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Improved Change Detection with Trajectory-Based Approach: Application to Quantify Cropland Expansion in South Dakota

Author

Listed:
  • Lan H. Nguyen

    (Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA)

  • Deepak R. Joshi

    (Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57007, USA)

  • Geoffrey M. Henebry

    (Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48824, USA
    Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA)

Abstract

The growing demand for biofuel production increased agricultural activities in South Dakota, leading to the conversion of grassland to cropland. Although a few land change studies have been conducted in this area, they lacked spatial details and were based on the traditional bi-temporal change detection that may return incorrect rates of conversion. This study aimed to provide a more complete view of land conversion in South Dakota using a trajectory-based analysis that considers the entire satellite-based land cover/land use time series to improve change detection. We estimated cropland expansion of 5447 km 2 (equivalent to 14% of the existing cropland area) between 2007 and 2015, which matches much more closely the reports from the National Agriculture Statistics Service—NASS (5921 km 2 )—and the National Resources Inventory—NRI (5034 km 2 )—than an estimation from the bi-temporal approach (8018 km 2 ). Cropland gains were mostly concentrated in 10 counties in northern and central South Dakota. Urbanizing Lincoln County, part of the Sioux Falls metropolitan area, is the only county with a net loss in cropland area over the study period. An evaluation of land suitability for crops using the Soil Survey Geographic Database (SSURGO) indicated a scarcity in high-quality arable land available for cropland expansion.

Suggested Citation

  • Lan H. Nguyen & Deepak R. Joshi & Geoffrey M. Henebry, 2019. "Improved Change Detection with Trajectory-Based Approach: Application to Quantify Cropland Expansion in South Dakota," Land, MDPI, vol. 8(4), pages 1-11, April.
    • Handle: RePEc:gam:jlands:v:8:y:2019:i:4:p:57-:d:219657
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    Citations

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    Cited by:

    1. Ioannis Manakos & Garik Gutman & Chariton Kalaitzidis, 2021. "Monitoring Land Cover Change: Towards Sustainability," Land, MDPI, vol. 10(12), pages 1-2, December.
    2. Jiří Šandera & Přemysl Štych, 2020. "Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier," Land, MDPI, vol. 9(11), pages 1-20, October.
    3. Woubet G. Alemu & Geoffrey M. Henebry & Assefa M. Melesse, 2020. "Land Cover and Land Use Change in the US Prairie Pothole Region Using the USDA Cropland Data Layer," Land, MDPI, vol. 9(5), pages 1-20, May.
    4. Hillary Mugiyo & Vimbayi G. P. Chimonyo & Mbulisi Sibanda & Richard Kunz & Cecilia R. Masemola & Albert T. Modi & Tafadzwanashe Mabhaudhi, 2021. "Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review," Land, MDPI, vol. 10(2), pages 1-24, January.

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