Zoom in on the map above and click your area of interest or use the Tile Index linked below to determine which package(s) you require for download. The DSM data is available in the form of 1-km by 1-km non-overlapping tiles grouped into packages for download.This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters and sensors may vary by project. See the detailed User Guide linked below for additional information.

You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page.

Now also available through a web service which exposes the data for visualization, geoprocessing and limited download. The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connectionusing the REST endpoint URL. The service draws using the Web Mercator projection.

For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca.

Service Endpoints

https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer https://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer (Government of Ontario Internal Users)

Additional Documentation

Ontario DSM (Lidar-Derived) - User Guide (DOCX)

OMAFRA Lidar 2016-2018 -Cochrane-Additional Contractor Metadata (PDF) OMAFRA Lidar 2016-2018 -Peterborough-AdditionalContractorMetadata (PDF) OMAFRA Lidar 2016-2018 -Lake Erie-AdditionalContractorMetadata (PDF) CLOCA Lidar 2018 - Additional Contractor Metadata (PDF) South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF) OMAFRA Lidar 2022 - Lake Huron - Additional Contractor Metadata (PDF) OMAFRA Lidar 2022 - Lake Simcoe - Additional Contractor Metadata (PDF) Huron-Georgian Bay Lidar 2022-23 - Additional Contractor Metadata (Word) Kawartha Lakes Lidar 2023 - Additional Contractor Metadata (Word) Sault Ste Marie Lidar 2023-24 - Additional Contractor Metadata (Word) Thunder Bay Lidar 2023-24 - Additional Contractor Metadata (Word) Timmins Lidar 2024 - Additional Contractor Metadata (Word)

Ontario DSM (Lidar-Derived) - Tile Index (SHP) Ontario Lidar Project Extents (SHP)

Product Packages Download links for the Ontario DSM (Lidar-Derived) (Word) Projects:

LEAP 2009 GTA 2014-18 OMAFRA 2016-18 CLOCA 2018 South Nation CA 2018-19 Muskoka 2018-23 York-Lake Simcoe 2019 Ottawa River 2019-20 Ottawa-Gatineau 2019-20 Lake Nipissing 2020 Hamilton-Niagara 2021 Huron Shores 2021 Eastern Ontario 2021-22 OMAFRA Lake Huron 2022 OMAFRA Lake Simcoe 2022 Belleville 2022 Digital Elevation Data to Support Flood Mapping 2022-26 Huron-Georgian Bay 2022-23 Kawartha Lakes 2023 Sault Ste Marie 2023-24 Sudbury 2023-24 Thunder Bay 2023-24 Timmins 2024

Greater Toronto Area Lidar 2023

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ontario Ministry of Natural Resources - Geospatial Ontario,geospatial@ontario.ca

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products using historic aerial imagery and Structure from Motion (SfM) photogrammetry methods. A high-resolution orthomosaic of the South Cow Mountain Recreational Area was generated from stereo historical aerial imagery acquired in by the BLM in May of1977. The aerial imagery were downloaded from the USGS Earth Resources Observation and Science (EROS) Data Center's USGS Single Aerial Frame Photo archive and an orthomosaic was created using USGS guidelines. Photo alignment, error reduction, and dense point cloud generation followed guidelines documented in Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6— Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039. Photo-identifiable points, selected as synthetic ground-control points, followed guidelines documented in Sherwood, C.R.; Warrick, J.A.; Hill, A.D.; Ritchie, A.C.; Andrews, B.D., and Plant, N.G., 2018. Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry https://doi.org/10.2112/JCOASTRES-D-18-00016.1 Additional post-processing of the 1977 dense point cloud, using Iterative Closest Point (ICP) analysis, was used to improve the alignment with the 2015 LiDAR point cloud. The ICP analysis is explained in Low, K.L., 2004. Linear least-squares optimization for point-to-plane ICP surface registration. Chapel Hill, University of North Carolina, 4(10), pp.1-3. http://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf Data were processed using photogrammetry to generate a three-dimensional point cloud that identifies pixels of an object from multiple images taken from various angles and calculates the x, y, and z coordinates of that object/pixel. The point cloud was processed to create a digital surface model of the study area (57.3 cm resolution). Finally, source images were stitched together based on shared pixels and orthogonally adjusted to the digital surface model to create a high resolution (approximately 18.3 cm) orthoimage for the study area.

Zoom in on the map above and click your area of interest to determine which package(s) you require for download.

