Landsat products can also be discovered through ESA's EO Catalogue (EO CAT), which allows users to browse and download products among the available datasets from ESA and Third Party Missions and instruments, using various criteria (spatial, temporal).
This dataset contains all the Landsat-7 Enhanced Thematic Mapper high-quality ortho-rectified L1T dataset (or L1Gt where not enough GCPs are available) over Kiruna, Maspalomas, Matera and Neustrelitz visibility masks.
Landsat 7 Etm+ Data Download
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As of March 31, 2004, Landsat 7 ETM+ data will no longer be available for search and order through the LP DAAC and the EOS Data Gateway (EDG). All orders submitted to the LP DAAC prior to this date will be filled, provided payment is received within a reasonable amount of time. Search, browse, and order capability for most of these datasets will be migrated to USGS systems in the near future.
This change is driven by long standing agreements to transfer land remotely sensed data from NASA to the USGS for long-term archiving, as well as specific agreements made between NASA and the USGS during the Landsat 7 program. Both agencies will be working to make the transfer as seamless to the users as possible, and to continue the current high level of service.
Although the method of data access will change, there will be no other change to the processing or format of the products. Level 1 and Level 0 products that were available through the EDG will now be orderable through the USGS Earth Explorer and the Global Visualization Viewer (GloVis), and processing will be done by the same systems currently in use (LPGS and NLAPS). If you have any questions, please contact LP DAAC User Services or USGS EDC Customer Services.
For example, after comparing the retrieved surface reflectances from ALI with those from ETM+ and Landsat-4, 5 TM and considering the fact that ALI is a sensor launched for validation of new sensor technologies, Bryant et al. [5] concluded that the ALI sensor performed extremely well. Chander et al. [3] conducted a cross calibration of ALI and ETM+ sensors and their results of the radiometric comparison indicate that the relative sensor chip assemblies gains agree with the ETM+ visible/near infrared (VNIR) band gains to within 2% and with the short-wave infrared (SWIR) bands to within 4%. In discriminating forests with Hyperion, ALI and ETM+ images, Goodenough et al. [9] compared capabilities of the three sensors' data used for forest classification at various classification levels. Their experimental results indicated that Hyperion (overall accuracy of 90%) outperformed ALI (85%) and ETM+ (75%) in forest classification and that ALI classification results were much better than ETM+. Furthermore, Neuenschwander et al. [6] demonstrated higher classification accuracy of mapping flood features in the Okavango Delta, Botswana when using ALI compared to ETM+. In our previous work to compare the capabilities of the three sensors (Hyperion, ALI and ETM+) by the effect of individual bands on estimating forest crown closure (CC) and leaf area index (LAI), we found the Hyperion data consistently outperformed the ALI and ETM+ data while ALI was better than ETM+ [7].
The most commonly used vegetation indices are simple algorithms based on the dissimilar interaction of red and near-infrared (NIR) electromagnetic radiance with vegetation canopies. Among them, the ratio-based normalized difference vegetation index (NDVI, [21]), and the simple ratio vegetation index (SR, [22]) are the most frequently used to correlate with CC, LAI and other canopy structure parameters (e.g., [23-25]). Besides using red and NIR bands, Gong et al. [19] also tested SWIR bands and NIR bands with Hyperion hyperspectral data to construct NDVI and SR VIs and found the VIs constructed with SWIR and NIR bands better than these constructed using red and NIR bands only.
Because of possible non-linear relationship between VIs and canopy structure parameters, three nonlinear VIs: MSR [31], NLI [32] and MNLI [19] were considered in the prediction models of CC and LAI. The MSR and MLI non-linear vegetation indices attempt to linearize relationships with surface parameters that tend to be nonlinear. In order to adopt merits from the two VIs to improve their performance correlating with canopy LAI, Gong et al. [19] also tested MNLI by modifying NLI and considering merits of Soil Adjust Vegetation Index coupled with NLI and proved that MNLI had a higher correlation with LAI than either NLI or MSR. While using red and NIR bands for ALI and ETM+ data to construct NLI and MSR, Hyperion bands located in SWIR and NIR (Table 2) were used. The MNLI is only for Hyperion sensor.
Forest CC and LAI maps produced with the three sensors' data. CC maps produced with ALI (a), ETM+ (b), and Hyperion (c) data; LAI maps produced with ALI (d), ETM+ (e), and Hyperion (f) data. The Blodgett study area is bounded in a white line in the six CC and LAI maps. In the figure, the darker the image pixels show, the higher the forest CC or LAI values.
