Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery
type: Dataset
description: 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.
issued: 2026-02-11T15:27:36.197013
modified: 2026-02-11T15:27:36.197017
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Volume 12 Issue 7
type: Distribution
issued: 2021-09-16T18:07:30.319751
modified: 2026-02-11T15:27:36.189302
access URL: https://www.mdpi.com/2072-4292/12/7/1086/htm

AmericaView
type: Agent