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Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat. © 2019 The United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) are gratefully acknowledged for support and encouragement of the 2012–2017 Landsat Science Team ( https://landsat.usgs.gov/landsat-science-teams ). The Editor and Reviewers are thanked for the valuable insights and constructive suggestions made to improve this manuscript.

Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia. Our analysis found an 18,154,000 (±1,580,000) ha, or 34.8%, increase in the area of cropland in East Africa. Conversion occurred primarily from Open Grasslands, Wooded Grasslands, and Open Forests, causing a large-scale reduction in woody vegetation classes. We observed far more conversion (by approximately 20 million hectares) of woody classes to less-woody classes than succession in the direction of increasing trees and shrubs. Spatial patterns within our sample highlight regional land cover conversion hotspots, such as the Central Zambezian Miombo Woodlands, as potential areas of concern related to the conservation of natural ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and ecoregions most impacted by three decades of human development, both spatially and statistically.

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time? 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance interpreter consistency are critical elements to document and ensure the quality of these reference data.

Automated cloud and cloud shadow identification algorithms designed for Landsat Thematic Mapper (TM) and Thematic Mapper Plus (ETM+) satellite images have greatly expanded the use of these Earth observation data by providing a means of including only clear-view pixels in image analysis and efficient cloud-free compositing. In an effort to extend these capabilities to Landsat Multispectal Scanner (MSS) imagery, we introduce MSS clear-view-mask (MSScvm), an automated cloud and shadow identification algorithm for MSS imagery. The algorithm is specific to the unique spectral characteristics of MSS data, relying on a simple, rule-based approach. Clouds are identified based on green band brightness and the normalized difference between the green and red bands, while cloud shadows are identified by near infrared band darkness and cloud projection. A digital elevation model is incorporated to correct for topography-induced illumination variation and aid in identifying water. Based on an accuracy assessment of 1981 points stratified by land cover and algorithm mask class for 12 images throughout the United States, MSScvm achieved an overall accuracy of 84.0%. Omission of thin clouds and bright cloud shadows constituted much of the error. Perennial ice and snow, misidentified as cloud, also contributed disproportionally to algorithm error. Comparison against a corresponding assessment of the Fmask algorithm, applied to coincident TM imagery, showed similar error patterns and a general reduction in accuracy commensurate with differences in the radiometric and spectral richness of the two sensors. MSScvm provides a suitable automated method for creating cloud and cloud shadow masks for MSS imagery required for time series analyses in temperate ecosystems.

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Landsat occupies a unique position in the constellation of civilian earth observation satellites, with a long and rich scientific and applications heritage. With nearly 40 years of continuous observation – since launch of the first satellite in 1972 – the Landsat program has benefited from insightful technical specification, robust engineering, and the necessary infrastructure for data archive and dissemination. Chiefly, the spatial and spectral resolutions have proven of broad utility and have remained largely stable over the life of the program. The foresighted acquisition and maintenance of a global image archive has proven to be of unmatched value, providing a window into the past and fueling the monitoring and modeling of global land cover and ecological change. In this paper we discuss the evolution of the Landsat program as a global monitoring mission, highlighting in particular the recent change to an open (free) data policy. The new data policy is revolutionizing the use of Landsat data, spurring the creation of robust standard products and new science and applications approaches. Open data access also promotes increased international collaboration to meet the Earth observing needs of the 21st century.

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67–0.74, Kappa: 0.41–0.52) and increased by 0.09–0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%–20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.

The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.

