Land‐cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling

A land‐cover classification is needed to deduce surface boundary conditions for a soil–vegetation–atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster–Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster–Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.     

Digital terrain analysis using Landsat‐7 ETM+ imagery and SRTM DEM: a case study of Nevsehir province (Cappadocia), Turkey

A three‐dimensional (3D) model of land‐use/land‐cover (LULC) and a digital terrain model of Nevsehir province (Cappadocia), Turkey, were generated and analysed using a Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) multispectral image set and a Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM). Stream drainage patterns, lineaments and structural‐geological features (landforms) were extracted and analysed. In the process of analysing and interpreting the multispectral images of geological features, criteria such as colour and colour tones, topography and stream drainage patterns were used to acquire information about the geological structures of the land, including as geomorphological, topographic and tectonic structures. Landsat‐7 ETM+ multispectral imagery and an SRTM DEM of the study region were used experimentally for classification and analysis of a digital terrain model. Using the multispectral image data, the LULC types were classified as: settlement (1.2%); agricultural land (70.1%); forest (scrubland, orchard and grassland) (2.9%); bare ground (25.5%); and water bodies (lakes and rivers) (0.3%) of the study area (5434 km²). The results of the DEM classification in the study area were: river flood plain (11.3%); plateau (52.3%); high plateau (28.4%); mountain (7.6%); and high mountain (0.3%). Lineament analysis revealed that the central Kizilirmak River divides the region into two nearly equal parts: the Kirsehir Plateau in the north and the Nevsehir Plateau in the south. In terms of the danger of catastrophe, the settlements of Kozakli, Hacibektas and Acigol were found to be at less risk of earthquake and/or flooding than those of Avanos, Gulsehir, Urgup, Nevsehir, Gumuskent and Derinkuyu, which are located on river flood plains and/or the main stream drainage channels, particularly stream beds, where the lineaments are deep valleys or fracture or fault‐line indicators.     

Sequential masking classification of multi‐temporal Landsat7 ETM+ images for field‐based crop mapping in Karacabey,Turkey

Three Landsat7 ETM+ images acquired in May, July and August during the 2000 crop growing season were used for field‐based mapping of summer crops in Karacabey, Turkey. First, the classification of each image date was performed on a standard per pixel basis. The results of per pixel classification were integrated with digital agricultural field boundaries and a crop type was determined for each field based on the modal class calculated within the field. The classification accuracy was computed by comparing the reference data, field‐by‐field, to each classified image. The individual crop accuracies were examined on each classified data and those crops whose accuracy exceeds a preset threshold level were determined. A sequential masking classification procedure was then performed using the three image dates, excluding after each classification the class properly classified. The final classified data were analysed on a field basis to assign each field a class label. An immediate update of the database was provided by directly entering the results of the analysis into the database. The sequential masking procedure for field‐based crop mapping improved the overall accuracies of the classifications of the July and August images alone by more than 10%.     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

Biomass measurements and relationships with Landsat‐7/ETM+ and JERS‐1/SAR data over Canada's western sub‐arctic and low arctic

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10–100 t ha^?1 for woodlands, 1–100 t ha^?1 for shrub sites, and 0.5–10 t ha^?1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r ² = 0.72 when using a one‐step approach (i.e. using all points) and 0.78 when using a two‐step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two‐step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two‐step approach over the one‐step approach, however, is not significant for the validation dataset, suggesting that the one‐step approach is as good as the two‐step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report.     

The first detailed land‐cover map of Socotra Island by Landsat/ETM+ data

Present study has produced first detailed land‐cover map of Socotra Island. A Landsat 7 ETM+ dataset was used as a main source of remotely sensed data. From numerous reference points (more than 250) coming from the ground data verification the set of training fields and the set of evaluation fields were digitised. As a classification method the supervised maximum likelihood classification without prior probabilities was used in combination with rule‐based post‐classification sorting, providing results of sufficient accuracy and subject resolution. Estimates of the area and degree of coverage of particular land‐cover classes within Socotra Island have brought excellent overview on state of island biotopes. Overall accuracy of the map achieved is more than 80%, 19 terrestrial land‐cover classes (including three types of Shrublands, three types of Woodlands, two types of Forests and Mangroves) have been distinguished. It consequently allows estimates of the current and potential occurrence of endemic plant populations, proposals of management and conservation plans and agro‐forestry planning.     

