Object-based Image Analysis Techniques Research Articles

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques

Monitoring burned areas in Thailand and other tropical countries during the post-harvest season is becoming increasingly important. High-resolution remote sensing data from Sentinel-2 satellites, which have a short revisit time, is ideal for accurately and efficiently mapping burned regions. However, automating the mapping of agriculture residual on a national scale is challenging due to the volume of information and level of detail involved. In this study, a Sentinel-2A Level-1C Multispectral Instrument image (MSI) from February 27, 2018 was combined with object-based image analysis (OBIA) algorithms to identify burned areas in Mae Chaem, Chom Thong, Hod, Mae Sariang, and Mae La Noi Districts in Chiang Mai, Thailand. OBIA techniques were used to classify forest, agricultural, water bodies, newly burned, and old burned regions. The segmentation scale parameter value of 50 was obtained using only the original Sentinel-2A band in red, green, blue, near infrared (NIR), and Normalized Difference Vegetation Index (NDVI). The accuracy of the produced maps was assessed using an existing burned area dataset, and the burned area identified through OBIA was found to be 85.2% accurate compared to 500 random burned points from the dataset. These results suggest that the combination of OBIA and Sentinel-2A with a 10 m spatial resolution is very effective and promising for the process of burned area mapping.

Remote Sensing Based Deforestation Monitoring at the Change Hotspot Area in Hindu Kush Himalayan Region

Forests play an essential role in providing sustainable ecosystem services and livelihood options for a growing population. In the Hindu Kush Himalayan (HKH) region, forests have been continuously reduced due to increasing demand for timber, fuelwood, and agriculture. Identification of deforestation hotspots and monitoring changes in those hotspots will be highly useful for forest managers to prevent illegal deforestation. In this paper, we identified forest cover change hotspots and areas for annual monitoring in those hotspots. For that factors like land cover from 1990 and 2010, shuttle radar topography mission (SRTM) digitalelevation model (DEM), slope, curvature, and distance from the settlement and roads for all four countries which could influence loss in forest area and overplayed to determine forest hotspots, were considered. Land cover maps of 1990 and 2010 and other GIS layers were used for identification of hotspots using the model builder ArcGIS software. For monitoring of deforestation in the hotspot areas, Landsat 8 images (2013, 2014 and 2015) and geographic object-based image analysis (GEOBIA) technique was used. The method was validated in four study sites in Bangladesh, Nepal, Bhutan and Pakistan. The study revealed that the sites in Bangladesh have higher deforestation during 2013-2014 and 2014-2015 with forest loss of 1121.58 and 1773.18 ha respectively. The web-based forest monitoring system provides information on deforestation useful for forest managers to enforce annual management plans.

Mapping and Assessment of Housing Informality Using Object-Based Image Analysis: A Review

Research on the detection of informal settlements has increased in the past three decades owing to the availability of high- to very-high-spatial-resolution satellite imagery. The achievement of development goals, such as the Sustainable Development Goals, requires access to up-to-date information on informal settlements. This review provides an overview of studies that used object-based image analysis (OBIA) techniques to detect informal settlements using remotely sensed data. This paper focuses on three main aspects: image processing steps followed when detecting informal settlements using OBIA; informal settlement indicators and image-based proxies used to detect informal settlements; and a review of studies that extracted and analyzed informal settlement land use objects. The success of OBIA in detecting informal settlements depends on the understanding and selection of informal settlement indicators and image-based proxies used during image classification. To meet the local ontology of informal settlements, the transfer of OBIA mapping techniques requires the fine-tuning of the rulesets. Machine learning OBIA techniques using image proxies derived from multiple sensors increase the opportunities for detecting informal settlements on the city or national level.

Assessment of Spatial Patterns of Backyard Shacks Using Landscape Metrics

Urban informality in developing economies like South Africa takes two forms: freestanding shacks are built in informal settlements, and backyard shacks are built in the yard of a formal house. The latter is evident in established townships around South African cities. In contrast to freestanding shacks, the number of backyard shacks has increased significantly in recent years. The study assessed the spatial patterns of backyard shacks in a formal settlement containing low-cost government houses (LCHs) using Unmanned Aerial Vehicle (UAV) products and landscape metrics. The backyard shacks were mapped using Object-Based Image Analysis (OBIA), which uses height information, vegetation index, and radiometric values. We assessed the effectiveness of rule-based and Random Forest (RF) OBIA techniques in detecting formal and informal structures. Informal structures were further classified as backyard shacks using spatial analysis. The spatial patterns of backyard shacks were assessed using eight shapes, aggregation, and landscape metrics. The analysis of the shape metrics shows that the backyard shacks are primarily square, as confirmed by a higher shape index value and a lower fractional dimension index value. The contiguity index of backyard shack patches is 0.6. The values of the shape metrics of backyard shacks were almost the same as those of formal and informal dwelling structures. The values of the assessed aggregation metrics of backyard shacks were more distinct from formal and informal structures compared with the shape metrics. The aggregation metrics show that the backyard shacks are less connected, less dense, and more isolated from each other compared with formal and freestanding shacks. The Shannon’s Diversity Index and Simpson’s Evenness Index values of informal settlements and formal areas with backyard shacks are almost the same. The results achieved in this study can be used to understand and manage informality in formal settlements.