This article describes a series of fundamental analyses designed to test and compare the utility of various MODIS data and products for detecting land cover change over a large area of the tropics. The approach for estimating proportional forest cover change as a continuous variable was based on a reduced major axis regression model. The model relates multispectral and multi-temporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for several study sites across the Central American region. Three MODIS data sets with diverse attributes were evaluated on model consistency, prediction accuracy and practical utility in estimating change in forest cover over multiple time intervals and spatial extents. A spectral index based on short-wave infrared information (normalized difference moisture index), calculated from half-kilometer Calibrated Radiances data sets, generally showed the best relationships with the reference data and the lowest model prediction errors at individual study areas and time intervals. However, spectral indices based on atmospherically corrected surface reflectance data, as with the Vegetation Indices and Nadir Bidirectional Reflectance Distribution Function - Adjusted Reflectance (NBAR) data sets, produced consistent model parameters and accurate forest cover change estimates when modeling over multiple time intervals. Models based on anniversary date acquisitions of the one-kilometer resolution NBAR product proved to be the most consistent and practical to implement. Linear regression models based on spectral indices that correlate with change in the brightness, greenness and wetness spectral domains of these data estimated proportional change in forest cover with less than 10% prediction error over the full spatial and temporal extent of this study.

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16-day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth-Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller-scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.

Satellite sensors are well suited to monitoring changes on the Earth's surface through provision of consistent and repeatable measurements at a spatial scale appropriate for many processes causing change on the land surface. Here, we describe and test a new conceptual approach to change detection of forests using a dense temporal stack of Landsat Thematic Mapper (TM) imagery. The central premise of the method is the recognition that many phenomena associated with changes in land cover have distinctive temporal progressions both before and after the change event, and that these lead to characteristic temporal signatures in spectral space. Rather than search for single change events between two dates of imagery, we instead search for these idealized signatures in the entire temporal trajectory of spectral values. This trajectory-based change detection is automated, requires no screening of non-forest area, and requires no metric-specific threshold development. Moreover, the method simultaneously provides estimates of discontinuous phenomena (disturbance date and intensity) as well as continuous phenomena (post-disturbance regeneration). We applied the method to a stack of 18 Landsat TM images for the 20-year period from 1984 to 2004. When compared with direct interpreter delineation of disturbance events, the automated method accurately labeled year of disturbance with 90% overall accuracy in clear-cuts and with 77% accuracy in partial-cuts (thinnings). The primary source of error in the method was misregistration of images in the stack, suggesting that higher accuracies are possible with better registration.

Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) to capture the extent and distributionofpeatlandsintheSt.PetersburgregionofRussiabyanalyzingtherelationshipsbetweenpeatlandcoverfractionsderived from reference maps and ∼1-km resolution MODIS Nadir BRDF-Adjusted Reflectance (NBAR) data from year 2002. First, we characterized and mapped 50 peatlands from forest inventory and peat deposit inventory data. The peatlands represent three major nutritional types (oligotrophic, mesotrophic, eutrophic) and different sizes (0.6–7800 ha). In addition, parts of 6 peatlands were mined for peat and these were mapped separately. The reference maps provided information on peatland cover for 1105 MODIS pixels. We performed regression analysis on 50% of the pixels and reserved the remainder for model validation. Canonical correlation analysis on the MODIS reflectance bands and the peatland cover fractions produced a multi-spectral peatland cover index (PCI), which served as the predictor in a reduced major axis (RMA) regression model. The results suggest a high potential for mapping peatlands with MODIS. The RMA regression models explained much of the variance in the PCI (r 2 =0.74 for mined and r 2 =0.81 for unmined peatlands). Model validation showed high correlation between observed versus predicted peatland cover (mined: r=0.87; unmined: r=0.92). We used the models to derive peatland cover estimates for the St. Petersburg region and compared the results to current MODIS land cover maps. © 2006 Elsevier B.V. All rights reserved.

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery. The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km2 study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed

Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km 2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites — often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites — often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters.

