Spatial Resolution Imagery Research Articles

Does community-based tenure prevent land grabbing? The oil palm case in Mexico

The recent oil palm expansion has resulted in significant land losses for rural communities globally, raising concerns about food security, poverty, and the loss of common resources. This study investigates whether community-based tenure regimes in Mexico, particularly ejidos, prevent land grabbing and land concentration in oil palm producing regions. By mapping oil palm plantation types (smallholdings, mid-sized and large-scale plantations) across major land tenure regimes (ejido, communal and private property) using high spatial resolution imagery from Google Earth and ESRI/Maxar, we explore the relationship between tenure forms and land concentration. Our findings suggest that ejido lands largely prevents land grabbing by oil palm, although neoliberal reforms have favored land concentration, especially under private tenured land (southern Campeche) but also in some ejidos facing illegal land-based investments (Lacandon rainforest). This research contributes to broader debates on oil palm, tenure regimes and land grabbing, highlighting the need for land tenure policies that protect rural communities from industrial plantation encroachments.

Normalized Difference Red-NIR-SWIR: A new Sentinel-2 three-band spectral index for mapping freshly-opened swiddens in the tropics

Swidden agriculture is undergoing a rapid but overlooked transition and transformation in the tropics, complicating global carbon budgeting and sustainable livelihood assessment of swiddeners. Remotely sensed algorithms for accurately detecting swiddening practices have been slowly developed to generate annual updates on their dynamics. This is primarily because, using medium spatial resolution imagery (≥30 m), it is challenging to identify the exact boundary of swidden patches. Spectral-based approaches have by far dominated the detection and mapping of swidden agriculture, but the potential of Sentinel-2 has not been examined. To reconstruct annual information of swidden agriculture, a new Sentinel-2 three-band spectral index, i.e., the Normalized Difference Red, Near-infrared (NIR), and Shortwave-infrared (SWIR), or NDRII, has been developed to map freshly-opened swiddens in tropical regions. As Red (visible), NIR, and SWIR spectral band combinations (i.e., the VNIR-SWIR spectroscopy) are sensitive to vegetation-moisture variations and thermal anomalies caused by slash and burn in tropical uplands during the dry season, NDRII delineates exact patches of freshly opened swiddens. The latest 20-m map facilitates probing into the landscape patterns of newly opened swiddens and underlines their prevalence in Laos for the first time. Established within the VNIR-SWIR spectroscopy of Sentinel-2 and Landsat-8 Operational Land Imager, the NDRII algorithm contributes to reconstructing historical datasets of tropical swiddens via integrating state-of-the-art approaches that use temporally stacked observations with available VNIR/SWIR satellite imagery and further understanding the dynamics of landscape pattern and disturbance due to rapid transition and transformation.

A Hybrid Spatio-Temporal Fusion Method for High Spatial Resolution Imagery: Fusion of Gaofen-1 and Sentinel-2 over Agricultural Landscapes

A Hybrid Spatio-Temporal Fusion Method for High Spatial Resolution Imagery: Fusion of Gaofen-1 and Sentinel-2 over Agricultural Landscapes

Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2

AbstractAssessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.

Evaluating the spatial representativeness of ground-based observations for satellite total ozone products

External verification based on ground-based data is commonly used to assess the accuracy of satellite products for total ozone. However, the spatial representativeness of ground-based observations at the pixel scale and the effects of the spatial mismatch between ground observations and satellite pixels are often overlooked. Therefore, this study focused on an Ozone Monitoring Instrument (OMI) and proposed a novel method to evaluate spatial representativeness using the total ozone product itself without relying on higher spatial resolution imagery from other sensors. A Pixel-level Spatial Mismatch Index (PSMI) was developed to quantify the peaks and stable intervals of the spatial response to the relative deviation of total ozone observations. The analysis revealed that increased uncertainty at the pixel edges can cause significant relative deviation, with the peak deviation occurring within a range of 4–7 km near the pixel boundary. The position of the high precision interval in PSMI was found to be <–22.38 km or >10 km, with mean values of 3.70 % and standard deviations of 3.45 % for the relative deviation. This interval is spatially distance-independent and can be observed on both sides of the pixel boundary. These findings highlight the importance of considering spatial patterns between satellite pixels and ground stations for external validation. Furthermore, avoiding a spatial response peak of the relative deviation at the pixel edge is crucial. Overall, this research provides new perspectives and insights for the verification of total ozone products. By considering these factors mentioned above, future verification of total ozone products can be improved, leading to enhanced their evaluation systems.