The Ontario Radar DSM has the following features:

source data: 1 arc second spaceborne C-Band Interferometric Synthetic Aperture Radar (IFSAR) data MNR Lambert Conformal Conic Projection vertical datum in both EGM96 and CGVD28, separately elevation value: floating local Polynomial Interpolation from vector elevation points spatial resolution: 30 meter surface elevation model This product offers significant advancements in elevation data in the province.

Read the details about these advancements and other technical specifications, including data processing, major spatial characteristics of the Radar DSM, and the steps to generate the Northern Ontario Radar DSM.

Additional Documentation

Ontario Radar DSM User Guide (Word) Ontario Radar DSM Accuracy Assessment (PDF) Ontario Radar DSM Tile Index (Shapefile)

Product Packages

Ontario Radar DSM - North Ontario Radar DSM - South

Status Completed: production of the data has been completed

Maintenance and Update Frequency Not planned: there are no plans to update the data

Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

For a description of the DEM and the steps for its compilation see or download the accompanying pdf document .For the DEM and Hillshade data, download the zip file.Files and information are also available here: https://www.academia.edu/23922627/A_Digital_Elevation_Model_for_Cyprus_based_on_the_ALOS_2_W3D30_Digital_Surface_ModelAll data for the production of this DEM are © Japan Aerospace Exploration Agency (JAXA).Data used for the production of the 1:5000 coastline used to clip the DEM are © Department of Lands and Surveys Cyprus (DLS).The dataset is available to use with no charge and is provided under the same conditions set by JAXA, as follows:- When the user provides or publishes the products and services to a third party using this dataset, it is necessary to display that the original data is provided by JAXA.- You are kindly requested to show the copyright (© JAXA) and the source of data, when you publish the fruits using this dataset.- JAXA does not guarantee the quality and reliability of this dataset and JAXA assume no responsibility whatsoever for any direct or indirect damage and loss caused by use of this dataset. Also, JAXA will not be responsible for any damages of users due to changing, deleting or terminating the provision of this dataset.

A 'Digital Elevation Model (DEM)' is a 3D approximation of the terrain's surface created from elevation data. The term 'Digital Surface Model (DSM)' represents the earth's surface and includes all objects including e.g. forests, buildings. The Digital Elevation Model over Europe from the GMES Reference Data Access project (EU-DEM) is a Digital Surface Model (DSM) representing the first surface as illuminated by the sensors. EU-DEM covers the 39 member and cooperating countries of EEA. The EU-DEM is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach. Different products have been derived from the EU-DEM, including raster’s of the slope, terrain aspect and hillshade. The different products are made available in both full-European coverage as in a set of 25 tiles covering 1000x1000km each. The EU-DEM map shows a colour shaded relief image over Europe, which has been created by EEA using a hillshade dataset derived from the ETRS89-LAEA version of EU-DEM. As this data cannot be used for analysis purposes (and that there are some known artefacts West of Norway), the downloadable data are single band raster’s with values relating to the actual elevation. The datasets are encoded as GeoTIFF with LZW compression (tiles) or DEFLATE compression (European mosaics as single files). The Web maps include WFS, WMS and WCS services. The EU-DEM statistical validation documents a relatively unbiased (-0.56 meters) overall vertical accuracy of 2.9 meters RMSE, which is fully within the contractual specification of 7m RMSE and the full report can be found at [1].

[1] https://cws-download.eea.europa.eu/in-situ/eudem/Report-EU-DEM-statistical-validation-August2014.pdf

Overview:
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 100 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps:
The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in VRT format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:

gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 100 m, bilinear resampling was performed in GRASS GIS (using r.proj and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

Projection + EPSG code:
ETRS89-extended / LAEA Europe (EPSG: 3035)

Spatial extent:
north: 6874000
south: -485000
west: 869000
east: 8712000

Spatial resolution:
100 m

Pixel values:
meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)

Original dataset license:
https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

The EU-DEM is a Digital Surface Model (DSM) representing the first surface as illuminated by the sensors. EU-DEM covers the EEA39 countries and it has been produced by a consortium led by Indra, Intermap edited the EUDEM and AGI provided the water mask. The EU-DEM is a 3D raster dataset with elevations captured at 1 arc second postings (2.78E-4 degrees) or about every 30 meter. It is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach. Ownership of EU-DEM belongs to European Commision, DG Enterprise and Industry.

The projection onto an Inspire compliant grid of 25m resolution and the computation of a Slope raster have been performed by the Joint Research Centre of the European Commission (see file documentation/SPEC010_a100421-SLOP.pdf).

