Snowfield mapping with K-band radar

Watte, William P.; Macdonald, H. C.

doi:10.1016/s0034-4257(70)80016-5

Cited by 20 publications

(9 citation statements)

References 6 publications

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“…The contrasting dielectrics should be sufficient to provide maximum contrasting returns on the SLAR imagery, which would allow accurate delineation of these two different soil types. Support for this hypothesis was revealed in a recent study by Waite and MacDonald [1970] in which the radar signal return from glacial ice was shown to be among the lowest returns recorded on the imagery. The signal contrast was sufficient to map areas of glacier ice accurately.…”

Section: Applicabilitymentioning

confidence: 75%

Soil Moisture Detection with Imaging Radars

Macdonald¹,

Waite²

1971

Water Resources Research

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The high degree of correlation between the electrical properties of soil and soil moisture content are well known. Considerable experimental work has been conducted by using radar for the determination of soil moisture. However, in the transition from a controlled measurement device to an operational imaging radar, the effects of soil moisture have been extremely difficult to separate from other terrain parameters influencing the radar return signal. The data presented in this study suggest that presently available dual polarized, K‐band, side‐looking imaging radars provide a capability for revealing a qualitative estimate of soil moisture content. When used as a supplement to aerial photography in temperate climates, radar imagery analysis will decrease the ambiguity of soil type reconnaissance. In the Arctic, an imaging radar may provide data for mapping regions of permafrost, and this process could be accomplished in a sequential manner regardless of weather or time of day. The use of additional multifrequency multipolarization imaging radars and the relative foliage penetration of each should be investigated as a possible means of gathering quantitative soil moisture information.

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Section: Applicabilitymentioning

confidence: 75%

Soil Moisture Detection with Imaging Radars

Macdonald¹,

Waite²

1971

Water Resources Research

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“…For example, both spectral classification and traditional methods of aerial photograph interpretation perform poorly in the identification of sparse woody vegetation (open woodlands and shrublands) and native non‐woody vegetation (grasslands and some wetland types). The discrimination of sparse woody vegetation from non‐woody vegetation and post‐clearing woody regrowth has posed particular difficulties, as has the discrimination of native grasslands from exotic pastures, and remnant native grassland from ‘secondary’ grassland derived from clearing of native woody vegetation (Andrews & Flemons 1997; DLWC 2001; ERIC 2001; Walter & Schelling 2004). The causes and magnitude of these problems vary spatially over such a large and diverse region as NSW.…”

Section: Methodsmentioning

confidence: 99%

A protocol for assessment and integration of vegetation maps, with an application to spatial data sets from south‐eastern Australia

Keith¹,

Simpson²

2008

Austral Ecology

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Maps are important tools in natural resource management. Often, there may be multiple maps that represent the same resource, which have been constructed using very different philosophies and methods, at different scales, for different dates and areas. In such cases, conservation planners and other natural resource managers are faced with a choice of map that will best serve their decision making. However, the best available information for a given purpose is often a combination of data from a number of different source maps. In this paper we present a protocol for assessing and integrating multiple maps of vegetation for a particular area of interest. The protocol commences with a consideration of management or policy context and technical issues to determine the basic specifications for the map. It then defines and assesses a set of measurable attributes, representing the concepts of theme, accuracy, precision and currency, for all candidate maps available for compilation.The resulting ranks for accuracy, precision and currency are used to compute a suitability index, which is used to assemble a composite map from the most suitable candidate maps. The final step in the protocol is to display spatial patterns in thematic consistency, accuracy, precision and currency for the composite map. We demonstrate the application of the protocol by constructing a map that discriminates structurally intact native vegetation from cleared land for the whole of New South Wales, south-eastern Australia. The source data include 46 maps that cover various parts of the region at various scales and which were made at different dates using different methods. The protocol is an explicit and systematic method to evaluate the strengths and weaknesses of alternative data sets. It implements spatial integration in a way that promotes overall accuracy, precision and currency of map data. It also promotes transparent reporting of map limitations, to help map users accommodate risks of map errors in their decision making, and to inform priorities for future survey and mapping.

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“…X-band or lower frequencies (longer wavelengths) are not generally useful for detecting and mapping dry snow because the size of snow particles is much smaller than the size of the radar wavelength. Thus there is little chance for the signal to be intercepted and scattered by the relatively small ice crystals comprising a snowpack (Waite & MacDonald, 1970). Longer wavelengths travel almost unaffected through dry snow.…”

Section: Snowmentioning

confidence: 99%