Remote Sensing for Grassland Management in the Arid Southwest

Marsett, Robert; Qi, Jiaguo; Heilman, Philip; Biedenbender, Sharon; Watson, M. Carolyn; Amer, Saud A.; Weltz, Mark A.; Goodrich, David C.; Marsett, R.

doi:10.2111/05-201r.1

Cited by 210 publications

(100 citation statements)

References 30 publications

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“…Observing dry-season herbaceous vegetation in semi-arid areas is important for a number of applications: forage monitoring, resource management, fire risk and emissions, and also erosion risk assessment (Marsett et al, 2006;Hagen et al, 2012;Ludwig and Tongway, 1995). Still, satellite survey of dry-season vegetation has received far less attention than green vegetation so far.…”

Section: Introductionmentioning

confidence: 99%

Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

Kergoat

Hiernaux

Dardel

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

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Section: Introductionmentioning

confidence: 99%

Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

Kergoat

Hiernaux

Dardel

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

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“…However, broad-scale data on such indicators are generally sparse for such ecosystem types that are not intensively managed. This low sampling density occurs because measuring these indicators in the field is time-intensive and expensive, especially in remote areas (Elzinga et al 1998, Holthausen et al 2005, and broad-scale remote sensing approaches typically cannot yet produce consistent measurements with the required accuracy and precision for long-term monitoring (Marsett et al 2006).…”

mentioning

confidence: 99%

Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability

Duniway

Karl

Schrader

et al. 2011

Environ Monit Assess

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Collection of standardized assessment and monitoring data is critically important for supporting policy and management at local to continental scales. Remote sensing techniques, including image interpretation, have shown promise for collecting plant community composition and ground cover data efficiently. More work needs to be done, however, evaluating whether these techniques are sufficiently feasible, cost-effective, and repeatable to be applied in large programs. The goal of this study was to design and test an image-interpretation approach for collecting plant community composition and ground cover data appropriate for local and continental-scale assessment and monitoring of grassland, shrubland, savanna, and pasture ecosystems. We developed a geographic information system image-interpretation tool that uses points classified by experts to calibrate observers, including point-by-point training and quantitative quality control limits. To test this approach, field data and high-resolution imagery (∼3 cm ground sampling distance) were collected concurrently at 54 plots located around the USA. Seven observers with little prior experience used the system to classify 300 points in each plot into ten cover types (grass, shrub, soil, etc.). Good agreement among observers was achieved, with little detectable bias and low variability among observers (coefficient of variation in most plots <0.5). There was a predictable relationship between field and image-interpreter data (R (2) > 0.9), suggesting regression-based adjustments can be used to relate image and field data. This approach could extend the utility of expensive-to-collect field data by allowing it to serve as a validation data source for data collected via image interpretation.

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“…Probably, the spatial resolution of temperature image make it difficult to discern fine resolution patterns of viscacha Second-order texture measure in a 3×3 moving window: CON contrast, ENT entropy Second-order texture measure in a 3×3 moving window: CON contrast, ENT entropy rat occurrence, so the variability explained by univariate model of temperature was very low. Moreover, our results showed that SATVI was the best greenness index; SATVI is sensitive to green and senescent vegetation because it uses the shortwave infrared band reflectance (bands 5 and 7 in the TM sensor) and furthermore includes the effect of bare soil factor (Marsett et al 2006), an important variable to be considered in arid environments. This greenness index was the best predictor of cover vegetation in the Monte Desert (Goirán et al 2012).…”

Section: Discussionmentioning

confidence: 88%

“…We evaluated green indices with a correction factor for soil background effects (Huete 1988) and sensitive to both, green and senescent vegetation (Goirán et al 2012). We tested raw greenness indices as independent variables in models: NDVI and GI from TC (Kauth and Thomas 1976;Crist and Cicone 1984) both sensitive to green vegetation, NDSVI (Normalized Difference Senescent Vegetation Index; Marsett et al 2006), which quantifies senescent and green biomass, SAVI (Soil Adjusted Vegetation Index; Huete 1988), which is sensitive to green vegetation but includes a parameter that normalizes the effect of bare soil factor, and SATVI (Soil Adjusted Total Vegetation Index; Marsett et al 2006), which is sensitive to both green and senescent vegetation, and includes a parameter that normalizes the effect of bare soil factor. In SAVI and SATVI, the parameter L has values of 1 for low vegetation cover, 0.5 for intermediate values, and 0.25 for high vegetation cover (Huete 1988).…”

Section: Remote Sensing Variablesmentioning

confidence: 99%

Remote sensing variables as predictors of habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling mammal living in a desert environment

2015

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Identifying high-quality habitats across large areas is a central goal in biodiversity conservation. Remotely sensed data provide the opportunity to study different habitat characteristics (e.g., landscape topography, soil, vegetation cover, climatic factors) that are difficult to identify at high spatial and temporal resolution on the basis of field studies. Our goal was to evaluate the applicability of remotely sensed information as a potential tool for modeling habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling species that lives in a desert ecosystem. We fitted models considering raw indices (i.e., green indices, Brightness Index (BI) and temperature) and their derived texture measures on locations used by and available for the viscacha rat. The habitat preferences identified in our models are consistent with results of field studies of landscape use by the viscacha rat. Rocky habitats were well differentiated by the second-order contrast of BI, instead of BI only, making an important contribution to the global model by capturing the heterogeneity of the substratum. Furthermore, rocky habitats are able to maintain more vegetation than much of the surrounding desert; hence, their availability might be estimated using SATVI (Soil Adjusted Total Vegetation Index) and its derived texture measures: second-order contrast and entropy. This is the first study that evaluates the usefulness of remotely sensed data for predicting and mapping habitat suitability for a small-bodied rock dwelling species in a desert environment. Our results may contribute to conservation efforts focused on these habitat specialist species by using good predictors of habitat quality.

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Remote Sensing for Grassland Management in the Arid Southwest

Cited by 210 publications

References 30 publications

Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability

Remote sensing variables as predictors of habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling mammal living in a desert environment

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