Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Snow and Ice Applications of AVHRR in Polar Regions: Report of a Workshop

The third symposium on Remote Sensing of Snow and Ice, organized by the International Glaciological Society, took place in Boulder, Colorado, 17-22 May 1992. As part of this meeting a total of 21 papers was presented on snow and ice applications of Advanced Very High Resolution Radiometer (AVHRR) satellite data in polar regions. Also during this meeting a NASA sponsored Workshop was held to review the status of polar surface measurements from AVHRR. In the following we have summarized the ideas and recommendations from the workshop, and the conclusions of relevant papers given during the regular symposium sessions. The seven topics discussed include cloud masking, ice surface temperature, narrow-band albedo, ice concentration, lead statistics, sea-ice motion and ice-sheet studies with specifics on applications, algorithms and accuracy, following recommendations for future improvements. In general, we can affirm the strong potential of AVHRR for studying sea ice and snow covered surfaces, and we highly recommend this satellite data set for long-term monitoring of polar process studies. However, progress is needed to reduce the uncertainty of the retrieved parameters for all of the above mentioned topics to make this data set useful for direct climate applications such as heat balance studies and others. Further, the acquisition and processing of polar AVHRR data must become better coordinated between receiving stations, data centers and funding agencies to guarantee a long-term commitment to the collection and distribution of high quality data

MULTIPLE HYPOTHESIS TRACKING AND STRONG POINT ANALYSIS FOR STORM TRACKING WITH WEATHER RADAR

Storm tracking using radar observations of weather is a valuable tool both for the study of weather conditions and for the issuance of advance warning when such conditions are severe. Centroid-based storm tracking algorithms are a common tool used to provide short-term forecasting of storms. In these algorithms, cells representing intense areas of storms are identified. The cells are compared to cells identified at regular intervals, and similar cells are assigned to tracks which indicate the history of the underlying storm. These tracks may then be used to generate forecasts. The Strong Point Analysis, Multiple Hypothesis Tracking (SPA-MHT) technique is a centroid-based algorithm which draws on methods effective in storm tracking and in the more general field of object tracking. Strong Point Analysis identifies storm cells using the erosion and dilation operations from the field of mathematical morphology. Multiple Hypothesis Tracking assigns these cells to tracks, resolving ambiguities by incorporating data from multiple sequential sets of observations. In this thesis, the motivation for the design of SPA-MHT is presented in the context of previous storm tracking algorithms. The suitability of Strong Point Analysis for storm identification and Multiple Hypothesis Tracking for track association is considered. An implementation of each technique is also presented. The combined implementation is evaluated on radar data from the NEXRAD network of Doppler weather radars. This constitutes the first evaluation of Multiple Hypothesis Tracking on actual weather radar data. Both qualitative visual comparisons and quantitative comparisons are considered. SPA-MHT is found to outperform the current NEXRAD tracking algorithm when tracking storms that are isolated or forming clusters, but performance between the two methods is more comparable in challenging situations such as squall lines

Report of the 2005 Workshop on Ocean Ecodynamics Comparison in the Subarctic Pacific

