Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. The study demonstrates the potential of this methodology by generating spatial layers at the landscape scale to inform on the state of rangeland ecosystems. The workflow showcases the power of remote sensing technology to map ecological states and addresses limitations in spatial coverage by integrating UAV and satellite data. By utilizing the bare ground LPI metric, which indicates the connectedness of bare ground, the methodology enables the classification of ecological states at a regional scale. This cost-effective approach potentially offers a standardized and reproducible method applicable across different sites and regions. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. The workflow serves as a blueprint for scaling up ecological states mapping in similar semi-arid rangelands. Further work should involve refining the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological states mapping.
KeywordsEcological SitesEcological StatesRangelandLargest Patch Index
Briggs, J.M., et al. (2005) An Ecosystem in Transition: Causes and Consequences of the Conversion of Mesic Grassland to Shrubland. BioScience, 55, 243-254. https://doi.org/10.1641/0006-3568(2005)055[0243:AEITCA]2.0.CO;2
Bestelmeyer, B.T., Brown, J.R., Havstad, K.M., Alexander, R., Chavez, G. and Herric, J.E. (2003) Development and Use of State-and-Transition Models for Rangelands. Journal of Range Management, 56, 114-126. https://doi.org/10.2458/azu_jrm_v56i2_bestelmeyer
Bestelmeyer, B.T., et al. (2017) State and Transition Models: Theory, Applications, and Challenges. In: Briske, D.D., Ed., Rangeland Systems: Processes, Management and Challenges, Springer Series on Environmental Management, Springer International Publishing, Cham, 303-345. https://doi.org/10.1007/978-3-319-46709-2_9
Bestelmeyer, B.T., Goolsby, D.P. and Archer, S.R. (2011) Spatial Perspectives in State-and-Transition Models: A Missing Link to Land Management? Journal of Applied Ecology, 48, 746-757. https://doi.org/10.1111/j.1365-2664.2011.01982.x
Stringham, T.K., Krueger, W.C. and Shaver, P.L. (2003) State and Transition Modeling: An Ecological Process Approach. Journal of Range Management, 56, 106-113. https://doi.org/10.2458/azu_jrm_v56i2_stringham
Brown, J.R. and Bestelmeyer, B.T. (2016) An Introduction to the Special Issue “Ecological Sites for Landscape Management”. Rangelands, 38, 311-312. https://doi.org/10.1016/j.rala.2016.11.001
U.-A. NRCS (2023) National Ecological Site Handbook. https://directives.sc.egov.usda.gov
U.-A. NRCS (2022) The Ecosystem Dynamics Interpretive Tool (EDIT). https://edit.jornada.nmsu.edu
Borrelli, P., et al. (2017) An Assessment of the Global Impact of 21st Century Land Use Change on Soil Erosion. Nature Communications, 8, Article No. 2013. https://doi.org/10.1038/s41467-017-02142-7
Herrick, J.E., et al. (2010) National Ecosystem Assessments Supported by Scientific and Local Knowledge. Frontiers in Ecology and the Environment, 8, 403-408. https://doi.org/10.1890/100017
Weltz, M.A., et al. (2014) Estimating Conservation Needs for Rangelands Using USDA National Resources Inventory Assessments. Transactions of the ASABE, 57, 1559-1570. https://doi.org/10.13031/trans.57.10030
Bolo, P.O., Sommer, R., Kihara, J., Kinyua, M., Nyawira, S. and Notenbaert, A.M.O. (2019) Rangeland Degradation: Causes, Consequences, Monitoring Techniques and Remedies. Working Paper, International Center for Tropical Agriculture, Nairobi, 23 p. https://cgspace.cgiar.org/handle/10568/102393
UAV
Remote Sensing
Westoby, M., Walker, B. and Noy-Meir, I. (1989) Opportunistic Management for Rangelands Not at Equilibrium. Journal of Range Management, 42, 266-274. https://doi.org/10.2307/3899492
Allred, B.W., et al. (2021) Improving Landsat Predictions of Rangeland Fractional Cover with Multitask Learning and Uncertainty. Methods in Ecology and Evolution, 12, 841-849. https://doi.org/10.1111/2041-210X.13564
Hagen, S.C., et al. (2012) Mapping Total Vegetation Cover across Western Rangelands with Moderate-Resolution Imaging Spectroradiometer Data. Rangeland Ecology & Management, 65, 456-467. https://doi.org/10.2111/REM-D-11-00188.1
Jones, M.O., et al. (2018) Innovation in Rangeland Monitoring: Annual, 30 m, Plant Functional Type Percent Cover Maps for U.S. Rangelands, 1984-2017. Ecosphere, 9, e02430. https://doi.org/10.1002/ecs2.2430
Ludwig, J.A., Bastin, G.N., Wallace, J.F. and McVicar, T.R. (2007) Assessing Landscape Health by Scaling with Remote Sensing: When Is It Not Enough? Landscape Ecology, 22, 163-169. https://doi.org/10.1007/s10980-006-9038-6
Steele, C.M., Bestelmeyer, B.T., Burkett, L.M., Smith, P.L. and Yanoff, S. (2012) Spatially Explicit Representation of State-and-Transition Models. Rangeland Ecology & Management, 65, 213-222. https://doi.org/10.2111/REM-D-11-00047.1
Williamson, J.C., Bestelmeyer, B.T. and Peters, D.P.C. (2012) Spatiotemporal Patterns of Production Can Be Used to Detect State Change across an Arid Landscape. Ecosystems, 15, 34-47. https://doi.org/10.1007/s10021-011-9490-2
Tongway, D.J. and Hindley, N.L. (1995) Manual for Soil Condition Assessment of Tropical Grasslands. CSIRO Division of Wildlife and Ecology, Canberra.