Using Remote Sensing Data in Urban Crime Analysis: A Systematic Review of English-Language Literature from 2003 to 2023

Drawing from environmental criminology principles, this article explores the existing literature to assess the utility of remote sensing data in detecting and analysing features in the urban environment that are associated with crime occurrence. A systematic review of the literature in the English language from 2003 until the first half of 2023 from two major databases, Scopus and Science Direct, is carried out. As many as 910 publications were selected, from which 36 publications satisfied the selection criteria. Findings show that neighborhood's design has a quantifiable imprint that is possible to be observed with very high spatial-resolution imagery. Given its high spatial and temporal resolution, remote sensing data can to different degrees support the identification of criminogenic features in urban environments (streets and roads, property boundaries, housing density, characteristics and density of vegetation as well as luminosity levels), but when it is used for the detection of potentially illegal activities, infringement of people's privacy and methods lacking validation still present serious concerns. The article concludes with a discussion of the opportunities and challenges of using remote sensing data in crime analysis.

Using the automated HYPERNETS hyperspectral system for multi-mission satellite ocean colour validation in the Río de la Plata, accounting for different spatial resolutions

Validation of water reflectance using in situ data is essential to ensure the quality of ocean colour satellite-derived products useful for water quality monitoring, like turbidity and chlorophyll-a concentration. Since December 2021, the HYPERNETS automated hyperspectral system has been collecting data in the optically complex and highly turbid waters of the Río de la Plata, an ideal scenario for testing atmospheric correction algorithms’ performance. The site, located 60 km south of Buenos Aires (Argentina), is described in relation to the water reflectance spectral features and variability using high spatial resolution imagery and a methodology is proposed to objectively select a sensor-specific location of a reference pixel for satellite validation. Six months of data is used to evaluate surface water reflectance operational products from multi-spectral systems like Landsat 8&amp;amp;9/OLI (L89/OLI), Sentinel-2/MSI (S2/MSI) &amp;amp; Sentinel-3/OLCI (S3/OLCI), and PlanetScope SuperDoves (PS/SD), and also non standard products for Aqua/MODIS (Aqua/MODIS) and SNPP&amp;amp;JPSS1/VIIRS (SJ/VIIRS) missions. Moreover, the standard surface water reflectance product from the hyperspectral PRISMA mission could also be evaluated. The matchups show general good results when in situ measurements are compared to L2 standard products of high spatial resolution sensors that use land-based atmospheric correction approach, if sun glint contamination is avoided. Low mean relative percentage difference was found for S2/MSI (2.45%) and L89/OLI (−3.52%), but higher for PS/SD (30.7%). In turn, S3/OLCI medium resolution also showed low mean relative differences (2.31%), while SJ/VIIRS and Aqua/MODIS showed larger and negative differences (−16.35 for SJ/VIIRS and −35.6% for Aqua/MODIS) which showed a clear increase towards the shortest blue bands. The results show the great potential of the HYPERNETS automated system to provide high quality and quantity of data for validation of satellite data at all visible and near infrared (VNIR, 400–900 nm) wavelengths in a multi-mission perspective.

Effects of tidal creeks on Spartina Alterniflora expansion: A perspective from multi-scale remote sensing

