GloVis

EarthExplorerUse the USGS EarthExplorer (EE) to search, download, and order satellite images, aerial photographs, and cartographic products. In addition to data from the Landsat missions and a variety of other data providers, EE provides access to MODIS land data products from the NASA Terra and Aqua missions, and ASTER level-1B data products over the U.S. and Territories from the NASA ASTER mission. Registered users of EE have access to more features than guest users.Earth Explorer Distribution DownloadThe EarthExplorer user interface is an online search, discovery, and ordering tool developed by the United States Geological Survey (USGS). EarthExplorer supports the searching of satellite, aircraft, and other remote sensing inventories through interactive and textual-based query capabilities. Through the interface, users can identify search areas, datasets, and display metadata, browse and integrated visual services within the interface.The distributable version of EarthExplorer provides the basic software to provide this functionality. Users are responsible for verification of system recommendations for hosting the application on your own servers. By default, this version of our code is not hooked up to a data source so you will have to integrate the interface with your data. Integration options include service-based API's, databases, and anything else that stores data. To integrate with a data source simply replace the contents of the 'getDataset' and 'search' functions in the CWIC.php file.Distribution is being provided due to users requests for the codebase. The EarthExplorer source code is provided "As Is", without a warranty or support of any kind. The software is in the public domain; it is available to any government or private institution.The software code base is managed through the USGS Configuration Management Board. The software is managed through an automated configuration management tool that updates the code base when new major releases have been thoroughly reviewed and tested.Link: https://earthexplorer.usgs.gov/

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Landsat TM, and ETM+ data are provided in GeoTIFF for Level 1T (terrain corrected) products, or for either Level 1Gt (systematic terrain corrected) or Level 1G (systematic corrected) products, if Level 1T processing is not available. GeoTIFF defines a set of publicly available TIFF tags that describe cartographic and geodetic information associated with TIFF images. GeoTIFF is a format that enables referencing a raster image to a known geodetic model or map projection.

The initial tags are followed by image data that, in turn, may be interrupted by more descriptive tags. By using the GeoTIFF format, both metadata and image data can be encoded into the same file. The Landsat 7 ETM+ GeoTIFF file format is described in detail in the"Landsat 7 ETM+ Level 1 Product Data Format Control Book (DFCB), LSDS-272": http://landsat.usgs.gov/documents/LSDS-272.pdf. The Landsat 4-5 TM GeoTIFF file format is described in detail in the "Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control Book (DFCB), LS-DFCB-20": http://landsat.usgs.gov/documents/LS-DFCB-20.pdf.

For more information on GeoTIFF visit: http://trac.osgeo.org/geotiff

Dataset History

ORGANIZATION

Each band of Landsat data in the GeoTIFF format is delivered as a grayscale, uncompressed, 8-bit string of unsigned integers. A metadata (MTL) file is included with data processed through the Level-1 Product Generation System (LPGS). A file containing the ground control points (GCP) used during image processing is also included. A processing history (WO) file is included with data processed through the National Landsat Archive Production System (NLAPS). Landsat 7 ETM+ SLC-off products processed after December 11, 2008, will include an additional directory (gap_mask) that contains a set of flat binary scan gap mask files (one per band). (Please note that the processing date and acquisition date are not necessarily the same.)

* DATA FILE NAMES

The file naming convention for Landsat LPGS-processed GeoTIFF data

is as follows:

LMSppprrrYYYYDOYGSIVV_BN.TIF where:

 L      = Landsat 

 M      = Mission (E for ETM+ data; T for TM data; M for MSS)

 S      = Satellite (7 = Landsat 7, 5 = Landsat 5, 4 = Landsat 4)

 ppp     = starting path of the product

 rrr     = starting and ending rows of the product

 YYYY    = acquisition year

 DOY     = Julian date

 GSI     = Ground Station Identifier 

 VV     = 2 digit version number

 BN     = file type:

   B1     = band 1

   B2     = band 2

   B3     = band 3

   B4     = band 4

   B5     = band 5

   B6_VCID_1 = band 6L (low gain) (ETM+)

