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Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, AngolaFollowing the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.
Document ID
20240010493
Acquisition Source
Langley Research Center
Document Type
Other - DEVELOP Summer 2024 Technical Report
Authors
Faith Macdonald
(Analytical Mechanics Associates (United States) Hampton, Virginia, United States)
Emmanuel N Aklie
(Analytical Mechanics Associates (United States) Hampton, Virginia, United States)
Wei Lun Tay
(Analytical Mechanics Associates (United States) Hampton, Virginia, United States)
Lukka Wolff
(Analytical Mechanics Associates (United States) Hampton, Virginia, United States)
Date Acquired
August 13, 2024
Publication Date
September 9, 2024
Publication Information
Publisher: National Aeronautics and Space Administration
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
CONTRACT_GRANT: 80LARC23FA024
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Professional Review
Keywords
remote sensing
landcover classification
Google Earth Engine
random forest classifier
arid environment
Landsat
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