Detecting trend and seasonal changes in satellite image time series

A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01-0.07 σ) and seasonal amplitudes (0.1-0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur.     

Landscape pattern and species richness; regional scale analysis from remote sensing

Concern about the future of biodiversity in the wider countryside is stimulating the development of methods for species and ecosystem monitoring over large areas. The objective of this paper is to explore the potential of remotely sensed data for measuring landscape structure as an important determinant of species diversity. Data from the satellite Land Cover Map of Great Britain, a land cover classification of Landsat Thematic Mapper scenes, were used to derive a set of simple measures of landscape structure within 2km x 2km tetrads for three vascular plant families. Results from a model to predict plant diversity from landscape structure alone proved difficult to interpret ecologically and highlighted the need to obtain data on both landscape quality and landscape structure.     

Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images

The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l’Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM⁺) (2006) was high compared with that from the PC image of Landsat ETM⁺ (2006). From 1986 – 2006 the area covered by settlements increased by 0.8% (230,380.00 km²), agricultural land decreased by 7.5% (1009.40 km²), vegetation cover decreased by 0.9% (114.00 km²) while waterbody increased by 0.2% (25.91 km²). Also, from 1986 – 2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale.

The PCA technique has been used quite widely to study changes in land cover and land use in many ‘developed’ countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.     

Time–space radiometric normalization of TM/ETM+ images for land cover change detection

A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization (SRRN) uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes (human-induced, natural disaster, etc.) while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress (e.g. drought), which occur at varying spatial and temporal scales, depending on landscape position, vegetation type, season, precipitation history and historic episodes of local disturbance. The SRRN approach was tested for a study area on the Californian border between the USA and Mexico using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus satellite imagery. Change products were generated from imagery radiometrically normalized using the SRRN procedure and with imagery normalized using a traditional empirical line technique. Reference data derived from high spatial resolution airborne imagery were utilized to validate the two change products. The SRRN procedure provided several benefits and was found to improve the overall accuracy of detecting abrupt land cover changes by nearly 20%.     

Recent publications

Abstract

Although the idea of using a satellite as a source of data for settlement studies is not new, actual measurements of the areal extent of settlements on a large scale has been meagre. A digital analytical approach has been used to identify and measure the areal extent of settlements in the Oyo-Ogbomoso-Ilorin area of Nigeria. Using 95 sampled pixels selected from Ilorin, Ogbomoso and Oyo as training areas, an area of about 249 km² has been classified as settlements. It was observed that settlements with built-up areas of about 300 h were identifiable from Landsat. Despite this observation, there is a need for further research to determine the level of accuracy of settlement identification and areal measurements which is possible from Landsat MSS data. The procedure of selecting subscenes to define known settlements is suggested as being a more accurate way of measuring the areal extent of settlements.     

Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

Modelling the dynamics of landscape transformations and population growth in the highlands of Ethiopia using remote-sensing data

This study models landscape transformations and settlement dynamics in a highland area of Ethiopia over a 56 year period (1957–2013). The analyses were performed using aerial photographs, satellite images, and field data. The remotely sensed images were geometrically and radiometrically corrected. Visual interpretation of aerial photographs and supervised classification of multispectral satellite images using the maximum likelihood algorithm were chosen for land-cover mapping. The population size was estimated by counting the houses on the aerial photographs and on the high-resolution images, and by direct census. The overall trend showed an increase of cropland and a decrease of other types of land cover. Landscape transformation rates recently slowed down due to ownership and policy restrictions. The average cropland holding size per family has decreased from 2.6 to 1.1 ha due to the exponential growth of the population. The relationship between settlement and cropland expansion is statistically significant. Models of logistic growth were fitted to the cropland area, and models of exponential and logistic growth to the population development to estimate the carrying capacity. The concomitant increase of population and the decrease of cropland per head resulted in a shortage of food and energy, highlighting the importance of policy decisions on land management.     

Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic

To aid in the environmental planning and management of Los Haitises National Park, a neotropical park in the Dominican Republic, a land cover change analysis was performed on the lower Yuna River watershed, within which a portion of the park exists and which contains a diversity of agricultural practices. Separate image classifications were performed on a 1973 Landsat MSS image and a 1985 Landsat TM image with resulting overall classification accuracies of 77.3 per cent and 81.3 per cent, respectively. In both classifications, spectral similarities between the various growth stages of rice, mangrove, orchard, and permanent grassland made separation and delineation of these classes difficult. The implications of land cover trends within the watershed for ecologic and economic management issues which affect both the watershed and the park were discussed.     

Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area

Urbanization process is a major factor of change in the Mediterranean region where pre-urban cities and new urban settlements have raised over the past decades. Several cities rapidly became regional centres or international nodes according to economic and political pressures. Urbanization (and informal settlement) causes land cover changes which can lead to deeper social, economic and environmental changes. The main objective of this paper is to provide time-series information to define and locate the evolution trends of the Tunis Metropolitan Area. In a first step, satellite imagery has been used (1986-1996 SPOT XS) to extract the land cover, identify the urbanization processes and estimate the changes. One of the main aspects is to locate informal settlement areas that grow significantly along the roadway networks in the Tunisian capital. Results show a global progression of the built-up areas of 13% in 10 years. In a second step, the urban growth evolution has been approached by using a potential model that provides general trends of feasible urban expansion, taking into account protection laws of natural and agricultural land. This type of model has not been tested on developing cities and as such it corresponds to a new planning contribution for planners who have no concept of spatially how their urban areas have changed over time and where the growth is occurring. In this case, it has been calibrated over the period of 1986-1996, and then applied to predict the location of the built-up growth over the next 10 years (1996-2006). These results can provide local authorities and other stakeholders with information towards decision support documents for future planning and monitoring plans. Moreover, they can be updated systematically through the integration of remote sensing data.     

Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001).     

Analysis of the Impacts of armed conflict on the Eastern Afromontane forest region on the South Sudan — Uganda border using multitemporal Landsat imagery

The impacts of armed conflict on ecosystems are complex and difficult to assess due to restricted access to affected areas during wartime making satellite remote sensing a useful tool for studying direct and indirect effects of conflict on the landscape. The Imatong Central Forest Reserve (ICFR) in South Sudan together with the nearby Dongotana Hills and the Agoro-Agu Forest Reserve (AFR) in Northern Uganda share a boundary and encompass a biologically diverse montane ecosystem. This study used satellite data combined with general human population trends to examine the impact of armed conflict and its outcome on similar forest ecosystems both during and after hostilities have occurred. A Disturbance Index (DI) was used to investigate the location and extent of forest cover loss and gain in three areas for two key time periods from mid-1980s to 2001 and 2003 to 2010. Results indicate that the rate of forest recovery was significantly higher than the rate of disturbance both during and after wartime in and around the ICFR and the net rate of forest cover change remained largely unchanged for the two time periods. In contrast, the nearby Dongotana Hills experienced relatively high rates of disturbance during both periods; however, post war period losses were largely offset by some gains in forest cover. For the AFR in Uganda, the rate of forest recovery was much higher during the second period, coinciding with the time people began leaving overcrowded camps. The diversity and merging of floristic regions in a very narrow band around the Imatong Mountains makes this area biologically distinct and of outstanding conservation importance; therefore, any future loss in forest cover is important to monitor — particularly in South Sudan where large numbers of people continue to return following the 2005 peace agreement and the 2011 Referendum on Independence.     

Spatial pattern analysis for monitoring protected areas

The management of diverse biota within protected areas is affected by both land cover change within a protected area and habitat loss and fragmentation in the surrounding landscape. Satellite images provide a synoptic view of land cover patterns, but the use of such imagery requires careful consideration of sensor type, resolution, extent, and the metrics used to quantify ecologically significant change. We examined these factors for landscape monitoring applications in four small National Parks near Washington, DC: Antietam National Battlefield, Catoctin Mountain Park, Prince William Forest Park and Rock Creek Park. Using 4 m Ikonos, 10 m SPOT, 15 m pan-sharpened Landsat ETM+ and 30 m Landsat ETM+ imagery, the parks and surrounding areas were mapped to National Land Cover system classes. For each park, we examined four methods for defining map extent, including park administrative boundaries, two variable buffer widths, and watershed boundaries, and then analyzed patterns of forest habitat for the maps using a graph theoretic approach (critical dispersal threshold distance) and common landscape metrics (number of patches, percent forest, forest edge density, and forest area-weighted mean patch size). As expected, landscape metrics for maps derived at differing resolutions varied significantly, but map extent often yielded even larger differences. We found that for most applications, coarser scale data (e.g., 30 m Landsat) are adequate for characterizing landscape pattern, although ultimately data from multiple sensors may be appropriate or necessary based on different objectives of landscape monitoring (e.g., mapping single trees vs. forest stands) and the scale at which a resource of interest interacts with the larger landscape (e.g., birds vs. herptiles). Our results provide a strong caution regarding the practical issues associated with combining data sources from multiple satellite sensors for monitoring applications.     

Regional land cover mapping in the humid tropics using combined optical and SAR satellite data—a case study from Central Sulawesi,Indonesia

The frequent mapping of the spatial extent of land cover and its change from satellite data at the regional level provides essential input to spatially explicit land use analysis and scenario modelling. The accuracy of a land cover map is the key factor describing the quality of a map, and hence affecting the results of land use modelling. In tropical regions, land cover mapping from optical satellites is hampered by cloud coverage and thus alternative data sources have to be evaluated. In the present study, data from Landsat‐ETM+ and Envisat‐ASAR satellite sensors were tested for their ability to assess small scaled landscape patterns in a tropical environment. A focus was on the detection of intensively managed perennial and intra‐annual cropping systems (cocoa, rice). The results confirm previous knowledge about the general potential and advantages of multi‐temporal SAR data compared to mono‐temporal SAR‐based mapping but also show the limitations of different polarization modes in SAR analysis for land cover mapping. In the present case study, cross‐polarized data from Envisat‐ASAR did not yield notable profit for tropical land cover mapping compared to common, co‐polarized time series of ASAR data. However, the general outcome of the study underlines the synergy of optical and radar satellite data for land cover mapping in tropical regions.     

Spatio-temporal Characteristics of Urban Land Cover Structures in Prefecture-level Cities of Inner Mongolia Autonomous Region based on Remote Sensing Imagery

The urban land cover structures play an important role in providing urban ecological service and altering the quality of human settlements environment.In this study,2000~2015 Landsat TM series satellite data along with fine resolution remote sensing images were used to capture information of each 5-year land cover structures in 12 prefecture-level cities of Inner Mongolia Autonomous Region.Subsequently,there land cover information was used to monitor and analyze the spatiotemporal dynamics of urban expansion,and differences of land cover structures and expansion types.The results showed that：in 2000~2015,the overall changes of land cover structures in 12 prefecture level cities of Inner Mongolia Autonomous Region were rapid.Specifically,the urban area expanded by 278.93 km².By comparison,the proportion of urban area expansion in 2010~2015 was 1.61 times and 1.91 times than that of the first two periods (in 2000~2005 and in 2005~2010).Since 2010,the most dramatic changes has been observed.Particularly,obvious urban impervious surface expansion was found.Also urban vegetation showed obviously increased with varying degrees.At the past decade,urban expansion has undergone three stages.Specifically,the main process experienced from urban interior filling to urban interior filling and then to urban extension,of which Baotou and Hulunbeier belonging to the internal filling city.Population growth and socio-economic development are responsible for these differences.     

Reconstructing disturbance history using satellite-based assessment of the distribution of land cover in the Russian Far East

Russian boreal forests are the largest forested zone on Earth and a tremendous pool of organic carbon. Current limited records on forest structure, composition, successional stage and disturbances contribute to large uncertainties in estimates of carbon stocks and fluxes in this zone. Our ability to monitor ongoing changes in forest cover has improved with the influx of remotely sensed data products since 2000 from multiple satellite platforms. Here we present a method aimed at reconstructing disturbance history from a known distribution of land cover. We developed and tested the method over a biologically and topographically diverse region of the Russian Far East. This method explores capabilities introduced through fusion of the long-term but spatially limited Landsat data archive and the spatially continuous but temporally limited 2000-present data record from the Moderate Resolution Spectroradiometer (MODIS). Landsat data from 1972 to 2002 were used to develop a reference disturbance dataset to train and validate a MODIS-based decision tree classification. The results showed a reliable differentiation of disturbed and mature forests with an overall accuracy of 88% (Kappa 0.73). Individual disturbances by type and decade were estimated with an overall accuracy of 70% (Kappa 0.64).     

Landsat continuity: Issues and opportunities for land cover monitoring

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.     

Landsat TM spectral information to enhance the land cover of Scotland 1988 dataset

The Scottish Office's Land Cover of Scotland 1988 Survey (LCS88), was announced in May 1987 and was intended to provide the first-ever detailed census of land cover in Scotland. It came about as a result of increasing concern about the nature and rate of land use change in rural Scotland and the need to obtain objective baseline information on which to build and evaluate future countryside policy. One of the recommendations of a Scottish Office feasibility study carried out prior to the LCS88 survey, was that satellite remotely-sensed data should be considered for measuring landscape change in the future. This paper relates specifically to this recommendation and presents the results of an evaluation study to investigate the use of limited acquisition satellite imagery from Landsat Thematic Mapper, to derive a land cover classification and spectral segmentation information to enhance the existing LCS88 dataset. Although a successful land cover, primary as well as some individual cover features, was obtained from the satellite data, the overall accuracy comparison with the LCS88 cover features was limited. However, the opportunistic mapping of important agricultural crops and primary cover types, such as oilseed rape and forestry cover features, or the interpretation of some of the considerable confusion between semi-natural grassland and improved grassland cover features, provided for an enhanced LCS88 dataset. This was also true for the illustration of the considerable potential of a satellite classification and spectral data, for identifying the component parts of LCS88 Mosaic cover features and estimating vegetation quality.     

Spatial and temporal probabilities of obtaining cloud‐free Landsat images over the Brazilian tropical savanna

Remotely sensed data are the best and perhaps the only possible way for monitoring large‐scale, human‐induced land occupation and biosphere‐atmosphere processes in regions such as the Brazilian tropical savanna (Cerrado). Landsat imagery has been intensively employed for these studies because of their long‐term data coverage (>30 years), suitable spatial and temporal resolutions, and ability to discriminate different land‐use and land‐cover classes. However, cloud cover is the most obvious constraint for obtaining optical remote sensing data in tropical regions, and cloud cover analysis of remotely sensed data is a requisite step needed for any optical remote sensing studies. This study addresses the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat‐like sensors. Percent cloud cover from more than 35 500 Landsat quick‐looks were estimated by the K‐means unsupervised classification technique. The data were examined by month, season, and El Niño Southern Oscillation event. Monthly observations of any part of the biome are highly unlikely during the wet season (October–March), but very possible during the dry season, especially in July and August. Research involving seasonality is feasible in some parts of the Cerrado at the temporal satellite sampling frequency of Landsat sensors. There are several limitations at the northern limit of the Cerrado, especially in the transitional area with the Amazon. During the 1997 El Niño event, the cloudiness over the Cerrado decreased to a measurable but small degree (5% less, on average). These results set the framework and limitations of future studies of land use/land cover and ecological dynamics using Landsat‐like satellite sensors.     

基于遥感与GIS的农村居民点景观特征比较

以河北省阜平县、武邑县 ,福建省清流县、惠安县作为研究区 ,利用 2 0 0 0年 TM遥感图像 ,通过解译、判读得到景观结构矢量图 ,然后利用景观生态学数量方法分析了研究区农村居民点景观特征的差异及空间分布格局。研究表明 ,研究区农村居民点规模较小 ,平原地区平均面积不到 2 0 hm2 ,山区农村居民点平均面积低于 10 hm2。农村居民点距离较近 ,山区农村居民点平均距离小于 2 .5 km,平原地区农村居民点距离小于 0 .4 hm。农村居民点规模小 ,分布零散 ,适应于农业经济的发展。为了节约居民点用地 ,应采取加快城镇及中心村发展的方针 ,促进农村居民点布局的优化     

The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally

The U.S. Landsat satellite series provide the longest dedicated land remote sensing data record with a balance between requirements for localized high spatial resolution studies and global monitoring. As with any other optical wavelength satellite sensor, cloud contamination greatly compromises image usability for land surface studies. Additionally, selective scene acquisition due to payload, ground station and mission cost constraints further reduces Landsat image availability. Since the 1999 launch of the Landsat Enhanced Thematic Mapper Plus (ETM+) a Long-term Acquisition Plan (LTAP) has been used to anticipate user requests with the goal of annually refreshing a global daytime archive of cloud-free ETM+ data. This research evaluates the availability of cloud-free Landsat ETM+ data over the conterminous U.S. and globally using 3 years of ETM+ cloud fraction metadata archived by the U.S. Landsat project. Landsat application requirements including obtaining at least one cloud-free observation in a year, a season, and two different seasons, or at least a pair of cloud-free observations occurring no more than 16, 32, 48, 64, and 80 days apart within a year and season are considered. Probabilistic analyses indicate that over the conterminous U.S., land applications requiring at least one cloud-free observation in a year, a season, two different seasons, or at least two cloud-free observations occurring within any period of the year, are on average largely unaffected by cloud cover, except for certain Winter applications and cloudy scenes near the U.S.-Canada border and the Great Lakes. Cloud becomes a constraint when at least two cloud-free observations are required from the same season over the conterminous U.S., especially when the separation between observations is restricted to short time intervals. Global applications requiring at least one cloud-free observation in a season, in two different seasons, and applications requiring at least two cloud-free observations in a year, are all severely affected by cloud and data availability constraints; and globally it is generally not practical to consider land applications that require at least two cloud-free observations in any season. Globally, only land applications requiring at least one cloud-free observation per year are largely unaffected by cloud cover and the reduced global ETM+ data availability. These results are specific only to the U.S. Landsat ETM+ archive; they suggest the need for an increased global Landsat acquisition rate for the current and future Landsat missions and/or the development of new approaches to mitigating cloud contamination in the U.S. global Landsat ETM+ archive.