Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors

Arora, Gaurav; Wolter, Peter T.

doi:10.1080/1747423x.2018.1466001

Cited by 20 publications

(25 citation statements)

References 24 publications

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“…We found a net increase in cropland area of approximate 14% (5447 km 2 ) in South Dakota. This result confirms substantial cropland expansion into grassland reported by previous studies [12][13][14]. Scarcity of land suitable for further cropland expansion was identified, pointing to the need for careful joint design and implementation of agriculture and energy policies to allow enable bioenergy demands to be met while protecting remaining wetlands and grasslands.…”

Section: Discussionsupporting

confidence: 87%

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

Nguyen

Joshi

Henebry

2019

Land

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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 km2 (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 km2)—and the National Resources Inventory—NRI (5034 km2)—than an estimation from the bi-temporal approach (8018 km2). 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.

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Section: Discussionsupporting

confidence: 87%

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

Nguyen

Joshi

Henebry

2019

Land

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“…These trends match the general findings of other studies (Arora & Wolter, ; Auch et al., ; Johnston, ), although those studies report statewide or regional values. Reporting cropland increase at the cropping district scale allows for a large enough region of interest to make meaningful conclusions while still including areas with similar physiographic, soil, and climatic conditions (USDA‐ERS, ).…”

Section: How Are Cropping Patterns Changing?supporting

confidence: 90%

“…These types of analyses are improving due to increasing availability of datasets, tools, and new methodology for geospatial analyses. These techniques have been applied recently to smaller scale research to identify local, more subtle trends in land use and cropping patterns specific to the Dakotas (Arora & Wolter, ; Auch et al., ; Otto et al., ; Reitsma et al., ).…”

Section: How Are Cropping Patterns Changing?mentioning

confidence: 99%

“…For example, some report corn and soybean area has increased primarily through conversion of marginal grasslands (Lark et al., ; Wright & Wimberly, ), while others maintain that the increase occurred mainly due to the return of Conservation Reserve Program (CRP) land into crop production (Johnston, ; Wimberly et al., ). While the sources of conversion and rates of change are disputed, these discrepancies are often the result of reporting over different study periods (Arora & Wolter, ).…”

Section: How Are Cropping Patterns Changing?mentioning

confidence: 99%

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Cropping pattern changes diminish agroecosystem services in North and South Dakota, USA

et al. 2020

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In the past several decades, North and South Dakota have experienced increased agricultural expansion and crop rotation simplification, namely an increase in corn (Zea mays L.)– soybean [Glycine max (L.) Merr.] systems. This review investigates the nature and extent of those changes, the underlying causes, and the consequences they have on ecosystem services. The framework of ecosystem services is underutilized in agricultural research, but it can be used to describe the sustainability, resistance, and resilience of the system in relation to these land use changes. The current trends are focused on maximizing provisioning services (i.e., food and fuel) at the expense of regulating, cultural, and supporting services. The decline of regulating services can be seen in increased peak river flow (up to 100% and 200% increases in South and North Dakota, respectively), as well as by the 100% increase in area treated by chemicals, partially due to diminished bioregulation of pests and weeds. The effects on supporting services are demonstrated by altered C balances and water cycling, while the loss of cultural connection to the land is evidenced by a 40% decrease in land conservation since 1997. Overall, these changes are making the land in North and South Dakota more susceptible to stressors, such as drought, crop pests, or even economic trends that could greatly harm these agroecosystems and have nationwide ramifications. To address these changes, producers need to provide a balance of agroecosystem services by optimizing currently available management strategies and possibly transforming agricultural practices for long‐term system stability.

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“…Studies on the Corn Belt using remote sensing have utilized mostly imaging-type instruments, such as MODIS. Many studies focus on the mapping of crops (Zhong et al, 2016) and the change in agricultural use of land (Arora and Wolter, 2018), monitoring growing conditions and droughts (Liu et al, 2018;Wu et al, 2015) and crop yield predictions (Johnson, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new space-borne perspective of crop productivity variations over the US Corn Belt

Somkuti

Boesch

Parker

et al. 2020

Preprint

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We analyse inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010&#8211;2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r&#8239;>&#8239;0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r&#8239;>&#8239;0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT&#8217;s sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation.[1] Somkuti et al.,&#160;"A new space-borne perspective of crop productivity variations over the US Corn Belt." Agricultural and Forest Meteorology 281 (2020): 107826.&#160;

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Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors

Cited by 20 publications

References 24 publications

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

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

Cropping pattern changes diminish agroecosystem services in North and South Dakota, USA

A new space-borne perspective of crop productivity variations over the US Corn Belt

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