Mosquito-born infectious diseases are a serious public health concern, not only for the less developed countries, but also for developed countries like the U.S. Larviciding is an effective method for vector control and adverse effects to non-target species are minimized when mosquito larval habitats are properly surveyed and treated. Remote sensing has proven to be a useful technique for large-area ground cover mapping, and hence, is an ideal tool for identifying potential larval habitats. Locating small larval habitats, however, requires data with very high spatial resolution. Textural and contextual characteristics become increasingly evident at higher spatial resolution. Per-pixel classification often leads to suboptimal results. In this study, we use pan-sharpened Ikonos data, with a spatial resolution approaching 1 meter, to classify potential mosquito larval habitats for a test site in South Korea. The test site is in a predominantly agricultural region. When spatial characteristics were used in conjunction with spectral data, reasonably good classification accuracy was obtained for the test site. In particular, irrigation and drainage ditches are important larval habitats but their footprints are too small to be detected with the original spectral data at 4-meter resolution. We show that the ditches are detectable using automated classification on pan-sharpened data.
The spatial resolution of spaceborne instrument has increased substantially in the three decades since Landsat-1 was launched. Higher spatial resolution has made some applications possible. But it has also brought about new challenges in ground cover classification. At a resolution around 1 meter, vegetation often displays distinct textures. Hence texture may make differentiation among some cover types possible. Ikonos panchromatic and multispectral data are used to examine how spatial features improve classification accuracy. In this study, textural features are extracted from co-occurrence matrices, contextual features are derived from neighborhood properties, and maximum likelihood method is used for classifications. It is shown that for the test data both types of spatial features, and especially the contextual measures, can significantly improve the classification accuracies. Discrete wavelet transform is used to extract textural features for two types of vegetation. Transformed divergence, a measure of separability, is shown much enhanced when textural features are included.
Knowledge discovery from online journals, abstracts and citation indices, cross-referenced with the NASA Distributed Active Archive Center (DAAC) user/order database to close the data-knowledge loop. Knowledge discovery in database (KDD) has been defined as the nontrivial process of discovering valid, novel, potentially useful, and ultimately understandable patterns from data. The core step of the KDD process is data mining. Data mining is all about extracting patterns from an organization's stored or warehoused data. These patterns can be used to gain insight into aspects of the organization's operations and predict outcomes for future situations. Patterns often concern the categories to which situations belong. For example, here is the situation, to decide if a journal paper used the NASA DAAC data or not, starting from the Goddard DAAC user/order database record, a rule-based classifier was developed and rules were found firstly with training samples, then these rules were applied to recognize new patterns.
Whether the Sun has significantly influenced the climate during the last century has been under extensive debates for almost two decades. Since the solar irradiance varies very little in a solar cycle, it is puzzling that some geophysical parameters show proportionally large variations which appear to be responding to the solar cycles. For example, variation in low-altitude clouds is shown correlated with solar cycle, and the onset of Forbush decrease is shown correlated with the reduction of the vorticity area index. A possible sun-climate connection is that galactic cosmic rays modulated by solar activities influence cloud formation. In this paper, we apply wavelet transform to satellite and surface data to examine this hypothesis. Data analyzed include the time series for solar irradiance, sunspots, UV index, temperature, cloud coverage, and neutron counter measurements. The interactions among the elements in the Earth system under the external and internal forcings give out very complex signals. The periodicity of the forcings or signals could range widely. Since wavelet transforms can analyze multi-scale phenomena that are both localized in frequency and time, it is very useful techniques for detecting, understanding and monitoring climate changes.
The Earth's temperature has risen approximately 0.5 degrees C in the last 150 years. Because the atmospheric concentration of carbon dioxide has increased nearly 30 percent since the industrial revolution, a common conjecture, supported by various climate models, is that anthropogenic greenhouse gases have contributed to global warming. Another probable factor for the warming is the natural variation of solar irradiance. Although the variation is as small as 0.1 percent, it is hypothesized that it contributes to part of the temperature rise. Warmer or cooler ocean temperature at one part of the Globe may manifest as abnormally wet or dry weather patterns some months or years later at another part of the globe. Furthermore, the lower atmosphere can be affected through its coupling with the stratosphere, after the stratospheric ozone absorbs the UV portion of the solar irradiance. In this paper, we use wavelet transforms based on Morlet wavelet to analyze the time-frequency properties in several datasets, including the radiation Budget measurements, the long-term total solar irradiance time series, the long-term temperature at two locations for the North and the South Hemisphere. The main solar cycle, approximately 11 years, are identified in the long-term total solar irradiance time series. The wavelet transform of the temperature datasets show annual cycle but not the solar cycle. Some correlation is seen between the length of the solar cycle extracted from the wavelet transform and the North Hemisphere temperature time series. The absence of the 11-year cycle in a time series does not necessarily imply that the geophysical parameter is not affected by the solar cycle; rather it simply reflects the complex nature of the Earth's response to climate forcings.
Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from co- occurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.
