Geographic Object-based Image Analysis Research Articles

Radiometric landscape: a new conceptual framework and operational approach for landscape characterisation and mapping

ABSTRACT Landscape mapping has the potential to address some of the most pressing research issues of our time, including climate change, sustainable development, and human well-being. In this paper, we propose an original method that lays the foundations for landscape mapping and overcomes some of the major limitations of existing biophysical methods. Based on the assumption that the primary components of the landscape can be extracted directly from the radiometric information of satellite image time series, this paper presents a new approach to landscape characterization and mapping based solely on remote sensing data. The approach relies on a conceptual model, which links the description, characteristics, structure and functions of the landscape to a set of Remote Sensing-based Essential Landscape Variables (RS-ELVs). The RS-ELVs are then processed according to geographic object-based image analysis (GEOBIA) approach to produce a radiometric landscape map. The model and the remote sensing data processing chain are tested on a case study in central Madagascar (about 13 000 km2) composed of contrasting landscapes resulting from different climatic conditions and agricultural practices. The RS-ELVs are extracted from MODIS image time series for the temporal and spectral variables, and from MODIS and Sentinel-2 images for the texture variables. The parameterization of the segmentation and clustering algorithms is determined by statistical optimization. The final result is a radiometric landscape map in six classes. The landscape classes are then characterized using an independent set of remote sensing variables, a global land cover map and ground observations. The approach successfully identifies and delineates the gradient and major landscape types of the complex region of central Madagascar, confirming our initial hypothesis. The production of such radiometric landscape maps opens the way for integrated territorial development, including the planning and protection of the living environment and human well-being, and the implementation of sectoral policies.

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.

Performance Comparison of Deep Learning (DL)-Based Tabular Models for Building Mapping Using High-Resolution Red, Green, and Blue Imagery and the Geographic Object-Based Image Analysis Framework

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.

Maize Crop Detection through Geo-Object-Oriented Analysis Using Orbital Multi-Sensors on the Google Earth Engine Platform

Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes.

Pairing remote sensing and clustering in landscape hydrology for large-scale change identification: an application to the subarctic watershed of the George River (Nunavik, Canada)

Abstract. For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised geographic object-based image analysis (GeOBIA) paired with the fuzzy c-means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985–2019 time period, each with a frequency of 5 years. Our study site is the George River watershed (GRW), a 42 000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015–2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985–2014 to investigate past changes in hydrological regime. The time series of seven classifications showed a homogenization of subwatershed types associated with increases in vegetation productivity and in water contents in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evaporation and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km3 yr−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote-sensing-based approaches, particularly in areas of discontinuous and sporadic permafrost.

SMALL WETLANDS IN THE BRAZILIAN SAVANNA(CERRADO): AN UNDERESTIMATED FRESHWATER RESOURCE

In Brazil, in scientific research and public policy have largely ignored small wetlands, they are not specifically covered by current environmental laws and reference standards. Furthermore, the lack of information about, small wetlands has made their protection and management difficult. Large-scale land use changes that modify the hydrology of low-order streams often lead to the accelerated degradation and loss of small wetlands. The aim of this study conducted in Brazilian Cerrado Biome area, was to map the type, size, and distribution of small wetlands in ancatchment area). Areas represented in 12 high-resolution multispectral images were individually classified in a geographic object-based image analysis (GEOBIA), resulting in a sensitivity or recall 0.872 (~87%). The 7,132 mapped wetlands had a total area of 163km2, corresponding to a density of 4.27wetlands km−2, with smallest wetlands smaller than 4.0 hectares (90.5%). The following descriptors were applied to map small wetlands: feature: asymmetry, rectangular fit, mean nir band, compactness, density, and length. Seven types of natural wetlands and four types of man-made wetlands were accordingly identified in the study area. Their classification within a system that follows the hierarchy of the Brazilian wetland classification system is proposed. Recommendations for the future study and protection of wetlands was are also provided.

Evaluating decadal space-temporal mangrove dynamics using geographic object-based image analysis (GEOBIA) on the Doce River delta-ES, southeastern Brazilian coast

Mangroves are ecosystems present in a large part of the Brazilian coastal zone that are home to a wide diversity of organisms, providing direct and indirect resources, as well as nursery and foraging habitats. Using images in 5-year intervals (2010, 2015, and 2020), the present study carried out a decadal mapping of the remaining mangroves of the Doce River delta along the tidal flats of the Mariricu River, in the coastal region of the city of São Mateus-ES. In this context, object-oriented classification methodology (GEOBIA) was used, which allowed the generation of high-resolution data. This methodology achieved excellent results, as evidenced by the global accuracy numbers and Kappa index, which gave an average of 96.78% and 93.5% respectively; and by low values of disagreement, with an average of 3.22%. The change detection analysis showed alterations in the landscape over time, with 9% of the mangrove areas becoming extinct, 12% of the areas expanding, and 79% of the areas remaining preserved. Therefore, our mapping data are consistent with the data published by the State Institute for the Environment and Water Resources of the State of Espírito Santo (IEMA), as well as with scientific works that recorded areas of reduction and expansion for mangroves, but mostly with large preserved areas. With that, our results demonstrate the highly effective application of geotechnologies for coastal environmental analyses with low cost and high speed.

