The residential pattern of military personnel associated with Offutt Air Force Base, Nebraska, 1970

A particular method of inspecting data is known to all scholars as the geographic method, based on charting the limits or range of phenomena, features, or traits that have a localized distribution on the earth. Geographers have long been concerned with the distribution of phenomena, whether they be species of trees, refugees, cities, land use, resources, glaciers, farms, ethnic groups, religions, or a myriad of other examples, and distributional analyses are fundamental to the discipline of geography. During the infancy of geography as a discipline, the studies of distributions were mainly descriptive, but as the subject grew and became, more sophisticated, comparison with other patterns and explanation became important. As Taaffe has noted, the geographer still describes and analyzes patterns found on maps, but behavior and process are becoming important considerations. This paper, too, is designed to study the patterns found on maps, that is, the residential patterns associated with the military personnel of Offutt Air Force Base, Nebraska, Process is also one of the considerations

Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters

In this study we tested the applicability of a method, originally developed for terrestrial plant leaves, to retrieve chlorophyll-a concentrations from reflectance spectra of turbid productive waters. We tuned the conceptual model according to the optical characteristics of the aquatic medium, and accurately predicted chlorophyll-a concentrations in water bodies over a wide range of optical conditions. Our results provide evidence that this technique may be considered as a general solution, independent of the type of medium, for assessing chlorophyll concentration in optically deep media using remotely sensed data

Remote Estimation of Crop Health [ABSTRACT]

First sentence of abstract: In this paper we discuss developed techniques to remotely assess the fraction of photosynthetically active radiation absorbed by green vegetation [fAPAR-GREEN=fAPAR*(green LAI/total LAI)], fractional green vegetation cover (FGVC), green leaf area index (GLAI) green leaf biomass (GLB) and net ecosystem carbon dioxide exchange (NEE) in crops

Differential Spatial Gradients of Wheat Streak Mosaic Virus into Winter Wheat from a Central Mite-Virus Source

The wheat curl mite (WCM), Aceria tosichella Keifer, transmits three potentially devastating viruses to winter wheat. An increased understanding of mite movement and subsequent virus spread through the landscape is necessary to estimate the risk of epidemics by the virus in winter wheat. Owing to the small size of WCMs, their dispersal via wind is hard to monitor; however, the viruses they transmit produce symptoms that can be detected with remote sensing. The objective of this study was to characterize the spatial dispersal of the virus from a central mite-virus source. Virus infection gradients were measured spatially by using aerial remote sensing, ground measurements, geostatistics, and a geographic information system between 2006 and 2009. The red edge position vegetation index as measured via aerial imagery was significantly correlated with in-field biophysical measurements. The occurrence of virus symptoms extended differentially in all directions from mite-virus source plots, and predictions from cokriging revealed an oval pattern surrounding the source but displaced to the southeast. The variable dispersal in different directions appeared to be influenced by the mite source density and wind direction and speed, but temperature also seemed likely to have affected mite spread. The spatial spread revealed in this study may be used to estimate the potential sphere of influence of mite-infested volunteer wheat in production fields. These risk parameter estimates require further validation, but they may potentially aid growers in making better virus management decisions regarding differential virus spread potential away from a central source

Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach

Coral reefs and associated benthic habitats are heterogeneous in nature. A remote sensor designed to discriminate these environments requires a high number of narrow, properly placed bands which are not currently available in existing satellite sensors. Optical hyperspectral sensors mounted on aerial platforms seem to be appropriate for overcoming the lack of both high spectral and spatial resolution of satellite sensors. This research presents results of an innovative coral reef application by such a sensor. Using hyperspectral Airborne Imaging Spectroradiometer for Applications (AISA) Eagle data, the approach presented solves the confounding influence of water column attenuation on substrate reflectance on a per-pixel basis. The hyperspectral imagery was used in band ratio algorithms to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). The water column correction technique produced a bottom albedo image which revealed that the dark regions comprised of sea grasses and benthic algae had albedo values ≈15%, whereas sand- and coral-dominated areas had albedos \u3e30% and ≈15–35%, respectively. The retrieved bottom albedo image was then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment

Elements of an Integrated Phenotyping System for Monitoring Crop Status at Canopy Level

Great care is needed to obtain spectral data appropriate for phenotyping in a scientifically rigorous manner. This paper discusses the procedures and considerations necessary and also suggests important pre-processing and analytical steps leading to real-time, non-destructive assessment of crop biophysical characteristics. The system has three major components: (1) data-collection platforms (with a focus on backpack and tractor-mounted units) including specific instruments and their configurations; (2) data-collection and display software; and (3) standard products depicting crop-biophysical characteristics derived using a suite of models to transform the spectral data into accurate, reliable biophysical characteristics of crops, such as fraction of green vegetation, absorbed photosynthetically active radiation, leaf area index, biomass, chlorophyll content and gross primary production. This system streamlines systematic data acquisition, facilitates research, and provides useful products for agriculture

Remote estimation of leaf area index and green leaf biomass in maize canopies

Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to- high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6