The widespread availability of satellite imagery and image processing software has made it relatively easy for ecologists to use satellite imagery to address questions at the landscape and regional scales. However, as often happens with complex tools that are rendered easy to use by computer software, technology may be misused or used without an understanding of some of the limitations or caveats associated with a particular application. The results can be disappointment when maps are less accurate than expected or incorrect decisions when they are treated as truth. In this paper, we discuss several key issues which are critical to ensuring the effectiveness and value of remote sensing products, but which are also sometimes sources of confusion: (1) direct versus indirect models of land surface properties and processes, (2) differences between class-based and continuous mapping models, (3) scale, and (4) accuracy assessment. We illustrate our points with examples from the application of satellite imagery to forest management issues in the Pacific Northwest, USA. While our examples focus largely on Landsat image data, the issues we discuss have broad relevance across sensor data types, land cover properties, and geographic locations.

Extensive estimates of forest productivity are required to understand the relationships between shifting land use, changing climate and carbon storage and fluxes. Aboveground net primary production of wood (NPPAw) is a major component of total NPP and of net ecosystem production (NEP). Remote sensing of NPP and NPPAw is generally based on light use efficiency or process-based biogeochemistry models. However, validating these large area flux estimates remains a major challenge. In this study we develop an independent approach to estimating NPPAw, based on stand age and biomass, that could be implemented over a large area and used in validation efforts. Stand age is first mapped by iterative unsupervised classification of a multi-temporal sequence of images from a passive optical sensor (e.g. Landsat TM). Stand age is then cross-tabulated with estimates of stand height and aboveground biomass from lidar remote sensing. NPPAw is then calculated as the average increment in lidar-estimated biomass over the time period determined using change detection. In western Oregon, productivity estimates made using this method compared well with forest inventory estimates and were significantly different than estimates from a spatially distributed biogeochemistry model. The generality of the relationship between lidar-based canopy characteristics and stand biomass means that this approach could potentially be widely applicable to landscapes with stand replacing disturbance regimes, notably in regions where forest inventories are not routinely maintained.

As a source of atmospheric carbon, biomass burning emissions associated with deforestation in the Amazon are globally significant. Once deforested, these lands continue to be sources of substantial burning emissions for many years due to frequent pasture burning. The objective of this research was to quantify biomass-burning emissions at a local scale. We estimated carbon emissions from three sources: fires associated with primary forest slash, regenerating forest slash, and pastures. The study was conducted on a 94370-ha section of land surrounding Jamari, RondGnia, Brazil. In the emissions com- putation, we integrated site-specific, ground-based data (biomass, emission factors for flam- ing and smoldering combustion, and combustion factors for land-cover types) with a Landsat TM land cover change map of the study area for 1984-1992. This map was used to ascertain changes in land cover based on TM image dates during a period of early colonization and rapid deforestation in Rond8nia. Emissions of CO, CO,, CH,, and other hydrocarbon trace gases were calculated. Between 1984 and 1992, we found CO, emissions generated by primary forest slash burning were 694 379 Mg C (920 kg C-ha-I-yr-I) and regenerating forest slash burning contributed 23 436 Mg C (31 kg C.ha-'.yr-l), whereas cumulative pasture burning produced 238 180 Mg C (316 kg C.ha-l.yr-l). CO and CH, folIowed the same trends by land cover type. Primary forest slash burns contributed 73% of the total C emissions whereas burning regenerating forest slash and pasture produced 2% and 2596, respectively. The major finding of this work is the identification of pasture burning as a potentially important source of pyrogenic emissions, but better data on biomass and com- bustion efficiency could confirm this finding. Further we demonstrated a novel method for estimating area burned that links land cover type and change to burn frequency.

Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km 2 ) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm—daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms.

