Abstract Tidal wetlands are critical ecosystems for coastal sustainability, yet despite growing regulatory protection, they continue to decline globally. Their long-term resilience to interacting chronic stressors and extreme events remains uncertain, in part because comprehensive, high-frequency monitoring has been lacking. While direct land-use conversion has been substantially restricted in the United States, the true trajectory of these protected habitats has remained unclear. Here, we use four decades of high-resolution satellite records to analyze the shifting dynamics of US tidal wetlands. We reveal a widespread and previously unquantified acceleration in the rate of tidal wetland loss, amounting to a net loss of −1640 km2 at the rate of −40.53 km2 year−1, accelerating by −0.73 km2 year−2, of which tidal marsh contributed the majority of this loss with a cumulative decline of 1567 km2. Furthermore, we show that the drivers of this decline are shifting: while chronic stressors like relative sea level rise have caused the largest cumulative loss (~60% of the total area loss), acute shocks from extreme weather now dominate (1.4 times that of the chronic stressors) the acceleration of that loss. By contrast, direct human activities were a minor driver, accounting for only 4% of total observed losses. These findings indicate that the resilience of these protected ecosystems is declining. It provides an urgent warning that existing conservation strategies, initially concerned with direct human impacts and increasingly focused on relative sea level rise as a slow-moving pressure, are ill-equipped for a future of increasing extreme weather events and highlights the need to redesign adaptation policies.

Tidal marsh spectral signals change with tidal stage as inundation and sediment exposure vary, complicating extent mapping and change detection. AlphaEarth annual embeddings encode multi-source Earth-observation data with spatial and temporal context at 10 m, but their value for intertidal ecosystems, and whether accurate annual maps yield reliable change, remains untested. We evaluated statistic and seasonal composites, harmonic time-series features (each with and without low-tide filtering), AlphaEarth embeddings, and continuous monitoring with and without explicit tidal modeling, within a common framework of ~73,683 extent samples and interpreted change samples. For extent mapping, predictors using all valid observations outperformed those restricted to comparable tidal conditions, contrary to common low-tide image selection, indicating that tidal variation provides discriminatory signal rather than only noise. AlphaEarth achieved the highest extent-mapping accuracy (F₁ = 0.90). However, differencing its independently classified annual maps overestimated change (F₁ = 0.55): the most accurate annual maps did not produce the most reliable change. We therefore integrated AlphaEarth with DEtection and Characterization of cOastal tiDal wEtlands change (DECODE). DECODE detects spectral breaks and defines temporally stable segments, while AlphaEarth embeddings replace the fitted coefficients conventionally used as cover predictors. Because cover type is assigned per segment, change is inferred without change-training samples, which are scarce and poorly transferable. The integrated framework reduced false change over stable areas and raised change-detection F₁ to 0.83. Applying it, we generated annual 10 m tidal marsh extent and change maps for temperate North America, 2017-2024 (overall accuracy 98.75%; 95% CI: 97.98-99.51%). In 2024, 75.8% of mapped patches were smaller than 0.5 ha and 48.1% smaller than 0.1 ha, subpixel to few-pixel scales in 30 m imagery. As independently classified annual maps become a default foundation-model output, this asymmetry between extent accuracy and change reliability likely extends beyond tidal marshes.

Reliable resilience indicators are urgently needed to monitor global forest health under increasing climate stress. Lag-1 temporal autocorrelation (TAC) of satellite vegetation indices captures forest recovery speed and is widely used as a resilience metric, but its mechanistic underpinnings and methodological reliability have been questioned. Here we use Landsat observations and field data from the Amazon to investigate how satellite-derived TAC relates to in situ measurements of the hydraulic safety margin (HSM), a key physiological trait for plants' capacity to accommodate drier conditions. We find a strong direct correlation between HSM and the resilience proxy (1-TAC) (R2 up to 0.57, P = 0.0116), providing evidence that forest recovery speed is constrained by drought resistance. A model incorporating both HSM and cumulative water deficit explains up to 83% of the variance in resilience, indicating that hydraulic strategies and local climate act as a joint, primary axis of forest resilience. Importantly, we show that this relationship is contingent on the selected vegetation index, the temporal frequency of satellite observations and the rolling window size. Taken together, our findings establish the physiological fidelity of TAC as a forest resilience metric and offer methodological guidance.