   B6_VCID_2 = band 6H (high gain) (ETM+)

   B6     = band 6 (TM and MSS)

   B7     = band 7 

   B8     = band 8 (ETM+)

   MTL    = Level-1 metadata

   GCP    = ground control points

 TIF     = GeoTIFF file extension

The file naming convention for Landsat NLAPS-processed GeoTIFF data

is as follows:

LLNppprrrOOYYDDDMM_AA.TIF where:

 LL     = Landsat sensor (LT for TM data)

 N      = satellite number

 ppp     = starting path of the product

 rrr     = starting row of the product

 OO     = WRS row offset (set to 00)

 YY     = last two digits of the year of 

        acquisition

 DDD     = Julian date of acquisition

 MM     = instrument mode (10 for MSS; 50 for TM)

 AA     = file type:

   B1     = band 1

   B2     = band 2

   B3     = band 3

   B4     = band 4

   B5     = band5

   B6     = band 6

   B7     = band 7

   WO     = processing history file 

 TIF     = GeoTIFF file extension

* GAP MASKS

All Landsat 7 ETM+ SLC-off imagery processed on or after December 11, 2008, will include gap mask files. (Please note the difference between acquisition date and processing date, files dates are not necessarily the same.) The gap mask files are bit mask files showing the locations of the image gaps (areas that fall between ETM+ scans). One tarred and gzip-compressed gap mask file is provided for each band in GeoTIFF format. The file naming convention for gap mask files is identical to that described above for LPGS-processed GeoTIFF data, with "_GM" inserted before file type.

If gap mask files are not included with the data, a tutorial for creating them can be found at: http://landsat.usgs.gov/gap_mask_files_are_not_provided_can_I_create_my_own.php

* README

The README_GTF.TXT (or README.GTF) is an ASCII text file and is this file.

* READING DATA

Delivered via file transfer protocol (FTP): data files are tarred and g-zip compressed and will need to be unzipped and untarred before the data files can be used. UNIX systems should have the "gunzip" and "tar"

commands available for uncompressing and accessing the data. For PC users, free software can be downloaded from an online source. Otherwise, check your PC, as you may already have appropriate software available.

No software is included on this product for viewing Landsat data.

GENERAL INFORMATION and DOCUMENTATION

Landsat Project Information:

http://landsat.usgs.gov

Landsat data access:

* USGS Global Visualization Viewer (GloVis): http://glovis.usgs.gov

* USGS EarthExplorer: http://earthexplorer.usgs.gov

* USGS LandsatLook Viewer: http://landsatlook.usgs.gov

* Landsat International Ground Station (IGS) network:

         http://landsat.usgs.gov/about_ground_stations.php

FGDC metadata:

http://www.fgdc.gov/metadata

Data restrictions and citation:

https://lta.cr.usgs.gov/citation

* National Snow and Ice Data Center (NSIDC)

Radarsat Antarctic Mapping Project (RAMP) elevation data citation:

Liu, H., K. Jezek, B. Li, and Z. Zhao. 2001.

Radarsat Antarctic Mapping Project digital elevation model version 2.

Boulder, CO: National Snow and Ice Data Center. Digital media.

For information on the data, please refer to the data set documentation

available at the following web site:

http://nsidc.org/data/nsidc-0082.html

PRODUCT SUPPORT

For further information on this product, contact USGS

EROS Customer Services:

Customer Services (ATTN: Landsat)

U.S. Geological Survey

Earth Resources Observation and Science (EROS) Center

47914 252nd Street

Sioux Falls, SD 57198-0001

Tel: 800-252-4547

Tel: 605-594-6151

Email: custserv@usgs.gov

For information on other products from USGS EROS:

http://eros.usgs.gov/ or https://lta.cr.usgs.gov/

For information on other USGS products:

http://ask.usgs.gov/

or call 1-888-ASK-USGS (275-8747)

DISCLAIMER

Any use of trade, product, or firm names is for descriptive

purposes only and does not imply endorsement by the U.S.

Government.

