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The monitoring of forest ecosystems is significantly affected by the lack of consistent historical data of low-severity (forest partially disturbed) or gradual disturbance (e.g. eastern spruce budworm epidemic). The goal of this paper is to explore the use of a subset of Landsat time series and deep learning models to identify both the type and the year of disturbances, including low-severity and gradual disturbances, in the boreal forest of eastern Canada at the pixel level. Remote sensing data such as the spectral information from Landsat time series are the best available option for large scale observations of disturbances that go back decades. Traditional modeling approaches, like LandTrendr, require substantial handcrafted pre-processing to remove noise and to extract temporal features from the image sequences before using them as input to a classical machine-learning model. Deep-learning models can autonomously discern which features are relevant within the coarse temporal and spectral information from the Landsat annual dense time series. We evaluated the performance of TempCNN and Transformer model in detecting and classifying the type and the year of the forest disturbance using Landsat time series subsequences. Our findings resulted in the generation of four disturbance maps outlining the forest history from 1986 to 2021 within the eastern Canadian boreal forest. Our experimental outcomes demonstrate several significant benefits of employing deep learning models. Firstly, using noisy Landsat time series they achieve comparable accuracy for classifying fire and total harvesting than existing publicly available disturbance maps. Secondly, the use of shorter time series subsequence with deep learning models enables to map adequately different overlapping disturbances occurring in the complete time series. Finally, they increase the number of distinguishable disturbance classes by adding partial harvesting, gradual disturbances, and forest recovery from older events, making them useful approaches for obtaining the first remote sensing-based map for areas affected by the eastern spruce budworm. • TempCNN, Transformer tested on Landsat time series for disturbance classification. • The type and the year of disturbance events were classified with good accuracy. • Deep learning effective for identifying partial and progressive disturbances. • Landsat subsequences extract overlapping disturbances. [ABSTRACT FROM AUTHOR]

A variety of evidence suggests that the boreal forests of Canada are responding to climate change. Specifically, several studies have inferred that widespread browning trends detected in time series of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) reflect the response of boreal forests to longer growing seasons, increased summer drought stress, and higher frequency of fires. Data from the Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) sensors onboard Landsat 5 and 7, respectively, span essentially the same time period as the AVHRR record, but provide data with substantially higher radiometric and spatial fidelity, and by extension, a much improved basis for evaluating decadal-scale trends in spectral vegetation indices such as the NDVI. However, detection of trends, which are often subtle, requires careful attention to ensure that artifacts associated with the quality and stability of inter- and intra-sensor calibration do not lead to spurious conclusions in results from time series analyses. In this paper, we use time series of TM5 and ETM+ images for fifteen sites distributed across the Canadian boreal forest zone to explore if and how sensor geometry and inter- and intra-sensor calibration affect trends in spectral vegetation indices derived from multi-decadal Landsat time series. To do this, we created annual cloud-free composites for each Landsat spectral band based on peak summer NDVI at each site from 1984 to 2011 using all available TM5 and ETM+ data. To distinguish trends arising from long term climate change from those related to disturbance, we isolated areas within each site that were undisturbed during the Landsat record, and used these locations to analyze sources of variance in time series of red reflectance, near-infrared (NIR) reflectance, the NDVI, and the Enhanced Vegetation Index (EVI). Our results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors. In particular, differences in sensor view geometry across adjacent overlapping Landsat scenes cause vegetated pixels in the eastern portion of Landsat scenes to have higher reflectances in the red and NIR bands (by 5 and 6 percent, respectively) than pixels in the western portion of scenes. While this effect does not significantly change NDVI values, it does affect EVI values. We also found modest, but potentially significant, differences between the red band reflectance of each sensor, with TM5 data having 14 percent higher red reflectance on average for vegetated pixels, which can introduce spurious trends in time series that combine TM5 and ETM+ data. More generally, the results from this work demonstrate that while the 30 + year Landsat archive provides unprecedented opportunities for studying changes to the Earth's terrestrial biosphere over the last three decades, care must be taken when inferring trends in these data without considering how sources of variance unrelated to surface processes affect the integrity of Landsat time series. [ABSTRACT FROM AUTHOR]

The western Canadian mixedwood boreal forests were projected to be significantly affected by regional drought. However, drought degrees were spatially different across elevations, longitudes and latitudes, which might cause different tree growth responses to climate change in different sub-regions within western Canada. In this way, regional classification of western Canadian boreal forests and understanding spatial tree growth responses to climate might be necessary for future forest management and monitoring. In this paper, tree-ring chronologies of two dominant tree species, trembling aspen ( Populus tremuloides Michx.) and white spruce ( Picea glauca (Moench.) Voss), were obtained from mixed forest stands distributed across western Canada to study spatial tree growth response to climate based on three regional classification schemes (a phytogeographic sub-region classification, a natural sub-region classification and non-classification). Phytogeographic sub-region classification was estimated based on tree ring samples we collected in this study, while natural sub-region classification was previously developed based on analysis of regional differences in vegetation, soil, site and climate conditions. Results showed that air temperature did not significantly increase, while drought stress became more severe between 1985 to 2010. Relationships between trembling aspen growth and temperature differed between north and south parts of the study area, resulting from spatial difference in water supply. Trembling aspen growth was influenced by temperature or moisture variables of the previous years. White spruce growth was influenced primarily by moisture variables (current or previous year), and response coefficients between white spruce and drought conditions (represented by drought code) were negative in all phytogeographic sub-regions, suggesting that white spruce was more sensitive to drought stress under climate change. As a late-successional dominant species, increasingly drought stress on white spruce might cause significant alteration in forest composition of western Canadian boreal forest. [ABSTRACT FROM AUTHOR]