Release 1.1 For Zenodo
===========================

Authors

Raphaël GRANDIN¹ and Arthur DELORME²

1 : Université de Paris, Institut de Physique du Globe de Paris. Email: grandin@ipgp.fr

2: Université de Paris, Institut de Physique du Globe de Paris. Email: delorme@ipgp.fr

===========================
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:

===========================

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]

Images were acquired from approximately 80 m above ground surface on the 12th of February 2021, using a Phantom 4 Advanced drone with an FC330 camera. The images are in file input_images.zip.

The mission planning software DJI GS Pro was used to automatically acquire images at suitable locations across the survey area to enable the reconstruction of a three dimensional model.

Images 422 to 531 were imported to the photogrammetry software Pix4D (version 4.6.4). The created Pix4D project is Station12Feb2021_limited.p4d, and the processing report is Station12Feb2021_limited_report.pdf.

Four three-dimensional ground control points were used to improve the positioning of the model. No two dimensional control points or check points were used.

These points were in ITRF 2000@2000 datum (UTM Zone 49S), with co-ordinates as per the table below:

Label, Type, X(m), Y(m), Z(m), Accuracy Horz(m), Accuracy Vert(M) BM05, 3D GCP, 478814.460, 2648561.910, 38.558, 0.050, 0.100 EW-05, 3D GCP, 478635.540, 2648617.260, 27.260, 0.050, 0.100 FuelFlange, 3D GCP, 478970.810, 2648642.250, 21.920, 0.050, 0.100 MeltbellFootingA, 3D GCP, 478680.270, 2648466.547, 35.850, 0.050, 0.100

BM-05 is a survey benchmark near the Casey flagpoles, see https://data.aad.gov.au/aadc/survey/display_station.cfm?station_id=600 EW-05 is a 44 gallon drum used as a groundwater extraction well by the remediation project Fuel Flange is the last fuel flange located on the elevated fuel line prior to the fuel line “dipping” under the wharf road. Meltbell footing A is a concrete footing for the Casey melt bell (surveyed in 2019/20).

No point cloud processing (e.g. removal of errant points) was done prior to orthomosaic and model generation.

The resulting orthomosaic (Station12Feb2021_limited_transparent_mosaic_group1.tif) has an average ground sampling distance of 2.9 cm, and covers an area of approximately 15.8 hectares, encompassing the majority of buildings along “main street” at Casey. The quarry, biopiles, helipad, and upper fuel farm area are all visible.

Contour lines were generated in Pix4D at 0.5 m intervals.

Due to the limited number of ground control points, and their imprecision, the estimated residual mean squared error across three dimensions is 0.17 m (17cm), and will be worse on the periphery of the imaged area.

The orthomosaic was exported from ArcGIS to a Google Earth file (CaseyStation Orthomosaic Feb 12 2021.kmz) using XTools Pro Version 17.2.

A map was created in ArcGIS showing the orthomosaic with a background showing contour lines obtained from the AADC data product windmill_is.mdb.

The map was exported in .jpg and .pdf format at 250 dpi. Casey Station Orthomosaic Feb 12 2021.pdf Casey Station Orthomosaic Feb 12 2021.jpg

The Pix4D folder structure has been copied across (with the exception of the temp folder) and is included in this dataset.

Pix4D Folder Structure:

Station12Feb2021_limited.zip 1_intitial • Contains Pix4D files created during the project • Contains the final processing report (as .pdf) 2_densification • Contains the 3D mesh as an .obj file • Contains the point cloud as a .LAS and .PLY file • Contains the point cloud as a .p4b file 3_dsm_ortho • Contains the digital surface model as a georeferenced .tif file • Contains the orthomosaic as a georeferenced .tif file

A text readable log file from the project processing is in the file Station12Feb2021_limited.log

This product is superseded by version 4.2. See: https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4-2

Citation and Main Description:

This product is described in Chapter 5 of the 2018 DWR Delta Modeling Section annual report, produced jointly with USGS.

https://water.ca.gov/-/media/DWR-Website/Web-Pages/Library/Modeling-And-Analysis/Files/Modeling-and-Analysis-PDFs/FINAL6BayDelta39thProgress-Report071918.pdf

Domain and Product

Changes between 4.0 and 4.1 are documented in the change log below and are most pronounced in the Suisun Marsh region and in the incorporation of some improvements in the South Delta.

Changes in version 4 relative to prior products are limited to the region east of the Carquinez Strait (starting around Carquinez Bridge). To facilitate compatibility between products released by DWR and USGS/NOAA partners, DWR distributes the region west of the active work at 10m resolution but does not actively work in this region. The San Pablo Bay boundary of active revision in the present product in a place where its source data matches that of other Bay elevation models, e.g., the 2m seamless high-resolution bathymetric and topographic DEM of San Francisco Bay by USGS Earth Resources Observation and Science Center (EROS) (https://topotools.cr.usgs.gov/coned/sanfrancisco.php ), the 2010 San Francisco Bay DEM by National Oceanic and Atmospheric Administration (https://www.ngdc.noaa.gov/metaview/page?xml=NOAA/NESDIS/NGDC/MGG/DEM/iso/xml/741.xml&view=getDataView&header=none ) or the prior (version 3) 10m digital elevation model (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3 ).The 10m DEM for the Bay-Delta is based on the first on the list, i.e. EROS’ 2m DEM for the Bay

New work reported here was done at 2m resolution, although the improvements have been incorporated into the 10m products as much as possible. Relative to the previous DWR release (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3), the 2m DEM product reported here consolidates work at this resolution into a small number of larger surfaces representing approximately one-third of the Delta (link to the Coverage Areas page). Laterally, the 2m models now extend over the levee crest as needed to match well with Delta LiDAR (http://www.atlas.ca.gov/download.html#/casil/imageryBaseMapsLandCover/lidar2009 ), the main terrestrial source of data used in this work. The 10m product (link to the Coverage Areas page) is based on the updated USGS DEM (https://www.sciencebase.gov/catalog/item/58599681e4b01224f329b484 ). In places where updated 2m models overlap the 10 meters, the 10m base elevation model was updated by resampling the new 2m model and adding levee enforcement. At the border between the 2m and 10m models, the two resolutions were locally edge-matched over a small region to maintain smoothness. For more information, please refer to the report: A Revised Continuous Surface Elevation Model for Modeling (link to Chapter 5 in the 2018 Delta Modeling Section Annual Report (https://water.ca.gov/-/media/DWR-Website/Web-Pages/Library/Modeling-And-Analysis/Files/Modeling-and-Analysis-PDFs/FINAL6BayDelta39thProgress-Report071918.pdf). Please note that by agreement with our data providers we distribute only our own integrated maps, not the original source point data. (https://water.ca.gov/-/media/DWR-Website/Web-Pages/Library/Modeling-And-Analysis/Files/Modeling-and-Analysis-PDFs/FINAL6BayDelta39thProgress-Report071918.pdf

Version: 4.1

• Time Completed: December 2018
• Horizontal Datum: NAD83
• Spheroid:GRS1980
• Projection:UTM_Zone_10N (meters)
• Vertical Datum:NAVD88 (meters)

Data Sources

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4/resource/981a57b5-e16f-4632-b7e0-4a533534a79c

Coverage Areas

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4/resource/2e3f2b6e-9f0d-4719-b32a-dff449be18af

Changes since 4.0

• correct the year of survey for COE single-beam (OBJECTID 25) from 1988 to 1988
• update 2m DEM for Montezuma Slough based on newly connected multibean data by NCRO (August 2018 for Montezuma Slough and Hunter Cut)
• cut the portion of DEM for Calaveras from v3 2m San Joaquin River (dem_calaveras_river_2m_20120902
• Changes only occurs in part 1 of "dem_north_delta_2m_v4_20171220" (v4 final): 1.apply patch at the intersection of Sacramento River and Feather River; 2.add Fremont Weir from the 2m Yolo DEM
• The following modifications are applied to "dem_yolo_merge3_mod_20161202" (v4 final; D:\dem_list_review\yolo_bypass):
• Improved merge with Sacramento River on the north:
• Improved merge with 2m Shag/Stairstep dem (dem_shag_stairstep_toe_drain_2m_v4_beta_20150518.tif ) which goes beyond multibeam data, derived based on mixed single-beam datasets using GAM
• Improved patching and compatibility with USGS 10m release
• Create 2m DEM for the Old River between SWP/CVP to Five points based on new bathymetry data collected in June 2018
• Add the newly developed 2m Montazuma Slough DEM as part of v4 release. Update 10m Delta DEM to be consistent with this addition and update data source polygon accordingly.
• Create 2m DEM for the Old River near Coney Island based on new bathymetry data collected in June 2018
• Incorporate modifications done for Yolo and North Delta into 10m DEM for Delta and Bay-Delta.
• The 20181120 version of the 2m South Delta DEM missed a small region that is included in the old version
• Add LiDAR to Shag Slough in 2m Yolo DEM (dem_yolo_2m_20181012), The existing DEM for Shag Slough did not extend sufficiently over banks.

Many Ontario lidar point cloud datasets have been made available for direct download by the Government of Canada through the federal Open Government Portal under the LiDAR Point Clouds – CanElevation Series record. Instructions for bulk data download are available in the Download Instructions document linked from that page. To download individual tiles, zoom in on the map in GeoHub and click a tile for a pop-up containing a download link. See the LIO Support - Large Data Ordering Instructions to obtain a copy of data for projects that are not yet available for direct download. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index in shapefile format. Data sizes by project area are listed below. The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The minimum point cloud classes are Unclassified, Ground, Water, High and Low Noise. The data is structured into non-overlapping 1-km by 1-km tiles in LAZ format. This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters, accuracy and sensors vary by project. Some projects have additional classes, such as vegetation and buildings. See the detailed User Guide and contractor metadata reports linked below for additional information, including information about interpreting the index for placement of data orders. Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived). You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Additional Documentation Ontario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX) OMAFRA Lidar 2016-18 - Cochrane - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Peterborough - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Lake Erie - Additional Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Metadata (Word)Timmins Lidar 2024 - Additional Metadata (Word) OMAFRA Lidar Point Cloud 2016-18 - Cochrane - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18- Peterborough - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18 - Lake Erie - Lift Metadata (SHP)CLOCA Lidar Point Cloud 2018 - Lift Metadata (SHP)South Nation Lidar Point Cloud 2018-19 - Lift Metadata (SHP)York-Lake Simcoe Lidar Point Cloud 2019 - Lift Metadata (SHP)Ottawa River Lidar Point Cloud 2019-20 - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Huron - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Simcoe - Lift Metadata (SHP)Eastern Ontario Lidar Point Cloud 2021-22 - Lift Medatadata (SHP)DEDSFM Huron-Georgian Bay Lidar Point Cloud 2022-23 - Lift Metadata (SHP)DEDSFM Kawartha Lakes Lidar Point Cloud 2023 - Lift Metadata (SHP)DEDSFM Sault Ste Marie Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Sudbury Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Thunder Bay Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Timmins Lidar Point Cloud 2024 - Lift Metadata (SHP)GTA 2023 - Lift Metadata (SHP) Ontario Classified Point Cloud (Lidar-Derived) - Tile Index (SHP)Ontario Lidar Project Extents (SHP)Data Package SizesLEAP 2009 - 22.9 GBOMAFRA Lidar 2016-18 - Cochrane - 442 GBOMAFRA Lidar 2016-18 - Lake Erie - 1.22 TBOMAFRA Lidar 2016-18 - Peterborough - 443 GBGTA 2014 - 57.6 GBGTA 2015 - 63.4 GBBrampton 2015 - 5.9 GBPeel 2016 - 49.2 GBMilton 2017 - 15.3 GBHalton 2018 - 73 GBCLOCA 2018 - 36.2 GBSouth Nation 2018-19 - 72.4 GBYork Region-Lake Simcoe Watershed 2019 - 75 GBOttawa River 2019-20 - 836 GBLake Nipissing 2020 - 700 GBOttawa-Gatineau 2019-20 - 551 GBHamilton-Niagara 2021 - 660 GBOMAFRA Lidar 2022 - Lake Huron - 204 GBOMAFRA Lidar 2022 - Lake Simcoe - 154 GBBelleville 2022 - 1.09 TBEastern Ontario 2021-22 - 1.5 TBHuron Shores 2021 - 35.5 GBMuskoka 2018 - 72.1 GBMuskoka 2021 - 74.2 GBMuskoka 2023 - 532 GBDigital Elevation Data to Support Flood Mapping 2022-26:Huron-Georgian Bay 2022 - 1.37 TBHuron-Georgian Bay 2023 - 257 GBHuron-Georgian Bay 2023 Bruce - 95.2 GBKawartha Lakes 2023 - 385 GBSault Ste Marie 2023-24 - 1.15 TBSudbury 2023-24 - 741 GBThunder Bay 2023-24 - 654 GBTimmins 2024 - 318 GBCataraqui 2024 - 50.5 GBGTA 2023 - 985 GBStatusOn going: Data is continually being updated Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Structure-from-Motion (SfM) photogrammetry can be used with digital underwater photographs to generate high-resolution bathymetry and orthomosaics with millimeter-to-centimeter scale resolution at relatively low cost. Although these products are useful for assessing species diversity and health, they have additional utility for quantifying benthic community structure, such as coral growth and fine-scale elevation change over time, if accurate length scales and georeferencing are included. This georeferencing is commonly provided with “ground control,” such as pre-installed seafloor benchmarks or identifiable “static” features, which can be difficult and time consuming to install, survey, and maintain. To address these challenges, we developed the SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5), a towed surface vehicle with an onboard survey-grade Global Navigation Satellite System (GNSS) and five rigidly mounted downward-looking cameras with overlapping views of the seafloor. The cameras are tightly synchronized with both the GNSS and each other to collect quintet photo sets and record the precise location of every collection event. The system was field tested in July 2019 in the U.S. Florida Keys, in water depths ranging from 3 to 9 m over a variety of bottom types. Surveying accuracy was assessed using pre-installed stations with known coordinates, machined scale bars, and two independent surveys of a site to evaluate repeatability. Under a range of sea conditions, ambient lighting, and water clarity, we were able to map living and senile coral reef habitats and sand waves at mm-scale resolution. Data were processed using best practice SfM techniques without ground control and local measurement errors of horizontal and vertical scales were consistently sub-millimeter, equivalent to 0.013% RMSE relative to water depth. Survey-to-survey repeatability RMSE was on the order of 3 cm without georeferencing but could be improved to several millimeters with the incorporation of one or more non-surveyed marker points. We demonstrate that the SQUID-5 platform can map complex coral reef and other seafloor habitats and measure mm-to-cm scale changes in the morphology and location of seafloor features over time without pre-existing ground control.

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices.
Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘.
In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:
• Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)
• Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )
• Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)
• associated reports (PDF; statistical and additional information on the classifiaction results and workflow)
The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided:
• Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)
• Color Coding table for the visualization of the classifiaction units (.xlsx)

This digital data set, compiled from new 10-meter digital elevation model (DEM) data, represents the physiography of the Willamette Valley, Oregon. This new physiographic data is useful because the improved resolution allows for better visualization of flood and fluvial features in the low lying areas of the Willamette Valley. Many scientist are interested in the Willamette Valley because it is subject to a variety of earthquake hazards, and its water and geologic resources are under pressure from rapid urbanization (see sheets for a brief description). Further, this Open-File report details the techniques used to create these maps (See readme.pdf). It is the author's purpose to publish these techniques and data so others may use this report to generate their own gray scale and/or color shaded-relief maps. All information about the data and methods used to create this report are in the readme.pdf file and this document.

This digital dataset was compiled from newly released U.S. Geological Survey 10-meter digital elevation model (DEM) data, along with stream and transportation coverages previously published on the internet. This report consists of a digital representation of the physiography of the Willamette Valley. Contained in this dataset is: 1) 10-meter DEM data for the entire Willamette Valley; 2) the ARC/INFO grids used to create the color shaded-relief and gray scale shaded-relief images; 3) the necessary data ARC/INFO data to used to plot these data; and 4) several reports detailing the data formats (this docuement) and producers used to create these datasets. The scale of the original 10-meter DEM data should not be violated. Any use of these original data smaller than the intended scale (1:24,000) will not yield improved accuracy.

The databases in this report were compiled in ARC/INFO, a commercial Geographic Information System (Environmental Systems Research Institute, Redlands, California, with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). The files are in either GRID (ARC/INFO raster data) format or COVERAGE (ARC/ INFO vector data) format. Coverages are stored in uncompressed ARC export format (ARC/INFO version 8.0.2). ARC/INFO export files (files with the .e00 extension) can be converted into ARC/ INFO coverages in ARC/INFO (see below) and can be read by some other Geographic Information Systems, such as MapInfo via ArcLink and ESRI's ArcView (version 1.0 for Windows 3.1 to 3.11 is available for free from ESRI's web site: http://www.esri.com). The digital compilation was done in version 8.0.2 of ARC/INFO with version 3.0 of the menu interface ALACARTE (Fitzgibbon and Wentworth, 1991, Fitzgibbon, 1991, Wentworth and Fitzgibbon, 1991). Custom AMLs were written to compile the 10-meter DEM data from 7.5-minute quadrangles into large composite datasets. The data was compiled as ARC/INFO grids and then converted to decimeter integer grids. This procedure greatly reduces the file sizes without downgrading the data quality. Stream coverages were merged with the grids used to create the color shaded-relief grid composite. Further details on the techniques used to generate these maps are available in the readme file of this report.