Shifts in the demographic and economic character of the Greater Yellowstone Ecosystem (GYE) are driving patterns of land cover and land use change in the region. Such changes may have important consequences for ecosystem functioning. The objective of this paper is to quantify the trajectories and rates of change in land cover and use across the GYE for the period 1975-1995 using satellite imagery. Spectral and geographic variables were used as inputs to classification tree regression analysis (CART) to find ''rules'' which defined land use and land cover classes on the landscape. The resulting CART functions were used to map land cover and land use across seven Landsat TM scenes for 1995. We then used a thresholding technique to identify locations that differed in spectral properties between the 1995 and 1985 time periods. These ''changed'' locations were classified using CART functions derived from spectral and geographic data from 1985. This was similarly done for the year 1975 based on Landsat MSS data. Differences between the 1975, 1985, and 1995 maps were considered change in land cover and use. We calibrated and tested the accuracy of our models using data acquired through manual interpretation of aerial photos. Elevation and vegetative indices derived from the remotely sensed satellite imagery explained the most variance in the land use and land cover classes (i.e., defined the ''rules'' most often). Overall accuracies from our study were good, ranging from 94% at the coarsest level of detail to 74% at the finest. The largest changes over the study period were the increases in burned, urban, and mixed conifer-herbaceous classes and decreases in woody deciduous, mixed woody deciduous-herbaceous, and conifer habitats. These changes have important implications for ecological function and biodiversity. The expansion of mixed conifer classes may increase fuel loads and enhance risk to the growing number of rural homes. The reduction of woody deciduous cover types is likely reducing population sizes for the numerous plant and animal species that specialize on this habitat type. Some of these species are also negatively influenced by the increase of rural homes in and near woody deciduous habitats.

Light use efficiency (LUE) algorithms are a potentially effective approach to monitoring global net primary production (NPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, these algorithms are applied at relatively coarse spatial resolutions ( � 1 km), which may subsume significant heterogeneity in vegetation LUE (en, gM J � 1 ) and, hence, introduce error. To examine the effects of spatial heterogeneity on a LUE algorithm, imagery from the Advanced Very High Resolution Radiometer (AVHRR) at � 1-km resolution was used to implement a LUE approach for NPP estimation over a 25-km 2 area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several en formulations were compared with a NPP reference surface based on measured NPPs and a high spatial resolution land cover surface derived from Landsat ETM+. Determination of en based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that en of soybean was 68% of that for corn. When a LUE algorithm for estimating NPP was implemented in the study area using the assumption of homogeneous cropland and the en for corn, the estimate for total biomass production was 126% of that from the NPP reference surface. Because of counteracting errors, total biomass production using the soybean en was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign en resulted in a total NPP very similar to the reference NPP because differences in leaf phenology between the crop types were small except early in the growing season. These results suggest several alternative approaches to accounting for land cover heterogeneity in en when implementing LUE algorithms at coarse resolution. D 2002 Elsevier Science Inc. All rights reserved.

A multiseasonal Landsat Thematic Mapper (TM) data set consisting of five image dates from a single year was used to characterize agricultural and related land cover in the Willamette River Basin (WRB) of western Oregon. Image registration was accomplished using an automated ground control point selection program. Radiometric normalization was performed using a semiautomated approach based on the identification of no-change pixels in forest, urban, and water classes. Reference data were developed using existing data sets, including low-level 35-mm color slide photographs, 1:24,000 color airphotos, and ancillary geographic information system (GIS) coverages. Preliminary examination of the data structure included plotting of training set temporal trajectories in spectral space with reference to existing crop calendars. A subsequent stratified, unsupervised classification algorithm, in combination with a geoclimatic rule set and regression analysis, was used to label mapped cells. A map of 20 land cover classes was developed. Classes included agricultural crops and orchards, forest and natural cover types, and urban building densities. An accuracy assessment indicated a final map error of only 26%. The map is now being used to model present and future landscapes for the basin.

