Geology topics

E. Raymond Hunt

Publications and source records attributed to E. Raymond Hunt.

3 recordsLinked to original sources

USGS2024

A global view of remote sensing of rangelands: Evolution, applications, future pathways

The application of digital remote sensing to rangelands is as long as the history of digital remote sensing itself. Before the launch of the Earth Resources Technology Satellite (ERTS) – later renamed Landsat, scientists were evaluating the use of multispectral aerial imagery to map soils and range vegetation (Yost and Wenderoth 1969). During the late 1960’s, the promise of ERTS, designed to drastically improve our ability to update maps and study earth resources, particularly in developing countries, was eagerly anticipated by a number of government agencies (Carter 1969). With the ERTS launch on July 23, 1972, a flurry of research activity aimed at the application of this new data source to map earth resources began. Practitioners who pioneered the use of satellite based digital remote sensing found the new data source a significant value for rangeland assessments (e.g., Rouse et al., 1973, Rouse et al., 1974, Bauer 1976). This early work established many of the basic techniques still in use today to assess and monitor global rangelands. The following sub-sections discuss the evolution of remote sensing data, methods, and approaches in various decades.

Book chapter↗

USGS2014

Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications

An important application of unmanned aircraft systems (UAS) may be remote-sensing for precision agriculture, because of its ability to acquire images with very small pixel sizes from low altitude flights. The objective of this study was to compare information obtained from two different pixel sizes, one about a meter (the size of a small vegetation plot) and one about a millimeter. Cereal rye (Secale cereale) was planted at the Beltsville Agricultural Research Center for a winter cover crop with fall and spring fertilizer applications, which produced differences in biomass and leaf chlorophyll content. UAS imagery was simulated by placing a Fuji IS-Pro UVIR digital camera at 3-m height looking nadir. An external UV-IR cut filter was used to acquire true-color images; an external red cut filter was used to obtain color-infrared-like images with bands at near-infrared, green, and blue wavelengths. Plot-scale Green Normalized Difference Vegetation Index was correlated with dry aboveground biomass ( ${mbi {r}} = 0.58$ ), whereas the Triangular Greenness Index (TGI) was not correlated with chlorophyll content. We used the SamplePoint program to select 100 pixels systematically; we visually identified the cover type and acquired the digital numbers. The number of rye pixels in each image was better correlated with biomass ( ${mbi {r}} = 0.73$ ), and the average TGI from only leaf pixels was negatively correlated with chlorophyll content ( ${mbi {r}} = -0.72$ ). Thus, better information for crop requirements may be obtained using very small pixel sizes, but new algorithms based on computer vision are needed for analysis. It may not be necessary to geospatially register large numbers of photographs with very small pixel sizes. Instead, images could be analyzed as single plots along field transects.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS1995

A remote sensing based vegetation classification logic for global land cover analysis

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Remote Sensing of Environment↗