Representative Landscapes in the Forested Area of Canada

Canada is a large nation with forested ecosystems that occupy over 60% of the national land base, and knowledge of the patterns of Canada’s land cover is important to proper environmental management of this vast resource. To this end, a circa 2000 Landsat-derived land cover map of the forested ecosystems of Canada has created a new window into understanding the composition and configuration of land cover patterns in forested Canada. Strategies for summarizing such large expanses of land cover are increasingly important, as land managers work to study and preserve distinctive areas, as well as to identify representative examples of current land-cover and land-use assemblages. Meanwhile, the development of extremely efficient clustering algorithms has become increasingly important in the world of computer science, in which billions of pieces of information on the internet are continually sifted for meaning for a vast variety of applications. One recently developed clustering algorithm quickly groups large numbers of items of any type in a given data set while simultaneously selecting a representative—or “exemplar”—from each cluster. In this context, the availability of both advanced data processing methods and a nationally available set of landscape metrics presents an opportunity to identify sets of representative landscapes to better understand landscape pattern, variation, and distribution across the forested area of Canada. In this research, we first identify and provide context for a small, interpretable set of exemplar landscapes that objectively represent land cover in each of Canada’s ten forested ecozones. Then, we demonstrate how this approach can be used to identify flagship and satellite long-term study areas inside and outside protected areas in the province of Ontario. These applications aid our understanding of Canada’s forest while augmenting its management toolbox, and may signal a broad range of applications for this versatile approach

Uncovering Dominant Land-Cover Patterns of Quebec: Representative Landscapes, Spatial Clusters, and Fences

Mapping large areas for planning and conservation is a challenge undergoing rapid transformation. For centuries, the creation of broad-extent maps was the near-exclusive domain of expert specialist cartographers, who painstakingly delineated regions of relative homogeneity with respect to a given set of criteria. In the satellite era, it has become possible to rapidly create and update categorizations of Earth’s surface with improved speed and flexibility. Land cover datasets and landscape metrics offer a vast set of information for viewing and quantifying land cover across large areas. Comprehending the patterns revealed by hundreds of possibly relevant landscape metric values, however, remains a daunting task. We studied the information content of a large set of landscape pattern metrics across Quebec, Canada, asking whether they were capable of making consistent, spatially cohesive distinctions among patterns in landscapes. We evaluated the possibility of metrics to identify representative landscapes for efficient sampling or conservation, and determined areas where differences in nearby landscape patterns were the most and least pronounced. This approach can serve as a template for a landscape perspective on the challenges that will be faced in the near future by planners and conservationists working across large areas

BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine

Remote sensing is undergoing a fundamental paradigm shift, in which approaches interpreting one or two images are giving way to a wide array of data-rich applications. These include assessing global forest loss, tracking water resources across Earth’s surface, determining disturbance frequency across decades, and many more. These advances have been greatly facilitated by Google Earth Engine, which provides both image access and a platform for advanced analysis techniques. Within the realm of land-use/land-cover (LULC) classifications, Earth Engine provides the ability to create new classifications and to access major existing data sets that have already been created, particularly at global extents. By overlaying global LULC classifications—the 300-m GlobCover 2009 LULC data set for example—with sharper images like those from Landsat, one can see the promise and limits of these global data sets and platforms to fuse them. Despite the promise in a global classification covering all of the terrestrial surface, GlobCover 2009 may be too coarse for some applications. We asked whether the LULC labeling provided by GlobCover 2009 could be combined with the spatial granularity of the Landsat platform to produce a hybrid classification having the best features of both resources with high accuracy. Here we apply an improvement of the Bayesian Updating of Land Cover (BULC) algorithm that fused unsupervised Landsat classifications to GlobCover 2009, sharpening the result from a 300-m to a 30-m classification. Working with four clear categories in Mato Grosso, Brazil, we refined the resolution of the LULC classification by an order of magnitude while improving the overall accuracy from 69.1 to 97.5%. This “BULC-U” mode, because it uses unsupervised classifications as inputs, demands less region-specific knowledge from analysts and may be significantly easier for non-specialists to use. This technique can provide new information to land managers and others interested in highly accurate classifications at finer scales

