Medium Spatial Resolution Satellite Imagery Research Articles

Reconstruction of Cloud-free Sentinel-2 Image Time-series Using an Extended Spatiotemporal Image Fusion Approach

Time-series for medium spatial resolution satellite imagery are a valuable resource for environmental assessment and monitoring at regional and local scales. Sentinel-2 satellites from the European Space Agency (ESA) feature a multispectral instrument (MSI) with 13 spectral bands and spatial resolutions from 10 m to 60 m, offering a revisit range from 5 days at the equator to a daily approach of the poles. Since their launch, the Sentinel-2 MSI image time-series from satellites have been used widely in various environmental studies. However, the values of Sentinel-2 image time-series have not been fully realized and their usage is impeded by cloud contamination on images, especially in cloudy regions. To increase cloud-free image availability and usage of the time-series, this study attempted to reconstruct a Sentinel-2 cloud-free image time-series using an extended spatiotemporal image fusion approach. First, a spatiotemporal image fusion model was applied to predict synthetic Sentinel-2 images when clear-sky images were not available. Second, the cloudy and cloud shadow pixels of the cloud contaminated images were identified based on analysis of the differences of the synthetic and observation image pairs. Third, the cloudy and cloud shadow pixels were replaced by the corresponding pixels of its synthetic image. Lastly, the pixels from the synthetic image were radiometrically calibrated to the observation image via a normalization process. With these processes, we can reconstruct a full length cloud-free Sentinel-2 MSI image time-series to maximize the values of observation information by keeping observed cloud-free pixels and calibrating the synthetized images by using the observed cloud-free pixels as references for better quality.

The urban greenness score: A satellite-based metric for multi-decadal characterization of urban land dynamics

Canada’s urban areas have experienced extensive growth over the past quarter century; however, there has been no consistent, spatially explicit approach for quantifying the loss and gain of greenness in cities nationally. Herein, we developed a novel urban greenness score metric using greenness fractions from a multi-decadal time series (1984–2016) of spectrally unmixed annual Landsat satellite image composites to characterize final year (2016) greenness and its overall change for 18 major Canadian urban areas, summarized by census dissemination area (DA). The applied validation procedure confirmed correlation coefficients (ρ) ranging from 0.67 – 0.85 between reference and estimated greenness fractions, indicating that spectral unmixing is an appropriate method for extracting urban greenness from a time series of medium spatial resolution satellite imagery. Most DAs across Canada sustained a moderate (∼20 % – 40 %) or low (≲ 20 %) level of greenness between 1984 and 2016, but overall there was a decreasing trend in greenness. Eastern urban areas maintained the most greenness over time, while urban areas in the Prairies had the greatest increase in greenness. Densely populated urban areas experienced the greatest loss in greenness (16 % of DAs); whereas, urban areas with a moderately-low density experienced the greatest increase (14 % of DAs). In agreement with previous studies, we found that greenness was negatively associated with urban infilling, with lower greenness levels typically found in urban cores, and greenness loss most often found in the urban periphery in conjunction with urban expansion. Methods presented in this analysis take advantage of the open and longstanding Landsat archive, as well as multiple spatial scales, including sub-pixel unmixing techniques, pixel level greenness faction data summarized for management units, and analysis conducted nationally. The developed urban greenness score provides a comprehensive framework to understand current urban greenness and relate it to its recent past, which supports long-term strategic planning, and can be transferred to other regions across spatial and temporal scales.

Monitoring Perubahan Pola Alur Sungai Menggunakan Citra Satelit Resolusi Spasial Menengah Berbasis Spectral Classification

The development of remote sensing technology allows humans to acquire and process data remotely and temporally. Changes in the flow of the Progo river from the last few years are significant, this can be caused by natural factors and human factors. The influence of the intensity of flow and the level of sedimentation in the Progo river causes a massive flow pattern change in the Progo river body. The data used in this research is Medium Spatial Resolution Satellite Imagery, Landsat 5 satellite imagery acquired in 1995 and Landsat 8 acquired in 2017. Monitoring of changes in river flow pattern is generally done by using the method of terrestrial or conventional measurement, which takes a long time, for that the use of methods and remote sensing data can be used to save time. The method used is multispectral classification approach with maximum likelihood algorithm. The results of extraction using digital classification method (maximum likelihood) resulted in the appearance of flow pattern quickly and representative, so this method is suitable for the purpose of rapid detection of changes in flow pattern. The results obtained from the extraction of the Progo river flow pattern show an intricate river flow pattern with many river rubbers on the image appearance of 1995, whereas in the image extraction results in 2017 the river banks and turns do not look dominant, it shows that erosion and sedimentation activities continue to occur massively.

Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery

Increases in extreme weather events associated with climate change have the potential to put currently healthy forests at risk. One option to minimize this risk is the application of forest management measures aimed at generating species mixtures predicted to be more resilient to these threats. In order to apply such measures appropriately, forest managers need up-to-date, accurate and consistent forest maps at relatively fine spatial resolutions. Cost efficiency is a major factor when creating such maps. Taking European spruce (Picea abies) and Scots pine (Pinus sylvestris) as an example, this paper describes an innovative approach for mapping two tree species using a combination of commercial very high resolution WorldView-2 (WV2) images and Landsat time series data. As a first step, this study used a supervised object-based classification of WV2 images covering relatively small test sites distributed across the region of interest. Using these classification maps as training data, wall-to-wall mapping of fractional coverages of spruce and pine was achieved using multi-temporal Landsat data and Random Forests (RF) regression. The method was applied for the entire state of Bavaria (Germany), which comprises a total forested area of approximately 26,000km2. As applied here, this two-step approach yields consistent and accurate maps of fractional tree cover estimates with a spatial resolution of 1ha. Independent validation of the fractional cover estimates using 3780 reference samples collected through visual interpretation of orthophotos produced root-mean-square errors (RMSE) of 11% (for spruce) and 14% (for pine) with almost no bias, and R2 values of 0.74 and 0.79 for spruce and pine, respectively. The majority of the validation samples (75% (spruce) and 84% (pine)) were modeled within the assumed uncertainty of ±15% of the reference sample. Accuracies were significantly better compared to those achieved using a single-step classification of Landsat time series data at the pixel level (30m), because the two-step approach better captures regional variation in the spectral signatures of target classes. Moreover, the increased number of available reference cells mitigates the impact of occasional errors in the reference data set. This two-step approach has great potential for cost-effective operational mapping of dominant forest types over large areas.

An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015

Researchers have been attending increasingly to impervious surface dynamics to better understand the urbanization process and its impacts on urban environments. Previously, numerous studies have only estimated and mapped impervious surface dynamics at annual or decadal time scales. It is challenging to estimate impervious surface dynamics at a finer time scale, such as on a monthly scale, while using a single source of medium spatial resolution satellite imagery. However, urban infrastructure construction could cause changes in impervious surfaces in a short period of time. This paper aimed at developing a new methodology for evaluating monthly impervious surface dynamics by fusing Landsat and MODIS time series. The Pearl River Delta in China, is located in a humid subtropical region and was selected as the study area due to its dramatic urbanization in the past three decades. Available Landsat images with cloud cover <90%, 7-Day MODIS NDVI 250m smooth time series, and daily MODIS LST 1000m time series from 2000 to 2015 were downloaded. These data were used to develop temporal features of land covers (i.e., monthly Landsat NDVI and LST time series) and to monitor impervious surface dynamics using semi-supervised time series fuzzy clustering method. The results showed the effectiveness of temporal features in differentiating land covers. Additionally, the average overall classification accuracy yielded reasonable accuracies (up to 89.36%). The proposed methodology has illustrated numerous, considerable advantages over previous methods. It has offered consistent maps of impervious surfaces on a monthly time scale as well as enhanced distinguishability of land covers with similar spectral characteristics. This study can be utilized to establish relationships between urban expansion, climate change, urban environment, population, and other socio-economic variables on a monthly basis. The study is also crucial for predicting the timing, duration, and density of ecological change for increased impervious surfaces.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index