Rapid expansion of Spartina Alterniflora (S. alterniflora) has led to severe ecological consequences in coastal wetlands in China. Better understanding of the mechanism of S. alterniflora expansion is of great importance for its control and management. Although previous research has reported that the expansion of S. alterniflora is closely related to tidal creeks via field investigations, few studies have quantitatively investigated their relationships using remote sensing at different scales. In this study, we utilized time series satellite remote sensing imagery with different spatial resolutions to investigate the effects of tidal creeks on S. alterniflora expansion at both landscape scale and patch scale. We took the Yellow River Delta (YRD), China as our study area, where S. alterniflora has spread across the intertidal wetlands during the past decade. At landscape scale, we first derived the yearly maps of S. alterniflora and tidal creeks during 2009–2020 based on Landsat-series satellite imagery, then analyzed the spatio-temporal relationship between S. alterniflora and tidal creeks. By using grid analysis method, we evaluated the change of S. alterniflora area and tidal creek length and compared their change time. The results showed that 30.46 % of the grids experienced no change in tidal creeks while expansion of S. alterniflora, and 54.02 % of the grids experienced S. alterniflora area increase after the lengthening of tidal creeks. The results indicated that relationship between tidal creeks and S. alterniflora in YRD was dominated by “tidal creeks promoting S. alterniflora expansion”. We then simulated the S. alterniflora expansion at patch scale using an improved spatially explicit individual-based model based on S. alterniflora maps derived from Landsat imagery (30 m) and GF-1 high spatial resolution imagery (2 m), respectively. The results showed that consideration of long-distance dispersal of seeds along the direction of tidal creeks considerably enhanced the simulation accuracy, regardless of the spatial resolution of the S. alterniflora maps. High-resolution S. alterniflora maps yielded higher simulation accuracies. Our study suggests that the long-distance dispersal of seeds led by tidal creeks is an important manner for S. alterniflora expansion. Satellite remote sensing imagery have great potential to discover the mechanism of S. alterniflora expansion, facilitating control of S. alterniflora at the early invasion stage.

Cooling effects in urban communities: Parsing green spaces and building shadows

The thermal environment in urban communities significantly impacts the comfort and well-being of residents. Reducing the surface temperature in these areas is critical for improving the quality of life of residents and mitigating the urban heat island effect. Various factors, including buildings, roads, and green spaces, can influence the cooling effects in community areas. However, there is currently a lack of research focusing on examining the relationship between the surface temperature and community characteristics in urban settings. In this paper, using highly urbanized Beijing as an example, 1-m spatial resolution imagery (GaoFen-2) was used to extract 241 communities with different vegetation and building characteristics. High-resolution surface temperature data, calculated using the Google Earth Engine (GEE), facilitated the investigation of the cooling efficiency of surfaces in communities. We then evaluated the contributions of the green space ratio, building shadows, and green space pattern to the surface temperature. This study revealed the following: (1) an inverse nonlinear relationship was found between the green space ratio and surface temperature. The cooling efficiency decreased by 2/3 when the green space ratio exceeded 0.43–0.46. (2) The proportions of green spaces and building shadows were the primary drivers of cooling, accounting for more than 85% of the overall cooling effect. (3) In communities with high-rise buildings, the cooling effect of building shadows was greater than that of vegetation patterns. This study could provide a reference for renovating existing communities and planning green spaces in newly developed urban areas.

Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai

As global warming intensifies, monitoring carbon dioxide (CO2) has increasingly become a focal point of research. Investigating urban XCO2 emission systems holds paramount importance, given the pivotal role of cities as major contributors to carbon emissions. Consequently, this study centers on urban locales, employing Shanghai as a case study for a comprehensive evaluation of regional XCO2 levels. We utilized high spatial resolution imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite to conduct an XCO2 assessment over the Baoshan District with a 30 m spatial resolution from April 2021 to October 2022. Our XCO2 analysis was conducted in two steps. Firstly, we conducted a sensitivity analysis on key parameters in the inversion process, where cloud cover severely interfered with inversion accuracy. Therefore, we developed the Fmask 4.0 cloud removal and iterative maximum a posteriori differential optical absorption spectroscopy (FIMAP-DOAS) algorithm. This novel integration eliminated cloud interference during the inversion process, achieving high-precision CO2 detection in the region. Secondly, we compared the XCO2 of the region with Level-2 data from carbon monitoring satellites such as OCO-2. The comparison results showed a strong consistency, with a root mean squared error (RMSE) of 0.75 ppm for Shanghai XCO2 data obtained from the PRISMA satellite compared to OCO-2 Level-2 data and an RMSE of 1.49 ppm compared to OCO-3. This study successfully established a high-accuracy and high-spatial-resolution XCO2 satellite monitoring system for the Shanghai area. The efficacy of the FIMAP-DOAS algorithm has been demonstrated in CO2 monitoring and inversion within urban environments, with potential applicability to other cities.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages, vital carriers of traditional culture, have faced significant alterations due to urbanization in recent years, urgently necessitating artificial intelligence data updates. This study integrates high spatial resolution remote sensing imagery with deep learning techniques, proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images. Using 0.54 m spatial resolution imagery of traditional village areas as the data source, this method analyzes the geometric and spectral image characteristics of village building rooftops. It constructs a deep learning feature sample library tailored to the target types. Employing a semantically enhanced version of the improved Mask R-CNN (Mask Region-based Convolutional Neural Network) for building recognition, the study conducts experiments on localized imagery from different regions. The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops, achieving an of 0.7520 and an of 0.7400. It improves the current problem of misidentification and missed detection caused by feature heterogeneity. This method offers a viable and effective approach for industrialized data monitoring of traditional villages, contributing to their sustainable development.

Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale

Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 × 106 km2) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy.

Development and implementation of a stand-level satellite-based forest inventory for Canada

Abstract Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada’s forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada’s forest-dominated ecosystems. In so doing, the entirety of Canada’s forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access.

A Cross-Domain Object-Semantic Matching Framework for Imbalanced High Spatial Resolution Imagery Water-Body Extraction

A Cross-Domain Object-Semantic Matching Framework for Imbalanced High Spatial Resolution Imagery Water-Body Extraction

ViT-UNet: A Vision Transformer Based UNet Model for Coastal Wetland Classification Based on High Spatial Resolution Imagery

ViT-UNet: A Vision Transformer Based UNet Model for Coastal Wetland Classification Based on High Spatial Resolution Imagery

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Transferability of a Mask R–CNN model for the delineation and classification of two species of regenerating tree crowns to untrained sites

Following harvest, monitoring reforestation success is a crucial component of sustainable management. In Alberta, Canada, like other jurisdictions, the efficiency of the current plot-based forest regeneration monitoring regime is challenged by the cost of accessibility and the declining availability of qualified field crews. Fine spatial resolution imagery and deep learning have been proposed as alternative monitoring tools and have proven successful under experimental conditions, yet how successfully models can be applied and transferred between a range of untrained sites and conditions remains unclear.In this research, we repurposed a mask region-based convolutional neural network (Mask R–CNN) model that was previously trained to delineate coniferous tree crowns to instead segment instances of two species of regenerating conifers. We transferred learned parameters by replacing original single-class labels with photo-interpreted species information and retraining a selection of the network's parameters. We assessed the transferability of the new model by testing on five untrained sites, representing a range of forest types and densities typical of reforestation in the region. Results yielded a mean average precision (mAP) of 72% and average class F1 scores of 69% and 78% for lodgepole pine (Pinus contorta) and white spruce (Picea glauca), respectively, demonstrating successful transferability. We then investigated an additional transfer learning scenario by iteratively adding data from four of the five sites to the training set while reserving data from the remaining site for testing. On average, this improved mAP by 5%, lodgepole pine F1 by 7%, and white spruce F1 by 3%, and demonstrated that trained models can be continuously improved as sufficiently representative data becomes available.

Image to Image Deep Learning for Enhanced Vegetation Height Modeling in Texas

Vegetation canopy height mapping is vital for forest monitoring. However, the high cost and inefficiency of manual tree measurements, coupled with the irregular and limited local-scale acquisition of airborne LiDAR data, continue to impede its widespread application. The increasing availability of high spatial resolution imagery is creating opportunities to characterize forest attributes at finer resolutions over large regions. In this study, we investigate the synergy of airborne lidar and high spatial resolution USDA-NAIP imagery for detailed canopy height mapping using an image-to-image deep learning approach. Our main inputs were 1 m NAIP image patches which served as predictor layers and corresponding 1 m canopy height models derived from airborne lidar data, which served as output layers. We adapted a U-Net model architecture for canopy height regression, training and validating the models with 10,000 256-by-256 pixel image patches. We evaluated three settings for the U-Net encoder depth and used both 1 m and 2 m datasets to assess their impact on model performance. Canopy height predictions from the fitted models were highly correlated (R2 = 0.70 to 0.89), precise (MAE = 1.37–2.21 m), and virtually unbiased (Bias = −0.20–0.07 m) with respect to validation data. The trained models also performed adequately well on the independent test data (R2 = 0.62–0.78, MAE = 3.06–4.1 m). Models with higher encoder depths (3,4) and trained with 2 m data provide better predictions than models with encoder depth 2 and trained on 1 m data. Inter-comparisons with existing canopy height products also showed our canopy height map provided better agreement with reference airborne lidar canopy height estimates. This study shows the potential of developing regional canopy height products using airborne lidar and NAIP imagery to support forest productivity and carbon modeling at spatially detailed scales. The 30 m canopy height map generated over Texas holds promise in advancing economic and sustainable forest management goals and enhancing decision-making in natural resource management across the state.

