Skip to main content
SpringerLink
Log in

Integrating drone imagery with existing rangeland monitoring programs

Environmental Monitoring and Assessment volume 192, Article number: 269 (2020) Cite this article

Abstract

The recent availability of small and low-cost sensor carrying unmanned aerial systems (UAS, commonly known as drones) coupled with advances in image processing software (i.e., structure from motion photogrammetry) has made drone-collected imagery a potentially valuable tool for rangeland inventory and monitoring. Drone-imagery methods can observe larger extents to estimate indicators at landscape scales with higher confidence than traditional field sampling. They also have the potential to replace field methods in some instances and enable the development of indicators not measurable from the ground. Much research has already demonstrated that several quantitative rangeland indicators can be estimated from high-resolution imagery. Developing a suite of monitoring methods that are useful for supporting management decisions (e.g., repeatable, cost-effective, and validated against field methods) will require additional exploration to develop best practices for image acquisition and analytical workflows that can efficiently estimate multiple indicators. We embedded with a Bureau of Land Management (BLM) field monitoring crew in Northern California, USA to compare field-measured and imagery-derived indicator values and to evaluate the logistics of using small UAS within the framework of an existing monitoring program. The unified workflow we developed to measure fractional cover, canopy gaps, and vegetation height was specific for the sagebrush steppe, an ecosystem that is common in other BLM managed lands. The correspondence between imagery and field methods yielded encouraging agreement while revealing systematic differences between the methods. Workflow best practices for producing repeatable rangeland indicators is likely to vary by vegetation composition and phenology. An online space dedicated to sharing imagery-based workflows could spur collaboration among researchers and quicken the pace of integrating drone-imagery data within adaptive management of rangelands. Though drone-imagery methods are not likely to replace most field methods in large monitoring programs, they could be a valuable enhancement for pressing local management needs.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Allen, C. R., Angeler, D. G., Fontaine, J. J., Garmestani, A. S., Hart, N. M., Pope, K. L., & Twidwell, D. (2017). Adaptive management of rangeland systems. In Rangeland Systems: Processes, management and challenges (pp. 373–394).

  • Baena, S., Moat, J., Whaley, O., & Boyd, D. S. (2017). Identifying species from the air: UAVs and the very high resolution challenge for plant conservation. PLoS One, 12(11), e0188714. https://doi.org/10.1371/journal.pone.0188714.

    Article  CAS  Google Scholar 

  • Booth, D. T., & Cox, S. E. (2008). Image-based monitoring to measure ecological change in rangeland. Frontiers in Ecology and the Environment, 6(4), 185–190. https://doi.org/10.1890/070095.

    Article  Google Scholar 

  • Booth, D. T., & Cox, S. E. (2009). Dual-camera, high-resolution aerial assessment of pipeline revegetation. Environmental Monitoring and Assessment, 158, 23–33. https://doi.org/10.1007/s10661-008-0562-5.

    Article  Google Scholar 

  • Booth, D., & Cox, S. (2011). Art to science: tools for greater objectivity in resource monitoring. Rangelands, 33(4), 27–34. https://doi.org/10.2111/1551-501x-33.4.27.

    Article  Google Scholar 

  • Breckenridge, R. P., Dakins, M., Bunting, S., Harbour, J. L., & White, S. (2011). Comparison of unmanned aerial vehicle platforms for assessing vegetation cover in sagebrush steppe ecosystems. Rangeland Ecology & Management, 64(5), 521–532. https://doi.org/10.2111/REM-D-10-00030.1.

    Article  Google Scholar 

  • Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3), 233–249. https://doi.org/10.1016/S0304-3800(03)00139-X.

    Article  Google Scholar 

  • Carbonneau, P. E., & Dietrich, J. T. (2016). Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry. Earth Surface Processes and Landforms, 42(3), 473–486. https://doi.org/10.1002/esp.4012.