Artificial light at night (ALAN) marks the global impact of humanity1,2. Yet, our understanding of its true ebb and flow has been limited, often based on temporally aggregated satellite data that obscure finer dynamics. Here, using daily night-time satellite imagery3 and a continuous change detection approach4,5, we created global maps of high-frequency ALAN dynamics (2014-2022). Our findings challenge the prevailing perspective that changes in light radiance are largely gradual and unidirectional. Instead, the nightlights of Earth are surprisingly dynamic, characterized by frequent and coexisting brightening and dimming. On average, each location experiencing change underwent 6.6 distinct shifts over the 9 years. Driven by this volatility, the cumulative area of total ALAN change comprised 2.05 million km2 of abrupt changes and 19.04 million km2 of gradual changes. Brightening contributed a radiance increase equivalent to 34% of the 2014 global baseline, whereas dimming offset this by 18%. Notably, both brightening and dimming have markedly intensified over the past decade. This evidence of increasing volatility in human night-time activity provides an important dynamic dimension for understanding urban evolution, energy transitions, policy impacts and ecological consequences of rapidly changing illuminated nights.

Saltwater intrusion is an increasing concern for coastal ecosystems. While groundwater models have made progress in simulating aquifer salinization, their boundary conditions - potentially informed by ocean model simulations in shallow water systems and intertidal zones - remain constrained. Here we presented a 3D unstructured-grid model that covers the Gulf of Maine and the Mid-Atlantic Bight, and most areas of the South-Atlantic Bight along the North American Atlantic Coast ("NAAC") for 2 decades, with a focus on the salinity simulations. This model resolves detailed geometric features of tidal tributaries down to 100 m while maintaining a resolution of 6.5 km in the coastal ocean. The two-decadal simulations from 2001 to 2020 were evaluated using a comprehensive observational dataset of elevation, temperature, and salinity. The mean absolute error in the M2 amplitude across the NOAA tidal gauges within the domain is 0.11 m. The root-mean-square deviation for salinity and temperature measurements are 0.27 PSU and 0.12 degrees C, respectively. The model reasonably captured the currents and circulations. For the first time, we extended a regional continental scale ocean model to the tidal wetlands to include compound flooding process. The two-decade of simulations of hydrodynamic and hydrological connectivity along the Atlantic Coast have significantly addressed numerous observational gaps in many systems. Specifically, saltwater intrusion patterns in major estuaries of the Mid-Atlantic, such as Chesapeake Bay, Delaware Bay, and other tributaries within the same hydrologic unit, exhibit significant correlations. The seamless cross-scale capability of this model facilitates future applications to land-sea interactions, such as carbon fluxes.

Wetlands are significant carbon sinks, yet methane emissions partially offset this function due to its high global warming potential. Coastal tidal wetlands, unlike non-tidal wetlands, are regulated by oceanic drivers like salinity gradients and tidal inundation, which strongly influence methane production and release but remain poorly represented in regional assessments. Here, we estimate methane emissions from U.S. East Coast tidal marshes, by integrating ocean model, remote sensing datasets, empirical relationships from metadata. Spatially, emissions reflect the combined effects of marsh extent and per-unit-area flux rates, with hotspots occurring under lower salinity, higher inundation, and lower latitudes. Temporally, temperature and salinity dominate decadal-scale interannual variability. Between 2001 to 2020, total methane emissions are estimated at 0.019 - 0.038 Tg yr-1, with local fluxes rate ranging from 0 to 20 g m-2 day-1. Following pronounced hydrological variability in the early 2000s, emissions have increased steadily since 2007 at approximately 802 t yr-1, driven by warming, freshening, and enhanced inundation. Projections under IPCC climate scenarios indicate that increasing inundation will amplify methane emissions with sea-level rise, until a threshold near 0.75 m SLR, beyond which saltwater intrusion increasingly suppresses further growth, highlighting the critical role of salinity-inundation interactions in coastal methane dynamics.

Land disturbances are fundamental drivers of terrestrial ecosystem dynamics, influencing biodiversity, carbon cycling and land–atmosphere interactions. An understanding of changes in their regimes is crucial for predicting future ecosystem trajectories and guiding sustainable land management. Here we leverage the long-term record of Landsat imagery to create high-resolution (30 m) maps of annual land disturbance agents across the contiguous USA from 1988 to 2022. We find that 178.50 million hectares of US land have been cumulatively disturbed over this period. Human-directed disturbances account for 65

Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 +/- 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.