Statistical models relating habitat characteristics to species occurrences are increasingly used to evaluate the consequences of forest management activities and conservation plans over large spatial and temporal scales. In practice, such models do not always generalize to other locations, hence, they should be validated using independent data. In this paper, we assess the predictive ability of 16 songbird habitat models developed in the Calling Lake region of the boreal mixedwood forest of Alberta using both internal and external validation approaches. Internal validation relied on the same dataset used to develop the models while external validation utilised independent data collected within four validation landscapes in the same ecological region. Two aspects of predictive accuracy were evaluated: the agreement between observations and predicted values (calibration) and the models’ ability to classify locations into those in which species are present or absent (discrimination). Calibration was assessed using the Hosmer–Lemeshow (H–L) statistic and plots showing predicted versus observed probabilities of occurrence. Discrimination was assessed using receiver operating characteristic (ROC) curves and the associated area under the ROC curve. With internal validation, the calibration plots were reasonable for all species, however the H–L statistic indicated a good fit for only eight species. Model discrimination between occupied and unoccupied sites, on the other hand, was reasonable for 14 of 16 models. With external validation, both the calibration plots and the H–L statistic identified only three species with a reasonable fit. The remaining models generally over- or under-predicted probability of occurrence. External model discrimination was reasonable for 10 of 16 species. When we re-estimated the models using all available data, the calibration plots were again reasonable for all species, with the H–L statistic indicating good fit for 12 species. Similarly, model discrimination was reasonable for 14 of 16 species. Several factors may help to explain why model performance using external data was generally poorer and more variable than when using internal data. These include differences in landscape structure and disturbance history as well as differences in the frequency of occurrence of individual species. Overall, our analyses identified several species whose habitat models degraded considerably, especially as measured by calibration. By re-estimating the models, we increased the range of variation in our covariates which likely led to an improvement in model calibration. We discuss the importance of ongoing evaluation and refinement of habitat models that will be used in conservation planning or to evaluate management scenarios at large spatial and temporal scales. [Copyright &y& Elsevier]

Abstract: This paper describes the activity of a small meandering stream and the development of its floodplain during the last 4600 years (calendar years BP) in the northern boreal zone of Québec. Three trenches were excavated across the floodplain''s full width and permitted the interpretation of morphosedimentary units in relation to modern analogs. Chronological controls within trenches was provided by the dendrochronological and radiocarbon dating of buried tree trunks. From 4600 to 2900 cal. BP and from 1000 to 120 cal. BP, the channel migrated and constructed its floodplain at very slow rates, mostly because of low flow velocities, vegetated streambanks and the cohesive texture of marine sediments reworked by the channel. Vertical accretion rates were extremely variable on the floodplain, with high rates proximal to the modern channel and low rates over distal (also older) portions of the floodplain. Following a major channel shift (meander cut-off or avulsion) around 2900 cal. BP, channel migration appears to have been constrained to a narrow zone adjacent to the modern channel. Within this constrained zone, the migrating channel has reworked its own sediments leading to a marked unconformity between 2900–1000 cal. BP. It is thought that underlying marine sediments protrusions, and perhaps the forested banks, protected older alluvial sediments from being eroded during the last three millenniums. Our study shows that small boreal floodplains may contain, in a very small area, abundant and diversified archives of their evolution. [Copyright &y& Elsevier]

This paper investigates the potential for the Soil Moisture Active Passive (SMAP) L-band radiometer to estimate the percentage of frozen soil inside a pixel during fall periods. To evaluate the spatial and temporal variability of the autumn freeze in northeastern Canada boreal forest, a network of compact and self-recording temperature sensors (iButton) along transects averaging 10 km was deployed on the soil surface at two different sites. Results show important spatial variability in soil freezing timing with maximums, for the two sites, spanning from 7.5 to 9.5 weeks to reach a frozen state. It is known that L-band radiometry such as Soil Moisture Active Passive (SMAP) can monitor freeze/thaw (F/T) seasonality at low spatial resolution (~40 km). However, the possible strong surface variability within a 40 km × 40 km pixel can lead to spatial delay in fall freezing. Simulations of brightness temperature (TB) weighted by spatially distributed iButton F/T records and using the ω-τ vegetation model show good agreement with SMAP satellite observations during the fall transition period with an average RMSE of 3.5 K. A new intra-pixel frozen soil percentage retrieval algorithm using only SMAP observations and air temperature is proposed. The percentage of frozen soil in falls 2015 and 2016 from the new algorithm and the in situ measurements are in good agreement with a coefficient of determination (R2) ranging between 0.63 and 0.88. • Substantial spatial variability of soil freezing timing in boreal forest • L-band SMAP radiometer sensitivity to spatial variability of soil freezing • A new SMAP algorithm to estimate the intra-pixel fraction of frozen soil [ABSTRACT FROM AUTHOR]