Satellite based analysis of mangrove cover and density change in mangroves of Tulang Bawang District, Lampung Province, Indonesia

Abstract. Kaskoyo H, Hartati F, Bakri S, Febryano IG, Dewi BS, Nurcahyani N. 2023. Satellite based analysis of mangrove cover and density change in mangroves of Tulang Bawang District, Lampung Province, Indonesia. Biodiversitas 24: 3019-3028. Being a threatened ecosystem of the world, mangroves require regular monitoring to identify areas that need improvement in conservation measures. Considering this, the present study aimed to assess the mangrove cover and density change in mangrove forest of Tulang Bawang District, Lampung Province, Indonesia. Data was collected by remote sensing techniques using satellite imagery viz., Landsat 5 in 2000 and 2010 and Landsat 8 in 2020. It was then analyzed using remote sensing method, namely Object-Based Image Analysis techniques using the unguided classification method to obtain mangrove cover data; while mangrove density information was obtained using the Normalized Difference Vegetation Index algorithm. The results revealed the decrease in mangrove cover and density between 2000 and 2020. The mangrove cover decreased from 7529 ha in 2000 to 5551 ha in 2020. Furthermore, density of mangrove forest has also witnessed change from dense density class in 2000 to medium density class in 2020. From this result, it is evident that mangroves of East Rawajitu Sub-district are degrading and require focused efforts to prevent the degradation. Communities, entrepreneurs, and governments have different motives for utilizing mangrove forests that result in damage, so reasonable efforts are needed to manage mangrove forests sustainably.

Integrated mixture model and ensemble learning geographic object-based image analysis for road network extraction

ABSTRACT Delineation of road networks is often hindered by shadows, occlusions and spectrally similar objects. A hybrid Geographic object-based Image analysis (GeOBIA) technique that combines Mixture Model segmentation with Ensemble Learning algorithms to extract road networks is proposed. The Multispectral Local Dirichlet Mixture Model (MLDMM) highlights the built-up area. The bagging or subspace integrated ensemble learning-based classification makes the framework immune to overfitting and a novel statistical feature selection phase boosts the performance by 18%. Post-processing by path morphology achieves complete networks. MLDMM-SDEC proffers a precision of 99.86%, a recall of 90.90%, an F1-score of 95.23%, a detection quality of 94.73%, and an accuracy of 96.77%.

Application of UAV photogrammetry for the assessment of forest structure and species network in the tropical forests of Southern Nigeria

Using the object-based image analysis (OBIA) technique, this study examined structural characteristics, quantified carbon stocks, and identified tree species in the tropical forests of southwestern Nigeria. The Structure from Motion (SfM) technique was used to produce orthomosaics with a ground sampling distance of 10 m from orthophotos recorded during a survey and optimized for forest assessment with the near-infrared band. In addition to tree species variables, GPS coordinates, tree diameter, and tree crown were all employed to estimate carbon stock in the study area. Carbon stock estimates of 450 tons per hectare were estimated , with a tree-specific range of 65 kg per tree to 8,488 kg per tree. There was a correlation between tree canopies and diameters, but no conclusive relationships between tree species distribution and position in their natural formations. OBIA significantly enhanced the current carbon inventory and provided a spatial characterization of species distribution in the research area.

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques.

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO3-N), and soluble reactive phosphorus (SRP) for assessing water quality(WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULCtypes. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the "fair" status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land (r = 0.68, P < 0.01) and a significant negative association with the built-up area (r = - 0.94, P < 0.01). To the best of the authors' knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

Analysis of the association between image resolution and landscape metrics using multi-sensor LULC maps

This study aims to investigate the changes in landscape metrics with varying spatial resolution from Sentinel-2 (10 m), SPOT 7 (1.5 m), Pleaides (0.5 m), and Worldview-4 (0.3 m) images. We implemented Geographic Object-Based Image Analysis (GEOBIA) techniques to all images to identify 21 land use and land cover (LULC) classes, which were then used to calculate several landscape metrics. We performed the Welch hypothesis testing on the class-level landscape metrics and applied Standardized Principal Component Analysis (PCA) with the correlation matrix to reveal the multivariate pattern of landscape metrics. Our results showed that 10 m and even the 1.5 m spatial resolutions cannot guarantee the identification of all LULC classes, and class areas change with varying spatial resolution (sometimes with 200% differences). Sentinel-2 images have some limitations, specifically from the landscape ecological planning perspective; on the other hand, Pleaides and Worldview-4 seem good alternatives to understand habitats’ viability and landscape isolation/connectivity.

Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis

Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue; RGB) sensor to identify invasive Phragmites australis in multiple Minnesota wetlands using object-based image analysis (OBIA) and machine learning (ML) algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The addition of a post-ML classification OBIA workflow was tested to determine if ML classifications can be improved using OBIA techniques. Results from each ML algorithm were compared across study sites both with and without the post-ML OBIA workflow. ANN was identified as the best classifier when not incorporating a post-ML OBIA workflow with a classification accuracy of 88%. Each of the three ML algorithms achieved a classification accuracy of 91% when incorporating the post-ML OBIA workflow. Results from this study suggest that a post-ML OBIA workflow can increase the ability of ML algorithms to accurately identify invasive Phragmites australis and should be used when possible. Additionally, the decision of which ML algorithm to use for Phragmites mapping becomes less critical with the addition of a post-ML OBIA workflow.

Comparison of pixel, sub-pixel and object-based image analysis techniques for co-seismic landslides detection in seismically active area in Lesser Himalaya, Pakistan

Comparison of pixel, sub-pixel and object-based image analysis techniques for co-seismic landslides detection in seismically active area in Lesser Himalaya, Pakistan

Towards understanding informal settlement growth patterns: Contribution to SDG reporting and spatial planning

The study proposes a ratio of informal settlement land consumption rate to imperviousness growth rate to understand informal settlement growth patterns. To compute this ratio, information on the informal extent and impervious surface within an informal settlement over at least two time periods is required. This ratio can be used to understand the density of building structures within an informal settlement. We investigated this ratio in a densely populated township in Tshwane Metropolitan Municipality over a five-year interval between 2005 and 2020. Informal settlement extent was generated using the object-based image analysis technique which uses asymmetry of image objects to classify informal settlements. The impervious surface was mapped using a ruleset that uses soil adjusted vegetation index, border index and radiometric values of the blue band. The results show that the ratio of land consumption rate of informal settlement to the impervious surface growth rate was 0.70 during the 2005–2010 interval, decreased substantially to −11.25 during the 2010–2015 interval and increased to 0.85 during the 2015–2020 interval. The results can be used to support the management and planning of informal settlement upgrade projects and to monitor progress made towards achieving sustainable development goals.

A COGNITIVE APPROACH FOR LANDSYSTEM IDENTIFICATION USING A GRAPH DATABASE – TOWARDS THE IDENTIFICATION OF LANDFORMS IN CONTEXT

Abstract. A landform is any physical feature of the earth's surface having a characteristic, recognizable shape. Most landform identification methods rely on OBIA (Object-Based Image Analysis) techniques to segment the terrain data and classify segments into objects that are assumed to compose the landform. However, geomorphologists can visually recognize any landform, considering the characteristics of the surrounding environment that plays the role of context. This notion of context was not considered in previous landform identification methods. We propose to model it using the notion of landsystem. Landsystems are geomorphologic elements that result from a set of natural geomorphological processes. They are also easily recognized by geomorphologists. In this paper, we present a new knowledge-based method to automatically identify landsystems as the context for landform identification. We first present a conceptual model as a core ontology of geomorphologic elements including landsystems and landforms, capturing relevant geomorphologists’ knowledge. Then, we present how this model is extended to create a domain ontology for a chosen domain in geomorphology. We illustrate such an extension for the case of mountainous glacial valleys. We used the graph database engine Neo4J to implement the domain ontology and to develop a knowledge-based system (a framework) to automatically identify landsystems from spatial datasets. We present the architecture of our framework and discuss how it is used to support: 1) the knowledge acquisition tasks; 2) the spatial data preparation task; 3) the processing of the user’s request seeking landsystems in a chosen study area.

Change of land use / land cover in kurdistan region of Iraq: A semi-automated object-based approach