Spatially-distributed estimates of biologically-driven CO 2 flux are of interest in relation to understanding the global carbon cycle. Global coverage by satellite sensors offers an opportunity to assess terrestrial carbon (C) flux using a variety of approaches and corresponding spatial resolutions. An important consideration in evaluating the approaches concerns the scale of the spatial heterogeneity in land cover over the domain being studied. In the Pacific Northwest region of the United States, forests are highly fragmented with respect to stand age class and hence C flux. In this study, the effects of spatial resolution on estimates of total annual net primary production (NPP) and net ecosystem production (NEP) for a 96 km 2 area in the central Cascades Mountains of western Oregon were examined. The scaling approach was a simple ‘measure and multiply’ algorithm. At the highest spatial resolution (25 m), a stand age map derived from Landsat Thematic Mapper imagery provided the area for each of six forest age classes. The products of area for each age class and its respective NPP or NEP were summed for the area wide estimates. In order to evaluate potential errors at coarser resolutions, the stand age map was resampled to grain sizes of 100, 250, 500 and 1000 m using a majority filter reclassification. Local variance in near-infrared (NIR) band digital number at successively coarser grain sizes was also examined to characterize the scale of the heterogeneity in the scene. For this managed forest landscape, proportional estimation error in land cover classification at the coarsest resolution varied from 1.0 toC0.6 depending on the initial representation and the spatial distribution of the age class. The overall accuracy of the 1000 m resolution map was 42% with respect to the 25 m map. Analysis of local variance in NIR digital number suggested a patch size on the order of 100‐500 m on a side. Total estimated NPP was 12% lower and total estimated NEP was 4% lower at 1000 m compared to 25 m. Carbon flux estimates based on quantifying differences in total biomass stored on the landscape at two points in time might be affected more strongly by a coarse resolution analysis because the differences among classes in biomass are more extreme than the differences in C flux and because the additional steps in the flux algorithm would contribute to error propagation. Scaling exercises involving reclassification of fine scale imagery over a range of grain sizes may be a useful screening tool for stratifying regions of the terrestrial surface relative to optimizing the spatial resolution for C flux estimation purposes.

When mapping land cover with satellite imagery in montane tropical regions, varying illumination angles and ecological zones can obscure the differences between spectral responses of old-growth forest, secondary forest and agricultural lands. We used multi-date, Landsat Thematic Mapper (TM) imagery to map secondary forests, agricultural lands and old-growth forests in the Talamanca Mountain Range in southern Costa Rica. With stratification by illumination and ecological zone, the overall accuracy for this classification was 87% with a Kappa coefficient of 0.83. We also examined spectral responses to forest successional stage, ecological zone and aspect illumination for the TM Tasselled Cap indices, TM (2 x 6)/7, TM 4/5 and TM difference bands, and whether using digital data from multiple decades improved classification accuracy. Digital maps of ecological zones should be useful for large-scale mapping of land use and forest successional stage in complex montane regions such as those in Central America.

Validation, mapping, and modeling efforts require accurate methods to transform process rates and ecosystem attributes estimated from small field plots to the 250–1000-m-wide cells used by a new generation of land cover mapping sensors. We provide alternative scale transformations, each with attendant assumptions and limitations. The choice of method depends on spatial characteristics of the land cover variables in question and consequently may vary between biomes or with the intended application. We extend the fractal similarity dimension renormalization method, previously developed for binary maps, to continuous variables. The method can preserve both the mean and the multifractal properties of the image, thereby satisfying a major goal, namely, to provide accurate areal estimates without sacrificing information about within-site variation. The scale transformation enables the multifractal scaling exponents of landscapes or individual spectral bands to be brought in and out of register with each other, thereby opening another dimension upon which to detect the scales at which various land use or terrain processes operate. Alternatively, landscapes can be selectively rescaled to highlight patterns due to particular processes. We recommend geostatistical procedures with which to assess spatial characteristics both within a site and within individual image cells. We recommend that aggregation of fine-grain measurements during validation of the Moderate Resolution Imaging Spectrometer (MODIS) products be based on continuous variables to reduce errors that originate from uncertainties in binary maps.