Spatiotemporal trends of urban-induced land use and land cover change and implications on catchment surface imperviousness

Urbanization, changes in land use and land cover (LULC), and an increase in population collectively have significant impacts on urban catchments. However, a vast majority of LULC studies have been conducted using readily available satellite imagery, which often presents limitations due to its coarse spatial resolution. Such imagery fails to accurately depict the surface characteristics and diverse spectrum of LULC classifications contained within a single pixel. This study focused on the highly urbanized Dry Creek catchment in Adelaide, South Australia and aimed to determine the impact of urbanization on spatiotemporal changes in LULC and its implications for the land surface condition of the catchment. Very high spatial resolution imagery was utilized to examine changes in LULC over the past four decades. Support Vector Machine-learning-based image classification was utilized to classify and identify the changes in LULC over the study area. The classification accuracy showed strong agreement, with a kappa value greater than 0.8. The findings of this analysis showed that extensive urban development, which expanded the built-up area by 34 km2, were responsible for the decline in grass cover by 43.1 km2 over the last 40 years (1979–2019). Moreover, built-up areas, plantation, and water features, in contrast to grass cover, have demonstrated an increasing trend during the study period. The overall urban expansion over the study period was 136.6%. Urbanization intensified impervious area coverage, increasing the runoff coefficient, equivalent impervious area, and curve number by 60.6%, 60.6%, and 7.9%, respectively, while decreasing the retention capacity by 38.6%. These modifications suggest a potential variability in catchment surface runoff, prompting the need for further research to understand the surface runoff changes brought by the changes in LULC resulting from urbanization. The findings of this study can be used for land use planning and flood management.

Upscaling peatland mapping with drone-derived imagery: impact of spatial resolution and vegetation characteristics

ABSTRACT Northern peatland functions are strongly associated with vegetation structure and composition. While large-scale monitoring of functions through remotely sensed mapping of vegetation patterns is therefore promising, knowledge on the interdependency between spatial resolution of acquired imagery, spatial vegetation characteristics, and resulting mapping accuracy needs to be improved. We evaluated how classification accuracy of commonly used vegetation mapping units (microforms and plant functional types) was affected by spatial resolution of acquired imagery and several spatial characteristics of the vegetation itself (size, shape, configuration, and diversity). To this end, we collected very high-resolution drone imagery (<0.05 m) from eight Irish peatlands differing in vegetation composition and pattern complexity in September 2021 and 2022. We then resampled this imagery from pixel sizes of 0.027–1 m and classified both mapping units at all unique spatial resolutions. Hereafter, we computed spatial vegetation characteristics for each of the eight classified images at 0.027 m spatial resolution to determine their role in defining minimum spatial resolution requirements for both microforms and plant functional types. We found that overall classification accuracy of microforms and plant functional types was consistently high (>90%) for all studied peatlands until average spatial resolutions were reached of 0.5 m ± 0.2 m and 0.25 m ± 0.1 m, respectively. However, variability within mapping units was considerable, with overall minimum spatial resolution ranging between 0.25 and 0.7 m for microforms and between 0.15 and 0.35 m for plant functional types. Individual classes even varied from 0.05 to 1 m. Furthermore, spatial vegetation characteristics were important drivers of minimum spatial resolution for microforms, but not for plant functional types. Particularly, peatlands with larger, compacter, and more clustered microform patches could be classified with coarser spatial resolution imagery (up to 0.7 m), while peatlands with small, complex, diverse, and more finely distributed patches required higher spatial resolutions (minimally 0.25 m). Based on these findings, we conclude that spatial vegetation characteristics strongly determine minimum required spatial resolution and thus affect upscaling of peatland vegetation mapping beyond the landscape scale by constraining the use of specific remote sensing platforms.