Abstract
Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.
Highlights
Grasslands represent one of the Earth’s most common vegetation types [1,2], covering nearly a fifth of the planet’s land [3] and providing important ecological, economic, and cultural services
The three grass height land covers were all identified with accuracies of 82% or higher, with accuracies as high as 85% and 88% obtained (Table 3)
We looked at the importance of all the image bands to the overall classification, and we looked at the importance of bands to classifying the three grass height land cover types
Summary
Grasslands represent one of the Earth’s most common vegetation types [1,2], covering nearly a fifth of the planet’s land [3] and providing important ecological, economic, and cultural services. They are responsible for an estimated 16%–17% of global primary production [4,5,6], serve as hotspots for floral and faunal biodiversity [7,8], support endemic species [7,8,9], affect runoff and water quality [10], and contain up to 30% of the Earth’s total soil carbon, reducing greenhouse gas emissions [2,8]. Radar and optical sensors collect data in different, complementary bands of electromagnetic
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