Mapping and monitoring of leaf area index (LAI) is important for spatially distributed modeling of vegetation productivity, evapotranspiration, and surface energy balance. Global LAI surfaces will be an early product of the MODIS Land Science Team, and the requirements for LAI validation at selected sites have prompted interest in accurate LAI mapping at a more local scale. While spectral vegetation indices (SVIs) derived from satellite remote sensing have been used to map LAI, vegetation type, and related optical properties, and effects of Sun–surface–sensor geometry, background reflectance, and atmospheric quality can limit the strength and generality of empirical LAI–SVI relationships. In the interest of a preliminary assessment of the variability in LAI–SVI relationships across vegetation types, we compared Landsat 5 Thematic Mapper imagery from three temperate zone sites with on-site LAI measurements. The sites differed widely in location, vegetation physiognomy (grass, shrubs, hardwood forest, and conifer forest), and topographic complexity. Comparisons were made using three different red and near-infrared-based SVIs (NDVI, SR, SAVI). Several derivations of the SVIs were examined, including those based on raw digital numbers (DN), radiance, top of the atmosphere reflectance, and atmospherically corrected reflectance. For one of the sites, which had extreme topographic complexity, additional corrections were made for Sun–surface–sensor geometry. Across all sites, a strong general relationship was preserved, with SVIs increasing up to LAI values of 3 to 5. For all but the coniferous forest site, sensitivity of the SVIs was low at LAI values above 5. In coniferous forests, the SVIs decreased at the highest LAI values because of decreasing near-infrared reflectance associated with the complex canopy in these mature to old-growth stands. The cross-site LAI–SVI relationships based on atmospherically corrected imagery were stronger than those based on DN, radiance, or top of atmosphere reflectance. Topographic corrections at the conifer site altered the SVIs in some cases but had little effect on the LAI–SVI relationships. Significant effects of vegetation properties on SVIs, which were independent of LAI, were evident. The variability between and around the best fit LAI–SVI relationships for this dataset suggests that for local accuracy in development of LAI surfaces it will be desirable to stratify by land cover classes (e.g., physiognomic type and successional stage) and to vary the SVI.

To consider the regional scale effects of forest management requires complete and consistent data over large areas. We used Landsat Thematic Mapper and Multispectral Scanner (TM and MSS) imagery to map forest cover and detect major disturbances between 1975 and 1992 for a 4.2 x 106 ha area of interior British Columbia. Forested pixels were mapped into closed conifer, semiopen conifer, deciduous, and mixed forest classes, with further subdivision of the closed conifer type into three age-classes. The image-based estimate of harvested area was similar to an independent estimate from forest inventory data. Changes in landscape pattern from 1975 to 1992 were examined by calculating indices that describe overall landscape pattern and that of conifer and harvested patches in each biogeoclimatic zone. Harvesting affected 8.4% of the forest area outside provincial parks during the 17-year period. Harvested areas were consistently much smaller than conifer patches in all biogeoclimatic zones and had a lower percentage of interior area and perimeter/area ratio. Conifer patch-shape complexity varied between zones; harvested patches had simpler shapes and were similar in all zones. Results indicate that this landscape is only in the early stages of fragmentation, but a similar harvest pattern has been imposed on differing ecological zones.

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.

A 34 year time series (1972–2006) of Landsat imagery for a portion of Snohomish and King Counties, Washington (the Snohomish Water Resource Inventory Area (WRIA)) was analyzed to estimate the amount of land that was converted into impervious surface as a result of urban and residential development. Spectral unmixing was used to determine the fractional composition of vegetation, open, and shadow for each pixel. Unsupervised and supervised classification techniques were then used to derive preliminary land cover maps for each time period. Digital orthophotos were used to create agricultural, forest management, high elevation, and riparian masks. In conjunction with established Urban Growth Areas (UGAs), these masks were utilized for the application of spatial rules that identified impervious surface as a surrogate for urban and residential development. Temporal rules, that minimized classification error, were developed based on each pixel's classified trajectory over the time series of imagery. Overall cross-date classification accuracy for impervious v. non-impervious surface was 95%. The results of the analysis indicate that the area of impervious surface in the Snohomish WRIA increased by 255% over 34 years, from 3285 ha in 1972 to 11,652 ha in 2006. This approach demonstrates the unique value of the 35 year Landsat archive for monitoring impervious surface trends in rapidly urbanizing areas.

