scholarly journals Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework

2021 ◽  
Vol 13 (24) ◽  
pp. 5177
Author(s):  
Xi Chen ◽  
Wenzhi Zhao ◽  
Jiage Chen ◽  
Yang Qu ◽  
Dinghui Wu ◽  
...  
Keyword(s):  
Inventory Management ◽  
Forest Fire ◽  
Large Scale ◽  
Coupling Effect ◽  
Forest Disturbance ◽  
The United States ◽  
Vital Role ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Integrated Method

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km2) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90%. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.

scholarly journals A STUDY OF USING FULLY CONVOLUTIONAL NETWORK FOR TREETOP DETECTION ON REMOTE SENSING DATA

2018 ◽  
Vol IV-1 ◽  
pp. 163-169 ◽  
Author(s):  
C. Xiao ◽  
R. Qin ◽  
X. Huang ◽  
J. Li
Keyword(s):  
Remote Sensing ◽  
Inventory Management ◽  
Remote Sensing Data ◽  
Training Data ◽  
Surface Model ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Individual Tree ◽  
Fully Convolutional Network ◽  
Sensing Data

<p><strong>Abstract.</strong> Individual tree detection and counting are critical for the forest inventory management. In almost all of these methods that based on remote sensing data, the treetop detection is the most important and essential part. However, due to the diversities of the tree attributes, such as crown size and branch distribution, it is hard to find a universal treetop detector and most of the current detectors need to be carefully designed based on the heuristic or prior knowledge. Hence, to find an efficient and versatile detector, we apply deep neural network to extract and learn the high-level semantic treetop features. In contrast to using manually labelled training data, we innovatively train the network with the pseudo ones that come from the result of the conventional non-supervised treetop detectors which may be not robust in different scenarios. In this study, we use multi-view high-resolution satellite imagery derived DSM (Digital Surface Model) and multispectral orthophoto as data and apply the top-hat by reconstruction (THR) operation to find treetops as the pseudo labels. The FCN (fully convolutional network) is adopted as a pixel-level classification network to segment the input image into treetops and non-treetops pixels. Our experiments show that the FCN based treetop detector is able to achieve a detection accuracy of 99.7<span class="thinspace"></span>% at the prairie area and 66.3<span class="thinspace"></span>% at the complicated town area which shows better performance than THR in the various scenarios. This study demonstrates that without manual labels, the FCN treetop detector can be trained by the pseudo labels that generated using the non-supervised detector and achieve better and robust results in different scenarios.</p>

scholarly journals Supply Chain Management under the Threat of International Terrorism

2001 ◽  
Vol 12 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Yossi Sheffi
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Large Scale ◽  
Management Strategies ◽  
The United States ◽  
International Terrorism ◽  
Western World ◽  
New Era ◽  
New Public ◽  
Terrorist Acts

On the morning of September 11th, 2001, the United States and the Western world entered into a new era ‐ one in which large scale terrorist acts are to be expected. The impacts of the new era will challenge supply chain managers to adjust relations with suppliers and customers, contend with transportation difficulties and amend inventory management strategies. This paper looks at the twin corporate challenges of (i) preparing to deal with the aftermath of terrorist attacks and (ii) operating under heightened security. The first challenge involves setting certain operational redundancies. The second means less reliable lead times and less certain demand scenarios. In addition, the paper looks at how companies should organize to meet those challenges efficiently and suggests a new public‐private partnership. While the paper is focused on the US, it has worldwide implications.

Quality Control of Data in a Large-Scale Cancer Register Program

1966 ◽  
Vol 05 (02) ◽  
pp. 67-74 ◽  
Author(s):  
W. I. Lourie ◽  
W. Haenszeland
Keyword(s):  
United States ◽  
Quality Control ◽  
Cancer Patients ◽  
Large Scale ◽  
The United States ◽  
Newly Diagnosed ◽  
Related Data ◽  
Cancer Register ◽  
Independent Review ◽  
End Results

Quality control of data collected in the United States by the Cancer End Results Program utilizing punchcards prepared by participating registries in accordance with a Uniform Punchcard Code is discussed. Existing arrangements decentralize responsibility for editing and related data processing to the local registries with centralization of tabulating and statistical services in the End Results Section, National Cancer Institute. The most recent deck of punchcards represented over 600,000 cancer patients; approximately 50,000 newly diagnosed cases are added annually.Mechanical editing and inspection of punchcards and field audits are the principal tools for quality control. Mechanical editing of the punchcards includes testing for blank entries and detection of in-admissable or inconsistent codes. Highly improbable codes are subjected to special scrutiny. Field audits include the drawing of a 1-10 percent random sample of punchcards submitted by a registry; the charts are .then reabstracted and recoded by a NCI staff member and differences between the punchcard and the results of independent review are noted.