Plastic greenhouses (PGs) are an important agriculture development technique to protect and control the growing environment for food crops. The extensive use of PGs can change the agriculture landscape and affects the local environment. Accurately mapping and estimating the coverage of PGs is a necessity to the strategic planning of modern agriculture. Unfortunately, PG mapping over large areas is methodologically challenging, as the medium spatial resolution satellite imagery (such as Landsat data) used for analysis lacks spatial details and spectral variations. To fill the gap, the paper proposes a new plastic greenhouse index (PGI) based on the spectral, sensitivity, and separability analysis of PGs using medium spatial resolution images. In the context of the Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, the paper examines the effectiveness and capability of the proposed PGI. The results indicate that PGs in Landsat ETM+ image can be successfully detected by the PGI if the PG fraction is greater than 12% in a mixed pixel. A kappa coefficient of 0.83 and overall accuracy of 91.2% were achieved when applying the proposed PGI in the case of Weifang District, Shandong, China. These results show that the proposed index can be applied to identifying transparent PGs in atmospheric corrected Landsat image and has the potential for the digital mapping of plastic greenhouse coverage over a large area.

A New Spectral Index for Extraction of Built-Up Area Using Landsat-8 Data

This research paper presents development of a new spectral index, it was proposed to extract the built-up area from medium spatial resolution satellite imagery data; a Landsat Operational Land Imager, and the Thermal Infrared Sensor “OLI_TIRS”. The newly developed spectral index was derived from Band ratios, it was compared with four previously used spectral indices; the Band Ratio for Built-up Area (BRBA), the Normalized Built-up Area Index (NBAI), the New Built-up Index (NBI) and the Normalized Built-up Index (NDBI). Indices were applied to extract built- up area of Djelfa city, South Algiers, in the central part of North Algeria. The results indicated that newly developed spectral index for extract the built-up area, using overall accuracy and kappa coefficient, are superior as compared to ones of the other spectral indices.

Assessment of urbanization encroachment over Al-Monib island using fuzzy post classification comparison and urbanization metrics

Nile River has about 144 islands from Aswan to the Mediterranean Sea. In this research remotely sensed images have been used for the assessment of land cover changes in the Al-Monib island as part of an ongoing sustainable development of this island. The island has witnessed high rates of change in land use in the past few years. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas.The most common use of many of the change detection algorithms has been to identify the change in coarse to medium spatial resolution satellite imagery. Now there is great interest in identifying the change in high spatial resolution multispectral data such as SPOT5 and QuickBird. In order to improve the quality and accuracy, different cues have been extracted such as IHS or PCA and texture derived from color image. Fuzzy classification has been performed several times utilizing from Multi-Cue integration (resulted into six classifications) for each date. Assessment of different approaches of classification (six classifications) has been performed for each date. After that fuzzy post classification comparison has been made for the best case. Value of the urban expansions for the period of 2002–2009 was calculated as 0.11km2. The urban expansion rate had been realized as 3.04%. Another significant change was the decline in agricultural lands result was estimated to be 8.29%.The changes of landscape pattern were then analyzed using a series of spatial metrics (class level) which were derived from FRAGSTATS software.

Use of MODIS NDVI data to improve forest-area estimation

The objective of this article is to develop and test a methodology capable of using medium spatial resolution satellite imagery to improve forest-area statistics derived from ground sampling. The methodology builds on the evidence that multitemporal Normalized Difference Vegetation Index (NDVI) images bring significant information on the spatial distribution of forest surfaces. Consequently, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images are potentially useful to improve forest-area assessment based on ground data. This expectation is verified in Tuscany (central Italy) using forest-area references extracted from the Coordination of Information on Environment (CORINE) land-cover map. The accuracy of forest-area statistics obtained at province level by different reference samplings is first assessed. Next, locally calibrated regression analyses are applied to multitemporal MODIS NDVI images in order to obtain per-pixel forest-area estimates. Two statistical methods (the direct expansion and the regression estimator) are finally used to combine these estimates with the ground data and produce corrected per-province statistics. The experimental results confirm that MODIS NDVI data contain relevant information on forest distribution, which can be efficiently extended over the land surface by locally calibrated regressions. The obtained estimates can be combined with the ground data for enhancing forest-area assessment at province level. To this aim, the regression estimator gives the best performance for all sampling densities of the reference data.