Klasifikasi Spesies Mangrove Menggunakan Drone di Nusa Lembongan, Bali

Mapping of mangroves using remote sensing technology has been widely carried out, especially in coastal areas of Indonesia. The most used remote sensing technology is satellite imagery. However, there are still some weaknesses in the use of satellite imagery for mapping mangrove, especially at the species level, including taking a long time, high costs, and low spatial resolution. One of the breakthroughs in remote sensing is drone. Drones have advantages, namely time flexibility, relatively low cost, and high spatial resolution. The purpose of this study was to determine the ability to use drones in mapping mangrove up to the species level and distribution of mangrove species in Nusa Lembongan. The method used is classification of GEOBIA (Geographic Object Based Image Analysis). Drone images were obtained with four flight missions in the northern and eastern areas of the Nusa Lembongan mangrove forest. Field data collection was carried out using a systematic random sampling method and high-resolution single band drones. The results showed that drone observations were able to identify two mangrove species in the northern research area and four mangrove species in the eastern Nusa Lembongan research area. In the northern area there are mangrove species Rhizophora apiculata and Bruguiera gymnorrhiza. In the eastern region, mangrove species Rhizophora apiculata, Avicennia alba, Sonneratia alba, and Avicennia lanata were found. The accuracy test in both research areas in the northern and eastern areas of Nusa Lembogan obtained an overall value with an average of 65% and a kappa coefficient of 0.56.

Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi–Limpopo Confluence Area

Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi–Limpopo Confluence Area

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.

Impact of segmentation algorithms on multisensor LULC classification in a semiarid Mediterranean area

Geographic object-based image analysis methods usually provide better results than pixel-based methods for classifying land use and land cover from high and medium resolution satellite imagery. This study compares the results of Random Forest (RF) and Multilayer Perceptron (MLP) when used to classify the segments obtained on an RGB+NIR Sentinel-2 image using three different segmentation algorithms, Multiresolution (MR), Region Growing (RG), and Mean-Shift (MS). The hyperparameters of these algorithms were optimised minimising the intra-object heterogeneity and maximizing the inter-object heterogeneity, integrating them in an optimization loop. Geometric and two different centrality and dispersion statistics were computed from some Sentinel-1, Sentinel-2 and LiDAR variables over the segments, and used as features to classify the datasets. The highest segment cross-validation accuracies were obtained with RF using MR segments: 0.9048 (k=0.8905), while the highest accuracies calculated with test pixels were obtained with MLP using MR segments: 0.9447 (k=0.9303), both with the mean and standard deviation of the feature set. Although the overall accuracy is quite high, there are problems with some classes in the confusion matrix and, significant misclassification appear when a qualitative analysis of the final maps is performed, indicating that the accuracy metrics may be overestimated and that a qualitative analysis of the results may also be necessary.

Spectral Patterns of Pixels and Objects of the Forest Phytophysiognomies in the Anauá National Forest, Roraima State, Brazil

Forest phytophysiognomies have specific spatial patterns that can be mapped or translated into spectral patterns of vegetation. Regions of spectral similarity can be classified by reference to color, tonality or intensity of brightness, reflectance, texture, size, shape, neighborhood influence, etc. We evaluated the power of accuracy of supervised classification algorithms via per-pixel (maximum likelihood) and geographic object-based image analysis (GEOBIA) for distinguishing spectral patterns of the vegetation in the northern Brazilian Amazon. A total of 280 training samples (70%) and 120 validation samples (30%) of each of the 11 vegetation cover and land-use classes (N = 4400) were classified based on differences in their visible (RGB), near-infrared (NIR), and medium infrared (SWIR 1 or MIR) Landsat 8 (OLI) bands. Classification by pixels achieved a greater accuracy (Kappa = 0.75%) than GEOBIA (Kappa = 0.72%). GEOBIA, however, offers a greater plasticity and the possibility of calibrating the spectral rules associated with vegetation indices and spatial parameters. We conclude that both methods enabled precision spectral separations (0.45–1.65 μm), contributing to the distinctions between forest phytophysiognomies and land uses—strategic factors in the planning and management of natural resources in protected areas in the Amazon region.

