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This data publication provides access to (1) an archive of maps and statistics on MISR L1B2 GRP data products updated as described in Verstraete et al. (2020, https://doi.org/10.5194/essd-2019-210), (2) a user manual describing this archive, (3) a large archive of standard (unprocessed) MISR data files that can be used in conjunction with the IDL software repository published on GitHub and available from https://github.com/mmverstraete (Verstraete et al., 2019, https://doi.org/10.5281/zenodo.3519989), (4) an additional archive of maps and statistics on MISR L1B2 GRP data products updated as described for eight additional Blocks of MISR data, spanning a broader range of climatic and environmental conditions (between Iraq and Namibia), and (5) a user manual describing this second archive. The authors also make a self-contained, stand-alone version of that processing software available to all users, using the IDL Virtual Machine technology (which does not require an IDL license) from Verstraete et al., 2020: http://doi.org/10.5880/fidgeo.2020.011.(1) The compressed archive 'L1B2_Out.zip' contains all outputs produced in the course of generating the various Figures of the manuscript Verstraete et al. (2020b).Once this archive is installed and uncompressed, 9 subdirectories named Fig-fff-Ttt_Pxxx-Oyyyyyy-Bzzz are created, where fff, tt, xxx, yyyyyy and zzz stand for the Figure number, an optional Table number, Path, Orbit and Block numbers, respectively. These directories contain collections of text, graphics (maps and scatterplots) and binary data files relative to the intermediary, final and ancillary results generated while preparing those Figures.Maps and scatterplots are provided as graphics files in PNG format. Map legends are plain text files with the same names as the maps themselves, but with a file extension '.txt'. Log files are also plain text files. They are generated by the software that creates those graphics files and provide additional details on the intermediary and final results.The processing of MISR L1B2 GRP data product files requires access to cloud masks for the same geographical areas (one for each of the 9 cameras). Since those masks are themselves derived from the L1B2 GRP data and therefore also contain missing data, the outcomes from updating the RCCM data products, as described in Verstraete et al. (2020, https://doi.org/10.5194/essd-12-611-2020), are also included in this archive.The last 2 subdirectories contain the outcomes from the normal processing of the indicated data files, as well as those generated when additional missing data are artificially inserted in the input files for the purpose of assessing the performance of the algorithms.(2) The document 'L1B2_Out.pdf' provides the User Manual to install and explore the compressed archive 'L1B2_Out.zip'.(3) The compressed archive 'L1B2_input_68050.zip' contains MISR L1B2 GRP and RCCM data for the full Orbit 68050, acquired on 3 October 2012, as well as the corresponding AGP file, which is required by the processing system to update the radiance product. This archive includes data for a wide range of locations, from Russia to north-west Iran, central and eastern Iraq, Saudi Arabia, and many more countries along the eastern coast of the African continent. It is provided to allow users to analyze actual data with the software package mentioned above, without needing to download MISR data from the NASA ASDC web site.(4) The compressed archive 'L1B2_Suppl.zip' contains a set of results similar to the archive 'L1B2_Out.zip' mentioned above, for four additional sites, spanning a much wider range of geographical, climatic and ecological conditions: these are covering areas in Iraq (marsh and arid lands), Kenya (agriculture and tropical forests), South Sudan (grasslands) and Namibia (coastal desert and Atlantic Ocean). Two of them involve largely clear scenes, and the other two include clouds. The last case also includes a test to artificially introduce missing data over deep water and clouds, to demonstrate the performance of the procedure on targets other than continental areas. Once uncompressed, this new archive expands into 8 subdirectories and takes up 1.8 GB of disk space, providing access to about 2,900 files.(5) The companion user manual L1B2_Suppl.pdf, describing how to install, uncompress and explore those additional files.