During the conflict and in the years afterward, the Kurdistan Region of Iraq (KRI) saw substantial changes in land use. The mapping and monitoring of land use/land cover (LULC) is critical for its long-term development and natural resource management. Therefore in this study, we developed a semi-automated object-based land use/land cover classification to identify and quantify LULC changes and change detection analysis in the Kurdistan Region of Iraq for the period 1990 to 2020 using Landsat satellite data (TM, ETM+, and OLI). To determine the optimum segmentation scales for each phase, we first applied and evaluated different scales of a multi-resolution segmentation technique. After that, spatial (digital elevation information) and spectral information were combined in an object-based image analysis (OBIA) technique. For each LULC class, object features were found. We then used the standard nearest neighbor (SNN) approach to derive their individual properties. Field data and validation units collected from high resolution Google earth pro and historical maps in SAS planet open source were used to conduct accuracy evaluations based on the error matrix and kappa coefficient for each reference year. With overall accuracies ranging from 86.072% to 88.9% and Kappa coefficients of 0.845–0.878, ten LULCs were effectively recorded. After that, a post classification comparison was used to perform a change analysis. The findings demonstrated that LULC change trends were notably different, as all categories were altered at different times throughout the study. Strikingly, 52.1% of the land use/land cover in the Kurdistan Region has changed between 1990 and 2020. All changes corresponded to the KRI's own challenges throughout the last three decades. The OBIA-based approaches and features, we conclude, have a lot of potential for LULC mapping and monitoring. The results shall support state institutions and experts to manage the land in a more controlled way.

Optimized unsupervised CORINE Land Cover mapping using linear spectral mixture analysis and object-based image analysis

In this paper, the approach Linear Spectral Mixture Analysis and Object-Based Image Analysis (LSMA + OBIA) and Iterative Self-Organizing Data Analysis Technique and Object-Based Image Analysis (ISODATA + OBIA) are evaluated, for optimizing land cover mapping in high mountain areas from Landsat-8 multispectral images. Both approaches are applied to generate in a semiautomatic and unsupervised way a land cover map of the Santurbán-Berlín moorland, located in Colombia as a case study to carry out the evaluation. It has been found that LSMA + OBIA allows the generation of a land cover classification with a maximum global reliability of 88% compared to a reliability of 79% with ISODATA + OBIA.

Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC)

Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to complement information on the types and rates of LCLU multiannual changes with the distributions, rates, and consequences of these changes in the Crozon Peninsula, a highly fragmented coastal area. To evaluate the multiannual change detection (CD) capabilities using high-resolution (HR) satellite imagery, we implemented three remote sensing algorithms: a support vector machine (SVM), a random forest (RF) combined with geographic object-based image analysis techniques (GEOBIA), and a convolutional neural network (CNN), with SPOT 5 and Sentinel 2 data from 2007 and 2018. Accurate and timely CD is the most important aspect of this process. Although all algorithms were indicated as efficient in our study, with accuracy indices between 70% and 90%, the CNN had significantly higher accuracy than the SVM and RF, up to 90%. The inclusion of the CNN significantly improved the classification performance (5–10% increase in the overall accuracy) compared with the SVM and RF classifiers applied in our study. The CNN eliminated some of the confusion that characterizes a coastal area. Through the study of CD results by post-classification comparison (PCC), multiple changes in LCLU could be observed between 2007 and 2018: both the cultivated and non-vegetated areas increased, accompanied by high deforestation, which could be explained by the high rate of urbanization in the peninsula.

Unmanned Aerial Vehicle (UAV)-Based Mapping of Acacia saligna Invasion in the Mediterranean Coast

Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.

Assessing the impacts of land use and land cover change on water resources in the Upper Bhima river basin, India

Rapid economic development in India has intensified the changes in natural land cover and thus affects the availability of water resources. The present study aims to analyze past land use changes for three time periods and assess their impacts on water resources in the Upper Bhima basin. An object-based image analysis (OBIA) technique was used to identify the progressive land use changes for the year 1992, 2000, and 2009. The Soil and Water Assessment Tool (SWAT), an open source hydrological model was used to simulate hydrological processes and assess the impacts of land use change on surface runoff, groundwater flow, lateral flow, percolation, and evapotranspiration in the basin. The model was calibrated and validated for a baseline period (1985-1994) using 1992 land use/land cover (LULC) map. The model calibration and validation was found to be satisfactory. The optimized SWAT model parameters along with 1992 and 2009 LULC maps were used to quantify the impacts in the Upper Bhima basin. The major land use changes were identified as conversion from wastelands and agricultural land to built up area (2.6 % in 1992 to 7.3% in 2009). The modeling results showed that urbanization has a significant (p<0.001) positive relation with surface runoff (R = 0.96), water yield (R = 0.57), and a negative relationship with baseflow (R = -0.95), and percolation (R = -0.99). The study also revealed that the nature of LULC change has differential impacts at both basin and sub-basin scale. At basin level, the overall impacts of LULC change on hydrological parameters are small however at sub-basin level, the surface runoff and water yield has increased up to 13.6%, 8%, respectively. Similarly, the lateral flow changed from -9% in sub-basin 42 to 10% in sub-basin 20 and evapotranspiration changed from -3% in sub-basin 43 to 59% in sub-basin 6.

Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer’s accuracy for the Phragmites class ranged from 3 to 76%, and the user’s accuracies were above 90%. The Not Phragmites class had user’s and producer’s accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.