Evaluation of segment‐based gap‐filled Landsat ETM+ SLC‐off satellite data for land cover classification in southern Saskatchewan,Canada

This paper describes single‐date and multi‐date land‐cover classification accuracy results using segment‐based, gap‐filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single‐date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap‐filled Landsat data. Area estimates for the mid‐summer images differed, on average, from 0.6% to 1.9% for a four‐class and eight‐class classification, respectively. A multi‐date land‐cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%.     

Is high spatial resolution DEM data necessary for mapping palustrine wetlands?

Digital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user’s and producer’s accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user’s and producer’s accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable.     

An index and approach for water extraction using Landsat–OLI data

The extraction of water distribution is extremely useful in research and planning activities, including those associated with water resources, environments, disasters, local climates, and other factors. Remote-sensing images with moderate resolution have been the main data source due to the vast distribution of water and the high cost, access difficulty, and massive size of high-resolution images. Although some water indices and methods for water extraction have been proposed, there is still a lack of these resources to easily, accurately, efficiently, and automatically extract water. This paper focused on some improvements that mainly used the most traditional but also the newest Operational Land Imager (OLI) images in Landsat 8. This study first analysed the variation features of previous water indices. Secondly, taking the city of Beijing and its surrounding area as the experimental site, a spectral curve analysis was performed and a new water index was proposed. This index was compared to three typical indices. Thirdly, a new approach was proposed to accurately and easily extract water. It included four major steps: background partitioning, thresholding and preliminary segmentation, noise removal by patch size, and local region growth. Next, the stricter and more effective stratified random sampling method was used to test the accuracy. Then, we tested the generality of the proposed water index and extraction method using nine typical test sites from around the world and tried to simplify the workflow. Finally, this paper discusses threshold optimization issues, such as automatic selection and reduction of the number of thresholds. The results show that the normalized water index (NDWI), modified normalized water index (MNDWI), and normalized difference built-up index (NDBI) may fail in some situations due to the complex spectrum of the impervious surface class. Some shadow pixels were impossible to remove using only spectral analysis because both the digital number (DN) trends and values were similar to those of water. The proposed water index was easy and simple, but it corresponded better to water bodies. Additionally, it was more accurate and universal and showed greater potential for extracting water. This method relatively accurately and completely extracted various water bodies from plain city, plain country, and natural mountainous regions in many typical climate zones, eliminating interference caused by dark impervious surfaces, plants, sand, suspended sediments, snow, ice, bedrock, reservoir drawdown areas, shadows from mountains and buildings, mixed pixels, etc. The mean kappa coefficients were 0.988, 0.982, and 0.984 in plain city, plain country, and natural mountainous regions, respectively. This paper suggests that thresholds can be automatically determined by comparing the accuracy changes of different thresholds according to preselected sample and test points. Furthermore, the combined use of the maximum class square error method (also known as the Ostu algorithm) and the adaptive thresholding method exhibits great potential for automatic determination of thresholds in regions without many noises with higher water index values. In addition, water bodies could also be accurately extracted by setting these thresholds to fixed values based on the results at more test sites.     

Characterizing post‐fire vegetation recovery of California chaparral using TM/ETM+ time‐series data