This data publication provides access to (1) an archive of maps and statistics on MISR RCCM data products updated as described in Verstraete et al. (2020, https://doi.org/10.5194/essd-2019-77), (2) a user manual describing this archive, and (3) a large archive of standard (unprocessed) MISR data files that can be used in conjunction with the IDL software repository published on GitHub and available from https://github.com/mmverstraete (Verstraete et al., 2019, https://doi.org/10.5281/zenodo.3240018). The authors also make a self-contained, stand-alone version of that processing software available to all users, using the IDL Virtual Machine technology (which does not require an IDL license) from Verstraete et al., 2020: http://doi.org/10.5880/fidgeo.2020.008.(1) The compressed archive 'RCCM_Out.zip' contains all outputs produced in the course of generating the various Figures of the manuscript Verstraete et al. (2020).Once this archive is installed and uncompressed, 8 subdirectories named Pxxx-Oyyyyyy-Bzzz are created, where xxx, yyyyyy and zzz stand for the Path, Orbit and Block numbers, respectively. At a minimum, those directories contain a 'GM/RCCM' subdirectory that includes a collection of 75 text, graphics (maps in PNG format) and binary data files relative to the intermediary, final and ancillary results generated while preparing those Figures.The 3 subdirectories for Orbit 92981, as well as the one dedicated to Orbit 78010, contain a subdirectory labeled 'GM/RCCM__[cc]-[l1]-[l2]' instead, where [cc] indicates the camera whose data have been modified to evaluate the performance of the algorithms, and [l1] and [l2] indicate the first and last line that is artificially deemed to contain missing data: those subdirectories include collections of 93 text, graphics and binary data files; they include maps of the RCCM data product before and after this artificial modification.Two of those subdirectories (for Orbits 2111 and 78171) also contain a subdirectory 'GM/L1B2', which hosts a number of text and graphics (maps in PNG format) files obtained by plotting the top of atmosphere bidirectional reflectance measured by the MISR instrument. These images can be used to verify visually the state of the cloud cover in the corresponding cameras and spectral bands.Each map is accompanied by a similarly named text file providing the legend of the corresponding map. Those subdirectories also contain log files that report on the success rate of the replacement algorithm as well as accuracy of the replacement procedure, when some data are artificially removed.(2) The document 'RCCM_Out.pdf' provides the User Manual to install and explore the compressed archive 'RCCM_Out.zip'.(3) The compressed archive 'RCCM_input_68050.zip' contains MISR L1B2 GRP and RCCM data for the full Orbit 68050, acquired on 3 October 2012. This file includes data for a wide range of locations, from Russia to north-west Iran, central and eastern Iraq, Saudi Arabia, and many more countries along the eastern coast of the African continent. It is provided to allow users to analyze actual data with the software package mentioned above, without needing to download MISR data from the NASA ASDC web site.
(1) The compressed archive 'RCCM_Soft_Win.zip' includes the self-contained, executable IDL Virtual Machine software package that allows processing MISR RCCM data without requiring an IDL license. Users who do have access to an IDL license are encouraged to obtain the necessary source codes from the GitHub web site https://github.com/mmverstraete (Verstraete et al., 2019, https://doi.org/10.5281/zenodo.3240018) and to incorporate those functions in their own custom programs.(2) The document 'RCCM_Soft_Win.pdf' provides the User Manual to install and use the software package 'RCCM_Soft_Win.zip' on a PC running under the MS Windows operating system.In addition, the authors provide the test input data archive 'RCCM_input_68050.zip', available from Verstraete et al., 2020, http://doi.org/10.5880/fidgeo.2020.004, to allow users to explore for themselves the various steps of this missing data replacement process in actual MISR RCCM files.Background information:The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on- board the NASA Terra platform, launched on 18 December 1999. It features 9 cameras pointing at various angles along the track of the platform, each measuring the amount of solar radiation reflected by the Earth in 4 spectral bands. MISR started acquiring observations on 24 February 2000, and is still operating as of this writing, therefore providing almost 20 years of continuous global observations.One of the most basic data products generated by NASA after the initial pre-processing of MISR raw data is the Level 1B2 Georectified Radiance Product (GRP). This data product intermittently contains missing data that are often due to a temporary overload of the on- board computer. This process, which results in the dropping of lines of measurements while the computer resets itself, tends to occur especially when the MISR instrument is switched from the default Global Mode (GM) to the occasional Local Mode (LM) of operation, and conversely. As a result, those missing lines are unevenly distributed and tend to cluster around particular sites and dates.
