[null,null,[],[[["\u003cp\u003eThe Google Global Landsat-based CCDC Segments dataset provides precomputed results of the Continuous Change Detection and Classification (CCDC) algorithm applied to 20 years of Landsat data (1999-2019).\u003c/p\u003e\n"],["\u003cp\u003eCCDC detects breakpoints in time-series data using harmonic fitting with a dynamic RMSE threshold, identifying changes in land cover and land use.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset covers landmasses between -60° and +85° latitude at a 30-meter resolution, derived from Landsat 5, 7, and 8 Collection-1, Tier-1 surface reflectance data.\u003c/p\u003e\n"],["\u003cp\u003eEach pixel contains information about detected breakpoints, including start and end dates, magnitude of change, and harmonic model coefficients for various spectral bands.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available in Earth Engine and is intended for research, education, and non-profit use under a CC-BY-4.0 license.\u003c/p\u003e\n"]]],["The dataset, available from 1999-01-01 to 2020-01-01 via Google Earth Engine, provides precomputed results from the Continuous Change Detection and Classification (CCDC) algorithm applied to 20 years of Landsat data. Using harmonic fitting, CCDC identifies breakpoints in time-series data across landmasses between -60° and +85° latitude. The output, at 30-meter resolution, includes start, end, and breakpoint dates, observation counts, change probabilities, model coefficients, RMSE, and breakpoint magnitudes for various spectral bands.\n"],null,["# Google Global Landsat-based CCDC Segments (1999-2019)\n\nDataset Availability\n: 1999-01-01T00:00:00Z--2020-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Google](https://earthengine.google.com/)\n\nTags\n:\n[change-detection](/earth-engine/datasets/tags/change-detection) [google](/earth-engine/datasets/tags/google) [landcover](/earth-engine/datasets/tags/landcover) [landsat-derived](/earth-engine/datasets/tags/landsat-derived) [landuse](/earth-engine/datasets/tags/landuse) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) \n\n#### Description\n\nThis collection contains precomputed results from running the\nContinuous Change Detection and Classification (CCDC) algorithm on\n20 years of Landsat surface reflectance data. CCDC is a break-point\nfinding algorithm that uses harmonic fitting with a dynamic RMSE\nthreshold to detect breakpoints in time-series data.\n\nThe dataset was created from the Landsat 5, 7, and 8 Collection-1, Tier-1,\nsurface reflectance time series, using all daytime images between 1999-01-01\nand 2019-12-31. Each image was preprocessed to mask pixels identified as\ncloud, shadow, or snow (according to the 'pixel_qa' band), saturated pixels,\nand pixels with an atmospheric opacity \\\u003e 300 (as identified by the\n'sr_atmos_opacity' and 'sr_aerosol' bands). Pixels repeated in\nnorth/south scene overlap were deduplicated. The results were\noutput in 2-degree tiles for all landmasses between -60° and +85° latitude.\nThe images are suitable to simply mosaic() into one global image.\n\nThe CCDC algorithm was run with the default algorithm parameters except for\nthe dateFormat:\n\n- tmaskBands: \\['green', 'swir'\\]\n- minObservations: 6\n- chiSquareProbability: 0.99\n- minNumOfYearsScaler: 1.33\n- dateFormat: 1 (fractional year)\n- lambda: 20\n- maxIterations: 25000\n\nEach pixel in the output is encoded using variable length arrays. The outer\nlength of each array (axis 0) corresponds to the number of breakpoints\nfound at that location. The coefs bands contain 2-D arrays, where each inner\narray contains the scaling factors for the 8 terms in the linear harmonic\nmodel, in the order: \\[offset, t, cos(ωt), sin(ωt), cos(2ωt),\nsin(2ωt), cos(3ωt), sin(3ωt)\\], where ω = 2Π. The\nmodels are scale to produce refelectance units (0.0 - 1.0) for the optical\nbands and degrees (K) / 100.0 for the thermal band.\n\nNote that since the output bands are arrays and can only be downsampled\nusing a SAMPLE pyramiding policy. At lower zoom levels, the\nresults are usually no longer representative of the full-resolution data,\nand, for instance, tile boundaries can be seen due to the downsampled masks.\nIt's therefore not recommended to use this dataset at resolutions less than\n240m/pixel.\n\nThere are no current plans to add post-2019 assets to this dataset.\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Pixel Size | Description |\n|-------------------|------------|------------------------------------------------------------------------------------------------------------------------------------|\n| `tStart` | meters | 1-D Array containing the date of the start of each segment (fractional year). |\n| `tEnd` | meters | 1-D Array containing the date of the end of each segment (fractional year). |\n| `tBreak` | meters | 1-D Array containing the date of the detected breakpoint of each segment (fractional year). |\n| `numObs` | meters | 1-D Array containing the number of observations found in each segment. |\n| `changeProb` | meters | A pseudo-probability of the detected breakpoint being real. |\n| `BLUE_coefs` | meters | 2-D array containing harmonic model coefficients for the blue band, for each segment. |\n| `GREEN_coefs` | meters | 2-D array containing harmonic model coefficients for the green band, for each segment. |\n| `RED_coefs` | meters | 2-D array containing harmonic model coefficients for the red band, for each segment. |\n| `NIR_coefs` | meters | 2-D array containing harmonic model coefficients for the near-infrared band, for each segment. |\n| `SWIR1_coefs` | meters | 2-D array containing harmonic model coefficients for the shortwave-infrared (1.55μm-1.75μm) band, for each segment. |\n| `SWIR2_coefs` | meters | 2-D array containing harmonic model coefficients for the shortwave-infrared (2.09μm-2.35μm) band, for each segment. |\n| `TEMP_coefs` | meters | 2-D array containing harmonic model coefficients for the thermal band, for each segment. |\n| `BLUE_rmse` | meters | 1-D array containing the RMSE of the model for the blue band, for each segment. |\n| `GREEN_rmse` | meters | 1-D array containing the RMSE of the model for the green band, for each segment. |\n| `RED_rmse` | meters | 1-D array containing the RMSE of the model for the red band, for each segment. |\n| `NIR_rmse` | meters | 1-D array containing the RMSE of the model for the near-infrared band, for each segment. |\n| `SWIR1_rmse` | meters | 1-D array containing the RMSE of the model for the the shortwave-infrared (1.55μm-1.75μm) band, for each segment. |\n| `SWIR2_rmse` | meters | 1-D array containing the RMSE of the model for the shortwave-infrared (2.09μm-2.35μm) band, for each segment. |\n| `TEMP_rmse` | meters | 1-D array containing the RMSE of the model for the thermal band, for each segment. |\n| `BLUE_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the blue band, for each segment. |\n| `GREEN_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the green band, for each segment. |\n| `RED_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the red band, for each segment. |\n| `NIR_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the near-infrared band, for each segment. |\n| `SWIR1_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the shortwave-infrared-1 (1.55μm-1.75μm) band, for each segment. |\n| `SWIR2_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the shortwave-infrared-2 (2.09μm-2.35μm) band, for each segment. |\n| `TEMP_magnitude` | meters | 1-D array containing the magnitude of the detected breakpoint for the thermal band, for each segment. |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n**Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine) \n[Google Global Landsat-based CCDC Segments (1999-2019)](/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1) \nThis collection contains precomputed results from running the Continuous Change Detection and Classification (CCDC) algorithm on 20 years of Landsat surface reflectance data. CCDC is a break-point finding algorithm that uses harmonic fitting with a dynamic RMSE threshold to detect breakpoints in time-series data. The dataset was created from the ... \nGOOGLE/GLOBAL_CCDC/V1, change-detection,google,landcover,landsat-derived,landuse,landuse-landcover \n1999-01-01T00:00:00Z/2020-01-01T00:00:00Z \n-60 -180 72 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://earthengine.google.com/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1)"]]