Publication Date: July 2014

Dataset Citation

U.S. Geological Survey (2014) SYD Landsat raw data v01. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/fe7aa98d-ea2a-48fc-bc09-1d5ce3a50246.

Welcome to the LandsatLook Viewer!The LandsatLook Viewer is a prototype tool that was developed to allow rapid online viewing and access to the USGS Landsat image archives. This viewer allows you to:Interactively explore the Landsat archive at up to full resolution directly from a common web browserSearch for specific Landsat images based on area of interest, acquisition date, or cloud coverCompare image features and view changes through timeDisplay configurable map information layers in combination with the Landsat imageryCreate a customized image display and export as a simple graphic fileView metadata and download the full-band source imagerySearch by address or place, or zoom to a point, bounding box, or Sentinel-2 Tile or Landsat WRS-1 or WRS-2 Path/RowGenerate and download a video animation of the oldest to newest images displayed in the viewerWe welcome feedback and input for future versions of this Viewer! Please provide your comments or suggestions .About the ImageryThis viewer provides visual and download access to the USGS LandsatLook "Natural Color" imageproduct archive.BackgroundThe Landsat satellites have been collecting multispectral images of Earth from space since 1972. Each image contains multiple bands of spectral information which may require significant user time, system resources, and technical expertise to obtain a visual result. As a result, the use and access to Landsat data has been historically limited to the scientific and technical user communities.The LandsatLook “Natural Color” image product option was created to provide Landsat imagery in a simple user-friendly and viewer-ready format, based on specific bands that have been selected and arranged to simulate natural color. This type of product allows easy visualization of the archived Landsat image without any need for specialized software or technical expertise.LandsatLook ViewerThe LandsatLook Viewer displays the LandsatLook Natural Color image product for all Landsat 1-8 images in the USGS archive and was designed primarily for visualization purposes.The imagery within this Viewer will be of value to anyone who wants to quickly see the full Landsat record for an area, along with major image features or obvious changes to Earth’s surface through time. An area of interest may be extracted and downloaded as a simple graphic file directly through the viewer, and the original full image tile is also available if needed. Any downloaded LandsatLook image product is a georeferenced file and will be compatible within most GIS and Web mapping applications.If the user needs to perform detailed technical analysis, the full bands of Landsat source data may also be accessed through direct links provided on the LandsatLook Viewer.Image ServicesThe imagery that is visible on this LandsatLook Viewer is based on Web-based ArcGIS image services. The underlying REST service endpoints for the LandsatLook imagery are available at https://landsatlook.usgs.gov/arcgis/rest/services/LandsatLook/ImageServer .Useful linksLandsat- Landsat Mission (USGS)- Landsat Science (NASA)LandsatLook- Product Description- USGS Fact Sheet- LandsatLook image services (REST)Landsat Products- Landsat 8 OLI/TIRS- Landsat 7 ETM+- Landsat 4-5 TM- Landsat 1-5 MSS- Landsat Band DesignationsLandsatLook images are full-resolution files derived from Landsat Level-1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. It is recommended that these images not be used in image analysis.LandsatLook image files are included as options when downloading Landsat scenes from EarthExplorer, GloVis, or the LandsatLook Viewer (See Figure 1).Figure 1. LandsatLook and Level-1 product download optionsLandsatLook Natural Color ImageThe LandsatLook Natural Color image is a .jpg composite of three bands to show a “natural” looking (false color) image. Reflectance values were calculated from the calibrated scaled digital number (DN) image data. The reflectance values were scaled to a 1-255 range using a gamma stretch with a gamma=2.0. This stretch was designed to emphasize vegetation without clipping the extreme values.Landsat 8 OLI = Bands 6,5,4Landsat 7 ETM+ and Landsat 4-5 TM = Bands 5,4,3Landsat 4-5 MSS = Bands 2,4,1Landsat 1-3 MSS = Bands 7,5,4LandsatLook Thermal ImageThe LandsatLook Thermal image is a one-band gray scale .jpg image that displays thermal properties of a Landsat scene. Image brightness temperature values were calculated from the calibrated scaled digital number (DN) image data. An image specific 2 percent clip and a linear stretch to 1-255 were applied to the brightness temperature values.Landsat 8 TIRS = Band 10Landsat 7 ETM+ = Band 61-high gainLandsat 4-5 TM = Band 6Landsat 1-5 MSS = not availableLandsatLook Quality ImageLandsatLook Quality images are 8-bit files generated from the Landsat Level-1 Quality band to provide a quick view of the quality of the pixels within the scene to determine if a particular scene would work best for the user's application. This file includes values representing bit-packed combinations of surface, atmosphere, and sensor conditions that can affect the overall usefulness of a given pixel. Color mapping assignments can be seen in the tables below. For each Landsat scene, LandsatLook Quality images can be downloaded individually in .jpg format, or as a GeoTIFF format file (_QB.TIF) within the LandsatLook Images with Geographic Reference file.Landsat Collection 1 LandsatLook 8-bit Quality Images DesignationsLandsat 8 OLI/TIRSLandsat 7 ETM+, Landsat 4-5 TMLandsat 1-5 MSSColorBitDescriptionBitDescriptionBitDescription 0Designated Fill0Designated Fill0Designated Fill 1Terrain Occlusion1Dropped Pixel1Dropped Pixel 2Radiometric Saturation 2Radiometric Saturation ​2Radiometric Saturation 3Cloud3Cloud3Cloud 4Cloud Shadow4Cloud Shadow 4Unused 5Snow/Ice 5Snow/Ice 5Unused 6Cirrus 6Unused6Unused 7Unused7Unused7UnusedUnusedTable 1. Landsat Collection 1 LandsatLook 8-bit Quality Images Designations LandsatLook Images with Geographic ReferenceThe LandsatLook Image with Geographic Reference is a .zip file bundle that contains the Natural Color, Thermal, and the 8-bit Quality images in georeferenced GeoTiff (.TIF) file format.Figure 2. LandsatLook Natural Color Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013Figure 3. LandsatLook Thermal Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013Figure 4. LandsatLook Quality Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013 with background color set to dark grey. Additional Information About LandsatLook ImagesMany geographic information systems and image processing software packages easily support .jpg images. To create these files, Landsat data is mapped to a 1-255 range, with the fill area set to zero (if a no-data value is set to zero, the compression algorithm may introduce zero-value artifacts into the data area causing very dark data values to be displayed as no-data).