    Article  Google Scholar 

  • Cruzan, M. B., Weinstein, B. G., Grasty, M. R., Kohrn, B. F., Hendrickson, E. C., Arredondo, T. M., & Thompson, P. G. (2016). Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology. Applications in Plant Sciences, 4(9), 1600041. https://doi.org/10.3732/apps.1600041.

    Article  Google Scholar 

  • Cunliffe, A., & Anderson, K. (2019. Measuring above-ground biomass with drone photogrammetry: data collection protocol. Protocol Exchange. https://doi.org/10.1038/protex.2018.134.

  • Cunliffe, A. M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment, 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019.

    Article  Google Scholar 

  • Duniway, M. C., Karl, J. W., Schrader, S., Baquera, N., & Herrick, J. E. (2012). Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability. Environmental Monitoring and Assessment, 184(6), 3789–3804. https://doi.org/10.1007/s10661-011-2224-2.

    Article  Google Scholar 

  • Eltner, A., Kaiser, A., Castillo, C., Rock, G., Neugirg, F., & Abellan, A. (2015). Image-based surface reconstruction in geomorphometry – merits, limits and developments of a promising tool for geoscientists. Earth Surface Dynamics Discussions, 3(4), 1445–1508. https://doi.org/10.5194/esurfd-3-1445-2015.

    Article  Google Scholar 

  • Fraser, R. H., Olthof, I., Lantz, T. C., & Schmitt, C. (2016). UAV photogrammetry for mapping vegetation in the Low-Arctic. Arctic Science, 102(June), 1–51. https://doi.org/10.1139/as-2016-0008.

    Article  Google Scholar 

  • Gearhart, A., Booth, D. T., Sedivec, K., & Schauer, C. (2013). Use of Kendall’s coefficient of concordance to assess agreement among observers of very high resolution imagery. Geocarto International, 28(6), 517–526. https://doi.org/10.1080/10106049.2012.725775.

    Article  Google Scholar 

  • Gillan, J. K., Karl, J. W., Duniway, M., & Elaksher, A. (2014). Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring. Journal of Environmental Management, 144, 226–235. https://doi.org/10.1016/j.jenvman.2014.05.028.

    Article  Google Scholar 

  • Gillan, J., Karl, J., Elaksher, A., & Duniway, M. (2017). Fine-resolution repeat topographic surveying of dryland landscapes using UAS-based structure-from-motion photogrammetry: assessing accuracy and precision against traditional ground-based erosion measurements. Remote Sensing, 9(5), 437. https://doi.org/10.3390/rs9050437.

    Article  Google Scholar 

  • Gillan, J. K., McClaran, M. P., Swetnam, T. L., & Heilman, P. (2019). Estimating forage utilization with drone-based photogrammetric point clouds. Rangeland Ecology & Management, 72(4), 575–585. https://doi.org/10.1016/j.rama.2019.02.009.

    Article  Google Scholar 

  • Hardin, P., Jackson, M., Anderson, V., & Johnson, R. (2007). Detecting Squarrose knapweed ( Centaurea virgata lam. Ssp. squarrosa Gugl.) using a remotely piloted vehicle: a Utah case study. GIScience & Remote Sensing, 44(3), 203–219. https://doi.org/10.2747/1548-1603.44.3.203.

    Article  Google Scholar 

  • Herrick, J. E., Zee, J. W. Van, McCord, S. E., Courtright, E. M., Karl, J. W., & Burkett, L. M. (2017). Monitoring manual for grassland, shrubland, and savanna ecosystems 2nd edn. Vol 1: Core Methods.

  • Hunt, E. R., Everitt, J. H., Ritchie, J. C., Moran, M. S., Booth, D. T., Anderson, G. L., et al. (2003). Applications and research using remote sensing for rangeland management. Photogrammetric Engineering & Remote Sensing, 69(6), 675–693. https://doi.org/10.14358/PERS.69.6.675.

    Article  Google Scholar 

  • James, M. R., & Robson, S. (2014). Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surface Processes and Landforms, 39(10), 1413–1420. https://doi.org/10.1002/esp.3609.