Bottom-up scaling of net ecosystem production (NEP) and net biome production (NBP) was used to generate a carbon budget for a large heterogeneous region (the state of Oregon, 2.5×105 km2) in the western United States. Landsat resolution (30 m) remote sensing provided the basis for mapping land cover and disturbance history, thus allowing us to account for all major fire and logging events over the last 30 years. For NEP, a 23-year record (1980–2002) of distributed meteorology (1 km resolution) at the daily time step was used to drive a process-based carbon cycle model (Biome-BGC). For NBP, fire emissions were computed from remote sensing based estimates of area burned and our mapped biomass estimates. Our estimates for the contribution of logging and crop harvest removals to NBP were from the model simulations and were checked against public records of forest and crop harvesting. The predominately forested ecoregions within our study region had the highest NEP sinks, with ecoregion averages up to 197 gC m−2 yr−1. Agricultural ecoregions were also NEP sinks, reflecting the imbalance of NPP and decomposition of crop residues. For the period 1996–2000, mean NEP for the study area was 17.0 TgC yr−1, with strong interannual variation (SD of 10.6). The sum of forest harvest removals, crop removals, and direct fire emissions amounted to 63% of NEP, leaving a mean NBP of 6.1 TgC yr−1. Carbon sequestration was predominantly on public forestland, where the harvest rate has fallen dramatically in the recent years. Comparison of simulation results with estimates of carbon stocks, and changes in carbon stocks, based on forest inventory data showed generally good agreement. The carbon sequestered as NBP, plus accumulation of forest products in slow turnover pools, offset 51% of the annual emissions of fossil fuel CO2 for the state. State-level NBP dropped below zero in 2002 because of the combination of a dry climate year and a large (200 000 ha) fire. These results highlight the strong influence of land management and interannual variation in climate on the terrestrial carbon flux in the temperate zone.

Exogenous disturbances are critical agents of change in temperate forests capable of damaging trees and influencing forest structure, composition, demography, and ecosystem processes. Forest disturbances of intermediate magnitude and intensity receive relatively sparse attention, particularly at landscape scales, despite influencing most forests at least once per generation. Contextualizing the spatial extent and heterogeneity of such damage is of paramount importance to increasing our understanding of forested ecosystems. We investigated patterns of intermediate wind disturbance across a forested landscape in the northern Great Lakes, USA. A vegetation change tracker (VCT) algorithm was utilized for processing near-biennial Landsat data stacks (1984–2009) spanning forests sustaining damage from four recent windstorms. VCT predominantly maps stand-clearing disturbance and regrowth patterns, which were used to identify forest boundaries, young stands, and disturbance patterns across space and time. To map wind damage severity, we compared satellite-derived normalized difference vegetation index (NDVI) values calculated from pre- and post-storm Landsat imagery. A geographic information system (GIS) was used to derive wind damage predictor variables from VCT, digital terrain, soils/landform, land cover, and storm tracking data. Hierarchical and random forests regressions were applied to rank the relative importance of predictor variables in influencing wind damage. A conservative estimate of aggregate damage from the intermediate windstorms (extrapolated to ∼150,000 ha, ∼25,500 severe) rivaled individual large, infrequent disturbances in the region. Damage patterns were relatively congruent among storms and became more spatially heterogeneous with increasing disturbance intensity. Proximity to forest-nonforest edge, stand age, and soils/landform were consistently important damage predictors. The spatial extent and distribution of the first two damage predictors are extremely sensitive to anthropogenic modifications of forested landscapes, the most important disturbance agent in the northern Great Lakes. This provides circumstantial evidence suggesting anthropogenic activities are augmenting and/or diminishing the ecological effects of the natural wind disturbance regime. Natural disturbances of intermediate size and intensity are significant agents of change in this region, and likely in other regions, deserving more attention from ecologists and biogeographers.