Table 4 presents some simple statistics derived from the validation results, used to judge CC and LAI map quality through comparison with the CC and LAI values derived by photo interpretation. Statistics include root mean squared error (RMSE) and mapped accuracy (MA) for each prediction model. By comparing statistical results in the table among different prediction models and between mapped results and interpreted results, it is worth noting that both mapped CC and LAI results with the lowest RMSE and highest MA values were produced by Hyperion data again, followed by ALI data, and the worst for ETM+ data.
Scatter plots showing the agreement degree and reliability between the interpreted values and corresponding mapped values. (a) CC and (b) LAI interpreted values vs. corresponding mapped values with ALI data; (c) CC and (d) LAI interpreted values vs. corresponding mapped values with ETM+ data; (e) CC and (f) LAI interpreted values vs. corresponding mapped values with Hyperion data;
In this study, our first objective was to estimate canopy height at locations unsampled by lidar, based on the statistical and geostatistical relationships between the lidar and Landsat ETM+ data at the lidar sample locations. We used basic data from lidar (maximum canopy height) and Landsat ETM+ (raw band values) and tested widely used, straightforward empirical estimation methods: ordinary least squares (OLS) regression, ordinary kriging (OK), and ordinary cokriging (OCK).
Separate stepwise multiple regression models were developed for the eight sampling strategies tested. In every case, ETM+ Band 7 was the first variable selected (Table 1). All nine independent variables contributed significantly, and were therefore included, in the four transect cases. The number of variables included in the point models decreased as sample data volume decreased, with only one variable selected in the lower extreme case (2000 m point strategy).
This work was funded by the NASA Terrestrial Ecology Program (NRA-97-MTPE-08), through the Enhanced Thematic Mapper Plus Lidar for Forested Ecosystems (ETM+LIFE) Project. Aeroscan data were provided by Mike Renslow of Spencer B. Gross, Portland, OR. We also thank Ralph Dubayah for his critical review and Jeff Evans for graphics assistance.
This data set includes orthorectified Landsat ETM+ scenes across the Legal Amazon region. At least one scene is provided for each spatial tile, representing the most cloud-free retrievals from mid-1999 through late 2001 (Fig. 1). Dates are therefore not continuous but include scenes from July 8, 1999 to November 13, 2001. Data have been atmospherically corrected and orthorectified.
Data files (and format) included for each scene are: six multispectral bands (tif), two thermal bands (tif), one panchromatic band (tif), two preview files (jpg), and one metadata file (txt). The individual Geotiff files have been g-zipped and subsequently all of the files for a scene have been g-zipped together for ordering convenience.
The LBA Data and Publication Policy [ _data_policy.html] is in effect for a period of five (5) years from the date of archiving and should be followed by data users who have obtained LBA data sets from the ORNL DAAC. Users who download LBA data in the five years after data have been archived must contact the investigators who collected the data, per provisions 6 and 7 in the Policy. This data set was archived in August of 2007. Users who download the data between August 2007 and July 2012 must comply with the LBA Data and Publication Policy.
Data users should use the Investigator contact information in this document to communicate with the data provider. Alternatively, the LBA Web Site [ ] in Brazil will have current contact information. Data users should use the Data Set Citation and other applicable references provided in this document to acknowledge use of the data.
This data set includes orthorectified Landsat ETM+ scenes across the Legal Amazon region. At least one scene is provided for each spatial tile, representing the most cloud-free retrievals from mid-1999 through late 2001. Dates are therefore not continuous and include scenes from July 8, 1999 to November 13, 2001.
Accurate knowledge of the spatial extents and distributions of an oil spill is very impor-tant for efficient response. This is because most petroleum products spread rapidly on the water surface when released into the ocean, with the majority of the affected area becoming covered by very thin sheets. This article presents a study for examining the feasibility of Landsat ETM+ images in order to detect oil spills pollutions. The Landsat ETM+ images for 1st, 10th, 17th May 2010 were used to study the oil spill in Gulf of Mexico. In this article, an attempt has been made to perform ratio operations to enhance the feature. The study concluded that the bands difference between 660 and 560 nm, division at 660 and 560 and division at 825 and 560 nm, normalized by 480 nm provide the best result. Multilayer perceptron neural network classifier is used in order to perform a pixel-based supervised classification. The result indicates the potential of Landsat ETM+ data in oil spill detection. The promising results achieved encourage a further analysis of the potential of the optical oil spill detection approach. e24fc04721
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