Abstract
Abstract. Planning sustainable use of land resources and environmental monitoring benefit from accurate and detailed forest information. The basis of accurate forest information is data on the spatial extent of forests. In Norway land resource maps have been carefully created by field visits and aerial image interpretation for over four decades with periodic updating. However, due to prioritization of agricultural and built-up areas, and high requirements with respect to the map accuracy, forest areas and outfields have not been frequently updated. Consequently, in some part of the country, the map has not been updated since its first creation in the 1960s. The Sentinel-2 satellite acquires images with high spatial and temporal resolution which provides opportunities for creating cloud-free mosaic images over areas that are often covered with clouds. Here, we combine object-based image analysis with machine learning methods in an automated framework to map forest area in Sentinel-2 mosaic images. The images are segmented using the eCogntion™ software. Training data are collected automatically from the existing land resource map and filtered using height and greenness information so that the training samples certainly represent their respective classes. Two machine learning algorithms, namely Random Forest (RF) and the Multilayer Perceptron Neural Network (MLP), are then trained and validated before mapping forest area. The effects of including and excluding some features on the classification accuracy is investigated. The results show that the method produces forest cover map at very high accuracy (up to 97%). The MLP performs better than the RF algorithm both in classification accuracy and in robustness against inclusion and exclusion of features.
Highlights
Detailed forest information is crucial for land use planning, land resource management, biodiversity and climate change monitoring and mitigation (Astrup et al 2019; Fichtner et al 2018; Gamfeldt et al 2013)
Optical satellite remote sensing has long been used in land use land cover mapping including forest cover
A better way to look at the performances is the balanced accuracy rather than the overall accuracy and the accuracies of individual classes
Summary
Detailed forest information is crucial for land use planning, land resource management, biodiversity and climate change monitoring and mitigation (Astrup et al 2019; Fichtner et al 2018; Gamfeldt et al 2013). Efficient methods of obtaining accurate forest cover is important in up-to-date resource inventory and environmental monitoring, especially with respect to climate change. Major parts of countries like Norway are covered by clouds throughout the year with only few days of clear sky. This severely limits the use of optical satellite images. Cloudfree mosaic images produced over that period can be considered as representative of the period. Beyond visualisation purposes, such mosaic images can be analysed in a similar approach as single scene images
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
