Remote Sensing for Grassland Management in the Arid Southwest

Abstract

We surveyed a group of rangeland managers in the Southwest about vegetation monitoring needs on grassland. Based on their responses, the objective of the RANGES (Rangeland Analysis Utilizing Geospatial Information Science) project was defined to be the accurate conversion of remotely sensed data (satellite imagery) to quantitative estimates of total (green and senescent) standing cover and biomass on grasslands and semidesert grasslands. Although remote sensing has been used to estimate green vegetation cover, in arid grasslands herbaceous vegetation is senescent much of the year and is not detected by current remote sensing techniques. We developed a ground truth protocol compatible with both range management requirements and Landsat’s 30 m resolution imagery. The resulting ground-truth data were then used to develop image processing algorithms that quantified total herbaceous vegetation cover, height, and biomass. Cover was calculated based on a newly developed Soil Adjusted Total Vegetation Index (SATVI), and height and biomass were estimated based on reflectance in the near infrared (NIR) band. Comparison of the remotely sensed estimates with independent ground measurements produced r2 values of 0.80, 0.85, and 0.77 and Nash Sutcliffe values of 0.78, 0.70, and 0.77 for the cover, plant height, and biomass, respectively. The approach for estimating plant height and biomass did not work for sites where forbs comprised more than 30% of total vegetative cover. The ground reconnaissance protocol and image processing techniques together offer land managers accurate and timely methods for monitoring extensive grasslands. The time-consuming requirement to collect concurrent data in the field for each image implies a need to share the high fixed costs of processing an image across multiple users to reduce the costs for individual rangeland managers.

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

“…The addition of the NDVI and soil characteristics in addition to microwave observations to these models reduced the RMSE for soil moisture retrieval by 30% approximately. This also opens the discussion for use of more sophisticated vegetation indices such as Fractional Green Vegetation Cover, Green Leaf Area Index, and Soil Adjusted Total Vegetation Index [63,64] to differentiate vegetation and soil response in soil moisture retrieval using microwave remote sensing data. Validation results showed that fuzzy logic and neural network models performed better compared to multiple regression.…”

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

“…Due to the issue of cloud cover, a set of images was selected representing only nine months of the year 2014 (January, February, April, May, Jun, July, August, September, October). In order to improve the predictive capability for toxic metals in soils and as we assumed that vegetation cover may provide important information for soil toxic metals' spatial modeling, the following spectral indices were derived from the satellite images: BCI, biophysical composition index [43]; EVI, enhanced vegetation index [44]; LSWI, land surface water index [45]; NDVI, normalized differential vegetation index [46]; SATVI, soil-adjusted total vegetation index [47,48]; SAVI, soil-adjusted vegetation index [49]; TVI, transformed vegetation index [50]; WDVI, weighted difference vegetation index [51,52]; and tasseled cap transformation including brightness, greenness and wetness [53]. The formulas used to derive these indices are shown in Table 2.…”

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

“…Early determination of the distribution and severity of rapidly spreading invasive populations of musk thistle is needed to implement mitigation treatments, but accurate assessments are often difficult or impossible to obtain with ground surveys, because of the extensive land area involved, time and labor required and inaccessibility of many areas [25][26][27]. Therefore, remote sensing has received considerable attention as a rapid, inexpensive and non-destructive method for assessing non-native noxious species invasions [6,27].…”