This study uses growth in vegetation during the monsoon season measured from LANDSAT imagery as a proxy for measured rainfall. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG) in southwestern Arizona, USA. The LANDSAT imagery (1986-2011) was downloaded from USGS’s GlobeVis website (http://glovis.usgs.gov/). Change in NDVI was calculated within a set of 2,843 Riparian Area Polygons (RAPs) up to 1 km in length defined in ESRI ArcMap 10.2. Because this work resulted from a Masters Thesis at Colorado State University, data will also be served to the public by CSU at https://dspace.library.colostate.edu/handle/10217/170347.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Several imagery datasets were available for the mapping project. Table 7 lists the types of imagery used in the LEWI mapping project, including the date the imagery was produced and the source of the data. Landsat satellite imagery was acquired from GLOVIS (http://glovis.usgs.gov/). SPOT 4 imagery was downloaded from EarthExplorer (http://edcsns17.cr.usgs.gov/NewEarthExplorer/). Landsat imagery at 30 m resolution consists of 7 bands: 3 visible, 2 mid-infrared, 1 shortwave infrared and 1 thermal band. SPOT 4 imagery consists of 4 bands: 2 visible (10m), 1 shortwave infrared (10m), and 1 mid-infrared (20 m). Imagery used was from the summer 2008 (Landsat) and late fall 2010 (SPOT 4) to provide a phenological contrast useful in differentiating vegetation types. Every homogeneous vegetation type has a unique reflectance which is referred to as a signature. This unique signature is often more apparent and distinct in the infrared wavelengths outside of the human eye visible spectrum, enabling a remote sensing expert to use these unique satellite signature snapshots in time to differentiate various vegetation types.

