Rangeland Monitoring Using Remote Sensing

Booth, D. Terrance; Tueller, Paul T.

doi:10.1080/713936105

Cited by 132 publications

(91 citation statements)

References 45 publications

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“…A number of studies have used remote sensing to estimate the percent cover, production and biophysical properties of grasslands [6,7,19]. Many of these studies have shown that the Normalized Difference Vegetation Index (NDVI) is highly correlated with green biomass, leaf area index, and radiation absorption by green vegetation (APAR green ) in grasslands [8,19], consistent with the simple LUE model approach, and allowing ready production estimates over large grassland areas.…”

Section: Introductionmentioning

confidence: 88%

“…Traditional methods of estimating prairie biomass can be subjective because they depend on the experience and skill of the field staff [7]. The estimation is also affected by the sample size and sampling method [9].…”

Section: Introductionmentioning

confidence: 99%

“…Prairie productivity is often estimated through biomass harvesting, which is expensive and time consuming [6][7][8]. Traditional methods of estimating prairie biomass can be subjective because they depend on the experience and skill of the field staff [7].…”

Section: Introductionmentioning

confidence: 99%

“…The estimation is also affected by the sample size and sampling method [9]. Accurate and fast methods of prairie production estimation that quickly acquire information over large areas are needed for range managers to evaluate the condition of prairies and adjust their management regimes [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, a major limitation in the application of satellite remote sensing in prairie management is the mismatch between the information that range managers need and the spatial scale of most satellite remote sensing. Due to the coarse pixel size (often several tens of meters to kilometers) [7] and broad bands of most satellite images, satellite remote sensing can best provide land cover or production estimates over large landscapes, or over regional or continental scales [2], but satellite data often have trouble providing detailed species distribution and prairie health information at the scale of small management units.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

et al. 2016

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Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies of grasslands have shown varying relationships between productivity and biodiversity, with most showing either a positive or a hump-shaped relationship where productivity peaks at intermediate diversity. In this study, we used airborne imaging spectrometry combined with ground sampling and eddy covariance measurements to estimate the spatial pattern of production and biodiversity for two sites of contrasting productivity in a southern Alberta prairie ecosystem. Resulting patterns revealed that more diverse sites generally had greater productivity, supporting the hypothesis of a positive relationship between production and biodiversity for this site. We showed that the addition of evenness to richness (using the Shannon Index of dominant species instead of the number of dominant species alone) improved the correlation with optical diversity, an optically derived metric of biodiversity based on the coefficient of variation in spectral reflectance across space. Similarly, the Shannon Index was better correlated with productivity (estimated via NDVI (Normalized Difference Vegetation Index)) than the number of dominant species alone. Optical diversity provided a potent proxy for other more traditional biodiversity metrics (richness and Shannon index). Coupling field measurements and imaging spectrometry provides a method for assessing grassland productivity and biodiversity at a larger scale than can be sampled from the ground, and allows the integrated analysis of the productivity-biodiversity relationship over large areas.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

et al. 2016

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Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

et al. 2018

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Innovations in machine learning and cloud‐based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services. We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per‐pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States from 1984 to 2017. Results were validated using three independent collections of plot‐level measurements, and resulting maps display land cover variation in response to changes in climate, disturbance, and management. The maps, which will be updated annually at the end of each year, provide exciting opportunities to expand and improve rangeland conservation, monitoring, and management. The data open new doors for scientific investigation at an unprecedented blend of temporal fidelity, spatial resolution, and geographic scale.

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A changing river: Long‐term changes of sinuosity and land cover in the Navasota River Watershed, Texas

Rhodes,

Talchabhadel,

Jordan

2024

River

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The Navasota River Basin, itself a tributary of the Brazos River in Texas, is a dynamic watershed undergoing many natural and anthropogenic changes. Local stakeholder involvement in this watershed is quite high, and many landowners in the southern portion of the watershed have concerns regarding the increasing frequency and duration of flooding on private property adjacent to the river, often attributing these impacts to the construction of the Lake Limestone dam. In this study, we examine historical flow data, channel morphology, land use/land cover, and precipitation. Our findings indicate that while there appears to be increasing flow in the northern portion of the watershed, temporal data gaps near the watershed outfall prevent the indication of such a trend in the southern portion of the watershed. Nevertheless, other natural and anthropogenic factors are evident in the watershed that may have a significant influence on downstream flooding. Overall river sinuosity (meandering) declined over the study period, with some river segments encountering significant straightening. Total river length declined by 4.3 km from 1972 to 2020. The number and length of offtake channels also decreased substantially during this period. Land use/land cover use shifted dramatically, with a 39.2% increase in impervious cover and a 12.5% decrease in herbaceous cover since 1972. Finally, yearly precipitation increased, with the change point occurring in 1972. Our findings suggest that the shortening and straightening of the river has reduced its volumetric capacity over time. Coupled with increasing impervious surface cover and precipitation, more water is being delivered downstream at a rate exceeding the watershed's ability to discharge it, thereby contributing to flooding issues expressed by stakeholders. We recommend that bathymetric data and supplemental flow monitoring and modeling within the watershed is needed to fully understand how anthropogenic and natural forces may further affect streamflow in the future.

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Rangeland Monitoring Using Remote Sensing

Cited by 132 publications

References 45 publications

Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

A changing river: Long‐term changes of sinuosity and land cover in the Navasota River Watershed, Texas

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