Abstract:
With advancements in data sources, software, and image analysis techniques,
remote sensing has become an efficient method for forest classification.
However, access to this technology has been limited for developing countries due
to the high cost of high-resolution images and analysis software. A potential
solution is that NASA and the European Space Agency provide free access to
mid-low resolution satellite images. In addition, Google Earth Engine, a free
cloud-based geospatial analysis platform, has allowed researchers from
developing countries to conduct research without relying on costly remote
sensing software. This study evaluates the suitability of the freely available
images and the Google Earth Engine platform for agroforestry applications in Sri
Lanka. Home garden is an agroforestry class seen in tropical countries often
overshadowed by global land cover classifications. As the home garden structure
and composition differs slightly from other forestry classes, it was necessary to
investigate the variables to distinguish the home garden from other agroforestry
classes. This study used a random forest classification algorithm to classify the
home garden, utilizing terrain data and Sentinel-2A images as the dataset. The
results confirmed that the red band of Sentinel-2 and textural metrics derived
from grey-level co-occurrence matrix analysis are effective in identifying home
gardens from other forestry classes. This research demonstrates that Google Earth
Engine and the freely available mid-low resolution satellite images make the
application of remote sensing in Sri Lanka a viable solution for the monitoring
and mapping of land cover.
