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Monitoring current forest characteristics in the taiga tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover >80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE. [ABSTRACT FROM AUTHOR]

Long-term observation of the earth is essential for studying the factors affecting global environmental changes. Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information. In this study, we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm. Our results show that Myanmar's forests have declined 0.68% annually over this six-year period. We validated our derived change results and found the overall accuracy to be greater than 88%. We also assessed forest loss from protected areas, areas close to roads, and areas subject to fire, which were most likely to lose forested area. The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion, fire, and the construction of highways. This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar. [ABSTRACT FROM AUTHOR]

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org). [ABSTRACT FROM PUBLISHER]

We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r2 of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data. [ABSTRACT FROM AUTHOR]

Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of land-change processes in urban areas requires long-term land cover and land use data at high spatial and temporal resolution. Extending an established procedure for deriving annual, fractional estimates of impervious surface cover (ISC) from per-pixel Landsat composites, here we propose a new post-classification methodology that characterizes ISC change as a continuous field in space and time. The method statistically derives the magnitude, timing and duration of ISC change at a per-pixel basis. We applied the method to a series of ISC maps of the Washington DC-Baltimore metropolitan region at an annual resolution from 1984 to 2010 to analyze the spatial, temporal and thematic patterns of urban/suburban land development. Our method was highly reliable for detecting and characterizing change, with producer’s accuracy of the change-year classes (timing layer) varying between 59.4% and 97.4% and user’s accuracy varying between 54.7% and 91.7%, based on a sample of independent validation points. Most misclassifications were found between neighboring years; relaxing the estimated change-year to ± 1 year increased both producer’s and user’s accuracy to > 80%. The derived change products showed that newly developed pixels on average had a 46% increase in ISC, with the majority (80%) reaching their ISC saturation level in ≤ 3 years. Across the study region, annual growth in impervious surface area accelerated from ~ 6 km 2 /year (0.7% of urban area in 1984) in the mid-1980s to ~ 12 km 2 /year in the late-2010s, with a large inter-annual variability among municipalities and over successive years. Of these emerged urban pixels, 89% were converted from non-urban to low- and medium-density urban features, mostly suburban residential land use. However, the relative proportions of low-, medium- and high-density urban development changed dramatically over the study period, indicating that urban sprawl of the metropolis experienced major transitions in terms of land use intensity in the past three decades. Although primarily designed for change detection, our method can also be used as a temporal smoothing technique to remove noise in time-series of land cover datasets. Our results highlight the value of mapping and monitoring urban land expansion as continuous fields along the spatial-temporal-thematic dimensions for understanding the dynamics of urban land-change. [ABSTRACT FROM AUTHOR]

Land-cover change detection using satellite remote sensing is largely confined to the era of Landsat satellites, from 1972 to present. However, the Corona, Argon, and Lanyard intelligence satellites operated by the U.S. government between 1960 and 1972 have the potential to provide an important extension of the long-term record of Earth’s land surface. Recently declassified, the archive of images recorded by these satellites contains hundreds of thousands of photographs, many of which have very high ground resolution- 6–9 ft (1.8–2.7 m) even by today’s standards. This paper demonstrates methods for extending the span of forest-cover change analysis from the Landsat-5 and -7 era (1984 to present) to the previous era covered by the Corona archive in two study areas: one area covered predominantly by urban and sub-urban land uses in the eastern US and another area by tropical forest in central Brazil. We describe co-registration of Corona and Landsat images, extraction of texture features from Corona images, classification of Corona and Landsat images, and post-classification change detection based on the resulting thematic dataset. Second-order polynomial transformation of Corona images yielded geometric accuracy relative to Landsat-7 of 18.24 m for the urban area and 29.35 m for the tropical forest study area, generally deemed adequate for pixel-based change detection at Landsat resolution. Classification accuracies were approximately 95% and 96% for forest/non-forest discrimination for the temperate urban and tropical forest study areas, respectively. Texture within 7 × 7- to 9 × 9-pixel (∼13.0–16.5 m) neighborhoods and within 11 × 11-pixel (∼30 m) neighborhoods were the most informative metrics for forest classification in Corona images in the temperate and tropical study areas, respectively. The trajectory of change from the 1960s to 2000s differed between the two study areas: the average annual forest loss rate in the urban area doubled from 0.68% to 1.9% from the 1960s to the mid-1980s and then decreased during the following decade. In contrast, deforestation in the Brazilian study area continued at a slightly increased pace between the 1960s and 1990s at annual loss rate of 0.62–0.79% and quickly slowed down afterward. This study demonstrates the strong potential of declassified Corona images for detecting historical forest changes in these study regions and suggests increased utility for retrieving a wide range of land cover histories around the world. [ABSTRACT FROM AUTHOR]