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.

This is a NASA Landsat 8 image of the South-east of Scotland which was acquired on 06/07/2013. You can view the metadata for this record here: http://glovis.usgs.gov/ImgViewer/showmetadata.cgi?scene_id=LC82040212013187LGN00 The image has 3.8% cloud cover and a quality rating of 9. This image is 32bit and will load in many GIS but may not load in standard image viewers. Downloaded from glovis.usgs.gov portal and manipulated into a true-color image using QGIS 2.2. Bands 2/3/4 where used to make the true-color image. Please reference Landsat NASA as the data source when using this dataset using the following: Landsat8 image (LC82040212013187LGN00), NASA 2013. Aerial or Satellite Imagery. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-05-29 and migrated to Edinburgh DataShare on 2017-02-22.

Backbarrier marsh widths observed from remote sensing of satellite imagery for each of the following islands: Smith, Myrtle, Ship Shoal, Wreck, Cobb, Hog, Paramore, Cedar, Metompkin, and Assawoman Islands. Backbarrier marsh is defined for the purposes of this dataset as those marshes that are connected in a straight line to the barrier island without being interrupted by open water (Open water does not include channels of widths less than 50m). ASTER satellite imagery used for this analysis was acquired from the USGS Global Visualization Viewer web portal (http://glovis.usgs.gov/) (Scene L1B_00305152004155804_2010120215, 2010). Measurements are made every 15m along shore-perpendicular transects. No measurements are made within 1,000m of island "tips" to avoid tidal inlet effects.

This dataset includes five scenes, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin, which were acquired on (yy-mm-dd) 2012-04-05, 2012-04-21, 2012-05-07, 2012-06-24, 2012-07-10. The data were all acquired around 11:50 (BJT) with data product of Level 2.
Landsat ETM+ dataset was downloaded from http://glovis.usgs.gov/.

Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in non- linear ways. This dataset is the synthesis outcome of 22 independent databases from studies of woody plant species recovery as part of the research project entitled "Impacts of landscape structure on secondary tropical forest regeneration". This work aimed to understand the effect of landscape-level disturbance on forest regeneration, specifically through the predictability of trajectories and the recovery rate of these forests. Using a multiscale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density, and basal area are less predictable in landscapes (4-km radius) with intermediate (40-60%) forest cover than in landscapes with high (>60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation in key ecological factors for forest recovery (e.g. seed dispersal, seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only the species richness is positively related to forest cover in relatively small (1-km radius) landscapes. These findings highlight the importance of using a spatially-explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1-4 km radius. Methods We compiled 22 independent databases from studies of woody plant species recovery across five Neotropical countries. Each study included plots established in secondary forest stands of different ages forming a chronosequence. We used taxonomic species richness, density of individuals, and total basal area per plot to evaluate the successional trajectories and recovery rate of vegetation structure. We calculated the extrapolated values of species richness considering the maximum sample coverage (?n = 1.0), following the protocols proposed by Chao & Jost (2012). To assess the predictability of successional trajectories, we related each community attribute (species richness, density of individuals, and basal area) to stand age for each chronosequence (n = 22). We derived the adjusted R²adj values from generalized additive models (GAMs) to use as a proxy for predictability of the successional trajectories. Because R²adj represents the fraction of the variance in the dependent variable that is explained by the independent variable (Crawley 2012), this parameter can be used as a proxy of the predictability of the relationship between each vegetation attribute and stand age. To assess the recovery rate of successional trajectories, we extracted the predicted value of the GAM relating each vegetation attribute and stand age for the fixed age of 15 and 20 years of succession. Then, we calculated the recovery rate values through the equation: [15 ????? ????????? ????? ― 20 ????? ????????? ????? / 5 ] , where 5 corresponds to the age interval in years. This measure was established under the assumption that five years is a short interval of recovery and therefore presents a lineal behavior. Imagery selection and pre-processing For each site we defined a landscape of 10-km radius from the centroid of the set of plots from each study. We choose this radius to standardize landscape size and enable an adequate analysis of landscape-level orest cover for the chronosequence stands in all databases. We obtained Landsat ETM+ and Landsat 8 satellite imagery with 30-m spatial resolution in the multispectral bands from the United States Geological Survey database (USGS, https://glovis.usgs.gov/app). Images were selected based on the location of the landscape of interest, the year of vegetation inventories of each study, and cloudiness. For databases containing data collected in different years, we selected imagery corresponding to the median year of the study and containing less than 10% cloudiness. During pre-processing of images, we corrected for cloud cover by creating both a cloud and a cloud shadow mask using the Cloud Masking tool and fmask function in QGIS 2.18.14 software (Quantum GIS Development Team 2009) as recommended for Landsat TM/ETM +/OLI/TIRS images (Zhu & Woodcock 2012). For each image in a landscape, we conducted panchromatic and spectral image fusion (i.e. pansharpend compound) to improve spatial resolution in the Landsat image using the IHS (Intensity Hue Saturation) method (Zhang 2002). Due to a failure in the Scan Line Corrector (SLC) of the Landsat ETM satellite sensor since May 2003, some images have wedge-shaped gaps on each side, resulting in the loss of ca. 22% of information. To correct this, we applied the Gapfill tool with the ENVI 4.7 program (ENVI 2008) according to the filling technique developed by Scaramuzza et al. (2004). This technique fills gaps in a Landsat image with data from another image and applies a linear transformation to adjust the corrected image based on the standard deviation and mean values of each band of each scene (Scaramuzza et al. 2004). Image classification and estimation of percent forest cover We carried out a supervised classification of images based on training data and validation. We considered three categories of land cover in the classification: native forest cover, agricultural lands, and other land covers (e.g. water, human settlements). Forest cover included both old-growth and late successional second-growth forests because vegetation structure in the later forest type is quite similar to old-growth forests (Poorter et al. 2016; Rozendaal et al. 2019). First, we selected regions of interest based on expert knowledge (i.e. polygons with land cover information) of the raster layer as a reference to classify unknown pixels by comparing the digital value of pixels with training data (Canty 2007). To this end, we used the Support Vector Machine non-parametric method for non-linear data (SVM). This method uses the Kernel class of algorithm (Foody & Mathur 2004; Tamma et al. 2013). Overall satellite image classification accuracy was relatively high (>85%). To reduce the salt and pepper effect, we applied post-classification Majority/Minority analysis. Next, we used the classified vectors to estimate the percent forest cover within each study landscape, using ten differently sized buffers, ranging from 1 to 10 km, at 1 km intervals (corresponding to landscapes of 314.1 to 31,415.6 ha). We next calculated forest cover for each buffer using the Dinamica EGO 4 program (http://csr.ufmg.br/dinamica). All classified vectors were sent to the authors of each database for revision and approval before estimation of the percent forest cover.

Single scene Tri-Decadal Global Landsat Orthorectified MSS, TM, ETM+, and ETM+ Pan-sharpened data, which may be browsed, searched, and downloaded through EarthExplorer or the USGS Global Visualization Viewer (Glovis). Ground control points are fixed, and images have been registered to the Universal Transverse Mercator (UTM) map projection and coordinate system and the World Geodetic System 1984 (WGS84) datum. All image bands have been individually resampled, using a nearest neighbor algorithm. Positional accuracy on the final image product has a Root Mean Square Error of better than 100 meters (MSS) and 50 meters (TM and ETM+). The Landsat data were acquired and processed through a National Aeronautics and Space Administration (NASA) contract with Earth Satellite Corporation, Rockville, Maryland, and are part of NASA's Scientific Data Purchase program.

This dataset includes: remote sensing data _ETM around 2000 in Western China; Data attributes: Pixel Size: 15-meter panchromatic: Band 8 30-meter: Bands 1-5 and Band 7 60-meter: Bands 6H and 6L Resampling Method: Cubic Convolution (CC) Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica. Image Orientation: Map (North Up) The data was downloaded from USGS: http://glovis.usgs.gov/ImgViewer/Java2ImgViewer.html?lat=38.3&lon=78.9&mission=LANDSAT&sensor=ETM. Part of the remote sensing images collected from various research projects. The folder contains ETM 8 band images (* .tif) and header files (* .met). The naming format of image files is row and column number _ETM image logo (7k, 7x, 7t), image acquisition time _ image 6 degree band number _ band number. The data also includes an image index map in shp format.