(1) The compressed archive 'L1B2 Soft Win.zip' includes the self-contained, executable IDL Virtual Machine software package that allows processing MISR L1B2 data without requiring an IDL license. Users who do have access to an IDL license are encouraged to obtain the necessary source codes from the GitHub web site https://github.com/mmverstraete (Verstraete et al., 2019, https://doi.org/10.5281/zenodo.3519989) and to incorporate those functions in their own custom programs.(2) The document 'L1B2_Soft_Win.pdf' provides the User Manual to install and use the software package 'L1B2_Soft_Win.zip' on a PC running under the MS Windows operating system.In addition, the authors provide the test input data archive 'L1B2_input_68050.zip', available from Verstraete et al., 2020, http://doi.org/10.5880/fidgeo.2020.012, to allow users to explore for themselves the various steps of this missing data replacement process in actual MISR L1B2 files.Background information:The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on- board the NASA Terra platform, launched on 18 December 1999. It features 9 cameras pointing at various angles along the track of the platform, each measuring the amount of solar radiation reflected by the Earth in 4 spectral bands. MISR started acquiring observations on 24 February 2000, and is still operating as of this writing, therefore providing 20 years of continuous global observations.One of the most basic data products generated by NASA after the initial pre-processing of MISR raw data is the Level 1B2 Georectified Radiance Product (GRP). This data product intermittently contains missing data that are often due to a temporary overload of the on- board computer. This process, which results in the dropping of lines of measurements while the computer resets itself, tends to occur especially when the MISR instrument is switched from the default Global Mode (GM) to the occasional Local Mode (LM) of operation, and conversely. As a result, those missing lines are unevenly distributed and tend to cluster around particular sites and dates.
Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories.To derive such multi-temporal landslide inventories, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from multi-sensor optical satellite time series data (Behling et al., 2016). The developed approach represents an extension of the original methodology (Behling et al., 2014, Behling and Roessner, 2020) and facilitates the integration of optical time series data acquired by different satellite systems. The goal of combining satellite data originating from variable sensor systems has been the establishment of longest possible time series for retrospective systematic assessment of multi-temporal landslide activity at highest possible temporal and spatial resolution. We applied the developed approach to a 2500 km² study area in Southern Kyrgyzstan using an optical satellite database acquired by the Landsat TM/ETM+, SPOT 1/5, IRS1-C LISSIII, ASTER, and RapidEye sensor systems covering a time period between 1986 and 2013. A multi-temporal landslide inventory from 2009-2013 derived from RapidEye satellite time series data is available as separate publications (Behling et al., 2014; Behling and Roessner, 2020).The resulting systematic multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2016), which describes the extended spatiotemporal landslide mapper in detail. This multi-sensor approach prioritizes most suitable images within the available multi-sensor satellite time series using parameters, such as spatial resolution, cloud coverage, similarity of sensor characteristics and seasonality related to vegetation characteristics with the goal of establishing a robust back-bone time series for initial detection of possible landslide objects. In a second step, this initial analysis gets more refined in order to achieve the best possible approximation of the date of landslide occurrence. For a more detailed description of the methodology of the extended spatiotemporal landslide mapper, please see Behling et al. (2016).In general, this landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to identify freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops).The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file 2020-002_Behling_et-al_2016_landslide_inventory_SouthernKyrgyzstan_1986_2013.zip and are described in more detail in the associated data description.
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