Earth observation with Landsat and other moderate resolution sensors is a vital component of a wide variety of applications across disciplines. Despite the widespread success of the Landsat program, recent problems with Landsat 5 and Landsat 7 create uncertainty about the future of moderate resolution remote sensing. Several other Landsat-like sensors have demonstrated applicability in key fields of earth observation research and could potentially complement or replace Landsat. The objective of this paper is to review the range of applications of 5 satellite suites and their Landsat-like sensors: SPOT, IRS, CBERS, ASTER, and ALI. We give a brief overview of each sensor, and review the documented applications in several earth observation domains, including land cover classification, forests and woodlands, agriculture and rangelands, and urban. We conclude with suggestions for further research into the fields of cross-sensor comparison and multi-sensor fusion. This paper is significant because it provides the remote sensing community a concise synthesis of Landsat-like sensors and research demonstrating their capabilities. It is also timely because it provides a framework for evaluating the range of Landsat alternatives, and strategies for minimizing the impact of a possible Landsat data gap.

Net ecosystem production (NEP) was estimated over a 10.9 × 10 4 km 2 forested region in western Oregon USA for 2 yr (2002–2003) using a combination of remote sensing, distributed meteorological data, and a carbon cycle model (CFLUX). High spatial resolution satellite data (Landsat, 30 m) provided information on land cover and the disturbance regime. Coarser resolution satellite imagery (MODIS, 1 km) provided estimates of vegetation absorption of photosynthetically active radiation. A spatially distributed (1 km) daily time step meteorology was generated for model input by interpolation of meteorological station data. The model employed a light use efficiency approach for photosynthesis. It was run over a 1 km grid. This approach captured spatial patterns in NEP associated with climatic gradients, ecoregional differences in NEP generated by different management histories, temporal variation in NEP associated with interannual variation in climate and changes in NEP associated with recovery from disturbances such as the large forest fire in southern Oregon in 2002. Regional NEP averaged 174 gC m −2 yr −1 in 2002 and 142 gC m −2 yr −1 in 2003. A diagnostic modelling approach of this type can provide independent estimates of regional NEP for comparison with results of inversion or boundary layer budget approaches. DOI: 10.1111/j.1600-0889.2006.00221.x

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by Landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on remote sensing for modeling biogeochemical cycles and for characterizing land cover, vegetation biophysical attributes, forest structure, and fragmentation in relation to biodiversity. Given the more than 30-year record of Landsat data, mapping land and vegetation cover change and using the derived surfaces in ecological models is becoming commonplace. In this article, we summarize this large body of work, highlighting the unique role of Landsat.

(1995). Cover Land Cover on the Western Slopes of the Central Oregon Cascade Range. International Journal of Remote Sensing: Vol. 16, No. 4, pp. 595-596.

Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.

Boreal peatlands play a major role in carbon and water cycling and other global environmental processes but understanding this role is constrained by inconsistent representation of peatlands on, or omission from, many global land cover maps. The comparison of several widely used global and continental-scale databases on peatland distribution with a detailed map for the St. Petersburg region of Russia showed significant under-reporting of peatland area, or even total omission. Analysis of the spatial agreement and disagreement with the detailed regional map indicated that the error of comission (overestimation) was significantly lower than the error of omission (underestimation) which means, that overall, peatlands were correctly classified as such in coarse resolution datasets but a large proportion (74–99%) was overlooked. The coarse map resolution alone caused significant omission of peatlands in the study region. In comparison to categorical maps, continuous field mapping approach utilizing MODIS sensor data showed potential for a greatly improved representation of peatlands on coarse resolution maps. Analysis of spectral signatures of peatlands with different types of surface vegetation suggested that improved mapping of boreal peatlands on categorical maps is feasible. The lower reflectance of treeless peatlands in the near- and shortwave-infrared parts of the electromagnetic spectrum is consistent with the spectral signature of sphagnum mosses. However, when trees are present, the canopy architecture appears to be more important in defining the overall spectral reflectance of peatlands. A research focus on developing remote sensing methods for boreal peatlands is needed for adequate characterization of their global distribution.