Scientific considerations and challenges for addressing cumulative effects in forest landscapes in Canada.

Traditionally, forest management has focused on forestry-related practices while other industries have been managed separately. Forest management requires the integration of all natural resource development activities, along with other anthropogenic and natural forest disturbances (e.g., climate change, pollution, wildfire, pest disturbance) to understand how human activities can change forested ecosystems. The term cumulative effects has been used to describe these attempts to integrate all disturbances to develop an understanding of past, current and future impacts on environmental, social and economic components of the system. In this review, we focus on the science required to understand the past, current and future impacts of the cumulative effects of anthropogenic and natural disturbances on forested ecosystems or their components. We have primarily focused on the terrestrial system with an emphasis on northern forests in Canada. Our paper is not intended to be a comprehensive review of all cumulative effects science but a synthesis of the challenges and approaches currently being used. Central repositories were identified as an approach to deal with issues of availability of remotely sensed data on anthropogenic and natural disturbances. Data integration projects, open data and well-designed large-scale data collection efforts are needed to provide sufficient data on environmental responses to cumulative effects. As well, large-scale integrated, modularized ecosystem models are needed to bring stressor and environmental response data together to explore responses to, and interactions between, multiple stressors, to project these effects into the future, and to identify future data collection needs.

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Leveraging Technology for Optimization of Health Survey Research

Journal of Health Management ◽

10.1177/0972063419884444 ◽

2019 ◽

Vol 21 (4) ◽

pp. 571-581

Author(s):

Shobana Sivaraman ◽

Punit Soni

Keyword(s):

Data Collection ◽

Information Management ◽

Survey Research ◽

Health Survey ◽

Operations Management ◽

Large Scale ◽

Open Data ◽

Information Management System ◽

Computer Assisted ◽

Quality Of Data

Public health deals with promotion of health, prevention and treatment of communicable and non-communicable diseases by designing appropriate health interventions and services to deliver through the health systems. There is a need for robust database on the magnitude of disease burden, socio-demographic characteristics and associated risk factors for evidence-based effective planning and developing appropriate strategies, their implementation, monitoring and evaluation. Although India has vast information available through various large-scale surveys and research studies, it still lacks a reliable health information management system. The available data are seldom analysed to draw meaningful conclusions, to develop evidence for policies and strategies and to measure effectiveness of health programmes. The challenges faced in the survey research are multifaceted, from data collection in the field to its rapid transmission of data to central data servers. There is an increasing trend in using technology, especially computer-assisted personal interviews (CAPI) which is not only expensive but also requires extensive training and information management for transmission of data and its storage. This article examines the application of technology in survey research for efficient data management and to improve data quality. A software called Open Data Kit (ODK) was used for data collection and real-time monitoring of interviewers in field to improve the quality of data collection, achieve desired response rate (RR) and for better field operations’ management. The data collection and field reporting forms designed using ODK act as a significant tool to demonstrate how technology can be used to articulate research expectations at various levels with lower cost and higher efficiency. The research article examines all possible aspects of using technology in Health Survey Research. It aims to introduce further discussion of using technology for field data collection and monitoring.

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Quantifying natural disturbances using a large‐scale dendrochronological reconstruction to guide forest management

Ecological Applications ◽

10.1002/eap.2189 ◽

2020 ◽

Vol 30 (8) ◽

Cited By ~ 2

Author(s):

Vojtěch Čada ◽

Volodymyr Trotsiuk ◽

Pavel Janda ◽

Martin Mikoláš ◽

Radek Bače ◽

...

Keyword(s):

Forest Management ◽

Large Scale ◽

Natural Disturbances

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Mortality in Forested Ecosystems: Suggested Conceptual Advances

Forests ◽

10.3390/f11050572 ◽

2020 ◽

Vol 11 (5) ◽

pp. 572

Author(s):

Mark E. Harmon ◽

David M. Bell

Keyword(s):

Forest Management ◽

Large Scale ◽

Tree Mortality ◽

Structure And Function ◽

Ecosystem Structure ◽

General Process ◽

A Chain ◽

And Function ◽

Forested Ecosystems ◽

Ecosystem Structure And Function

Mortality of trees is an important ecological process altering forest structure and function as well as influencing forest management decisions. Recent observations suggest that the overall rate of tree mortality is increasing at local to global scales. While more data on mortality is needed to document these changes, key concepts are also needed to guide the collection, interpretation, and use of this information. Mortality can be considered as a general process that includes all forms of tree-related death ranging from parts of trees to large-scale disturbances. Viewing mortality as a continuum allows one to examine how the lifespan of trees and their parts (e.g., branches), as well as multiple disturbances, influence ecosystem structure and function. Statistically, mortality does not follow the law of large numbers because, regardless of the scale analyzed, consequential, infrequent episodes can occur. This causes mortality to occur in irregular pulses. While the causes of mortality are indeed complex, this stems from the fact many processes, each with its own set of controls, can lead to mortality. By analyzing and predicting mortality using a chain of events influenced by specific mechanisms, a clearer understanding of this process should develop, leading to a more science-based and less reactive forest management.

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Quantifying Natural Disturbances Using a Large‐Scale Dendrochronological Reconstruction to Guide Forest Management

Bulletin of the Ecological Society of America ◽

10.1002/bes2.1759 ◽

2020 ◽

Vol 101 (4) ◽

Author(s):

Vojtěch Čada ◽

Volodymyr Trotsiuk ◽

Pavel Janda ◽

Martin Mikoláš ◽

Radek Bače ◽

...

