Cesar Alvites, Hannah O'Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani, Erika Bazzato
{"title":"树冠高度绘图仪:结合 GEDI 和多源数据预测全球树冠高度的谷歌地球引擎应用程序","authors":"Cesar Alvites, Hannah O'Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani, Erika Bazzato","doi":"10.1016/j.envsoft.2024.106268","DOIUrl":null,"url":null,"abstract":"Spatially and temporally discontinuous canopy height footprints collected by NASA's GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at <ce:inter-ref xlink:href=\"https://ee-calvites1990.projects.earthengine.app/view/ch-gee\" xlink:type=\"simple\">https://ee-calvites1990.projects.earthengine.app/view/ch-gee</ce:inter-ref>, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km<ce:sup loc=\"post\">2</ce:sup> at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"11 1","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data\",\"authors\":\"Cesar Alvites, Hannah O'Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani, Erika Bazzato\",\"doi\":\"10.1016/j.envsoft.2024.106268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spatially and temporally discontinuous canopy height footprints collected by NASA's GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at <ce:inter-ref xlink:href=\\\"https://ee-calvites1990.projects.earthengine.app/view/ch-gee\\\" xlink:type=\\\"simple\\\">https://ee-calvites1990.projects.earthengine.app/view/ch-gee</ce:inter-ref>, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km<ce:sup loc=\\\"post\\\">2</ce:sup> at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.envsoft.2024.106268\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.envsoft.2024.106268","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data
Spatially and temporally discontinuous canopy height footprints collected by NASA's GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at https://ee-calvites1990.projects.earthengine.app/view/ch-gee, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km2 at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.