A multimodality test outperforms three machine learning classifiers for identifying and mapping paddocks using time series satellite imagery. Issue 25 (13th December 2022)

• Record Type: Journal Article
• Authors: O'Hara, Rob; Zimmermann, Jesko; Green, Stuart
• Is Part Of: Geocarto international. Volume 37:Issue 25(2023)
• Page Start: 9748
• Page End: 9766
• Publication Date: 2022-12-13
• Subjects: Land use -- grassland -- intensification -- Earth observation -- remote sensing; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.2024278
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Rotational grazing in paddocks is a strong indicator of intensive grassland management. Uncertainty in the extent of grassland management intensity is a reported source of uncertainty in greenhouse gas budgeting. This article outlines a method of detecting paddock locations in Sentinel 2 satellite imagery using a statistical multimodality test. The test was compared to three machine learning algorithms (support vector machine, random forest and extreme gradient boosting). Photographic records of the Eurostat 2018 LUCAS survey were used as ground truth data to confirm the presence or absence of paddocks. The multimodality test achieved an overall accuracy of 88.4% versus the best machine learning accuracy of 82.4%. The test was also used to map paddock occurrence at a regional scale in the Republic of Ireland. Overall map accuracy was 85.7% versus validation data. The test can be applied in temperate grasslands with pre-mapped field boundaries where rotational grazing is practiced.