Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types

Abstract

Riparian zones fulfill diverse ecological and economic functions. Sustainable management requires detailed spatial information about vegetation and hydromorphological properties. In this study, we propose a machine learning classification workflow to map classes of the thematic levels Basic surface types (BA), Vegetation units (VE), Dominant stands (DO) and Substrate types (SU) based on multispectral imagery from an unmanned aerial system (UAS). A case study was carried out in Emmericher Ward on the river Rhine, Germany. The results showed that: (I) In terms of overall accuracy, classification results decreased with increasing detail of classes from BA (88.9%) and VE (88.4%) to DO (74.8%) or SU (62%), respectively. (II) The use of Support Vector Machines and Extreme Gradient Boost algorithms did not increase classification performance in comparison to Random Forest. (III) Based on probability maps, classification performance was lower in areas of shaded vegetation and in the transition zones. (IV) In order to cover larger areas, a gyrocopter can be used applying the same workflow and achieving comparable results as by UAS for thematic levels BA, VE and homogeneous classes covering larger areas. The generated classification maps are a valuable tool for ecologically integrated water management.

Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets

“…Vegetation mapping using machine learning techniques such as RF or SVM, for example, has often been implemented on a relatively small number of broad classes that are spectrally different, aggregating features with similar characteristics into a single class, e.g., different types of forests under an aggregated upland forest class [46][47][48][49]. Previous studies have shown that machine learning algorithms can effectively handle a relatively small number of broad classes and the inclusion of a larger number of more specific classes causes a significant drop in accuracy [50,51]. The use of spectrally similar classes, e.g., at the species level, also often causes a significant drop in accuracy [48,52,53].…”

An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution

“…Supervised classification of satellite images by employing machine learning classifiers such as Random Forests and XGBoost has been applied efficiently by previous studies for mapping of land cover and vegetation types [83,84]. The mapping suites presented in this research are built on the recent improvements of land cover and vegetation mapping through the utilization of machine learning and fusion of classification [85][86][87][88] and it indicates an enhanced performance by combining the probabilities of predictions from multiple classifiers. The ultra-resolution and very high-resolution suites proposed in this research should be effective and useful for land cover and vegetation mapping in other large regions of interest as well.…”

Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery

“…Also, the performance of different machine learning classifiers on high-resolution imagery was tested by many [33][34][35][36][37]. However, few have used UAS-derived imagery for a comparison of classifier performances [38][39][40], and even fewer have investigated the effect of the classifiers' hyperparameter settings on classification accuracies [41,42]. Due to variations between study areas, data collection procedures, and environmental conditions during data collection, it is difficult to derive generalized conclusions regarding the optimal choice of classification algorithm and hyperparameter settings for a specific task.…”