Smith, L., et al. (2014) Guide to Rangeland Monitoring and Assessment Basic Concepts for Collecting, Interpreting, and Use of Rangeland Data for Management Planning and Decisions. Vol. I, Arizona Grazing Lands Conservation Association, Las Cruces. https://aznrcd.files.wordpress.com/2014/10/guide-to-rangeland-monitoring-assessment.pdf
Herrick, J., Van Zee, J., Havstad, K., Burkett, L. and Whitford, W. (2005) Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems. Volume I: Quick Start. USDA-ARS Jornada Experimental Range, Las Cruces.
Kuehl, R.O., McClaran, M.P. and Va, J. (2001) Detecting Fragmentation of Cover in Desert Grasslands Using Line Intercept. Journal of Range Management, 54, 61-66. https://doi.org/10.2458/azu_jrm_v54i1_kuehl
Tongway, D. and Hindley, N. (2004) Landscape Function Analysis: A System for Monitoring Rangeland Function. African Journal of Range & Forage Science, 21, 109-113. https://doi.org/10.2989/10220110409485841
McGarigal, K. and Cushman, S. (2012) FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. https://fragstats.org/index.php
McGarigal, K. (2014) Landscape Pattern Metrics. Wiley StatsRef: Statistics Reference Online. John Wiley & Sons, Ltd., Hoboken. https://doi.org/10.1002/9781118445112.stat07723
McClaran, M.P. and Wei, H. (2014) Recent Drought Phase in a 73-Year Record at Two Spatial Scales: Implications for Livestock Production on Rangelands in the Southwestern United States. Agricultural and Forest Meteorology, 197, 40-51. https://doi.org/10.1016/j.agrformet.2014.06.004
Breckenfeld, D.J. and Robinett, D. (2003) Soil and Ecological Sites of the Santa Rita Experimental Range. Santa Rita Experimental Range: 100 Years (1903 to 2003) of Accomplishments and Contributions, Conference Proceedings, Vol. 30, Tucson, 30 October-1 November 2003, 157-165.
Gillan, J.K., Ponce-Campos, G.E., Swetnam, T.L., Gorlier, A., Heilman, P. and McClaran, M.P. (2021) Innovations to Expand Drone Data Collection and Analysis for Rangeland Monitoring. Ecosphere, 12, e03649. https://doi.org/10.1002/ecs2.3649
Planet, T. (2017) Planet Application Program Interface: In Space for Life on Earth. https://api.planet.com
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017) Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
Glasbey, C. (1993) An Analysis of Histogram-Based Thresholding Algorithms. CVGIP: Graphical Models and Image Processing, 55, 532-537. https://doi.org/10.1006/cgip.1993.1040
Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
Li, H. (2014) Smile. https://haifengl.github.io
Lloyd, C.D. (2010) Spatial Data Analysis: An Introduction for GIS Users. Oxford University Press, Oxford.
Rigge, M., et al. (2019) Using Remote Sensing to Quantify Ecosystem Site Potential Community Structure and Deviation in the Great Basin, United States. Ecological Indicators, 96, 516-531. https://doi.org/10.1016/j.ecolind.2018.09.037
Saraf, S., et al. (2023) Biophysical Drivers for Predicting the Distribution and Abundance of Invasive Yellow Sweetclover in the Northern Great Plains. Landscape Ecology, 38, 1463-1479. https://doi.org/10.1007/s10980-023-01613-1
O’Neill, R.V., et al. (1988) Indices of Landscape Pattern. Landscape Ecology, 1, 153-162. https://doi.org/10.1007/BF00162741
Nauman, T.W. and Thompson, J.A. (2014) Semi-Automated Disaggregation of Conventional Soil Maps Using Knowledge Driven Data Mining and Classification Trees. Geoderma, 213, 385-399. https://doi.org/10.1016/j.geoderma.2013.08.024