Los productos satelitales han sido generados por el Laboratorio de Teledetección y se han usado imágenes satelitales ASTER (fecha: 2000 - 2008), LANDSAT 7-8 (fechas: 1999-2013), y SRTM-30. Descargados del Global Visualization Viewe-GLOVIS; http://glovis.usgs.gov/ y EarthExplorer (http://eartheplorer.usgs.gov/).

1. Anaglifo LANDSAT-SRTM-30
2. Anomalías espectrales LANDSAT
3. Mosaico Banda 3 ASTER (estructural)
4. Mosaico RGB 931 (litología)
5. Mosaico de Índices de Cuarzo-Sílice, Carbonatos y Rocas Máficas RGB: Qi,Ci,Mi (litología).

Los datos se presentan en formato GEOTIFF.

Las capas han sido cortadas en cuadrículas (A-2, A-3, A-4, B-1, B-2, B-3, B-5, C-1, C-2, C-3, C-4, C-5, C-6, D-1, D-2, D-3, D-4, D-5, D-6), ligeramente modificadas de las Zonas Catastrales del Perú (INACC, 2004).

El Laboratorio de Tele detección de la Dirección de Laboratorios del INGEMMET ha procesado imágenes satelitales de las zonas afectadas por los últimos eventos relacionados al Niño Costero-2017, con el fin de que se pueda visualizar y pueda ser usado por las instituciones encargadas de la evaluación y gestión de las emergencias. Imágenes del satélite SENTINEL-2 del programa COPERNICUS de la ESA: Posee 13 bandas espectrales desde el visible hasta el infrarrojo de onda corta B2, B3, B4 y B8 (10m) B5, B6, B7, B8a, B11, B12 (20m) B1, B9 y B10 (60m) Imágenes de fechas: ¿ 31 de enero del 2017 ¿ 20 de febrero del 2017 ¿ 12 de marzo del 2017 ¿ 22 de marzo del 2017 ¿ 25 de marzo del 2017 .

Las imágenes se irán integrando a medida que sean adquiridas. Son imágenes de los satélites SENTINEL-1 (radar) y SENTINEL-2 (óptico) del programa COPERNICUS y se han descargado a través de su portal (https://scihub.copernicus.eu/), imágenes del satélite peruano PERUSAT-1 y las imágenes PLEIADES y SPOT obtenidas a través del portal del Centro Nacional de Imágenes Satelitales CNOIS (http://cof.cnois.gob.pe/) e imágenes de los satélites TERRA (Sensor ASTER), LANDSAT-8 (Sensor OLI) del Servicio Geológico de los Estados Unidos (https://glovis.usgs.gov/). Así también se pondrá a disposición productos temáticos extraídos de las mismas imágenes.

Se ha visto por conveniente, poner a disposición estas imágenes en el GEOCATMIN, con apoyo de apoyo de la Oficina de Sistemas de Información (OSI) del INGEMMET, ya que conjuntamente con las demás capas de información existentes, representan una plataforma que permite explotar los beneficios de un Sistema de Información Geográfica, muy útil en las etapas de evaluación de daños, y gestión de las emergencias permitiendo llevar a cabo los análisis correspondientes por las instituciones involucradas en el inventario, gestión de emergencias y reconstrucción. Eventualmente podrán ponerse como servicio WMS para que las demás instituciones puedan acceder a su visualización. El Laboratorio de Teledetección de la Dirección de Laboratorios del INGEMMET ha procesado imágenes satelitales de las zonas afectadas por los últimos eventos relacionados al Niño Costero-2017, con el fin de que se pueda visualizar y pueda ser usado por las instituciones encargadas de la evaluación y gestión de las emergencias.