I. Scientific Issues Posed by OECOS II. Participant Contributions to the OECOS Workshop A. ASPECTS OF PHYTOPLANKTON ECOLOGY IN THE SUBARCTIC PACIFIC Microbial community compositions by Karen E. Selph Subarctic Pacific lower trophic interactions: Production-based grazing rates and grazing-corrected production rates by Nicholas Welschmeyer Phytoplankton bloom dynamics and their physiological status in the western subarctic Pacific by Ken Furuya Temporal and spatial variability of phytoplankton biomass and productivity in the northwestern Pacific by Sei-ichi Saitoh, Suguru Okamoto, Hiroki Takemura and Kosei Sasaoka The use of molecular indicators of phytoplankton iron limitation by Deana Erdner B. IRON CONCENTRATION AND CHEMICAL SPECIATION Iron measurements during OECOS by Zanna Chase and Jay Cullen 25 The measurement of iron, nutrients and other chemical components in the northwestern North Pacific Ocean by Kenshi Kuma The measurement of iron, nutrients and other chemical components in the northwestern North Pacific Ocean by Kenshi Kuma C. PHYSICAL OCEANOGRAPHY, FINE-SCALE DISTRIBUTION PATTERNS AND AUTONOMOUS DRIFTERS The use of drifters in Lagrangian experiments: Positives, negatives and what can really be measured by Peter Strutton The interaction between plankton distribution patterns and vertical and horizontal physical processes in the eastern subarctic North Pacific by Timothy J. Cowles D. MICROZOOPLANKTON Microzooplankton processes in oceanic waters of the eastern subarctic Pacific: Project OECOS by Suzanne Strom Functional role of microzooplankton in the pelagic marine ecosystem during phytoplankton blooms in the western subarctic Pacific by Takashi Ota and Akiyoshi Shinada E. MESOZOOPLANKTON Vertical zonation of mesozooplankton, and its variability in response to food availability, density stratification, and turbulence by David L. Mackas and Moira Galbraith Marine ecosystem characteristics and seasonal abundance of dominant calanoid copepods in the Oyashio region by Atsushi Yamaguchi, Tsutomu Ikeda and Naonobu Shiga OECOS: Proposed mesozooplankton research in the Oyashio region, western subarctic Pacific by Tsutomu Ikeda Some background on Neocalanus feeding by Michael Dagg Size and growth of interzonally migrating copepods by Charles B. Miller Growth of large interzonal migrating copepods by Toru Kobari F. MODELING Ecosystem and population dynamics modeling by Harold P. Batchelder III. Reports from Workshop Breakout Groups A. PHYSICAL AND CHEMICAL ASPECTS WITH EMPHASIS ON IRON AND IRON SPECIATION B. PHYTOPLANKTON/MICROZOOPLANKTON STUDIES C. MESOZOOPLANKTON STUDIES IV. Issues arising during the workshop A. PHYTOPLANKTON STOCK VARIATIONS IN HNLC SYSTEMS AND TROPHIC CASCADES IN THE NANO AND MICRO REGIMES B. DIFFERENCES BETWEEN EAST AND WEST IN SITE SELECTION FOR OECOS TIME SERIES C. TIMING OF OECOS EXPEDITIONS D. CHARACTERIZATION OF PHYSICAL OCEANOGRAPHY V. Concluding Remarks VI. References (109 page document

Earth Resources: A continuing bibliography, issue 28

This bibliography lists 436 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1, 1980 and December 31, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems instrumentation and sensors, and economic analysis

A Hybrid Labeled Multi-Bernoulli Filter With Amplitude For Tracking Fluctuating Targets

The amplitude information of target returns has been incorporated into many tracking algorithms for performance improvements. One of the limitations of employing amplitude feature is that the signal-to-noise ratio (SNR) of the target, i.e., the parameter of amplitude likelihood, is usually assumed to be known and constant. In practice, the target SNR is always unknown, and is dependent on aspect angle hence it will fluctuate. In this paper we propose a hybrid labeled multi-Bernoulli (LMB) filter that introduces the signal amplitude into the LMB filter for tracking targets with unknown and fluctuating SNR. The fluctuation of target SNR is modeled by an autoregressive gamma process and amplitude likelihoods for Swerling 1 and 3 targets are considered. Under Rao-Blackwell decomposition, an approximate Gamma estimator based on Laplace transform and Markov Chain Monte Carlo method is proposed to estimate the target SNR, and the kinematic state is estimated by a Gaussian mixture filter conditioned on the target SNR. The performance of the proposed hybrid filter is analyzed via a tracking scenario including three crossing targets. Simulation results verify the efficacy of the proposed SNR estimator and quantify the benefits of incorporating amplitude information for multi-target tracking

Guidance for benthic habitat mapping: an aerial photographic approach

This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor