AquaBits.io Cutting Edge Water Management Solutions

JP Uncategorized

aquabits sensor
In the last few months DroneMapper has developed a terrestrial sensor (AquaBits IoT) focused on water resource management. This post describes our development, verification and capabilities for an affordable, real time device sensing flowing water in channels and continuous measurement of reservoir holding capacities. Previously we described the use of drone imagery collection, precision imagery processing and generation of reservoir basin contours from the DEM for water elevation correlation to storage capacity. This data can now be augmented with real time reporting of actual reservoir stage at any frequency of interest, with little latency to water managers.

AquaBits utilizes accurate hydrostatic pressure measurement of the water column and is self contained inclusive of a power subsystem and communication capability using either cellular (when available) or satellite for remote locations. Data is transmitted to our secure dashboard where it is archived and processed for discharge in a flowing channel or current water stage in a reservoir. A slide deck with an overview of AquaBits and recently collected data compared to Colorado State instrumentation can be found here: (pdf attached)

In other scenarios when equipped with the appropriate sensor(s), applications to consider may include soil nutrient/moisture measurement for agriculture, earth subsidence/movement, chemical contamination, water quality, audio/visual surveillance, limited only by the imagination... Feel free to contact us and discuss your specific application.

The DroneMapper Team

aquabits.io

Lowering the Cost of Continuous Streamflow Monitoring Award, January 21, 2020

JP Uncategorized

The Bureau of Reclamation and the US Geological Survey conducted a challenge in 2019 to solicit approaches for the reduction of cost associated with streamflow monitoring at gaging sites throughout the US. "...continuous streamflow monitoring stations are vital to water resources planning, design, management and research." With budget and project priority pressures the number of gaging sites nationally is in decline.

DroneMapper was one of five selected finalists in the challenge offering an approach using a 3D sensor for continuous remote sensing of the water surface and the use of UAS for stream channel characterization to improve hydrological modeling and simulation. The Bureau and USGS award press release is viewed here: https://www.usbr.gov/newsroom/stories/detail.cfm?RecordID=69224

We are very interested in pursuing this technology further for US Government, state and local use. We are in the process of identifying investment alternatives that would promote further development and verification of the 3D sensor. Please do not hesitate to contact us if you have or know of someone that has an interest in supporting this venture. Please contact Pierre Stoermer, CEO DroneMapper, (970) 417-1102

Many thanks from the DroneMapper Team!!

Data Science Basic Crop Analysis with UAV Imagery and Micasense Altum

JP Uncategorized

https://github.com/dronemapper-io/CropAnalysis

A jupyter notebook with crop analysis algorithms utilizing digital elevation models, dtm and multi-spectral imagery (R-G-B-NIR-Rededge-Thermal) from a MicaSense Altum sensor processed with DroneMapper Remote Expert.

Due to limitations on git file sizes, you will need to download the GeoTIFF data for this project from the following url: https://dronemapper.com/software/DroneMapper_CropAnalysis_Data.zip. Once that has been completed, extract the TIF files into the notebook data directory matching the structure below.

Included Data

  • data/DrnMppr-DEM-AOI.tif – 32bit georeferenced digital elevation model
  • data/DrnMppr-ORT-AOI.tif – 16bit georeferenced orthomosaic (Red-Green-Blue-NIR-Rededge-Thermal)
  • data/DrnMppr-DTM-AOI.tif – 32bit georeferenced dtm
  • data/plant_count.shp – plant count AOI
  • data/plots_1.shp – plot 1 AOI
  • data/plots_2.shp – plot 2 AOI
  • output/*.csv – output csv of dataframes

Algorithms

  • plot volume/biomass
  • plot canopy height
  • plot ndvi zonal statistics
  • plot thermals
  • plant count

Notes

These basic algorithms are intended to get you started and interested in multi-spectral processing and analysis.

The orthomosaic, digital elevation model, and dtm were clipped to an AOI using GlobalMapper. The shapefile plots were also generated using GlobalMapper grid tool. We highly recommend GlobalMapper for GIS work!

We cloned the MicaSense imageprocessing repository and created the Batch Processing DroneMapper.ipynb notebook which allows you to quickly align and stack a Altum or RedEdge dataset creating the correct TIF files with EXIF/GPS metadata preserved. These stacked TIF files are then directly loaded into DroneMapper Remote Expert for processing.

This notebook assumes the user has basic knowledge of setting up their python environment, importing libraries and working inside jupyter.

Do More!

Implement additional algorithms like NDRE or alternative methods for plant counts. Submit a pull request to the repo!

Load Digital Elevation Model and Orthomosaic

Orthomosaic-DigitalElevationModel

Load Plot 1 AOI and Generate NDVI

ndvi_zonal_statistics

Generate NDVI Zonal Statistics For Each Plot

 ndvi_zonal_statistics_plots
geometryLAYERMAP_NAMENAMEminmaxmeancountstdmedian
0POLYGON Z ((289583.708 5130289.226 0.000, 2895…Coverage/QuadUser Created Features20.1884610.8730920.43855944440.0789210.436500
1POLYGON Z ((289588.705 5130289.052 0.000, 2895…Coverage/QuadUser Created Features30.1932140.8879710.44528244400.0910900.425304
2POLYGON Z ((289593.702 5130288.877 0.000, 2895…Coverage/QuadUser Created Features40.2322220.8901470.55286444400.1124400.519746
3POLYGON Z ((289598.699 5130288.703 0.000, 2896…Coverage/QuadUser Created Features50.0908250.8650830.53029544440.1105700.515392
4POLYGON Z ((289603.696 5130288.528 0.000, 2896…Coverage/QuadUser Created Features60.1046970.9224500.53666044420.1327310.495813

Load Plot 2 AOI & Compute DEM Canopy Mean Height For Each Plot

 canopy_height_dem
geometryLAYERMAP_NAMENAMEminmaxmeancountstdmedian
0POLYGON Z ((289707.875 5130279.812 1182.502, 2…Coverage/QuadUser Created Features – Coverage/Quad1360.129120363.381683361.14129433181.091593360.441467
1POLYGON Z ((289712.189 5130279.586 1190.569, 2…Coverage/QuadUser Created Features – Coverage/Quad2360.131866363.382446361.71029733161.215926361.927017
2POLYGON Z ((289716.503 5130279.360 1183.212, 2…Coverage/QuadUser Created Features – Coverage/Quad3360.117279363.384766361.13859233101.122890360.425781
3POLYGON Z ((289720.817 5130279.134 1182.668, 2…Coverage/QuadUser Created Features – Coverage/Quad4360.110443363.387207361.91543633221.258644362.585251
4POLYGON Z ((289725.131 5130278.908 1182.782, 2…Coverage/QuadUser Created Features – Coverage/Quad5360.006683363.377991361.55850133201.305164360.546524

Compute Thermal Mean For Each Plot

The thermal band (5) in the processed orthomosaic shows stitching artifacts which could likely be improved using more accurate pre-processing alignment and de-distortion algorithms. You can find more information about these functions in the MicaSense imageprocessing github repository. See notes at the top of this notebook.

 
geometryLAYERMAP_NAMENAMEminmaxmeancountstdmedian
0POLYGON Z ((289707.875 5130279.812 1182.502, 2…Coverage/QuadUser Created Features – Coverage/Quad130008.030431.030196.6286923318120.98205830193.0
1POLYGON Z ((289712.189 5130279.586 1190.569, 2…Coverage/QuadUser Created Features – Coverage/Quad230068.030560.030333.1927023316123.85609330332.0
2POLYGON Z ((289716.503 5130279.360 1183.212, 2…Coverage/QuadUser Created Features – Coverage/Quad329792.030645.030266.0302113310170.83120730275.5
3POLYGON Z ((289720.817 5130279.134 1182.668, 2…Coverage/QuadUser Created Features – Coverage/Quad429790.030700.030386.1372673322201.26691930391.0
4POLYGON Z ((289725.131 5130278.908 1182.782, 2…Coverage/QuadUser Created Features – Coverage/Quad529618.030691.030209.9045183320292.29939230262.0

Load Plot 1 AOI & Compute Volume/Biomass For Each Plot

biomass
geometryLAYERMAP_NAMENAMEminmaxmeancountsumvolume_m3area_m2
0POLYGON Z ((289583.708 5130289.226 0.000, 2895…Coverage/QuadUser Created Features2-0.1534733.5255431.53252444446810.53848376.65036850.0
1POLYGON Z ((289588.705 5130289.052 0.000, 2895…Coverage/QuadUser Created Features3-0.0944823.5752261.64666444407311.18869082.28502150.0
2POLYGON Z ((289593.702 5130288.877 0.000, 2895…Coverage/QuadUser Created Features4-0.0706483.9924931.88459644408367.60827694.17467650.0
3POLYGON Z ((289598.699 5130288.703 0.000, 2896…Coverage/QuadUser Created Features50.0329284.5759892.969443444413196.202637148.51891550.0
4POLYGON Z ((289603.696 5130288.528 0.000, 2896…Coverage/QuadUser Created Features60.0741885.1054083.155879444214018.412506157.77261750.0

Load Plant Count AOI & Count Plants

plant_count
plant_count
Plant count: 310
UTMXUTMY
0289622.4111715130240.899705
1289621.8497055130244.325966
2289622.1015695130248.858655
3289621.8650865130253.863387
4289621.4362805130258.158493

Thanks! Keep an eye out for future notebooks and algorithms.

Using the Max Flow/Min Cut DEM algorithm for accurate volumetrics

JP Uncategorized

capacity-map

Figure 1

It's that time of the season again where water resource managers are checking their emptied reservoirs for possible silt-in and verification of water holding capacities. We recently flew one of these reservoirs on the south side of the Grand Mesa in western Colorado with our Phantom 3 and a set of ground surveyed aerial targets. The imagery and control were processed using two different DEM generation algorithms, the results of which are presented and discussed here.

Figure 1 illustrates the ortho of the area with the ground surveyed targets and other points depicted as black dots. Figure 2 shows the DEM construction using our standard algorithm. The black oval highlights DEM decorrelation where the vegetation and shadows obscure the NW bank of the reservoir causing erroneous elevation rendering. Figure 3 shows the DEM construction utilizing the max flow/min cut DEM algorithm with greatly enhanced correlation in the same area previously highlighted.

We use Global Mapper to generate contours at specific elevations of interest ranging from reservoir drain or dead pool to maximum capacity or spill elevation. Each contour generated is converted to a flat water elevation surface and the volume between that surface and the DEM reservoir surface is computed. Take a look at what happens when contours are generated on a locally decorrelated DEM in Figure 4. One can see the contours are moving into the NW vegetated area which produces higher volumetric and surface area estimates than reality. As a comparison Figure 5 using the Max/Flow/Min Cut algorithm demonstrates more accurate rendering of the contours and subsequently higher accuracy volume estimates.

Image

Figure 2

Image

Figure 3

Image

Figure 4

Image

Figure 5

Drone Mapping Video Tutorials

JP Uncategorized

We are generating a set of video tutorials to help users get setup and run your imagery collections efficiently using REMOTE EXPERT or RAPID. We hope that these will provide some insight on how your various processing needs can be met and the options you can select for your specific scene. We understand we're not all photogrammetry experts and provide these tutorials to help you select the best options for the dataset you collected and possibly more importantly, how you can modify your future collections to produce acceptable products for your applications and customers.

Please click on this link to access videos by topic: https://dronemapper.com/video-tutorials.

Check back often as we will updating this link with more material. Feel free to contact us with comments, critique and other topics you want to see.covered.

The very best from us, The DroneMapper Team