Semiautomatic Mapping of Center Pivot Irrigated Areas Using Sentinel-2 Images and GEOBIA Approach

Image analysis and feature extraction of remoted sensing data are significant for mapping irrigated agriculture areas as a source of information to improve water management and agricultural planning. This paper presents an image segmented base approach GEOBIA (Geographic Object-Based Image Analysis) to extract irrigated areas by Center Pivot Irrigation System (CPIS). This study suggests a semi-automated recognition of circular patterns for the mapping of irrigated regions by center pivots, using Sentinel -2 MSI images, 10 meters spatial resolution. A set of images from different seasons, humid and dry are used to maximize de CPIS’s occurrence. A multiresolution segmentation method was applied, and a large number of segment-based shape features was extracted and used as input to a feature selection procedure (shape descriptors: Area; Compactness; Circularity Factor; Length/Width; Radius of smallest enclosing ellipse; and Roundness). In addition, another shape descriptor “Circularity Factor” was developed in this research and played an important role during preliminaries classification processes. The accuracy assessment of preliminaries classifications has validated used the Circularity Factor together with the other chosen shape descriptors to reach better results to CPIS’s detection. Furthermore, 86.23% of the CPIS mapped in the classification process is in accordance with the ground truth map. This methodology can be used to map large areas in a relatively short time and provides a tool for monitoring irrigated areas.

Leveraging NAIP Imagery for Accurate Large-Area Land Use/land Cover Mapping: A Case Study in Central Texas

Large-area land use land cover (LULC) mapping using high-resolution imagery remains challenging due to radiometric differences between scenes, the low spectral depth of the imagery, landscape heterogeneity, and computational limitations. Using a random forest (RF)- supervised machine-learning algorithm, we present a geographic object-based image analysis approach to classifying a large mosaic of 220 National Agriculture Imagery Program orthoimagery into lulc categories. The approach was applied in central Texas, USA, covering over 6000 km2. We generated 36 variables for each object and accounted for spatial structures of sample data to determine the distance at which samples were spatially independent. The final rf model produced 94.8% accuracy on independent stratified random samples. In addition, vegetation and water indices, the mean and standard deviation of principal components, and texture features improved classification accuracy. This study demonstrates a cost-effective way of producing an accurate multi-class land use/land cover map using high-spatial/low-spectral resolution orthoimagery.

An open-source GIS approach to understanding dunefield morphologic variability at Kati Thanda (Lake Eyre), Central Australia

Future climate projections indicate an expansion of the world’s drylands, and with that a commensurate increase in the mobilization of unconsolidated desert sediments such as sand and dust. It is therefore increasingly important to investigate the large-scale formation of dryland landscapes such as dunefields in order to better understand the processes responsible for their genesis, evolution, and thresholds for mobilization. Assessing dunefield morphologic variability, including analysis of the morphologic relationship between aeolian bedforms and other landforms such as fluvial channels and bedrock uplands, underpins such investigations. So far, however, meaningful investigations of erg-scale geomorphic patterns have been limited. This is in part due to the technological limitations of geographic information system (GIS) tools, particularly in the case of open-source datasets and software, which has effectively hindered investigations by colleagues in drylands of the global south where many of the world’s dunefields are located. Recent years have overseen the increasing availability of open-source remote sensing datasets, as well as the development of freely available software which can undertake geographic object-based image analysis (GEOBIA). These new tools facilitate cartography and statistical analysis of dunefields at large scales. In this study we make use of open-source GIS to characterise a morphologically diverse linear dunefield southwest of Kati Thanda (Lake Eyre) in central Australia. We focus on three parameters; dune orientation, spacing and Y-junctions using semi-automated GEOBIA, and investigate these in the context of local fluvial channels, depressions (pans) and uplands. Our results suggest a possible correlation between dune orientation, wind regime and the role of uplands as deflective barriers to longitudinal dune migration; dune spacing and sediment supply, likely relating to the location of both ephemeral and abandoned fluvial channels; and Y-junction frequency with underlying topography. Our study provides a framework for understanding process-based interactions between dunes and other landforms, as well as the first completely open-source approach which can be applied to linear dunefields worldwide.

Remote sensing and GIS-based landslide susceptibility mapping using LNRF method in part of Western Ghats of India

Every year, the Western Ghats region experiences devastating landslides, resulting in significant loss of life and damage to both private and public assets. To mitigate these losses, it is essential to identify the areas most susceptible to landslides. This research aims to create an accurate landslide susceptibility map for a section of the Western Ghats region by employing a comprehensive approach that combines remote sensing and geographical information systems (GIS). The Landslide Numerical Risk Factor (LNRF) method is utilized to determine landslide susceptibility zones. The LNRF model incorporates a landslide inventory based on geographic object-based image analysis and influencing factors. The resulting LNRF model generates a landslide susceptibility map, classifying over 35% of the study region's land area as having a high probability of landslides. The model's accuracy is assessed using the receiver operating characteristic (ROC) method, which yields an area under the curve (AUC) value of 0.77. This indicates that the LNRF model exhibits good predictive performance, resulting in a reliable landslide susceptibility map.

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%.

Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery

Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index.

Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.