A time series of normalized difference vegetation index (NDVI) data derived from 11 TM/ETM+ images was used to examine the recovery characteristics of chaparral vegetation in a small watershed near Santa Barbara, California following a fire event in 1985. The NDVI recovery trajectory was compared to a generalized recovery trajectory of leaf area index (LAI) for the same region, which was established using a chronosequence approach and TM/ETM+ data. Post‐fire NDVI recovery trajectories were derived for the entire catchment and for individual vegetation types. Post‐fire NDVI spatial patterns on each image date were compared to the pre‐fire pattern to determine the extent to which the pre‐fire pattern was re‐established, and the rate of this recovery. Results indicated that the post‐fire recovery trajectory for the catchment area average NDVI was similar to the previously established regional LAI trajectory based on a chronosequence approach. The NDVI recovery was disrupted by drought stress and attained pre‐fire levels approximately 10 years after the fire. Individual vegetation types did not exhibit different rates of recovery and the recovery trajectories were only distinguished by the maximum post‐fire NDVI observed after 10 years. The post‐fire NDVI spatial pattern also showed a systematic return to pre‐fire conditions, but exhibited a more substantial disruption due to drought stress than was the case for the average NDVI recovery trajectory.     

Envisat multi‐polarized ASAR data for flood mapping

During the August 2002 Elbe river flood, different satellite sensor data were acquired, and especially Envisat Advanced Synthetic Aperture Radar (ASAR) data. The ASAR instrument was activated in Alternating Polarization (AP) and Image (IM) modes, providing high resolution datasets. Thus, the comparison with a quasi‐simultaneous ERS‐2 scene enables the evaluation of the contribution of polarization configurations to flood boundary delineation. This study highlights the increased capabilities of the Envisat ASAR instrument in flood mapping, especially the benefit of combining like‐ and cross‐polarizations for rapid mapping within a crisis context.     

Multi‐resolution dune morphology using Shuttle Radar Topography Mission (SRTM) and dune mobility from fuzzy inference systems using SRTM and altimetric data

The complex morphology of large sand dunes of the world's great deserts have significant importance on conservation and climate change and hence are of interest to a wide variety of scientific and environmental applications including studies on aeolian processes, paleoclimate, civilian infrastructure management, and design of blown‐sand control systems. Scientific studies on dune formation and dynamics have been limited to desert margins due to inaccessibility of the desert interior by conventional surveying and mapping techniques. Thus, dune morphology in the deep desert interiors is not well studied and much about the driving forces controlling dune activity and dynamics are still poorly understood.

We demonstrate the utility of space‐based observations to characterize dune morphology. Specifically, we used the Shuttle Radar Topography Mission (SRTM) C‐band data to investigate and compare morphologic attributes of dune fields of the Taklimakan and the Namib Deserts. Cross‐sectional amplitude roughness estimates have similar magnitude but stoss slopes are shallower in the Taklimakan Desert than in the Namib Desert. The high Height‐Width (H:W) ratio of 0.09 for the linear dunes in the Taklimakan Desert is indicative of its equilibrium with aeolian shear stress whereas the Namib Desert dunes are unstable. Multi‐resolution planimetric properties from SRTM Digital Elevation Model (DEM) using B‐spline wavelet decomposition reveal linear dunes in the Taklimakan Desert are superimposed on a dome‐like substrate whereas the linear dune in the Namib Desert are constructed on a knoll‐like submorphology. These long‐wavelength features, taken as paleotopography, may be a major controlling factor on wind patterns and dune sinuosity.

We demonstrate the utility of ANFIS to assess seasonal dune changes in the Namib Desert using ICESat observations and SRTM. ANFIS is a data‐driven prediction scheme. Predicted topography along ICESat tracks have low rms of 3.5m, a 30% increase over SRTM accuracy. Seasonal track comparison from August 2003 to January 2005 shows that most changes to dune topography occur at crestal deformation of isolated dunes. The results show seasonal waning and waxing of dune crests.     

Utilization of Landsat ETM+ data for mineral-occurrences mapping over Dalma and Dhanjori,Jharkhand, India: an Advanced Spectral Analysis approach