Medium Spatial Resolution Satellite Imagery to Estimate Gross Primary Production in an Urban Area

Remote sensing data with medium spatial resolution can provide useful information about Gross Primary Production (GPP), especially on the scale of urban areas. Most models of ecosystem carbon exchange that are based on remote sensing use some form of the light use efficiency (LUE) model. The aim of this work is to analyze the distribution of annual GPP in the urban area of Denpasar, Bali. Additional analysis using two types of satellite data (ALOS/AVNIR-2 and Aster) addresses the impact of spatial resolution on the detection of various ecosystem processes in Denpasar. Annual GPP estimated using ALOS/AVNIR-2 varied from 0.13 gC m−2 yr−1 to 2,586.18 gC m−2 yr−1. Meanwhile, the Aster estimate varied from 0.14 gC m−2 yr−1 to 2,595.26 gC m−2 yr−1. GPP as measured by ALOS/AVNIR-2 was lower than that from Aster because ALOS/AVNIR-2 has medium spatial resolution and a smaller spectral range than Aster. Variations in land use may influence the measured value of GPP via differences in vegetation type, distribution, and photosynthetic pathway type. The medium spatial resolution of the remote sensing data is crucial for discriminating different land cover types in heterogeneous urban areas. Given the heterogeneity of land cover over Denpasar, ALOS/AVNIR-2 detects a smaller maximum value of GPP than Aster, but the annual mean GPP from ALOS/AVNIR-2 is higher than that from Aster. Based on comparisons with previous work, we find that ALOS/AVNIR-2 and Aster satellite data provided more accurate estimates of maximum GPP in Denpasar and in the tropical Kalimantan-Indonesia and Amazon forest than estimates derived from the MODIS GPP product (MOD17).

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better.

Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery

A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and, in the case of French Guiana, compare such precision with the official inventory precision. We extract nine sample data sets from the official wall-to-wall deforestation map derived from satellite interpretations produced for the Brazilian Amazon for the year 2002 to 2003. This global sampling scheme estimate gives 2.81 million ha of deforestation (mean from nine simulated replicates) with a standard error of 0.10 million ha. This compares with the full population estimate from the wall-to-wall interpretations of 2.73 million ha deforested, which is within one standard error of our sampling test estimate. The relative difference between the mean estimate from sampling approach and the full population estimate is 3.1%, and the standard error represents 4.0% of the full population estimate. This global sampling is then intensified to a territorial level with a case study over French Guiana to estimate deforestation between the years 1990 and 2006. For the historical reference period, 1990, Landsat-5 Thematic Mapper data were used. A coverage of SPOT-HRV imagery at 20 m × 20 m resolution acquired at the Cayenne receiving station in French Guiana was used for year 2006. Our estimates from the intensified global sampling scheme over French Guiana are compared with those produced by the national authority to report on deforestation rates under the Kyoto protocol rules for its overseas department. The latter estimates come from a sample of nearly 17,000 plots analyzed from same spatial imagery acquired between year 1990 and year 2006. This sampling scheme is derived from the traditional forest inventory methods carried out by IFN (Inventaire Forestier National). Our intensified global sampling scheme leads to an estimate of 96,650 ha deforested between 1990 and 2006, which is within the 95% confidence interval of the IFN sampling scheme, which gives an estimate of 91,722 ha, representing a relative difference from the IFN of 5.4%. These results demonstrate that the intensification of the global sampling scheme can provide forest area change estimates close to those achieved by official forest inventories (<6%), with precisions of between 4% and 7%, although we only estimate errors from sampling, not from the use of surrogate data. Such methods could be used by developing countries to demonstrate that they are fulfilling requirements for reducing emissions from deforestation in the framework of an REDD (Reducing Emissions from Deforestation in Developing Countries) mechanism under discussion within the United Nations Framework Convention on Climate Change (UNFCCC). Monitoring systems at national levels in tropical countries can also benefit from pan-tropical and regional observations, to ensure consistency between different national monitoring systems.

Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991–2000, by using satellite images

The majority of the vast literature on remote sensing of urban landscapes has adopted a ‘hard classification’ approach, in which each image pixel is assigned a single land use and land cover category. Owing to the nature of urban landscapes, the confusion between land use and land cover definitions and the constraints of widely applied medium spatial resolution satellite images, high classification accuracy has been difficult to achieve with the conventional ‘hard’ classifiers. The prevalence of the mixed pixel problem in urban landscapes indicates a crucial need for an alternative approach to urban analyses. Identification, description and quantification, rather than classification, may provide a better understanding of the compositions and processes of heterogeneous landscapes such as urban areas. This study applied the Vegetation–Impervious Surface–Soil (V‐I‐S) model for characterizing urban landscapes and analysing their dynamics in Indianapolis, USA, between 1991 and 2000. To extract these landscape components from three dates of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images in 1991 1995 and 2000, we used the technique of linear spectral mixture analysis (LSMA). These components were further classified into urban thematic classes, and used for analysis of the landscape patterns and dynamics. The results indicate that LSMA provides a suitable technique for detecting and mapping urban materials and V‐I‐S component surfaces in repetitive and consistent ways, and for solving the spectral mixing of medium spatial resolution satellite imagery. The reconciliation between the V‐I‐S model with LSMA for Landsat imagery allowed this continuum landscape model to be an alternative, effective approach to characterizing and quantifying the spatial and temporal changes of the urban landscape compositions in Indianapolis from 1991 to 2000. It is suggested that the model developed in this study offers a more realistic and robust representation of the true nature of urban landscapes, as compared with the conventional method based on ‘hard classification’ of satellite imagery. The general applicability of this continuum model, especially its spectral, spatial and temporal variability, is discussed.

Vineyard area estimation using medium spatial resolution satellite imagery

The European Union requires member states to estimate their wine growing potential. For this purpose, most member states have developed or updated vineyard registers. The present study suggests locating vineyards using medium spatial resolution satellite imagery. The work was carried out using Landsat images that were validated for the Designation of Origin "Bierzo", León, Spain. The methodology described in this paper yields a producer's accuracy of 0.88 and a user's accuracy of 0.63. The vineyard areas for each municipality were estimated from the classified images by linear regression, with fits of R2 &gt; 0.80. The method gives good results at the municipal scale.

Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN

Remote sensing estimation of impervious surface is significant in monitoring urban development and determining the overall environmental health of a watershed, and it has therefore attracted more interest recently in the remote sensing community. The main objective of this paper is to examine and compare the effectiveness of two advanced algorithms for estimating impervious surfaces from medium spatial resolution satellite images, namely, linear spectral mixture analysis (LSMA) and artificial neural network (ANN). Terra's Advanced Spaceborne Thermal Emission and Reflection Radiometer [(ASTER); acquired on June 16,2001] and a Landsat Enhanced Thematic Mapper Plus (ETM+) image (acquired on June 22, 2000) of Indianapolis, IN, were used for the analysis. The LSMA was employed to generate high- and low-albedo, vegetation, and soil fraction images (endmembers), and an image of impervious surfaces was then estimated by adding high- and low-albedo fraction images. Furthermore, an ANN model, specifically the multilayer-perceptron feedforward network with the back-propagation learning algorithm, was employed as a subpixel image classifier to estimate impervious surfaces. Accuracy assessment was performed against a high- resolution digital orthophoto. The results show that ANN was more effective than LSMA in generating impervious surfaces with high statistical accuracy. For the ASTER image, the root-mean-square error (RMSE) of the impervious surface map with the ANN model was 12.3%, and the one that resulted from LSMA was 13.2%. For the ETM+ image, the RMSE with the ANN model was 16.7%, and the one from LSMA was 18.9%. The better performance of ANN over LSMA is mainly attributable to the ANN'S capability of handling the nonlinear mixing of image spectrum. In order to test the seasonal sensitivity of satellite images for estimating impervious surfaces, LSMA was applied to two additional ASTER images of the same area, which are acquired on April 5, 2004, and October 3, 2000, respectively. The results were then compared with the ASTER image acquired in June in terms of RMSE. The June image had the highest accuracy, whereas the October image was better than the one in April. Plant phenology caused changes in the variance partitioning and impacted the mixing-space characterization, leading to a less accurate estimation of impervious surfaces.

Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey

Coastline mapping and coastline change detection are critical issues for safe navigation, coastal resource management, coastal environmental protection, and sustainable coastal development and planning. Changes in the shape of coastline may fundamentally affect the environment of the coastal zone. This may be caused by natural processes and/or human activities. Over the past 30 years, the coastal sites in Turkey have been under an intensive restraint associated with a population press due to the internal and external touristic demand. In addition, urbanization on the filled up areas, settlements, and the highways constructed to overcome the traffic problems and the other applications in the coastal region clearly confirm an intensive restraint. Aerial photos with medium spatial resolution and high resolution satellite imagery are ideal data sources for mapping coastal land use and monitoring their changes for a large area. This study introduces an efficient method to monitor coastline and coastal land use changes using time series aerial photos (1973 and 2002) and satellite imagery (2005) covering the same geographical area. Results show the effectiveness of the use of digital photogrammetry and remote sensing data on monitoring large area of coastal land use status. This study also showed that over 161 ha areas were filled up in the research area and along the coastal land 12.2 ha of coastal erosion is determined for the period of 1973 to 2005. Consequently, monitoring of coastal land use is thus necessary for coastal area planning in order to protecting the coastal areas from climate changes and other coastal processes.

Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO‐1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high‐quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI‐derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion‐derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low‐albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low‐albedo surfaces came mainly from additional bands in the mid‐infrared region.

An approach for edge matching large-area satellite image classifications

Large-area land cover mapping based on remotely sensed data often requires combining individual or large groups of classified images to produce final map products. Operational and logistical considerations are typically confronted when classifying medium spatial resolution satellite imagery (i.e., Landsat), with the mapping partitioned by spectral, ecological, or political considerations, or combinations thereof. Visual discontinuities can emerge at the locations where logistically based production zones join. Transparent and systematic approaches for addressing discontinuities are desired for the Earth Observation for Sustainable Development of Forests (EOSD) project. This large-area land cover mapping project is producing map products for Canada's forested ecozones. A distributed implementation plan, largely based on grouping provincial and territorial political units, was followed for production. Scene-to-scene discontinuities are rare within each production zone and are primarily related to image acquisition date and phenological state. In contrast, discontinuities can emerge at the production zone boundaries because of differences in support data available or more commonly because of differences in the attribution of density classes. Of the over 475 scenes classified for the EOSD project, it is estimated that fewer than 30 (about 6.3% of total) will require processing to minimize the cross-boundary discontinuity. Options for mitigating the discontinuities are described and demonstrated in the context of different scenarios of overlap found along the EOSD production zone boundaries (complete overlap, partial overlap, and no overlap) using two subsets of a Landsat scene along the shared provincial border between British Columbia and Alberta, Canada. Analysis of image gradients provides a quantitative basis for identification of discontinuities and also relates the results of the likelihood-based relabelling process. Through this process, only density descriptors of cover types are altered, largely maintaining classification integrity. The process as presented is generic and is suitable for addressing edge discontinuities that can emerge when undertaking a large-area land cover classification project.

Detection of Storm Damage Tracks with EOS Data

Abstract The damage surveys conducted by the NWS in the aftermath of a reported tornadic event are used to document the location of the tornado ground damage track (pathlength and width) and an estimation of the tornado intensity. This study explores the possibility of using near-real-time medium and high spatial resolution satellite imagery from the NASA Earth Observing System satellites to provide additional information for the surveys. Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data were used to study the damage tracks from three tornadic storms: the La Plata, Maryland, storm of 28 April 2002 and the Ellsinore and Marquand, Missouri, storms of 24 April 2002. These storms varied in intensity and occurred over regions with significantly different land cover. It was found that, depending on the nature of the land cover, tornado damage tracks from intense storms (F1 or greater) and hail storms may be evident in ASTER, Landsat, and MODIS satellite imagery. In areas where the land cover is dominated by forests, the scar patterns can show up very clearly, while in areas of grassland and regions with few trees, scar patterns are not as obvious or cannot be seen at all in the satellite imagery. The detection of previously unidentified segments of a damage track caused by the 24 April 2002 Marquand, Missouri, tornado demonstrates the utility of satellite imagery for damage surveys. However, the capability to detect tornado tracks in satellite imagery depends on the ability to observe the ground without obstruction from space and appears to be as much dependent on the nature of the underlying surface and land cover as on the severity of the tornadic storm.