Since the 2008 opening of the USGS Landsat archive, research has focused on standardization and compositing of dense time series of Landsat images to increase measurement precision sufficiently for long-term and/or global land cover mapping. Most of these efforts rely on atmospherically corrected estimates of surface reflectance, some of which also incorporate corrections for effects of sun-target-sensor geometry. However, these effects have not yet been well-characterized in Landsat data. Using reflectance and Bidirectional Reflectance Distribution Function (BRDF) parameters derived from daily Moderate-resolution Imaging Spectroradiometer (MODIS) reflectance estimates and the sun-target-sensor geometry of the Landsat-5 orbit, we simulated the BRDF effects within the Landsat-5 data archive at three sites representing low, mid, and high latitudes. We found that bidirectional effects are prevalent in Landsat measurements, but their effect varies by latitude. Seasonal variations in illumination geometry affect reflectance as well as vegetation indices, adding spurious seasonality in otherwise stable evergreen phenology at a lower-latitude tropical forest site. The ±7.5° variation in View Zenith Angle (VZA), usually considered negligible, resulted in as much as 20% along-scan variation in reflectance at the lower latitude site. Although variation in Solar Zenith Angle (SZA) is less pronounced in the tropics than in the upper latitudes, its impact is most significant in the tropics due to the relative proximity of Landsat scans to the principle plane. For this reason, even small variations in viewing and illumination geometry have large impacts on reflectance and vegetation indices near the equator. In contrast, the amplitude of SZA and phenological variations have the greatest impact on reflectance estimates in mid-and upper latitudes, where interaction between phenological variation and Landsat’s 16-day orbital cycle can be greater than the combined effect of BRDF and orbital drift. Our analysis suggests that accounting or correction for BRDF effects will increase precision of land cover mapping and monitoring based on Landsat data alone or in combination with other sensors. [ABSTRACT FROM AUTHOR]

Historical baselines of forest cover are needed to understand the causes and consequences of recent changes and to assess the effectiveness of land-use policies. However, historical assessment of the global distribution of forest cover and change has been lacking due to obstacles in image acquisition, computational demands, and lack of retrospective reference data for image classification. As limitations of access to imagery and computational power are overcome, the possibility is increased of an automated retrospective classification of forest cover. We used locally fit classification trees to relate hind-cast observations of “stable pixels” of forest and non-forest cover from circa-2000 to Landsat spectral measurements taken from the circa-1990 epoch of the Global Land Survey collection of Landsat images. Based on analysis of nearly 30,000 Landsat images, forest-cover change between 1990 and 2000 epochs was detected based on joint probabilities of cover in the two epochs. Assessed across a sample of areas with coincident reference data in the conterminous United States, the resulting maps achieved 93% accuracy for forest cover and 84% for forest-cover change—comparable or even higher than many previous national efforts. Global accuracy assessment likewise showed accuracy of 88% for forest-cover change. The maps depict the global distribution of gross gains and losses in forest cover, as well as their net change. The initial analysis showed strong effects of extant land use in temperate regions and land-use change in the tropics over the period, while wildfire dominated in the boreal zone. Regions of high net forest loss (e.g., Amazonia) were associated with land-use changes into agriculture, and regions of high gross gains and losses (e.g., southeastern US, Sweden) were associated with intensive forestry. These results, including the global forest cover and forest cover change datasets, will be a basis for the estimation of the efficacy of policies and analyzing correlation between forest cover change and socio-economic factors. [ABSTRACT FROM AUTHOR]

Abstract: Global, long-term monitoring of changes in Earth's land surface requires quantitative comparisons of satellite images acquired under widely varying atmospheric conditions. Although physically based estimates of surface reflectance (SR) ultimately provide the most accurate representation of Earth's surface properties, there has never been a globally consistent SR dataset at the spatial resolution (<1ha) or temporal extent (~40years) of the Landsat mission. To increase the consistency and robustness of Landsat-based land cover monitoring, we atmospherically corrected the Global Land Survey (GLS) Landsat dataset using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) implementation of the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model. The GLS provides synoptic, orthorectified, cloud-free Landsat coverage of Earth's land area in four nominal epochs (1975, 1990, 2000, and 2005). This paper presents the resulting GLS surface reflectance dataset and a global assessment of the 2000- and 2005-epoch data against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) daily SR and Normalized Bidirectional Distribution Function-Adjusted Reflectance (NBAR) measurements. Agreement with respect to MODIS SR and NBAR data is very high, with overall discrepancies (Root-Mean-Squared Deviation (RMSD)) between 1.3 and 2.8percent reflectance for Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and between 2.2 and 3.5percent reflectance for Landsat-5 Thematic Mapper (TM). The resulting Landsat surface reflectance dataset and the associated quality metrics for each image are hosted on the Global Land Cover Facility web site for free download (http://www.landcover.org/data/gls_SR). This new repository will provide consistent, calibrated, multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences. [Copyright &y& Elsevier]

Abstract: Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat–MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions. [Copyright &y& Elsevier]