Multiple Images Improve Lake CDOM Estimation: Building Better Landsat 8 Empirical Algorithms across Southern Canada

Coloured dissolved organic matter (CDOM) is an important water property for lake management. Remote sensing using empirical algorithms has been used to estimate CDOM, with previous studies relying on coordinated field campaigns that coincided with satellite overpass. However, this requirement reduces the maximum possible sample size for model calibration. New satellites and advances in cloud computing platforms offer opportunities to revisit assumptions about methods used for empirical algorithm calibration. Here, we explore the opportunities and limits of using median values of Landsat 8 satellite images across southern Canada to estimate CDOM. We compare models created using an expansive view of satellite image availability with those emphasizing a tight timing between the date of field sampling and the date of satellite overpass. Models trained on median band values from across multiple summer seasons performed better (adjusted R2 = 0.70, N = 233) than models for which imagery was constrained to a 30-day time window (adjusted R2 = 0.45). Model fit improved rapidly when incorporating more images, producing a model at a national scale that performed comparably to others found in more limited spatial extents. This research indicated that dense satellite imagery holds new promise for understanding relationships between in situ CDOM and satellite reflectance data across large areas

Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems

Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia

Studies that relate changes in land cover with changes in river discharge at the small scale (100 km2) usually have not found similar relationships. Here we analyse a 50-year long time series of discharge of a tropical river, the Tocantins River at Porto Nacional (175,360 km2), as well as precipitation over this drainage area, during a period where substantial changes in land cover occurred in the basin (1949–1998). Based on agricultural census data, we estimate that, in 1960, about 30% of the basin was used for agriculture. Previous work indicates that by 1995, agriculture had increased substantially, with about 49% of the basin land used as cropland and pastures. Initially, we compare one period with little changes in land cover (period 1-1949–1968) with another with more intense changes in land cover (period 2-1979–1998). Our analysis indicates that, while precipitation over the basin is not statistically different between period 1 and period 2 (α=0.05), annual mean discharge in period 2 is 24% greater than in period 1 (P<0.02), and the high-flow season discharge is greater by 28% (P<0.01). Further analyses present additional evidence that the change in vegetation cover altered the hydrological response of this region. As the pressure for changes in land cover in that region continue to increase, one can expect important further changes in the hydrological regime of the Tocantins River

Context and Opportunities for Expanding Protected Areas in Canada

At present, 10.5% of Canada&#8217;s land base is under some form of formal protection. Recent developments indicate Canada aims to work towards a target of protecting 17% of its terrestrial and inland water area by 2020. Canada is uniquely positioned globally as one of the few nations that has the capacity to expand the area under its protection. In addition to its formally protected areas, Canada&#8217;s remote regions form de facto protected areas that are relatively free from development pressure. Opportunities for expansion of formally protected areas in Canada include official delineation and designation of de facto protected areas and the identification and protection of land to improve connectivity between protected areas (PA). Furthermore, there are collaborative opportunities for expanding PA through commitments from industry and provincial and territorial land stewards. Other collaborative opportunities include the contributions of First Nations aligning with international examples of Indigenous Protected Areas, or the incorporation and cultivation of private protection programs with documented inclusion in official PA networks. A series of incremental additions from multiple actors may increase the likelihood for achieving area-based targets, and expands stakeholder engagement and representation in Canada&#8217;s PA system. Given a generational opportunity and high-level interest in expansion of protected areas in Canada and elsewhere, it is evident that as a diverse number of stakeholders and rights holders collaboratively map current and future land uses onto forest landscapes, science-based conservation targets and spatial prioritizations can inform this process

Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally

Water temperature and ice cover are critical characteristics of the ecological, biogeochemical, and physical functioning of a lake. Site-specific observations of temperature and ice, however, are not available for most lakes in the world. Yet this information is crucial to understanding the global role of lakes in the functioning of the bio- and hydrosphere. Here, we present the LakeTEMP dataset, referring to the ~1.4 million lakes globally of the HydroLAKES database with a surface area exceeding 0.1 km2, and consisting of two subsets: (1) an observational dataset that contains lake surface water temperatures (LSWTs), derived from Landsat 8 thermal radiance observations between 2013 and 2021 extracted at the lake center points; and (2) a dataset with monthly and yearly LSWT summary statistics and predictions of average yearly ice cover durations, interpolated from the observational dataset using seasonal trendlines. All observations underwent extensive quality control and filtering, based on outlier detection, overlapping imagery removal, and the removal of observations taken from dry lake beds. Validation of the LSWT observations was carried out with in-situ data and yielded an R^2, RMSE and median of differences of 0.93, 1.71°C and 0.42°C, respectively. The global average yearly LSWT is 6.3°C, assuming 0°C during times of presumed ice cover, and 12.4°C when only considering periods of open water. About 8% of all lakes never freeze, ~6% have short or sporadic freezing periods, and ~86% freeze every year, corresponding to an estimated proportion of global lake surface area of 23%, 20%, and 57%, respectively. The warmest lakes in the world (average temperatures of up to 36°C) are all artificial lakes used in the power plant, mining, salt extraction, and aquaculture industries. LakeTEMP fills a crucial spatial data gap in large-scale limnological research, especially for the incorporation of small lakes and understudied geographies of remote regions. Moreover, easy linkage to other large-scale datasets that use the unique lake identifiers from HydroLAKES, most notably the LakeATLAS database (56 hydro-environmental variables for each lake including anthropogenic influences), allows to explore characteristics that may be correlated to or affected by LSWT and ice cover. The data are in an analysis-ready format and openly available at https://doi.org/10.6084/m9.figshare.23844660

Data from: Multi-purpose habitat networks for short-range and long-range connectivity: a new method combining graph and circuit connectivity

Biodiversity conservation in landscapes undergoing climate and land-use changes requires designing multipurpose habitat networks that connect the movements of organisms at multiple spatial scales. Short-range connectivity within habitat networks provides organisms access to spatially distributed resources, reduces local extinctions and increases recolonization of habitat fragments. Long-range connectivity across habitat networks facilitates annual migrations and climate-driven range shifts. We present a method for identifying a multipurpose network of forest patches that promotes both short- and long-range connectivity. Our method uses both graph-theoretic analyses that quantify network connectedness and circuit-based analyses that quantify network traversability as the basis for identifying spatial conservation priorities on the landscape. We illustrate our approach in the agroecosystem, bordered by the Laurentian and Appalachian mountain ranges, that surrounds the metropolis of Montreal, Canada. We established forest conservation priorities for the ovenbird, a Neotropical migrant, sensitive to habitat fragmentation that breeds in our study area. All connectivity analyses were based on the same empirically informed resistance surface for ovenbird, but habitat pixels that facilitated short- and long-range connectivity requirements had low spatial correlation. The trade-off between connectivity requirements in the final ranking of conservation priorities showed a pattern of diminishing returns such that beyond a threshold, additional conservation of long-range connectivity had decreased effectiveness on the conservation of short-range connectivity. Highest conservation priority was assigned to a series of stepping stone forest patches across the study area that promote traversability between the bordering mountain ranges and to a collection of small forest fragments scattered throughout the study area that provide connectivity within the agroecosystem. Landscape connectivity is important for the ecology and genetics of populations threatened by climate change and habitat fragmentation. Our method has been illustrated as a means to conserve two critical dimensions of connectivity for a single species, but it is designed to incorporate a variety of connectivity requirements for many species. Our approach can be tailored to local, regional and continental conservation initiatives to protect essential species movements that will allow biodiversity to persist in a changing climate