Understory plant communities are an integral component of deciduous forests, playing a vital role in the overall health of the ecosystem. However, remote sensing of understory plant communities is challenging due to the obstruction by the forest canopy. In this study, we proposed an automated dense Sentinel-2 time series-based approach for understory plant communities and created maps of four understory classes (i.e., native shrubs of greenbrier and mountain laurel, and invasive shrubs of barberry and the assemblage of mixed invasive) at 10 m resolution in Connecticut's deciduous forests in 2020. A harmonic time series model and three years of Sentinel-2 time series from 2019 to 2021 were used to classify understory species based on their unique, intra-annual phenology characteristics. The time series model coefficients captured the subtle phenology differences and created synthetic cloud-free images within a short temporal window in the spring prior to canopy leaf-on (hereafter called "observation window"). During the observation window, Sentinel-2 data penetrated the deciduous overstory canopy and observed the unique trajectories of different understory species due to their phenology differences. We also calculated spatial texture features (i.e., mean, second moment, and contrast from gray level co-occurrence matrix) based on the synthetic images created within the observation window to capture the different conditions of leaf growth and distinct spatial patterns within deciduous forests. By using the spectral, temporal, and spatial features as input variables from dense Sentinel-2 data, auxiliary data (i.e., LiDAR and soil drainage layer), a random forest classifier, and a new strategy to iteratively select representative sample (namely ISRS), understory species maps were created with an overall accuracy of approximately 93%, and the user's and producer's accuracies varied from 39% to 99% for the three mapped understory species and one assemblage of species. The proposed method created an accurate binary map of understory presence with an overall accuracy of 95%, a producer's accuracy of 84%, and a user's accuracy of 68%. Additionally, we separated the invasive (i.e. barberry and mixed invasive of multi-flora rose, oriental bittersweet, honeysuckle, winged euonymus, and autumn olive) and native (greenbrier and mountain laurel) species with an overall accuracy of 94%. We estimated that the invasive species cover an area of 649.33 +/- 140.59 km(2), which occupied a large proportion (similar to 53%) of the shrub understory in Connecticut's deciduous forests.

Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others - including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km(2) of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (similar to 2.6 km(2) per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.

Land disturbance can increase carbon emissions, cause detrimental environmental impacts, and threaten human life and property. Monitoring land disturbance in near-real-time is essential to mitigate their negative effects and prevent future losses. However, rapid and timely monitoring of land disturbance at a high spatial resolution is in its infancy. Here, we developed an algorithm for Near-Real-Time MOnitoring of laNd dIsturbance based on Time series of harmOnized Reflectance (NRT-MONITOR) from Landsats 7-8 and Sentinel-2 data at 30-m spatial resolution. It incorporates an online recursive algorithm called Forgetting Factor to improve efficiency in the determination of land disturbance to get fast detection based on the harmonized data. This algorithm is developed and validated by using 1200 samples created from the harmonized Landsats 7-8 and Sentinel-2 time series from 2015 to 2019 within the conterminous United States (CONUS). An overall accuracy of 70% has been achieved for monitoring a variety of land disturbance types. NRT-MONITOR improves the processing efficiency (11.5 times faster) compared to the COLD algorithm (Zhu et al., 2020). The mean time lag of NRT-MONITOR, defined as the delta days of confirming a land disturbance after its occurrence, is only 35 days, which is achieved by using the harmonized Landsats 7-8 and Sentinel-2 observations and reduced number of clear observations (from six to four) needed to confirm a land disturbance. Finally, NRT-MONITOR can be integrated into an alerting system to provide potential land disturbance probability maps that are updated every three days.

Built heritage documentation involves the 3D modelling of the geometry (typically using 3D computer graphics, photogrammetry and laser scanning techniques) and information management of semantic knowledge (i.e., using Geographic Information System (GIS) and ontology tools). The recent developed Building Information Modelling (BIM) technique combines 3D modelling and information management. One of its modern application is heritage documentation and has generated a new concept of Historic/Heritage Building Information Modelling (HBIM). This paper summarises the applications of these information techniques on the built heritage documentation. We utilise Web of Science Collection to monitor the publications on built heritage documentation. We analyse the research trend in heritage modelling by comparing the attention paid by researchers before and during the 2010s. The results show that photogrammetry is always the most popular method in heritage modelling. More and more works in heritage modelling have begun to use laser scanning, computer science, GIS and especially BIM techniques. Ontologies and 3D computer graphics are traditional ways for heritage documentation. Moreover, we pay attention to the roles of BIM on heritage documentation and conduct a detailed discussion on how to extend the HBIM capabilities by integrating with other techniques. The integration provides possible enhanced functions in HBIM, including accurate parametric modelling from computer graphics, automatic semantic segmentation of 3D point cloud from reality-based modelling, spatial information management and analysis by GIS, and knowledge modelling by ontology. (C) 2020 Elsevier Masson SAS. All rights reserved.

Cloud and cloud shadow detection is one of the most important tasks for optical remote sensing image preprocessing. It is not an easy task due to the variety and complexity of underlying surfaces, such as the low-albedo objects (water and mountain shadow) and the high-albedo objects (snow and ice). In this study, an end-to-end multiscale 3D-CNN method is proposed for cloud and cloud shadow detection in high resolution multispectral imagery. Specifically, a multiscale learning module is designed to extract cloud and cloud shadow contextual information of different levels. In order to make full use of band information, four band-combination images are inputted into the multiscale 3D-CNN. A joint spectral-spatial information of 3D-convolution layer is developed to fully explore the joint spatial-spectral correlations feature in the input data. Overall, in the experiments undertaken in this paper, the proposed method achieved a mean overall accuracy of 97.27% for cloud detection, with a mean precision of 96.02% and a mean recall of 95.86%. For cloud shadow detection, the proposed method achieved a mean precision of 95.92% and a mean recall of 92.86%. Experimental results on two validation datasets (GF-1 WFV validation data and ZY-3 validation data) show that the proposed multiscale-3D-CNN method achieved good performance with limited spectral ranges.

The first national product of Surface Water Dynamics in France (SWDF) is generated on a monthly temporal scale and 10-m spatial scale using an automatic rule-based superpixel (RBSP) approach. The current surface water dynamic products from high resolution (HR) multispectral satellite imagery are typically analyzed to determine the annual trend and related seasonal variability. Annual and seasonal time series analyses may fail to detect the intra-annual variations of water bodies. Sentinel-2 allows us to investigate water resources based on both spatial and temporal high-resolution analyses. We propose a new automatic RBSP approach on the Google Earth Engine platform. The RBSP method employs combined spectral indices and superpixel techniques to delineate the surface water extent; this approach avoids the need for training data and benefits large-scale, dynamic and automatic monitoring. We used the proposed RBSP method to process Sentinel-2 monthly composite images covering a two-year period and generate the monthly surface water extent at the national scale, i.e., over France. Annual occurrence maps were further obtained based on the pixel frequency in monthly water maps. The monthly dynamics provided in SWDF products are evaluated by HR satellite-derived water masks at the national scale (JRC GSW monthly water history) and at local scales (over two lakes, i.e., Lake Der-Chantecoq and Lake Orient, and 200 random sampling points). The monthly trends between SWDF and GSW were similar, with a coefficient of 0.94. The confusion matrix-based metrics based on the sample points were 0.885 (producer's accuracy), 0.963 (user's accuracy), 0.932 (overall accuracy) and 0.865 (Matthews correlation coefficient). The annual surface water extents (i.e., permanent and maximum) are validated by two HR satellite image-based water maps and an official database at the national scale and small water bodies (ponds) at the local scale at Loir-et-Cher. The results show that the SWDF results are closely correlated to the previous annual water extents, with a coefficient >0.950. The SWDF results are further validated for large rivers and lakes, with extraction rates of 0.929 and 0.802, respectively. Also, SWDF exhibits superiority to GSW in small water body extraction (taking 2498 ponds in Loir-et-Cher as example), with an extraction rate improved by approximately 20%. Thus, the SWDF method can be used to study interannual, seasonal and monthly variations in surface water systems. The monthly dynamic maps of SWDF improved the degree of land surface coverage by 25% of France on average compared with GSW, which is the only product that provides monthly dynamics. Further harmonization of Sentinel-2 and Landsat 8 and the introduction of enhanced cloud detection algorithm can fill some gaps of no-data regions.

Detecting the water surface area and its temporal changes is essential in water resource management. Multispectral satellite data are applied extensively to monitor the surface water dynamics because of their repeated coverage. This letter monitors waterbody dynamics in a high temporal resolution from Sentinel-2 and Landsat 8 images by using the Google Earth Engine (GEE) platform. Water index, automatic threshold and postprocessing of noise elimination are used to extract the water pixels from the background. Taking a part of the middle reaches of the Yangtze River () as a case study, the surface water dynamics are estimated by 47 dates from February 2017 to November 2019. The popular JRC Global Surface Water database includes 8 qualified monthly maps for the study area in 2017 and 2018, while our results display 32 maps covering 18 months. The surface water dynamic maps obtain an overall accuracy of 92.5% (using random sample points) and 82.0% (using manual-drawing thematic map). Also, water occurrence is calculated based on the water dynamics to indicate the permanent, seasonal and flooding areas. The high temporal dynamics of surface water are meaningful for the analysis of ephemeral lake and inundation extent.

This study illustrates the potential of alteration extraction in coal-bed methane (CBM) reservoirs using the recently available Sentinel-2 data. This study then evaluates the capabilities for mapping the altered minerals and vegetation. In the alteration mapping process, we separately analyzed the key remote sensing signatures of altered minerals and geobotanical anomalies based on the hydrocarbon micro-seepage theory. The diagnostic spectral characteristics of irons, clays and altered vegetation were concentrated and demonstrated the distribution of hydrocarbon micro-seepage. In the bare soil region, the altered minerals, including irons and clays were extracted through band math and principal component analysis (PCA) methods. In the vegetation area mapping, the diagnostic spectral feature parameters, such as the locations and slopes of three feature edges, were calculated. In addition, the mapping accuracy was assessed based on the extraction results of Hyperion data through full spectral profile matching method and the X-ray diffraction (XRD) analysis results. The results show that: 1) compared with band math and PCA methods, the different extraction methods were suitable for different minerals; 2) the extraction results of iron and clay minerals were most accurate (78.33% and 76.67%, respectively) with XRD analysis; 3) the highest rate of change of the feature edge slope was up to 39% with a reference spectrum; and 4) the distribution of alteration information was consistent with the Hudi coal mining area in Jincheng, Shanxi province. The potential geological application of Sentinel-2 data was revealed to identify the direction of CBM exploration in a large scale, highly efficient, convenient, and inexpensive way.

Built heritage has been documented by reality-based modeling for geometric description and by ontology for knowledge management. The current challenge still involves the extraction of geometric primitives and the establishment of their connection to heterogeneous knowledge. As a recently developed 3D information modeling environment, building information modeling (BIM) entails both graphical and non-graphical aspects of the entire building, which has been increasingly applied to heritage documentation and generates a new issue of heritage/historic BIM (HBIM). However, HBIM needs to additionally deal with the heterogeneity of geometric shape and semantic knowledge of the heritage object. This paper developed a new mesh-to-HBIM modeling workflow and an integrated BIM management system to connect HBIM elements and historical knowledge. Using the St-Pierre-le-Jeune Church, Strasbourg, France as a case study, this project employs Autodesk Revit as a BIM environment and Dynamo, a built-in visual programming tool of Revit, to extend the new HBIM functions. The mesh-to-HBIM process segments the surface mesh, thickens the triangle mesh to 3D volume, and transfers the primitives to BIM elements. The obtained HBIM is then converted to the ontology model to enrich the heterogeneous knowledge. Finally, HBIM geometric elements and ontology semantic knowledge is joined in a unified BIM environment. By extending the capability of the BIM platform, the HBIM modeling process can be conducted in a time-saving way, and the obtained HBIM is a semantic model with object-oriented knowledge.

Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS) imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN) architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%.

The development and evolution of trusted software is the focus of attention in the fields of trusted software and software engineering at home and abroad. In view of its complexity and diversity, this article proceeds with component, which is the basic element of software architecture, and discusses the refinement of trusted component. Refine one of the operations and its local environment using OR-transition colored Petri net to achieve the purpose of gradual refinement.