Utopias of One

2019 ◽  
Author(s):  
Joshua Kotin
Keyword(s):  
United States ◽  
Soviet Union ◽  
World War I ◽  
Large Scale ◽  
Jim Crow ◽  
Personal Autonomy ◽  
The United States ◽  
Soviet Period ◽  
Original Research ◽  
The Soviet Union

This book is a new account of utopian writing. It examines how eight writers—Henry David Thoreau, W. E. B. Du Bois, Osip and Nadezhda Mandel'shtam, Anna Akhmatova, Wallace Stevens, Ezra Pound, and J. H. Prynne—construct utopias of one within and against modernity's two large-scale attempts to harmonize individual and collective interests: liberalism and communism. The book begins in the United States between the buildup to the Civil War and the end of Jim Crow; continues in the Soviet Union between Stalinism and the late Soviet period; and concludes in England and the United States between World War I and the end of the Cold War. In this way it captures how writers from disparate geopolitical contexts resist state and normative power to construct perfect worlds—for themselves alone. The book contributes to debates about literature and politics, presenting innovative arguments about aesthetic difficulty, personal autonomy, and complicity and dissent. It models a new approach to transnational and comparative scholarship, combining original research in English and Russian to illuminate more than a century and a half of literary and political history.

The Obesity Epidemic in Chinese American Youth?: A Literature Review and Pilot Study

2008 ◽  
Vol 6 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Robyn Matloff ◽  
Angela Lee ◽  
Roland Tang ◽  
Doug Brugge
Keyword(s):  
Asian Americans ◽  
Ethnic Diversity ◽  
Large Scale ◽  
Community Health Center ◽  
Chinese American ◽  
The United States ◽  
Overweight And Obesity ◽  
Waiting Room ◽  
Obesity Epidemic ◽  
American Children

Despite nearly 12 million Asian Americans living in the United States and continued immigration, this increasingly substantial subpopulation has consistently been left out of national obesity studies. When included in national studies, Chinese-American children have been grouped together with other Asian Americans, Pacific Islanders or simply as “other,” yielding significantly lower rates of overweight and obesity compared to non-Asians. There is a failure to recognize the ethnic diversity of Asian Americans as well as the effect of acculturation. Results from smaller studies of Chinese American youth suggest that they are adopting lifestyles less Chinese and more Americans and that their share of disease burden is growing. We screened 142 children from the waiting room of a community health center that serves primarily recent Chinese immigrants for height, weight and demographic profile. Body Mass Index was calculated and evaluated using CDC growth charts. Overall, 30.1 percent of children were above the 85th we found being male and being born in the U .S. to be statistically significant for BMI > 85th percentile (p=0.039, p=0.001, respectively). Our results suggest that being overweight in this Chinese American immigrant population is associated with being born in the U.S. A change in public policy and framework for research are required to accurately assess the extent of overweight and obesity in Chinese American children. In particular, large scale data should be stratified by age, sex, birthplace and measure of acculturation to identify those at risk and construct tailored interventions.

Situational Breakdowns

2019 ◽  
Author(s):  
Anne Nassauer
Keyword(s):  
United States ◽  
Large Scale ◽  
Contextual Factors ◽  
The United States ◽  
Social Outcomes ◽  
Dynamic Processes ◽  
Collective Decisions ◽  
Different Types ◽  
The Impact

This book provides an account of how and why routine interactions break down and how such situational breakdowns lead to protest violence and other types of surprising social outcomes. It takes a close-up look at the dynamic processes of how situations unfold and compares their role to that of motivations, strategies, and other contextual factors. The book discusses factors that can draw us into violent situations and describes how and why we make uncommon individual and collective decisions. Covering different types of surprise outcomes from protest marches and uprisings turning violent to robbers failing to rob a store at gunpoint, it shows how unfolding situations can override our motivations and strategies and how emotions and culture, as well as rational thinking, still play a part in these events. The first chapters study protest violence in Germany and the United States from 1960 until 2010, taking a detailed look at what happens between the start of a protest and the eruption of violence or its peaceful conclusion. They compare the impact of such dynamics to the role of police strategies and culture, protesters’ claims and violent motivations, the black bloc and agents provocateurs. The analysis shows how violence is triggered, what determines its intensity, and which measures can avoid its outbreak. The book explores whether we find similar situational patterns leading to surprising outcomes in other types of small- and large-scale events: uprisings turning violent, such as Ferguson in 2014 and Baltimore in 2015, and failed armed store robberies.

Accidental Sustainability

2017 ◽  
Author(s):  
Geoffrey Jones
Keyword(s):  
Large Scale ◽  
Negative Impact ◽  
Positive Impact ◽  
Organic Food ◽  
The United States ◽  
Air Travel ◽  
Business Leaders ◽  
Natural Beauty ◽  
And Diffusion ◽  
Whole Foods

This chapter examines the scaling and diffusion of green entrepreneurship between 1980 and the present. It explores how entrepreneurs and business leaders promoted the idea that business and sustainability were compatible. It then examines the rapid growth of organic foods, natural beauty, ecological architecture, and eco-tourism. Green firms sometimes grew to a large scale, such as the retailer Whole Foods Market in the United States. The chapter explores how greater mainstreaming of these businesses resulted in a new set of challenges arising from scaling. Organic food was now transported across large distances causing a negative impact on carbon emissions. More eco-tourism resulted in more air travel and bigger airports. In other industries scaling had a more positive impact. Towns were major polluters, so more ecological buildings had a positive impact.

Ban Ki-moon, 2007–2016

2018 ◽  
Author(s):  
Richard Gowan
Keyword(s):  
United States ◽  
Decision Making ◽  
Security Council ◽  
Large Scale ◽  
Central African Republic ◽  
The United States ◽  
Cautious Approach ◽  
Peacekeeping Operations ◽  
Council Decision ◽  
The Face

During Ban Ki-moon’s tenure, the Security Council was shaken by P5 divisions over Kosovo, Georgia, Libya, Syria, and Ukraine. Yet it also continued to mandate and sustain large-scale peacekeeping operations in Africa, placing major burdens on the UN Secretariat. The chapter will argue that Ban initially took a cautious approach to controversies with the Council, and earned a reputation for excessive passivity in the face of crisis and deference to the United States. The second half of the chapter suggests that Ban shifted to a more activist pressure as his tenure went on, pressing the Council to act in cases including Côte d’Ivoire, Libya, and Syria. The chapter will argue that Ban had only a marginal impact on Council decision-making, even though he made a creditable effort to speak truth to power over cases such as the Central African Republic (CAR), challenging Council members to live up to their responsibilities.

Psychosocial Consequences of Body Weight and Obesity

2018 ◽  
pp. 274-286
Author(s):  
Deborah Carr ◽  
Vera K. Tsenkova
Keyword(s):  
Body Weight ◽  
Interpersonal Relationships ◽  
Large Scale ◽  
Well Being ◽  
The United States ◽  
Overweight And Obesity ◽  
The Body ◽  
Integrative Health ◽  
Psychosocial Consequences ◽  
Adults And Children

The body weight of U.S. adults and children has risen markedly over the past three decades. The physical health consequences of obesity are widely documented, and emerging research from the Midlife in the United States study and other large-scale surveys reveals the harmful impact of obesity on adults’ psychosocial and interpersonal well-being. This chapter synthesizes recent research on the psychosocial implications of body weight, with attention to explanatory mechanisms and subgroup differences in these patterns. A brief statistical portrait of body weight is provided, documenting rates and correlates of obesity, with a focus on race, gender, and socioeconomic status disparities. The consequences of body weight for three main outcomes are described: institutional and everyday discrimination, interpersonal relationships, and psychological well-being. The chapter concludes with a discussion of the ways that recent integrative health research on the psychosocial consequences of overweight and obesity inform our understanding of population health.

scholarly journals NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

2021 ◽  
Vol 11 (10) ◽  
pp. 4426
Author(s):  
Chunyan Ma ◽  
Ji Fan ◽  
Jinghao Yao ◽  
Tao Zhang
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Evaluation Criteria ◽  
Image Data ◽  
Basketball Player ◽  
Basketball Players ◽  
Convolutional Network ◽  
Atomic Actions ◽  
New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

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