Perry J. Hardin

Resolution enhancement of spaceborne scatterometer data

A method for generating enhanced resolution radar images of the Earths surface using spaceborne scatterometry is presented. The technique is based on an image reconstruction technique that takes advantage of the spatial overlap in scatterometer measurements made at different times to provide enhanced imaging resolution. The reconstruction algorithm is described, and the technique is demonstrated using both simulated and actual Seasat-A Scatterometer (SASS) measurements. The technique can also be used with ERS-1 scatterometer data. The SASS-derived images, which have approximately 4-km resolution, illustrate the resolution enhancement capability of the technique, which permits utilization of both historic and contemporary scatterometer data for medium-scale monitoring of vegetation and polar ice. The tradeoff between imaging noise and resolution inherent in the technique is discussed. >

Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities

Although potential applications abound, small-scale unmanned aerial vehicles have not yet been widely used for environmental remote sensing. Several challenges remain to be overcome until widespread adoption is possible. One problem is the challenge inherent in flying fragile small-scale aircraft with low weight limits and narrow center of gravity tolerances. Other challenges include: (1) the hostile natural environment in which the aircraft fly; (2) the limits of on-board power; (3) the paucity of commercially available sensors; (4) the difficulties involved in managing and analyzing the large imagery volume generated during a sortie; and (5) the federal regulations in the United States designed to ensure the safety of commercial and private air travel. Each of these challenges is formidable, and overcoming them will require the use of technologies that are currently experimental. However, within each challenge are opportunities for researchers willing to act as innovative pioneers in the remote sensing community.

Quantitative phosphorus measurement: A field test procedure for archaeological site analysis at Piedras Negras, Guatemala

Currently there is a wide interest in the use of chemical analyses for the evaluation of anthropogenically altered soils and other archaeological deposits. Because soil phosphorus levels increase in areas of human habitation, and leave a permanent signature that can only be removed by erosion of the soil itself, phosphorus mapping has become a popular field procedure to indicate areas of habitation where overt evidence of ancient occupance is absent. We have developed a methodology to obtain accurate acid-extractable phosphorus concentrations (mg/kg) in calcareous soils under the primitive field conditions of Piedras Negras, Guatemala. Predicated on Mehlich-II acid extractant and colorimetric methods, this procedure processed 36 samples per hour at very low cost per sample. Based on eight replicate measurements of a group of samples, the coefficient of variation of the procedure was 8.3%. Subsequent analysis of 35 soil samples in a controlled laboratory revealed a moderate correlation of 0.44 between the Mehlich-extractable phosphorus and total phosphorus. The correlation was 0.91 between the Mehlich procedure and Olsen bicarbonate extractable phosphorus, indicating that Mehlich-based results are similar to those obtainable using a traditional extractable phosphorus method on soils of neutral to alkaline pH. There was a moderate correlation between Mehlich P and ring-test rating (r = 0.42). The wider dynamic range of the Mehlich extraction, coupled with the use of a battery-operated colorimeter, facilitated the finding of a refuse midden within an area of phosphate enriched soils. Further tests indicated that phosphorus concentrations measured in the field deviated by only 7% from those made under controlled laboratory conditions.

Vegetation studies of the Amazon basin using enhanced resolution Seasat scatterometer data

The Seasat-A scatterometer (SASS) was designed to measure the near-surface wind field over the ocean by inferring the wind from measurements of the surface radar backscatter. While backscatter measurements were also made over land, they have been primarily used for the calibration of the instrument. This has been due in part to the low resolution of the scatterometer measurements (nominally 50 km). In a separate paper the present authors introduced a new method for generating enhanced resolution radar measurements of the Earths surface using spaceborne scatterometry. In the present paper, the method is used with SASS data to study vegetation classification over the extended Amazon basin using the resulting medium-scale radar images. The remarkable correlation between the Ku-band radar images and vegetation formations is explored, and the results of several successful experiments to classify the general vegetation classes using the image data are presented. The results demonstrate the utility of medium-scale radar imagery in the study of tropical vegetation and permit utilization of both historic and contemporary scatterometer data for studies of global change. Because the scatterometer provides frequent, wide-area coverage at a variety of incidence angles, it can supplement higher resolution instruments which often have narrow swaths with limited coverage and incidence angle diversity. >

Remote sensing/GIS integration to identify potential low-income housing sites

Abstract Perhaps the most notable problem in the rapid pace of urbanization in the developing world is the need for housing and the provision of related services. The quality of planning and decision making processes can be substantially improved when suitable data are appropriately and efficiently handled. This study reviews the development of remote sensing and geographic information system (GIS) techniques for urban analysis. It then applies these techniques to evaluate several types of planning related information in a raster based (GIS) to identify potential low income housing sites in the eastern portion of the Bangkok Metropolitan Area. This work demonstrates how satellite imagery can provide both site specific information on land cover for mapping urban residential land use, and also act as a medium to generate a variety of GIS coverages.

Mapping, Measuring, and Modeling Urban Growth

Immediately after World War II, developers in the United States took advantage of market demand and government incentives to build new housing subdivisions for returning soldiers anxious to marry, begin families, and resume civilian life. New developments such as Levittown (New York), Park Forest (Illinois) and Lakewood (California)1 sprang up and were quickly filled with affordable cookie-cutter homes for veterans seeking the American Dream of suburban home ownership (Hayden 2003). The baby boom followed. As a result of the boom and international immigration, the U.S. population grew from 151 million to 300 million between 1950 and 2007. To accommodate this expanding population growth, cities and towns in the U.S. rapidly spread into their rural hinterlands.

Investigating SeaWinds Terrestrial Backscatter: Equatorial Savannas of South America

Because tropical grasslands play an important role in the storage of global carbon, monitoring them is critical to evaluating global climate change. The goal of this research is to model seasonal SeaWinds Ku-band backscatter in five savanna areas of Colombia, Venezuela, and Brazil as a function of biophysical changes in the savanna landscape. Multiple regression modeling demonstrates that savanna Ku-band backscatter is a function of (1) savanna grass biomass/leaf area, (2) soil moisture, and (3) other soil characteristics. Fit for the regression models is excellent (R � 0.87 and 0.81, respectively, for the horizontal and vertical polarization case). The horizontal—vertical polarization difference is also moderately related to precipitation (R � 0.71). The results from this modeling are consistent with theory predicated on previous C- and X-band research. The possibility of monitoring savanna vegetation, soil moisture, and rainfall using Ku-band radar and scatterometry is discussed.

Detecting Squarrose Knapweed (Centaurea virgata Lam. Ssp. squarrosa Gugl.) Using a Remotely Piloted Vehicle: A Utah Case Study

Determining the location of founding weed populations is critical to minimizing the diffusion of weedy species. Remote sensing is a promising tool for early detection of these small weed patches. The objective of this study was to determine the capability of a small remotely piloted vehicle (RPV) (carrying a digital camera and GPS) to acquire aerial photography from which small infestations of squarrose knapweed (Centaurea virgata Lam. Ssp. squarrosa Gugl.) could be detected and mapped. Two Utah rangeland sites were studied. The location of squarrose knapweed found on the digital air aerial photography (true color) was compared to a complete census of knapweed conducted on the ground. Although the two study sites had different vegetation species mixes, site histories, and soil conditions, the results were comparable. Despite the large scale of the aerial photography, the knapweed detection rate on the spring photography was only 5%. In contrast, knapweed detection rates on late summer imagery were about 50%. False alarm rates at all seasons were extremely low. Despite the capability shown for weed detection with the RPV and digital camera system, the practical difficulties of using a small RPV in the field requires more research before the system can be operationalized for weed management tasks.

Introduction—Small-Scale Unmanned Aerial Systems for Environmental Remote Sensing

In a recently published article entitled “How UAVs Will Change Aviation,” David Esler (2010) discussed many ways that unmanned aerial systems (UAS) have changed (and will continue to change) traditional aviation. Simply stated, Esler believed that UAS are not only here now, but they are here to stay. We share that opinion regarding small-scale UAS for environmental remote sensing—while they may currently be experimental, their use in environmental monitoring, mapping, and management will certainly increase in the future. In their present state of development, UAS are popular in academic and research remote sensing where they have been successfully used to acquire high-spatial-resolution imagery and other environmental data. The appeal of UAS to researchers is obvious. Not only can UAS potentially obtain timely imagery over areas that are difficult or dangerous to access by traditional means, this imagery can usually be acquired at a lower cost relative to other collection methods (Hardin and Hardin, 2010). The popularity of unmanned aerial systems has coincided with the rapid deployment and use of unmanned vehicles by the military (Newcome, 2004). Militaries in many countries worldwide are either flying UAS on an operational basis or are actively seeking that capability. With this increased interest in UAS, the underpinning technologies—airframe design, materials, aviation electronics, sensor systems, etc.—have also continued to improve. However, the success and proliferation of military UAS has come with an unexpected consequence—more active governmental oversight over all UAS operations. The resulting regulatory burden that must be borne by practitioners wanting to fly civilian UAS represents a substantial impediment to widespread adoption of environmental remote sensing from small unmanned aircraft. Given the promise and challenges of using UAS in environmental remote sensing, more research is called for to: (1) remove the current limitations of the technology; and (2) build effective systems and methodologies for its successful use in environmental applications. Within the foregoing context, this special issue of GIScience & Remote Sensing focuses on the use of UAS for environmental remote sensing. The processing and analysis of the imagery acquired by small-scale UAS remains a significant challenge to efficient use of that imagery. The paper by Laliberte and Rango (2011) continues to advance the leading edge of mapping ground cover from very large scale aerial imagery using automated and object-oriented approaches. In the

A New Method to Correct Pushbroom Hyperspectral Data Using Linear Features and Ground Control Points

Data acquired using aerial pushbroom scanners often contain aircraft motion errors—especially aircraft roll error. This article describes a new algorithm to correct roll errors using both ground control points and linear features digitized on a reference map and the uncorrected image. The algorithm is different from previous methods used to correct aerial pushbroom data because it does not solely rely on a single or multiple straight lines. Rather, it is able to integrate both points and lines together to correct the error. With an associated loss of precision, it can also use points or lines separately. The method is tested on a hyperspectral dataset acquired in the Range Creek, Utah area during June 2009. It was found that the approach is an effective way to remove aircraft roll errors and prepare the image for additional georegistration. A program with a graphical user interface has been written using the algorithm and is available on the projects website.