La Soufrière volcano (Saint Vincent) Fusion of Pleiades (2014, 2 m) and Copernicus (2018, 30 m) digital elevation models

Release 1.1 For Zenodo =========================== Authors Raphaël GRANDIN1 and Arthur DELORME2 1 : Université de Paris, Institut de Physique du Globe de Paris. Email: [e-mail] 2: Université de Paris, Institut de Physique du Globe de Paris. Email: [e-mail] =========================== 1. Collection Overview This collection contains a digital surface model (DSM) of the Soufrière volcano (Saint Vincent) calculated from Pleiades images acquired in 2014, hole-filled with the 2018 Copernicus digital elevation model (DEM). The Pleiades dataset consists in three images acquired in 2014: * image A = `DS_PHR1A_201407041445368_FR1_PX_W062N13_1009_00974` * image B = `DS_PHR1A_201409271441564_FR1_PX_W062N13_1009_00974` * image C = `DS_PHR1A_201410161445303_SE1_PX_W062N13_1009_00974` By combining these three images, three different digital surface models (DSMs) were computed (AB, BC and ABC). The three Pleiades DSMs were then merged together, taking advantage of the different cloud cover in the three pairs / triplets. Areas that are not visible in any of the three DSMs due to clouds are subsequently filled with the Copernicus DEM. The collection includes five folders : 1. **Report**: * "SaintVincent_DEM_Pleiades_Copernicus_fusion_Grandin_Delorme_2021.pdf": report 2. **DSM**: the merged DSM in Geotiff format: * "SaintVincent_Pleiades_Copernicus_merged.tif": the merged Pleiades DSM + Copernicus DEM 3. **Data**: the three Pleiades DSMs in Geotiff format: * "SaintVincent_Pleiades_AB_dsm.tif": the Pleiades DSM computed from images A and B * "SaintVincent_Pleiades_AB_cor.tif": the correlation score betwen images A and B * "SaintVincent_Pleiades_BC_dsm.tif": the Pleiades DSM computed from images B and C * "SaintVincent_Pleiades_BC_cor.tif": the correlation score betwen images B and C * "SaintVincent_Pleiades_ABC_dsm.tif": the Pleiades DSM computed from images A, B and C * "SaintVincent_Pleiades_ABC_cor.tif": the correlation score betwen images A, B and C 4. **KMZ**: quickviews in KMZ format: * SaintVincent_Pleiades_Copernicus_merged_color.kmz": the merged Pleiades DSM + Copernicus DEM in KMZ format (color version) * "SaintVincent_Pleiades_Copernicus_merged_shaded.kmz": the merged Pleiades DSM + Copernicus DEM in KMZ format (hillshade version) 5. **Figures**: the figures shown in the report =========================== 2. Dataset Acknowledgement Access to Pleiades data was granted through the DINAMIS program (https://dinamis.teledetection.fr/) via project ID 2021-055-Sci (PI: Raphaël Grandin, IPGP). This work was supported by public funds received in the framework of GEOSUD, a project (ANR-10-EQPX-20) of the program "Investissements d’Avenir" managed by the French National Research Agency. Calculation of the Pleiades DSM used the S-CAPAD cluster of IPGP. =========================== 3. Dataset Attribution This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial). Attribution required for copies and derivative works: The underlying dataset from which this work has been derived includes Pleiades material ©CNES (2014), distributed by AIRBUS DS, and EO material ©CCME (2018), provided under COPERNICUS by the European Union and ESA, all rights reserved. =========================== 4. Dataset Citation Grandin and Delorme (2021). “La Soufrière volcano (Saint Vincent) – Fusion of Pleiades (2014, 2 m) and Copernicus (2018, 30 m) digital elevation models”. Dataset distributed on Zenodo: https://doi.org/10.5281/zenodo.4668734 Dataset distributed on GitHub: https://github.com/RaphaelGrandin/SaintVincent_DEM_Pleiades_Copernicus @misc{grandindelorme2021, title={{La Soufriere volcano (Saint Vincent) -- Fusion of Pleiades (2014, 2 m) and Copernicus (2018, 30 m) digital elevation models}}, author={Grandin, Raphael and Delorme, Arthur}, year={2021}, howpublished={Dataset on Zenodo}, doi={10.5281/zenodo.4668734} } =========================== 5. Collection Location Country: Saint Vincent and the Grenadines Bounding box: <north>13.387947</north> <south>13.293314</south> <east>-61.106448</east> <west>-61.244318</west> =========================== 6. Method Three digital surface models (DSMs) are computed from panchromatic images from the Pleiades satellite, whose ground sampling distance (GSD) is 0.5 m. As no stereoscopic acquisition is available on the volcano area in the archive catalog, the processed images are monoscopic acquisitions, taken on three dates: 04/07/2014 (image A, [Figure 1](Figures/DS_PHR1A_201407041445368_FR1_PX_W062N13_1009_00974.png?raw=true)), 27/09/2014 (image B, [Figure 2](Figures/DS_PHR1A_201409271441564_FR1_PX_W062N13_1009_00974.png)) and 16/10/2014 (image C, [Figure 3](Figures/DS_PHR1A_201410161445303_SE1_PX_W062N13_1009_00974.png)). This dataset, with images of different dates, which are partially covered by clouds, is not ideal for producing a DSM. The idea is therefore to produce several DSMs with different combinations of images, then to merge these DSMs, finally filling any hole by interpolation or with an external DSM, namely the Copernicus DEM (https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198). Considering the base-to-height ratio of the different pairs of images, three combinations of images seem prone to provide satisfactory results: A-B, B-C and A-B-C. Images are processed using the open source photogrammetry software MicMac (Rupnik et al., 2017). First, the geometry model of each image is translated into MicMac format (Convert2GenBundle command). Then tie points between images are extracted from each possible pair of images (Tapioca). A bundle block adjustment is performed between the three images to refine the geometry models (Campari). Finally, the three DSMs are computed separately, by correlation between images A-B (1), B-C (2) and A-B-C (3) (Malt). The GSD of the DSMs is 0.5 m, thanks to MicMac multi-scale approach and regularization criterion. They are downsampled to 2 m to reduce the signal to noise ratio ([Figure 4a](Figures/AB_dsm_raw.png), [Figure 4c](Figures/BC_dsm_raw.png), [Figure 4e](Figures/ABC_dsm_raw.png)). Each DSM comes with a correlation score for each pixel, which can be used to remove pixels whose correlation score is below a certain threshold ([Figure 4b](Figures/AB_cor_raw.png), [Figure 4d](Figures/BC_cor_raw.png), [Figure 4f](Figures/ABC_cor_raw.png)). The areas masked by clouds in the Pleiades DSM are then filled with the digital elevation model from Coper- nicus. A threshold on the correlation score is used to build a cloud mask. Finally, the three hole-filled DSMs are merged using the correlation score as a weighting factor ([Figure 5](Figures/Merged.png)). =========================== References [1] Ewelina Rupnik, Mehdi Daakir, and Marc Pierrot Deseilligny. Micmac–a free, open-source solution for pho- togrammetry. Open Geospatial Data, Software and Standards, 2(1):1–9, 2017. [Link]Zenodo 1.1版发布 =========================== 作者：拉斐尔·格兰丹（Raphaël GRANDIN）¹、亚瑟·德洛姆（Arthur DELORME）² ¹ 巴黎大学，巴黎地球物理研究所。邮箱：[e-mail] ² 巴黎大学，巴黎地球物理研究所。邮箱：[e-mail] =========================== 一、数据集概况 本数据集包含圣文森特苏弗里耶尔火山的数字表面模型（DSM），该模型由2014年获取的昴星团（Pleiades）卫星影像计算得到，并使用2018年哥白尼数字高程模型（DEM）填补孔洞。 本次昴星团数据集包含2014年获取的三幅影像： * 影像A：`DS_PHR1A_201407041445368_FR1_PX_W062N13_1009_00974` * 影像B：`DS_PHR1A_201409271441564_FR1_PX_W062N13_1009_00974` * 影像C：`DS_PHR1A_201410161445303_SE1_PX_W062N13_1009_00974` 通过组合这三幅影像，可计算得到三组不同的数字表面模型（AB、BC及ABC组合）。随后利用三组影像对/三幅影像组合的不同云覆盖情况，将这三幅昴星团卫星DSM进行融合。对于因云层遮挡而在三组DSM中均不可见的区域，则使用哥白尼DEM进行填补。 本数据集包含5个文件夹： 1. **报告（Report）**： * `SaintVincent_DEM_Pleiades_Copernicus_fusion_Grandin_Delorme_2021.pdf`：数据集研究报告 2. **DSM数据**：融合后的DSM，格式为Geotiff： * `SaintVincent_Pleiades_Copernicus_merged.tif`：融合昴星团DSM与哥白尼DEM得到的最终模型 3. **原始数据（Data）**：三组昴星团DSM数据，格式为Geotiff： * `SaintVincent_Pleiades_AB_dsm.tif`：由影像A与B计算得到的昴星团DSM * `SaintVincent_Pleiades_AB_cor.tif`：影像A与B的相关性评分 * `SaintVincent_Pleiades_BC_dsm.tif`：由影像B与C计算得到的昴星团DSM * `SaintVincent_Pleiades_BC_cor.tif`：影像B与C的相关性评分 * `SaintVincent_Pleiades_ABC_dsm.tif`：由影像A、B与C计算得到的昴星团DSM * `SaintVincent_Pleiades_ABC_cor.tif`：影像A、B与C的相关性评分 4. **KMZ预览文件**：KMZ格式的快速预览图： * `SaintVincent_Pleiades_Copernicus_merged_color.kmz`：融合后的DSM的彩色KMZ预览图 * `SaintVincent_Pleiades_Copernicus_merged_shaded.kmz`：融合后的DSM的晕渲KMZ预览图（Hillshade版） 5. **图表（Figures）**：研究报告中提及的所有图表 =========================== 二、数据集致谢 昴星团卫星影像的访问权限通过DINAMIS项目（https://dinamis.teledetection.fr/）提供，项目编号为2021-055-Sci（项目负责人：拉斐尔·格兰丹，巴黎地球物理研究所）。本研究获得法国国家科研署（ANR）管理的“未来投资计划”框架下GEOSUD项目（ANR-10-EQPX-20）的公共资金支持。昴星团DSM的计算工作依托巴黎地球物理研究所的S-CAPAD集群完成。 =========================== 三、数据集归属声明 本数据集采用知识共享署名-非商业性使用4.0国际许可协议（CC BY-NC 4.0）进行授权。复制或演绎本数据集内容需注明原作者：本数据集的原始数据源包含法国国家空间研究中心（CNES）2014年发布的昴星团卫星影像（由空客防务与航天公司分发），以及欧盟与欧洲空间局（ESA）通过哥白尼计划提供的2018年地球观测（EO）数据（©CCME 2018），保留所有权利。 =========================== 四、数据集引用规范 格兰丹与德洛姆（2021）。《圣文森特苏弗里耶尔火山——2014年昴星团卫星（2米分辨率）与2018年哥白尼计划（30米分辨率）数字高程模型融合》。Zenodo平台发布数据集：https://doi.org/10.5281/zenodo.4668734 GitHub平台发布数据集：https://github.com/RaphaelGrandin/SaintVincent_DEM_Pleiades_Copernicus 引用格式： bibtex @misc{grandindelorme2021, title={{La Soufriere volcano (Saint Vincent) -- Fusion of Pleiades (2014, 2 m) and Copernicus (2018, 30 m) digital elevation models}}, author={Grandin, Raphael and Delorme, Arthur}, year={2021}, howpublished={Dataset on Zenodo}, doi={10.5281/zenodo.4668734} } =========================== 五、数据集采集位置 国家：圣文森特和格林纳丁斯 Bounding box范围： * 北纬：13.387947 * 南纬：13.293314 * 东经：-61.106448（即西经61.106448°） * 西经：-61.244318（即西经61.244318°） =========================== 六、数据处理方法 本数据集从昴星团卫星的全色影像中计算得到三组数字表面模型，其地面采样距离（GSD）为0.5米。由于档案目录中无该火山区域的立体采集影像，本次处理采用的影像分别为三个日期的单视采集影像：2014年7月4日（影像A，[图1](Figures/DS_PHR1A_201407041445368_FR1_PX_W062N13_1009_00974.png?raw=true)）、2014年9月27日（影像B，[图2](Figures/DS_PHR1A_201409271441564_FR1_PX_W062N13_1009_00974.png)）及2014年10月16日（影像C，[图3](Figures/DS_PHR1A_201410161445303_SE1_PX_W062N13_1009_00974.png)）。 由于该数据集的影像分不同日期获取且均存在部分云遮挡，并非理想的DSM生产数据源。因此本研究采用不同影像组合方式生成多组DSM，随后对这些DSM进行融合，最终通过插值或外部数字高程模型（即哥白尼DEM，https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198）填补剩余孔洞。 考虑到不同影像对的基高比，三组影像组合（A-B、B-C及A-B-C）被认为可获得较好的计算结果。本研究使用开源摄影测量软件MicMac（Rupnik等，2017）进行影像处理：首先将每幅影像的几何模型转换为MicMac格式（Convert2GenBundle命令）；随后从所有可能的影像对中提取同名点（Tapioca工具）；对三幅影像进行光束法平差以优化几何模型（Campari工具）；最后分别通过影像A-B（1）、B-C（2）及A-B-C（3）的相关性计算得到三组DSM（Malt工具）。 得益于MicMac的多尺度处理与正则化准则，原始DSM的地面采样距离为0.5米。为降低信噪比，将DSM重采样至2米分辨率（[图4a](Figures/AB_dsm_raw.png)、[图4c](Figures/BC_dsm_raw.png)、[图4e](Figures/ABC_dsm_raw.png)）。每组DSM均附带每个像素的相关性评分，可用于剔除相关性评分低于阈值的像素（[图4b](Figures/AB_cor_raw.png)、[图4d](Figures/BC_cor_raw.png)、[图4f](Figures/ABC_cor_raw.png)）。 随后将昴星团DSM中被云层遮挡的区域用哥白尼DEM进行填补。通过相关性评分阈值生成云掩膜，最终以相关性评分作为权重因子，将三组已填补孔洞的DSM进行融合（[图5](Figures/Merged.png)）。 =========================== 参考文献 [1] Ewelina Rupnik、Mehdi Daakir及Marc Pierrot Deseilligny. MicMac：一款免费开源的摄影测量解决方案. 《开放地理空间数据、软件与标准》，2(1):1–9，2017. [链接]