    Article  Google Scholar 

  • James, M. R., Robson, S., D’Oleire-Oltmanns, S., & Niethammer, U. (2017). Optimising UAV topographic surveys processed with structure-from-motion: ground control quality, quantity and bundle adjustment. Geomorphology, 280, 51–66. https://doi.org/10.1016/j.geomorph.2016.11.021.

    Article  Google Scholar 

  • Jensen, J. L. R., & Mathews, A. J. (2016). Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sensing, 8(50). https://doi.org/10.3390/rs8010050.

    Article  Google Scholar 

  • Jones, M. O., Allred, B. W., Naugle, D. E., & Mestas, J. D. (2018). Innovation in rangeland monitoring : annual, 30 m, plant functional type percent cover maps for U. S. rangelands, 1984–2017, 9(September). https://doi.org/10.1002/ecs2.2430.

    Article  Google Scholar 

  • Karl, J. W., & Herrick, J. E. (2010). Monitoring and assessment based on ecological sites. Rangelands, 32(6), 60–64. https://doi.org/10.2111/RANGELANDS-D-10-00082.1.

    Article  Google Scholar 

  • Karl, J. W., Duniway, M. C., & Schrader, T. S. (2012). A technique for estimating rangeland canopy-gap size distributions from high-resolution digital imagery. Rangeland Ecology & Management, 65(2), 196–207. https://doi.org/10.2111/REM-D-11-00006.1.

    Article  Google Scholar 

  • Karl, J. W., Gillan, J. K., Barger, N. N., Herrick, J. E., & Duniway, M. C. (2014). Interpretation of high-resolution imagery for detecting vegetation cover composition change after fuels reduction treatments in woodlands. Ecological Indicators, 45, 570–578. https://doi.org/10.1016/j.ecolind.2014.05.017.

    Article  Google Scholar 

  • Kendall, W. L., & Moore, C. T. (2012). Maximizing the utility of monitoring to the adaptive management of natural resources. In R. A. Gitzen, J. J. Milspaugh, A. B. Cooper, & D. S. Licht (Eds.), Design and analysis of long-term ecological monitoring studies. Cambridge: University of Cambridge Press.

    Google Scholar 

  • Kuhn, M., & Quinlan, R. (2017). C5.0 decision trees and rule-based models. R package version 0.1.1.

  • Laliberte, A. S., & Rango, A. (2011). Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GIScience & Remote Sensing, 48(1), 4–23. https://doi.org/10.2747/1548-1603.48.1.4.

    Article  Google Scholar 

  • Laliberte, A. S., Browning, D. M., Herrick, J. E., & Gronemeyer, P. (2010a). Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment. Journal of Spatial Science, 55(1), 101–115. https://doi.org/10.1080/14498596.2010.487853.

    Article  Google Scholar 

  • Laliberte, A. S., Herrick, J. E., Rango, A., & Winters, C. (2010b). Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering & Remote Sensing, 76(6), 661–672. https://doi.org/10.14358/PERS.76.6.661.

    Article  Google Scholar 

  • Laliberte, A. S., Goforth, M. a., Steele, C. M., & Rango, A. (2011a). Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sensing, 3(12), 2529–2551. https://doi.org/10.3390/rs3112529.

    Article  Google Scholar 

  • Laliberte, A. S., Winters, C., & Rango, A. (2011b). UAS remote sensing missions for rangeland applications. Geocarto International, 26(2), 141–156. https://doi.org/10.1080/10106049.2010.534557.

    Article  Google Scholar 

  • Lass, L. W., & Calihan, R. H. (1997). Effects of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technology, 11, 248–256.

    Article  Google Scholar 

  • Leis, S. a., & Morrison, L. W. (2011). Field test of digital photography biomass estimation technique in tallgrass prairie. Rangeland Ecology & Management, 64(1), 99–103. https://doi.org/10.2111/REM-D-09-00180.1.

    Article  Google Scholar 

  • Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65–70. https://doi.org/10.1080/10106040108542184.

    Article  Google Scholar 

  • Lu, B., & He, Y. (2017). Species classification using unmanned aerial vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 73–85. https://doi.org/10.1016/j.isprsjprs.2017.03.011.

