Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors
Open Access
- 5 January 2021
- Vol. 21 (1), 320
- https://doi.org/10.3390/s21010320
Abstract
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.Keywords
Funding Information
- European Research Council (647038 BIODESERT)
This publication has 70 references indexed in Scilit:
- A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in DrylandsRemote Sensing, 2019
- Dryland changes under different levels of global warmingScience of The Total Environment, 2018
- Author Correction: Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylandsNature Ecology & Evolution, 2018
- Remote‐sensing‐derived fractures and shrub patterns to identify groundwater dependenceEcohydrology, 2018
- Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case StudyRemote Sensing, 2017
- Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metricsRemote Sensing of Environment, 2016
- Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetryRemote Sensing of Environment, 2016
- Early Warning Signals of Ecological Transitions: Methods for Spatial PatternsPLOS ONE, 2014
- Land degradation in drylands: Interactions among hydrologic–aeolian erosion and vegetation dynamicsGeomorphology, 2010
- The role of vegetation patterns in structuring runoff and sediment fluxes in drylandsEarth Surface Processes and Landforms, 2005