Esta colección contiene resultados calculados previamente a partir de la ejecución del algoritmo de detección y clasificación de cambios continuos (CCDC) en 20 años de datos de reflectancia superficial de Landsat. El CCDC es un algoritmo de detección de puntos de inflexión que usa la adaptación armónica con un umbral dinámico de RMSE para detectar puntos de inflexión en los datos de series temporales. El…
Este conjunto de datos contiene mapas de la ubicación y la distribución temporal del agua superficial de 1984 a 2021 y proporciona estadísticas sobre el alcance y el cambio de esas superficies de agua. Para obtener más información, consulta el artículo de la revista asociado: Cartografía de alta resolución de las aguas superficiales globales y sus …
El producto de datos de áreas quemadas de la versión 6.1 de MCD64A1 combinado de Terra y Aqua es un producto mensual global con cuadrícula de 500 m que contiene información de calidad y de áreas quemadas por píxel. El enfoque de mapeo de áreas quemadas MCD64A1 emplea imágenes de reflectancia superficial de MODIS de 500 m junto con observaciones de incendios activos de MODIS de 1 km. El algoritmo…
Este producto forma parte del paquete de datos del Sistema de supervisión de cambios en el paisaje (LCMS). Muestra el cambio modelado por el LCMS, la cobertura terrestre o las clases de uso de la tierra de cada año y abarca los Estados Unidos contiguos (CONUS), así como las áreas fuera de CONUS (OCONUS), incluidas Alaska (AK), Puerto …
El producto de datos de la versión 1 del área quemada (VNP64A1) del conjunto de radiómetros de imágenes infrarrojas visibles (VIIRS) de la NASA de la asociación nacional de órbita polar de Suomi (Suomi NPP) es un producto mensual global con cuadrícula de 500 m que contiene información de calidad y área quemada por píxel. El enfoque de mapeo de áreas quemadas de VNP64 emplea VIIRS de 750 m …
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Falta la información que necesito","missingTheInformationINeed","thumb-down"],["Muy complicado o demasiados pasos","tooComplicatedTooManySteps","thumb-down"],["Desactualizado","outOfDate","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Problema con las muestras o los códigos","samplesCodeIssue","thumb-down"],["Otro","otherDown","thumb-down"]],[],[[["\u003cp\u003eThis webpage features curated datasets focused on land change detection using satellite imagery.\u003c/p\u003e\n"],["\u003cp\u003eDatasets provide insights into surface water changes, burned areas, and land cover/use transformations.\u003c/p\u003e\n"],["\u003cp\u003eData sources include Landsat, MODIS, VIIRS, and algorithms like CCDC for analyzing temporal patterns.\u003c/p\u003e\n"],["\u003cp\u003eGlobal and regional coverage is offered, with examples such as the USFS Landscape Change Monitoring System.\u003c/p\u003e\n"],["\u003cp\u003eThe collection spans multiple decades, enabling long-term change analysis and monitoring efforts.\u003c/p\u003e\n"]]],[],null,["# Datasets tagged change-detection in Earth Engine\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Google Global Landsat-based CCDC Segments (1999-2019)](/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1) |\n | This 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 ... |\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-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### JRC Global Surface Water Mapping Layers, v1.4](/earth-engine/datasets/catalog/JRC_GSW1_4_GlobalSurfaceWater) |\n | This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its ... |\n | [change-detection](/earth-engine/datasets/tags/change-detection) [geophysical](/earth-engine/datasets/tags/geophysical) [google](/earth-engine/datasets/tags/google) [jrc](/earth-engine/datasets/tags/jrc) [landsat-derived](/earth-engine/datasets/tags/landsat-derived) [surface](/earth-engine/datasets/tags/surface) |\n\n-\n\n |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### MCD64A1.061 MODIS Burned Area Monthly Global 500m](/earth-engine/datasets/catalog/MODIS_061_MCD64A1) |\n | The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500m product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500m MODIS Surface Reflectance imagery coupled with 1km MODIS active fire observations. The algorithm ... |\n | [burn](/earth-engine/datasets/tags/burn) [change-detection](/earth-engine/datasets/tags/change-detection) [fire](/earth-engine/datasets/tags/fire) [geophysical](/earth-engine/datasets/tags/geophysical) [global](/earth-engine/datasets/tags/global) [mcd64a1](/earth-engine/datasets/tags/mcd64a1) |\n\n-\n\n |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### USFS Landscape Change Monitoring System v2024.10 (CONUS and OCONUS)](/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2024-10) |\n | This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS-modeled change, land cover, and/or land use classes for each year and covers the Conterminous United States (CONUS) as well as areas outside the CONUS (OCONUS) including Alaska (AK), Puerto ... |\n | [change-detection](/earth-engine/datasets/tags/change-detection) [forest](/earth-engine/datasets/tags/forest) [gtac](/earth-engine/datasets/tags/gtac) [landcover](/earth-engine/datasets/tags/landcover) [landuse](/earth-engine/datasets/tags/landuse) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### VNP64A1: Burned Area Monthly L4 Global 500m SIN Grid](/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP64A1) |\n | The daily Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500m product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750m VIIRS ... |\n | [burn](/earth-engine/datasets/tags/burn) [change-detection](/earth-engine/datasets/tags/change-detection) [fire](/earth-engine/datasets/tags/fire) [land](/earth-engine/datasets/tags/land) [nasa](/earth-engine/datasets/tags/nasa) [noaa](/earth-engine/datasets/tags/noaa) |"]]