    Article  Google Scholar 

  • Ludwig, J. A., Bastin, G. N., Chewings, V. H., Eager, R. W., & Liedloff, A. C. (2007). Leakiness: a new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data. Ecological Indicators, 7(2), 442–454. https://doi.org/10.1016/j.ecolind.2006.05.001.

    Article  Google Scholar 

  • MacKinnon, W. C., Karl, J. W., Toevs, G. R., Taylor, J. J., Karl, M., Spurrier, C. S., & Herrick, J. E. (2011). BLM core terrestrial indicators and methods. Tech Note 440. Denver: US Department of the Interior, Bureau of Land Management, National Operations Center. 

  • McCord, S. E., Buenemann, M., Karl, J. W., Browning, D. M., & Hadley, B. C. (2017). Integrating remotely sensed imagery and existing multiscale field data to derive rangeland indicators: application of Bayesian additive regression trees. Rangeland Ecology & Management, 1–12. https://doi.org/10.1016/j.rama.2017.02.004.

    Article  Google Scholar 

  • McGwire, K. C., Weltz, M. A., Finzel, J. A., Morris, C. E., Fenstermaker, L. F., & McGraw, D. S. (2013). Multiscale assessment of green leaf cover in a semi-arid rangeland with a small unmanned aerial vehicle. International Journal of Remote Sensing, 34(5), 1615–1632. https://doi.org/10.1080/01431161.2012.723836.

    Article  Google Scholar 

  • Meng, B., Gao, J., Liang, T., Cui, X., Ge, J., Yin, J., et al. (2018). Modeling of alpine grassland cover based on unmanned aerial vehicle technology and multi-factor methods: a case study in the east of Tibetan Plateau, China. Remote Sensing, 10(2), 320. https://doi.org/10.3390/rs10020320.

    Article  Google Scholar 

  • Mitchell, J. E. (2010). Criteria and indicators of sustainable rangeland management. University of Wyoming Cooperative Extension Publication No. SM-56.

  • Mitchell, J. J., Glenn, N. F., Anderson, M. O., Hruska, R. C., & Charlie, A. H. (2012). Unmanned aerial vehicle ( {UAV} ) hyperspectral remote sensing for dryland vegetation monitoring hyperspectral image and signal sensing. Idaho National Laboratory Preprint.

  • Moffet, C. a. (2009). Agreement between measurements of shrub cover using ground-based methods and very large scale aerial imagery. Rangeland Ecology & Management, 62(3), 268–277. https://doi.org/10.2111/08-244R.1.

    Article  Google Scholar 

  • Montealegre, A. L., Lamelas, M. T., & De La Riva, J. (2015). A comparison of open - source LiDAR filtering algorithms in a mediterranean forest environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8), 4072–4085. https://doi.org/10.1109/JSTARS.2015.2436974.

    Article  Google Scholar 

  • Navulur, K. (2007). Multi-spectral image analysis using the object-oriented paradigm. Boca Raton: CRC Press, Taylor and Francis Group.

    Google Scholar 

  • Olsoy, P. J., Shipley, L. A., Rachlow, J. L., Forbey, J. S., Glenn, N. F., Burgess, M. A., & Thornton, D. H. (2018). Unmanned aerial systems measure structural habitat features for wildlife across multiple scales. Methods in Ecology and Evolution, 9(3), 594–604. https://doi.org/10.1111/2041-210X.12919.

    Article  Google Scholar 

  • Rango, A., Laliberte, A., Herrick, J. E., Winters, C., Havstad, K., Steel, C., & Browning, D. (2009). Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3(1), 033542. https://doi.org/10.1117/1.3216822.

    Article  Google Scholar 

  • Sankey, T. T., McVay, J., Swetnam, T. L., McClaran, M. P., Heilman, P., & Nichols, M. (2017). UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/RSE2.44.

