impact the likelihood of flooding...

In this project, I analyzed NDVI trends to assess vegetation cover changes across various provinces in southern China, including Sichuan, Guizhou, Chongqing, Hubei, Hunan, Jiangxi, Anhui, Zhejiang, and Fujian. Utilizing Google Earth Engine (GEE) for time series analysis from 2000 to 2021, I employed techniques like gap filling and smoothing to enhance data quality. The findings revealed significant relationships between annual rainfall and vegetation greenness, highlighting trends in vegetation cover and health. This research contributes to understanding forest ecosystem dynamics and supports conservation and sustainable management efforts. https://code.earthengine.google.com/3303d3e2ae823cd1ee3a3ebdfaae5955

This study focuses on assessing two forested areas of Woodland Valley Farm to determine the level of damage caused by beavers. In my project, I collected data using a drone to effectively identify gaps in the canopy. By utilizing QGIS, I separated the trees from the ground layer for further analysis. After this separation, I applied an interpolation method called Inverse Distance Weighting (IDW). The results indicated that the presence of beavers positively impacts increasing species richness in the area.