The Advanced Spectral Analysis (ASA) technique, one of the most advanced remote-sensing tools, has been used as a possible means of identifying mineral occurrences over Dalma and Dhanjori. The ASA technique is a sixfold tool, which includes the continuous processes of (1) the reflectance calibration of Landsat Enhanced Thematic Mapper (ETM+) images of the study area, (2) the generation of minimum noise fraction (MNF) transformation, (3) the calculation of the pixel purity index (PPI), (4) the n-dimensional visualization and extraction of endmember spectra, (5) the identification of endmember spectra for mineral occurrences and (6) the mapping of mineral occurrences. The identification of the extracted endmember spectra is obtained by comparing it with available pre-defined library spectra (United States Geological Survey (USGS), John Hopkins University (JHU) and Jet Propulsion Laboratory (JPL) spectral libraries) using the Spectral Analyst tool of ENVI 4.1 software (Research Systems Inc., Boulder, CO, US), which provides scores of matching. Three techniques, namely Spectral Feature Fitting (SFF), Spectral Angle Mapping (SAM) and Binary Encoding (BE), are used for identification of the collected endmember spectra to produce a score between 0 and 1, where the value of 1 equals a perfect match showing the exact mineral type. A total of six endmember spectra are identified and extracted in the study area. Mapping of mineral occurrences is carried out using the Mixture-Tuned Matched Filtering (MTMF) technique over the study area on the basis of collected and identified endmember spectra. Results of the present study using the ASA technique ascertain that Landsat ETM+?data can be used to generate valuable mineralogical information.     

Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.     

A comparative assessment of different methods for Landsat 7/ETM+ pansharpening

This article compares a set of relevant methods, based on different mathematical approaches, for Landsat 7 Enhanced Thematic Mapper Plus (ETM+) pansharpening. These are classical procedures such as principal component analysis and fast intensity hue saturation; methods based on wavelet transforms, such as wavelet à trous, additive wavelet luminance proportional and multidirectional–multiresolution methods; a method of a geostatistical nature, called downscaling cokriging (DCK); and finally, a Bayesian method (1cor). The comparison of the fused images is based on the qualitative and quantitative evaluation of their spatial and spectral characteristics by calculating statistical indices and parameters that measure the quality and coherence of the images. Moreover, the quality of the spectral information is studied indirectly by means of the Iterative Self-Organizing Data Analysis Technique (ISODATA) classification of the products of fusion. The results show that DCK and 1cor methods yielded better results than the wavelet-based methods. Particularly, DCK does not introduce artefacts in the estimation of the digital numbers corresponding with the source multispectral image and, therefore, it can be considered as the most coherent method.     

Measurement of the left‐lateral displacement of Ms 8.1 Kunlun earthquake on 14 November 2001 using Landsat‐7 ETM+ imagery

An imageodesy study has been carried out, using pre‐ and post‐event Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) images, to reveal regional co‐seismic displacement caused by the Ms 8.1 Kunlun earthquake in November 2001. The two Landsat scenes, Kusai Lake and Buka Daban, cover an area of some 57 600 km² (320 km W–E and about 180 km N–S), which includes most of the fault rupture zone. The co‐seismic displacement measured in the Kusai Lake scene shows that the average left‐lateral shift along the Kunlun fault is 4.8 m (ranging from 1.5 to 8.1 m) and the maximum shift appears west of the Kusai Lake. The splayed nature of the fault to the west of Buka Daban, where the fault splits into three branches, causes the displacement pattern to become complicated. Here the average left‐lateral shift, between the south side of the southern branch and the north side of the northern branch, is 4.6 m (ranging from 1.0 to 8.2 m). Our results also illustrate that the south side of the fault is the ‘active’ block, moving significantly in an east–south‐easterly direction, relative to the largely ‘stable’ northern block.     

Combining airborne lidar and Landsat ETM+ data with photoclinometry to produce a digital elevation model for Langjökull,Iceland