    Article  Google Scholar 

  • Seefeldt, S. S., & Booth, D. T. (2006). Measuring plant cover in sagebrush steppe rangelands: a comparison of methods. Environmental Management, 37(5), 703–711. https://doi.org/10.1007/s00267-005-0016-6.

    Article  Google Scholar 

  • Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2015). Structure from motion photogrammetry in physical geography. Progress in Physical Geography, 40(2), 247–275. https://doi.org/10.1177/0309133315615805.

    Article  Google Scholar 

  • Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from Internet photo collections. International Journal of Computer Vision, 80(2), 189–210. https://doi.org/10.1007/s11263-007-0107-3.

    Article  Google Scholar 

  • Stiver, S. J., Thomas Rinkes, E., & Naugle, D. E. (2015). Sage-grouse habitat assessment framework: a multiscale assessment tool. Technical reference 6710–1. Denver, CO.

  • Swetnam, T. L., Gillan, J. K., Sankey, T. T., McClaran, M. P., Nichols, M. H., Heilman, P., & McVay, J. (2018). Considerations for achieving cross-platform point cloud data fusion across different dryland ecosystem structural states. Frontiers in Plant Science, 8(January), 2144. https://doi.org/10.3389/fpls.2017.02144.

    Article  Google Scholar 

  • Taylor, J., Kachergis, E., Toevs, G., Karl, J., Bobo, M., Karl, M, Miller, S., & Spurrier, C. (2014). AIM-monitoring: a component of the BLM assessment, inventory, and monitoring strategy. Tech Note 445. Denver: US Department of the Interior, Bureau of Land Management, National Operations Center.

  • Toevs, G. R., Karl, J. W., Taylor, J. J., Spurrier, C. S., Karl, M. S., Bobo, M. R., & Herrick, J. E. (2011). Consistent indicators and methods and a scalable sample design to meet assessment, inventory, and monitoring information needs across scales. Rangelands, 33(4), 14–20. https://doi.org/10.2111/1551-501X-33.4.14.

    Article  Google Scholar 

  • US Bureau of Land Management. (2007). Eagle Lake field office resource management plan and environmental impact statement. Susanville, CA.

  • Vautherin, J. (2016). Photogrammetric accuracy and modeling of rolling shutter cameras. EuroCOW 2016, the European Calibration and Orientation Workshop (Presentation), 10–12 February 2016, Lausanne, Switzerland. https://doi.org/10.5194/isprsannals-III-3-139-2016.

    Article  Google Scholar 

  • Webb, N. P., Herrick, J. E., & Duniway, M. C. (2014). Ecological site-based assessments of wind and water erosion : informing accelerated soil erosion management in rangelands. Ecological Applications, 24(6), 1405–1420. https://doi.org/10.1890/13-1175.1.

    Article  Google Scholar 

  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-motion’ photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021.

    Article  Google Scholar 

  • Xian, G., Homer, C., Rigge, M., Shi, H., & Meyer, D. (2015). Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment, 168, 286–300. https://doi.org/10.1016/j.rse.2015.07.014.

    Article  Google Scholar 

Download references

Acknowledgments

We thank Andrew Johnson, geographer at Eagle Lake Field Office, for help coordinating this research with field crews. We also thank Eric Panebaker, aviation manager of BLM Northern California District, for permitting drone flights.

Funding

Travel and data collection were funded by USDA ARS Jornada Experimental Range.

Author information

Authors and Affiliations

  1. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA

    Jeffrey K. Gillan & Willem J. D. van Leeuwen

  2. Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID, 83844, USA

    Jason W. Karl

  3. School of Geography and Development, University of Arizona, Tucson, AZ, 85721, USA

    Willem J. D. van Leeuwen

Authors
  1. Jeffrey K. Gillan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason W. Karl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Willem J. D. van Leeuwen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeffrey K. Gillan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 53.2 KB)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gillan, J.K., Karl, J.W. & van Leeuwen, W.J. Integrating drone imagery with existing rangeland monitoring programs. Environ Monit Assess 192, 269 (2020). https://doi.org/10.1007/s10661-020-8216-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-020-8216-3

Keywords

search

Navigation