The 2017 Digital Terrain Model (DTM) is a 2 foot pixel resolution raster in Erdas IMG format. This was created using the ground (class = 2) lidar points and incorporating the breaklines.

The DTMs were developed using LiDAR data. LiDAR is an acronym for LIght Detection And Ranging. Light detection and ranging is the science of using a laser to measure distances to specific points. A specially equipped airplane with positioning tools and LiDAR technology was used to measure the distance to the surface of the earth to determine ground elevation. The classified points were developed using data collected in April to May 2017. The LiDAR points, specialized software, and technology provide the ability to create a high precision three-dimensional digital elevation and/or terrain models (DEM/DTM). The use of LiDAR significantly reduces the cost for developing this information.

The DTMs are intended to correspond to the orthometric heights of the bare surface of the county (no buildings or vegetation cover). DTM data is used by county agencies to study drainage issues such as flooding and erosion; contour generation; slope and aspect; and hill shade images. This dataset was compiled to meet the American Society for Photogrammetry and Remote Sensing (ASPRS) Accuracy Standards for Large-Scale Maps, CLASS 1 map accuracy.

The U.S. Army Corps of Engineers Engineering and Design Manual for Photogrammetric Production recommends that data intended for this usage scale be used for any of the following purposes: route location, preliminary alignment and design, preliminary project planning, hydraulic sections, rough earthwork estimates, or high-gradient terrain / low unit cost earthwork excavation estimates. The manual does not recommend that these data be used for final design, excavation and grading plans, earthwork computations for bid estimates or contract measurement and payment.

This dataset does not take the place of an on-site survey for design, construction or regulatory purposes.

Canopy-intercepted light, or photosynthetically active radiation, is fundamentally crucial for quantifying crop biomass development and yield potential. Fractional photosynthetically active radiation (PAR) (fPAR) is conventionally obtained by measuring the PAR both below and above the canopy using a mobile lightbar platform to predict the potential yield of nut crops. This study proposed a feasible and low-cost method for accurately estimating the canopy fPAR using aerial photogrammetry-based canopy three-dimensional models. We tested up to eight different varieties in three experimental almond orchards, including California's leading variety of ‘Nonpareil’. To extract various canopy profile features, such as canopy cover and canopy volume index, we developed a complete data collection and processing pipeline called Virtual Orchard (VO) in Python environment. Canopy fPAR estimated by VO throughout the season was compared against midday canopy fPAR measured by a mobile lightbar platform in midseason, achieving a strong correlation (R2) of 0.96. A low root mean square error (RMSE) of 2% for ‘Nonpareil’. Furthermore, we developed regression models for predicting actual almond yield using both measures, where VO estimation of canopy fPAR, as a stronger indicator, achieved a much better prediction (R2 = 0.84 and RMSE = 195 lb acre−1) than the lightbar (R2 = 0.70 and RMSE = 266 lb acre−1) for ‘Nonpareil’. Eight different new models for estimating potential yield were also developed using temporal analysis from May to August in 2019 by adjusting the ratio between fPAR and dry kernel yield previously found using a lightbar. Finally, we compared the two measures at two different spatial precision levels: per-row and per-block. fPAR estimated by VO at the per-tree level was also assessed. Results showed that VO estimated canopy fPAR performed better at each precision level than lightbar with up to 0.13 higher R2. The findings in this study serve as a fundamental link between aerial-based canopy fPAR and the actual yield of almonds.

This data set consists of a southern African subset of the Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) data in both ASCII GRID and binary image file formats. The Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) is a global data set with horizontal grid spacing of 30 arc-seconds (0.008333... degrees) in latitude and longitude, resulting in dimensions of 21,600 rows and 43,200 columns. At the Equator a degree of latitude is about 111 kilometers. GLOBE has 120 values per degree, giving GLOBE slightly better than 1-km gridding at the Equator, with progressively finer gridding longitudinally toward the Poles. The horizontal coordinate system is seconds of latitude and longitude referenced to World Geodetic System 84 (WGS84). The vertical units represent elevation in meters above Mean Sea Level. The elevation values range from -407 to 8,752 meters on land. In GLOBE Version 1.0, ocean areas have been masked as no data and have been assigned a value of -500. Because of the nature of the raster structure of the DEM, small islands in the ocean less than approximately 1 square kilometer (specifically, those that are not characterized by at least one 30 grid cell and/or do not have coastlines digitized into Digital Chart of the World or World Vector Shoreline) may not be represented. More information about the procedure used to create the southern African subset is described in the accompanying file ftp://daac.ornl.gov/data/safari2k/almanac/globe_dem/comp/so_africa_dem_readme.pdf.