An incomplete airborne lidar survey of Langjökull, Iceland's second largest ice cap (?900 km²) and the surrounding area was undertaken in August 2007. Elevation data were interpolated between the lidar swaths using the technique of photoclinometry (PC), which relates Sun-parallel slope angles to image brightness. A Landsat Enhanced Thematic Mapper Plus (ETM+) image from March 2002 was used for this purpose. Different bands and band combinations were assessed and Band 4 (760–900 nm) was found to be the most appropriate. Parameters in the slope–brightness equation were derived empirically by comparing the image brightness with lidar elevation data in a 4 km × 4 km region in the centre of the ice cap. This relationship was then used to calculate the slopes, and, by integration between tie points of known lidar elevation, the elevations of the 30 m pixels that were not surveyed by lidar. The root-mean-square (RMS) precision (repeatability) of lidar elevations was 0.18 m and the accuracy was estimated to be 0.25 m. The 68.3% quantile of absolute difference relative to lidar (analogous to root-mean-square error (RMSE)) of all interpolated areas where PC assumptions are met was 5.44 m (4.66 m and 8.73 m for on- and off-ice areas, respectively). Where one or more PC assumptions were not met (e.g. self-shading, sensor saturation), the 68.3% quantile of absolute difference relative to lidar was 27.89 m (18.52 m on the ice cap and 32.91 m off-ice). These accuracies were applicable to 63%, 31%, and 6% of the ice cap and 59%, 28%, and 13% of the final digital elevation model (DEM), respectively. The area-weighted average 68.3% quantiles were 2.89 m for the ice cap and 6.75 m for the entire DEM. The PC technique applied to satellite imagery is a useful and appropriate method for interpolating a lidar survey of an ice cap.     

Peat resource mapping in Lewis using remote sensing techniques and automated cartography†

Abstract

The application of methodology developed for peat resource survey has illustrated the usefulness of multi-level and multi-band aerial photography in classifying Landsat multispectral imagery. Ground truthing to characterize the thematic maps plotted from the aerial photography and Landsat imagery was based primarily on vegetation surveys. The results of this work are illustrated with a series of maps and photographs and the usefulness of the approach is assessed.     

A high‐level data fusion and spatial modelling system for change‐detection analysis using high‐resolution airborne digital sensor data

A high‐level data fusion system that uses Bayesian statistics involving weights‐of‐evidence modelling is described to combine disparate information from airborne digital data such as digital surface model (DSM), colour, thermal infrared (TIR) and hyperspectral images at different time periods. To determine the efficacy of the system, an analysis of change detection was performed. The data fusion system is capable of detecting changes in man‐made features automatically in a densely populated area where there is little prior information. Multiclass segmented images were obtained from the data captured by four airborne remote sensing sensors. The system performs data fusion modelling by using binary images of each theme class and a total of 40 binary patterns were obtained. Through Bayesian methods, involving weights‐of‐evidence modelling, all the binary images were analysed and finally four binary patterns (indicator images) were identified automatically as significant for the change‐detection application. A weighted index overlay model available in the system combines these four patterns. Data fusion by weights‐of‐evidence modelling is found to be straightforward and unequivocal for predicting newly transformed locations. The results of the Bayesian method are accurate as the weights are based on statistical analysis. Changes in features such as colour of roofs, parking areas, openland areas, newly built structures, and the presence or absence of vehicles are extracted automatically by using the high‐level data fusion approach. The final predictor image shows the probability of change‐detected areas in a densely populated city in Japan.     

A multi‐scale segmentation approach to filling gaps in Landsat ETM+ SLC‐off images

On 31 May 2003, the Landsat Enhanced Thematic Plus (ETM+) Scan Line Corrector (SLC) failed, causing the scanning pattern to exhibit wedge‐shaped scan‐to‐scan gaps. We developed a method that uses coincident spectral data to fill the image gaps. This method uses a multi‐scale segment model, derived from a previous Landsat SLC‐on image (image acquired prior to the SLC failure), to guide the spectral interpolation across the gaps in SLC‐off images (images acquired after the SLC failure). This paper describes the process used to generate the segment model, provides details of the gap‐fill algorithm used in deriving the segment‐based gap‐fill product, and presents the results of the gap‐fill process applied to grassland, cropland, and forest landscapes. Our results indicate this product will be useful for a wide variety of applications, including regional‐scale studies, general land cover mapping (e.g. forest, urban, and grass), crop‐specific mapping and monitoring, and visual assessments. Applications that need to be cautious when using pixels in the gap areas include any applications that require per‐pixel accuracy, such as urban characterization or impervious surface mapping, applications that use texture to characterize landscape features, and applications that require accurate measurements of small or narrow landscape features such as roads, farmsteads, and riparian areas.