Surface vegetation is an important link in the coupling between the atmosphere and the biosphere. Monitoring the condition of vegetation cover on the Earth surface is essential for detecting the changes in climate. Advanced Very-High Resolution Radiometer 10-day composite data in 1 X 1 degree resolution from NASA/GSFC and a global vegetation ground truth in the same resolution from the University of Maryland's Geography Department are used in this study. A fully connected multilayer neural network is used for supervised classification. The normalized difference vegetation index, which is also called the greenness index, is used along with the surface reflectance and brightness temperature as the input features. Trainings and classifications are performed for two spatial modes and three multitemporal modes.
Increase in the levels of carbon dioxide and other greenhouse gases over the next half-century may result in an increase in global mean temperature. The recent discoveries of possible advance of arctic tree line into the tundra and earlier greening of northern vegetation provide additional warnings that global warming may indeed be occurring. On the Earth surface, land cover and its changes affect the coupling between the biosphere and the atmosphere, and control many important Earth system processes. Satellite remote sensing provides long-term, repeated coverage over extended area and is the essential data source for monitoring climate changes. An Advanced Very-High Resolution Radiometer (AVHRR) Pathfinder dataset from 1987, in 1 degree latitude-longitude resolution, is used in this study. Two reflective channels, two thermal channels, and Normalized Difference Vegetation Index are the input parameters. In conjunction with a global vegetation ground truth, a multi-layer neural network is trained and used for global vegetation characterization. As the same type of vegetation may appear very differently over different parts of the Earth at any given time, global classification is more difficult than local classification. It is shown that a multitemporal approach, in which data from multiple dates are used, may improve the accuracy.
Popularized by the images from weather satellites and other Earth observing satellites, remote sensing from space has already become a household term. Airborne remote sensing, however, still holds its important place in the development of the remote sensing technology and in many applications. Prototype, proof-of-concept instruments are flown on aircraft before their improved versions are deployed on space shuttles or satellites. Airborne remote sensing is also more practical for regional applications. Since an aircraft flies in the Earth's atmosphere, factors contributing to geometric distortion are less systematic and more random. Substantial amount of effort is usually required to rectify the measurements. In this study, a scanner model is developed to generate simulated aircraft measurements. A backpropagation network and other variations are used to map the measurement space to the physical space. For measurements conducted over extensive area, techniques of anchoring the training data is developed such that geometric rectification can be performed in segments. Advantages of the neural network methods over the traditional method, and the need of constrained optimization are discussed.
Global temperature has risen significantly during the past century, based on measurements at meteorological stations. The two important factors in climate forcings are anthropogenic activities and changes of incident solar irradiance. The 14 years of Nimbus-7 Earth radiation budget measurements, which started in November 1978 and continued through January 1993, provide an important long-term record of solar irradiance, absorbed solar energy, and outgoing long wave and net radiation. Wavelet transforms are powerful techniques to decompose time series in time and frequency domains, and to isolate relevant characteristics. Transforms based on Morlet wavelets and Mexican Hat wavelets are used to examine the periodicities in the 14-year Nimbus-7 measurements and the 9-year Solar Maximum Mission (SMM) measurements. Short-term variations with periods ranging from a few days to 30 or 40 days are identified. The importance of selecting wavelet kernels is illustrated, pointing toward the need of wavelet transform and adaptive wavelet transform. The superiority of wavelet analyses over short-time Fourier transform and Gabor transform is also demonstrated.
The unprecedented data volume in the era of NASA's Mission to Planet Earth (MTPE) demands innovative information extraction methods and advanced processing techniques. The neural network techniques, which are intrinsic to distributed parallel processings and have shown promising results in analyzing remotely sensed data, could become the essential tools in the MTPE era. To evaluate the information content of data with higher dimension and the usefulness of neural networks in analyzing them, measurements from the GER 63-channel airborne imaging spectrometer data over Cuprite, Nevada, are used. The data are classified with 3-layer Perceptron of various architectures. It is shown that the neural network can achieve a level of performance similar to conventional methods, without the need for an explicit feature extraction step.
Since the beginning of space-borne remote sensing less than two decades ago, sensor technologies have greatly advanced. State-of-the-art sensor systems, such as the Earth Observing System (Eos), will have higher spatial, spectral, radiometric resolutions, which are selected together to enhance the capabilities of differentiating surface categories. Multiple, pointable platforms covering different parts of electromagnetic spectrum will circle the earth, detect and monitor terrestrial changes, and measure the essential surface and atmospheric parameters. It is anticipated that sensors of future generations will have even greater spectral, spatial, and radiometric resolutions. However, resolutions cannot increase without bound. Noise of electronic, mechanical, optical, and atmospheric origins limits the effective resolutions of the measurements. In this paper, several aspects of the effects of radiometric resolution on remotely sensed data are examined. It is shown that higher radiometric resolution indeed improves information content. But to improve the utilization of the spectrometer, radiometric sensitivity must also be modified. Using clusters constructed from empirical signatures, it is shown that discriminability between clusters converges beyond 6 bits. It is also shown that the information content of current sensor measurements is not limited by the atmosphere, but by the sensitivity settings of the spectrometers. It is proposed that a spectrometer with variable sensitivity and capable of sampling scene radiance into full dynamic range be used as a means of optimizing information content. If implemented, the same amount of information content currently observed could be measured with fewer bits.