Keyword(s):

Forest Management ◽

Large Scale ◽

Natural Disturbances

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Forecasting the Cumulative Effects of Multiple Stressors on Breeding Habitat for a Steeply Declining Aerial Insectivorous Songbird, the Olive-sided Flycatcher (Contopus cooperi)

Frontiers in Ecology and Evolution ◽

10.3389/fevo.2021.635872 ◽

2021 ◽

Vol 9 ◽

Author(s):

Andrea R. Norris ◽

Leonardo Frid ◽

Chloé Debyser ◽

Krista L. De Groot ◽

Jeffrey Thomas ◽

...

Keyword(s):

Land Use ◽

Forest Management ◽

Boreal Forest ◽

Protected Areas ◽

Habitat Loss ◽

Habitat Suitability ◽

Multiple Stressors ◽

Simulation Models ◽

Cumulative Effects ◽

Western Boreal Forest

To halt ongoing loss in biodiversity, there is a need for landscape-level management recommendations that address cumulative impacts of anthropogenic and natural disturbances on wildlife habitat. We examined the cumulative effects of logging, roads, land-use change, fire, and bark beetle outbreaks on future habitat for olive-sided flycatcher (Contopus cooperi), a steeply declining aerial insectivorous songbird, in Canada’s western boreal forest. To predict the occurrence of olive-sided flycatcher we developed a suite of habitat suitability models using point count surveys (1997–2011) spatially- and temporally-matched with forest inventory data. Flycatcher occurrence was positively associated with small (∼10 ha) 10- to 20-year-old clearcuts, and with 10–100% tree mortality due to mountain pine beetle (Dendroctonus ponderosae) outbreaks, but we found no association with roads or distance to water. We used the parameter estimates from the best-fit habitat suitability models to inform spatially explicit state-and-transition simulation models to project change in habitat availability from 2020 to 2050 under six alternative scenarios (three management × two fire alternatives). The simulation models projected that the cumulative effects of land use conversion, forest harvesting, and fire will reduce the area of olive-sided flycatcher habitat by 16–18% under Business As Usual management scenarios and by 11–13% under scenarios that include protection of 30% of the land base. Scenarios limiting the size of all clearcuts to ≤10 ha resulted in a median habitat loss of 4–6%, but projections were highly variable. Under all three management alternatives, a 50% increase in fire frequency (expected due to climate change) exacerbated habitat loss. The projected losses of habitat in western boreal forest, even with an increase in protected areas, imply that reversing the ongoing population declines of olive-sided flycatcher and other migratory birds will require attention to forest management beyond protected areas. Further work should examine the effects of multiple stressors on the demographic mechanisms driving change in aerial insectivore populations, including stressors on the wintering grounds in South America, and should aim to adapt the design of protected areas and forest management policies to projected climate-driven increases in the size and frequency of wildfires.

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A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽

10.3390/epidemiologia2030024 ◽

2021 ◽

Vol 2 (3) ◽

pp. 315-324

Author(s):

Juan M. Banda ◽

Ramya Tekumalla ◽

Guanyu Wang ◽

Jingyuan Yu ◽

Tuo Liu ◽

...

Keyword(s):

Large Scale ◽

Social Dynamics ◽

Additional Data ◽

Open Data ◽

Data Sources ◽

Research Projects ◽

Research Groups ◽

The World ◽

Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

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A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Forest Ecosystems ◽

10.1186/s40663-021-00284-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Matieu Henry ◽

Zaheer Iqbal ◽

Kristofer Johnson ◽

Mariam Akhter ◽

Liam Costello ◽

...

Keyword(s):

Climate Change ◽

Forest Management ◽

Data Collection ◽

Land Cover ◽

Forest Inventory ◽

Sustainable Forest Management ◽

National Forest Inventory ◽

Forest Resources ◽

National Forest ◽

Land Cover Map

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

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Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

Remote Sensing ◽

10.3390/rs13050905 ◽

2021 ◽

Vol 13 (5) ◽

pp. 905

Author(s):

Chuyi Wu ◽

Feng Zhang ◽

Junshi Xia ◽

Yichen Xu ◽

Guoqing Li ◽

...

Keyword(s):

Damage Assessment ◽

Large Scale ◽

Binary Classification ◽

Open Data ◽

Building Damage ◽

Attention Mechanism ◽

Large Scale Dataset ◽

Data Program ◽

The Impact ◽

Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

Remote Sensing ◽

10.3390/rs13020176 ◽

2021 ◽

Vol 13 (2) ◽

pp. 176

Author(s):

Peng Zheng ◽

Zebin Wu ◽

Jin Sun ◽

Yi Zhang ◽

Yaoqin Zhu ◽

...

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Retrieval System ◽

Hyperspectral Image ◽

Parallel Implementation ◽

Remotely Sensed Data ◽

Web Interfaces ◽

Content Retrieval ◽

Service Mode ◽

Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

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Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽

10.3390/s21123982 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3982

Author(s):

Giacomo Lazzeri ◽

William Frodella ◽

Guglielmo Rossi ◽

Sandro Moretti

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Fire Severity ◽

Experimental Methodology ◽

Vegetation Recovery ◽

Burned Area ◽

Remotely Sensed Data ