Time Series Of Maps Research Articles

A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction

Context: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today's competitive environment for producing high-quality software. Objective: This paper proposes a neuro-fuzzy hybridized method by integrating self-organized- map (SOM) and fuzzy time series (FTS) forecasting for the reliability prediction of a software system. Methodology: In the proposed approach, a well-known supervised clustering algorithm SOM is incorporated with FTS forecasting for developing a hybrid model for software reliability prediction. To validate the proposed approach, an experimental study is done by applying proposed neuro-fuzzy method on a software failure dataset. In addition, a comparative study was conducted for evaluating the performance of the proposed method by comparing it with some of the existing FTS models. Results: Experimental outcomes show that the proposed approach performs better than the existing FTS models. Conclusion: The results show that the proposed approach can be used efficiently in the software industry for software reliability prediction.

Taylor-based optimized recursive extended exponential smoothed neural networks forecasting method

The development of a Time series Forecasting System is a major concern for Artificial Intelligence researchers. Commonly, existing systems only assess temporal features and analyze the behavior of the data over time, thus, resulting in uncertain forecasting accuracy. Although many forecasting systems were proposed in the literature; they have not yet answered the attending question. Hence, to overcome this problematic, we propose an innovative method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method, abbreviated as TOREESNN. Briefly explained, the proposed technique introduces three ideas to solve this issue: First, building an innovative framework for forecasting univariate time series based on Exponential Smoothed theory. Second, designing an Elman Classifier model for uncertainty prediction in order to correct the forecasted values. And finally hybrading the two recurrent systems in one framework to obtain the final results. Experimental results demonstrate that the proposed method has a high accuracy both in training and testing data in terms of Mean Squared Error (MSE) and outperforms the state-of-the-art Recurrent Neural Networks models on Mackey-Glass, Nonlinear Auto-Regressive Moving Average time series (NARMA), Lorenz, and Henon map datasets.

Analysis of Worldwide Time-Series Data Reveals Some Universal Patterns of Evolution of the SARS-CoV-2 Pandemic

Predicting the evolution of the current epidemic depends significantly on understanding the nature of the underlying stochastic processes. To unravel the global features of these processes, we analyse the world data of SARS-CoV-2 infection events, scrutinising two 8-month periods associated with the epidemic’s outbreak and initial immunisation phase. Based on the correlation-network mapping, K-means clustering, and multifractal time series analysis, our results reveal several universal patterns of infection dynamics, suggesting potential predominant drivers of the pandemic. More precisely, the Laplacian eigenvectors localisation has revealed robust communities of different countries and regions that break into clusters according to similar profiles of infection fluctuations. Apart from quantitative measures, the immunisation phase differs significantly from the epidemic outbreak by the countries and regions constituting each cluster. While the similarity grouping possesses some regional components, the appearance of large clusters spanning different geographic locations is persevering. Furthermore, characteristic cyclic trends are related to these clusters; they dominate large temporal fluctuations of infection evolution, which are prominent in the immunisation phase. Meanwhile, persistent fluctuations around the local trend occur in intervals smaller than 14 days. These results provide a basis for further research into the interplay between biological and social factors as the primary cause of infection cycles and a better understanding of the impact of socio-economical and environmental factors at different phases of the pandemic.

Extending the National Burned Area Composite Time Series of Wildfires in Canada

Wildfires are a major natural disturbance in Canada that are postulated to increase under a warming climate. To derive accurate trends in burned area and to quantify the effects of fire frequency, duration, and extent, a sufficiently long time series of reliable burned area maps is required. With that in mind, we extended Canada’s National Burned Area Composite (NBAC) dataset from its previous start year (2004) back to 1986. NBAC consists of annual maps in polygon format where the area burned in each fire event is represented by the best available delineation among various mapping methods and data sources of varying quality. Ordered from more to less reliability, in the new 35-year time series (1986 to 2020), 10% of the total burned area was derived from airborne and high-resolution (<5 m) satellite imagery, 81% from change detection methods using 30 m Landsat satellite imagery, and the remaining 9% was largely from aerial surveys. Total (Canada-wide) annual burned area estimates ranged from 215,797 ha in 2020 to 6.7 million ha (Mha) in 1989. We computed 95% confidence intervals for the estimate of each year from 1986 to 2020 based on the accuracy and relative contribution in that year of the different data sources, for both the new NBAC time series and the polygon version of the Canadian National Fire Database (CNFDB), a commonly used source of spatially explicit data on burned area in Canada. NBAC confidence intervals were on average ±9.7% of the annual figure, about one-third the width of the confidence intervals derived for CNFDB. The NBAC time series also included nearly 5000 fire events (totalling 4 Mha, with the largest event being 120,661 ha in size) that are missing in the CNFDB. In a regional analysis for the Northwest Territories, retroactive fire mapping from Landsat imagery reduced historical estimates by 3 Mha (16%), which would result in a 1.6 Mha increase in the reported undisturbed critical habitat for threatened woodland caribou. The NBAC dataset is freely downloadable from the Canadian Wildland Fire Information System.

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series.

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

Analysis of the spatiotemporal evolution of the coastline of Jiaozhou Bay and its driving factors

Studying the spatiotemporal changes in coastlines and the underlying mechanisms is important for the rational development and utilization of coastal resources and sustainable economic development. For the first time, this study analyzes twenty years of spatiotemporal changes in the coastline of Jiaozhou Bay (JB), Qingdao, by integrating high-resolution remote sensing images from Google Maps and Landsat time series data at five-year intervals from 2000 to 2020. Five coastlines were extracted using the normalized difference water index and Otsu threshold segmentation method. The index of coastline type diversity (ICTD), index of coastline utilization degree (ICUD), and end point rate were applied to the systematic study of the evolution of the coastline of JB for the first time. The results show that the total length of coastline decreased, with the natural coastline accounting for only approximately 10% of the sharp decrease in the first five years and slow increase in the last 15 years. The ICTD was >0.7, and the ICUD increased from 231.7 to 261.5, indicating an increase in the influence of humans. The coastline also moved seaward with an average accretion rate of 47.5 km/year, resulting in a 24.5 km2 reduction in the area of the bay over the 20 years. This was caused by human activities such as port construction. In addition, the degree of correlation between coastline changes and socioeconomic factors was analyzed using grey correlation analysis. Based on this, the forces driving the changes to the coastline were explored, and suggestions for coastal zone management were proposed.

Feedbacks between size and density determine rapid eco‐phenotypic dynamics

AbstractBody size is a fundamental trait linked to many ecological processes—from individuals to ecosystems. Although the effects of body size on metabolism are well‐known, the potential reciprocal effects of body size and density are less clear. Specifically, (a) whether changes in body size or density more strongly influence the other and (b) whether coupled rapid changes in body size and density are due to plasticity, rapid evolutionary change or a combination of both.Here, we address these two issues by experimentally tracking population density and mean body size in the protistTetrahymena pyriformisas it grows from low density to carrying capacity. We then use Convergent Cross Mapping time series analyses to infer the direction, magnitude and causality of the link between body size and population dynamics. We confirm the results of our analysis by experimentally manipulating body size and density while keeping the other constant. Last, we fit mathematical models to our experimental time series that account for purely plastic change in body size, rapid evolution in size, or a combination of both, to gain insights into the processes that most likely explain the observed dynamics.Our results indicate that changes in body size more strongly influence changes in density than the other way around, but also show that there is reciprocity in this effect (i.e., a feedback). We show that a model that only accounts for purely plastic change in size most parsimoniously explains observed, coupled phenotypic and ecological dynamics.Together, these results suggest (a) that body size can shift dramatically through plasticity, well within ecological timescales, (b) that rapid changes in body size may have a larger effect on population dynamics than the reverse, but (c) phenotypic and population dynamics influence each other as populations grow. Overall, we show that rapid plastic changes in functional traits like body size may play a fundamental—but currently unrecognized—role in familiar ecological processes such as logistic population growth.Read the freePlain Language Summaryfor this article on the Journal blog.

An Approach for Monitoring and Classifying Marshlands Using Multispectral Remote Sensing Imagery in Arid and Semi-Arid Regions

Marshlands in arid and semi-arid areas are considered constantly changing environments due to unsecured water supplies as a result of high evapotranspiration and limited and highly variable rainfall. Classification of marshlands in these regions and mapping of their land cover is not an easy task and maps need to be upgraded frequently. Satellites provide enormous amounts of information and data for the continuous monitoring of changes. The aim of this paper is to introduce an approach using multispectral satellite imagery that was adopted to classify and monitor the Al Hammar Marsh (Iraq) over several years and to suggest a relationship between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Water Index (NDWI), using Landsat 8 data with a resolution of 30 m × 30 m, validated with Sentinel-2 datasets at 10 m × 10 m. Six land cover classes were used: (1) open water, (2) dry area, (3) dense vegetation, (4) medium-density vegetation, (5) sparse vegetation, and (6) wet soil. Three indices, NDWI, NDMI, and NDVI, were chosen for the automatic classification of each pixel and the creation of a time series of land cover maps. The proposed method can efficiently classify and monitor marshlands and can be used to study different marshlands by adjusting the thresholds for NDVI, NDMI, and NDWI. Overall, the correlation for all classes (R) between Landsat 8 and Sentinel-2 is about 0.78. Thus, this approach will help to preserve marshes through improved water management.

AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), which simultaneously captures relevant aspects of both local and global dynamics in an easy way, so that researchers can use graph algorithms to mine the knowledge in time series and gain special latent graph representation features. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AvgNet, by utilizing GNN model DiffPool as the classifier. We then adopt AvgNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AvgNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task.

Assessing neighborhood-scale traffic from crowd-sensed traffic data: Findings from an environmental justice community in New York City

BackgroundThe waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. ObjectiveThe goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 and 2019. MethodsWe analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. ResultsIn the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. ConclusionsNeighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures.

Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume

The Darling River system in Australia is under pressure from water extraction and climate change. Management interventions such as environmental flow releases require understanding of water storage dynamics and the connectivity of floodplains and wetlands. Such knowledge can be gleaned from the long observational record of the Landsat series of satellite sensors and high (<5 m) resolution digital elevation models derived from airborne light detection and ranging (LiDAR). Here, for the first time, we develop and demonstrate an approach to reconstruct 16-day floodplain water dynamics, including extent, depth, and volume for a long Landsat time series (1987 to present). Time series mapping of surface water extent at 5-m resolution was achieved by topographic downscaling of Landsat-derived surface water data. We propose a simple and effective algorithm to restore missing data in the images caused by, e.g., cloud and shadows, swath edges and the Landsat 7 Scan Line Corrector (SLC) failure, thereby increasing the number of useable images five-fold. The 5-m surface water extent maps clearly delineate the narrow river channel and the boundary of floodplain wetlands. They can capture the development, peak and retreat of flood events. By combining Landsat and airborne LiDAR observations, we produced time series of surface water depth mapping at 5-m resolution, accounting for the degree of hydraulic surface water connectivity. Based on these maps, we derived 16-day floodplain volume dynamics for 1987 to present. The correlation coefficient between upstream river flow records and floodplain volume time series was 0.88, indicating that the estimates were robust. The algorithms developed can be used for ongoing very high-resolution mapping to assist in managing human water use and environmental health in the Murray-Darling Basin.

A Solar-cycle Study of Coronal Rotation: Large Variations, Rapid Changes, and Implications for Solar-wind Models

Abstract Information on the rotation rate of the corona, and its variation over latitude and solar cycle, is valuable for making global connections between the corona and the Sun, for global estimates of reconnection rates and as a basic parameter for solar-wind modeling. Here, we use a time series of tomographical maps gained from coronagraph observations between 2007 and 2020 to directly measure the longitudinal drift of high-density streamers over time. The method reveals abrupt changes in rotation rates, revealing a complex relationship between the coronal rotation and the underlying photosphere. The majority of rates are between −1.°0 to +0.°5 day−1 relative to the standard Carrington rate of 14.°18 day−1, although rates are measured as low as −2.°2 day−1 and as high as 1.°6 day−1. Equatorial rotation rates during the 2008 solar minimum are slightly faster than the Carrington rate, with an abrupt switch to slow rotation in 2009, then a return to faster rates in 2017. Abrupt changes and large variations in rates are seen at all latitudes. Comparison with a magnetic model suggests that periods of equatorial fast rotation are associated with times when a large proportion of the magnetic footpoints of equatorial streamers are near the equator, and we interpret the abrupt changes in terms of the latitudinal distribution of the streamer photospheric footpoints. The coronal rotation rate is a key parameter for solar-wind models, and variations of up to a degree per day or more can lead to large systematic errors over forecasting periods of longer than a few days. The approach described in this paper gives corrected values that can form a part of future forecasting efforts.

Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps

G protein-coupled receptors (GPCRs) are promising drug targets because they play a large role in physiological processes by modulating diverse signaling pathways in the human body. The GPCR-mediated signaling pathways are regulated by four types of ligands—agonists, neutral antagonists, partial agonists, and inverse agonists. Once each type of ligand is bound to the binding site, it activates, deactivates, or does not perturb signaling by shifting the conformational ensemble of GPCRs. Predicting the ligand's effect on the conformation at the binding moment could be a powerful screening tool for rational GPCR drug design. Here, we detected conformational differences by capturing the spatiotemporal residue pair pattern of the ligand-bound β2-adrenergic receptor (β2AR) using a 3-dimensional residual network, 3D-ResNets. The network was trained with the time series of protein distance maps extracted from hundreds of molecular dynamics (MD) simulation trajectories of ten β2AR-ligand complexes. The MD system was constructed with a lipid bilayer embedded in an inactive β2AR X-ray crystal structure and solvated with explicit water molecules. To train the network, three hyperparameters were tested, and it was found that the number of MD trajectories in the training set significantly affected the model's accuracy. The classification of agonists and neutral antagonists was successful, but inverse agonists were not. Between the agonists and antagonists, different residue pair patterns were spotted on the extracellular loop segment. This result demonstrates the potential application of a 3-D neural network in GPCR drug screening, as well as an analysis tool for protein functional dynamics

CLPVG: Circular limited penetrable visibility graph as a new network model for time series.

A visibility graph transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms. In this paper, based on the classic limited penetrable visibility graph method, we propose a novel mapping method named circular limited penetrable visibility graph, which replaces the linear visibility line in limited penetrable visibility graph with nonlinear visibility arc for pursuing more flexible and reasonable mapping of time series. Tests on degree distribution and some common network features of the generated graphs from typical time series demonstrate that our circular limited penetrable visibility graph can effectively capture the important features of time series and show higher robust classification performance than the traditional limited penetrable visibility graph in the presence of noise. The experiments on real-world time-series datasets of radio and electroencephalogram signals also suggest that the structural features provided by a circular limited penetrable visibility graph, rather than a limited penetrable visibility graph, are more useful for time-series classification, leading to higher accuracy. This classification performance can be further enhanced through structural feature expansion by adopting subgraph networks. All of these results demonstrate the effectiveness of our circular limited penetrable visibility graph model.

Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

This article presents Maximum Visibility Approach (MVA), a new time series forecasting method based on the Complex Network theory. MVA initially maps time series data into a complex network using the visibility graph method. Then, based on the similarity measures between the nodes in the network, MVA calculates the one-step-ahead forecasts. MVA does not use all past terms in the forecasting process, but only the most significant observations, which are indicated as a result of the autocorrelation function. This method was applied to five different groups of data, most of them showing trend characteristics, seasonal variations and/or non-stationary behavior. We calculated error measures to evaluate the performance of MVA. The results of statistical tests and error measures revealed that MVA has a good performance compared to the accuracy obtained by the benchmarks considered in this work. In all cases, MVA surpassed other forecasting methods in Literature, which confirms that this work will contribute to the field of time series forecasting not only in the theoretical aspect, but also in practice.

Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century

Spatiotemporally consistent data on global cropland extent is essential for tracking progress towards sustainable food production. In the present study, we present an analysis of global cropland area change for the first two decades of the twenty-first century derived from satellite data time-series. We estimate that, in 2019, the cropland area was 1,244 Mha with a corresponding total annual net primary production (NPP) of 5.5 Pg C year−1. From 2003 to 2019, cropland area increased by 9% and cropland NPP by 25%, primarily due to agricultural expansion in Africa and South America. Global cropland expansion accelerated over the past two decades, with a near doubling of the annual expansion rate, most notably in Africa. Half of the new cropland area (49%) replaced natural vegetation and tree cover, indicating a conflict with the sustainability goal of protecting terrestrial ecosystems. From 2003 to 2019, global per-capita cropland area decreased by 10% due to population growth. However, the per-capita annual cropland NPP increased by 3.5% as a result of intensified agricultural land use. The presented global, high-resolution, cropland map time-series supports monitoring of natural land appropriation at the local, national and international levels.

Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping

This study tested the ability of Sentinel-1 C-band to separate forest from other common land use classes (i.e., urban, low vegetation and water) at two different sites. The first site is characterized by temperate forests and rough terrain while the second by tropical forest and near-flat terrain. We trained a support vector machine classifier using increasing feature sets starting from annual backscatter statistics (average, standard deviation) and adding long-term coherence (i.e., coherence estimate for two acquisitions with a large time difference), as well as short-term (six to twelve days) coherence statistics from annual time series. Classification accuracies using all feature sets was high (&gt;92% overall accuracy). For temperate forests the overall accuracy improved by up to 5% when coherence features were added: long-term coherence reduced misclassification of forest as urban, whereas short-term coherence statistics reduced the misclassification of low vegetation as forest. Classification accuracy for tropical forests showed little differences across feature sets, as the annual backscatter statistics sufficed to separate forest from low vegetation, the other dominant land cover. Our results show the importance of coherence for forest classification over rough terrain, where forest omission error was reduced up to 11%.

Accurate tracking of forest activity key to multi-jurisdictional management goals: A case study in California

An essential component of sustainable forest management is accurate monitoring of forest activities. Although monitoring efforts have generally increased for many forests throughout the world, in practice, effective monitoring is complex. Determining the magnitude and location of progress towards sustainability targets can be challenging due to diverse forest operations across multiple jurisdictions, the lack of data standardization, and discrepancies between field inspections and remotely-sensed records. In this work, we used California as a multijurisdictional case study to explore these problems and develop an approach that broadly informs forest monitoring strategies. The State of California recently entered into a shared stewardship agreement with the US Forest Service (USFS) and set a goal to jointly treat one million acres of forest and rangeland annually by 2025. Currently, however, federal and state forest management datasets are disjoint. This work addresses three barriers stymying the use of federal and state archival records to assess management goals. These barriers are: 1) current databases from different jurisdictions have not been combined due to their distinct data collection processes and internal structures; 2) datasets have not been comprehensively analyzed, despite the need to understand the extent of previous treatments as well as the rate of current activity; and 3) the spatial accuracy of archival datasets has not been evaluated against remotely-sensed data. To reduce these barriers, we first aggregated existing archival forest management records between 1984 and 2019 from the USFS′ Forest Activity Tracking System (FACTS) and the California Department of Forestry and Fire Protection (CAL FIRE) using a qualitative scalar of treatment intensity. Combined FACTS and CAL FIRE completed footprint acres – defined as unique areas of land where a treatment was completed at any time since 1984 – have decreased since a peak in 2008. At most, 300,000 footprint acres are completed each year, 30% of the million-acre goal. Prescribed fires – defined as direct burning operations – have risen over time, according to the FACTS hazardous fuels dataset but prescribed fire records in CAL FIRE's dataset have rapidly increased since 2016. We also refined the spatial and temporal detail of the aggregated management record using the Continuous Change Detection and Classification algorithm on satellite remote sensing data to produce a state-wide time series map of harvest disturbances. A comparison of the algorithm's refined data to the archival record potentially suggests over-reporting in both FACTS and CAL FIRE's archival datasets. Our integrated dataset provides a better assessment of current treatments and the path towards the 1-million-acre a year goal. The refined dataset leverages the strengths of complementary, albeit imperfect, monitoring strategies from archives and remotely-sensed detection.