SRTM v4.1 is based on the finished-grade 2006 SRTM v2 release by NASA that was post-processed and published in 2008 by CGIAR-CSI (Consortium for Spatial Information). The SRTM v4.1 data set offers 3 arc-second (approximately 90 meters) spatial resolution and covers about 80% of Earth’s landmass, between 60° North and 56° South. SRTM v4.1 is divided onto 5° x 5° of latitude and longitude tiles in “geographic” projection, shown here.

The original SRTM v2 release contained voids (areas not or not well observed by the SRTM radar), mostly occurring in topographically steep terrain. The overcome this problem, CGIAR-CSI focused on filling the voids (holes) using various interpolation techniques, such as Kriging, moving window averaging, and importantly, the use of auxiliary elevation data sets (DEMs from other sources, e.g., national DEMs). CGIAR-CSI DEM v4.1 data comes at 5 deg x 5 deg tiles, and has a typical file size of 23 MB for one tile, which comprises two kinds of information; the DEM file and a mask file. The mask file is a binary file which identifies areas within the DEM that have been interpolated. The SRTM v4.1 datasets are available in ArcInfo ASCII and GeoTIFF (.tif) formats.

Geodetic information: The SRTM V4.1 DEMs are vertically referenced to the EGM96 geoid and horizontally referenced to the WGS84 (World Geodetic System 1984).

Further notes: This data set contains artefacts, e.g., pits or steps, over parts of the Himalayas, the Andes and other mountainous regions. Artefacts in SRTM v4.1 tend to occur over void-filled areas. The SRTM DEM represents bare ground elevations only where vegetation cover and buildings are absent. Over most areas, the DEM elevations reside between the bare ground (terrain) and top of canopies (surface), so are technically a mixture of a terrain and surface model.

Data access to the v4.1 data set: A detailed description is found at http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1 and access is possible via the data search page on http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp.

References:Reuter H.I, A. Nelson, A. Jarvis, 2007, An evaluation of void filling interpolation methods for SRTM data, International Journal of Geographic Information Science, 21:9, 983-1008. Available on http://srtm.csi.cgiar.org/download/Reuteretal2007.pdf

Digital Elevation Model (DEM) for British Columbia produced by GeoBC. This data is the TRIM DEM converted to the Canadian Digital Elevation Data (CDED)format. The data consists of an ordered array of ground or reflective surface elevations, recorded in metres, at regularly spaced intervals. The spacing of the grid points is .75 arc seconds north/south. The data was converted into 1:50,000 grids for distribution. The scale of this modified data is 1:250,000 which was captured from the original source data which was at a scale of 1:20,000. The CDED format specification are available at ftp://ftp.geogratis.gc.ca/pub/nrcan_rncan/elevation/cdem_mnec/doc/CDEM_product_specs.pdf

Aerial imagery was collected at the Lower Montane site (Pumphouse) in the East River Watershed, Colorado during the spring, summer, and fall seasons of 2017 and 2018 to improve the understanding of seasonal vegetation dynamics and their drivers. The datasets include Red-Green-Blue (RGB) ortho-mosaics and digital surface models (DSMs) inferred from the Unoccupied Aerial System (UAS) acquired aerial RGB imagery for June 3, June 19, July 7, and August 14, 2017, and for March 14, April 26, June 1, June 18, July 6, and August 7, 2018. Real-Time Kinematic Global Positioning System (RTK-GPS) surveyed Ground control points (GCPs) were used to increase the reconstruction accuracy. The reconstructed RGB mosaics and DSMs have been trimmed to cover a similar spatial domain. The accuracy of the RGB mosaics is considered high (~10 cm). DSM accuracy is highest (~10 cm) where sufficient GCPS are available, and more difficult to assess elsewhere (see reconstruction reports for uncertainty estimates). The dataset includes a total of 20 GeoTIFF (.tif) files, 10 PDF (.pdf) files, 3 data CSV (.csv) files, and 2 metadata CSV (.csv) files. Feel free to contact the authors with any questions or collaboration interests.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.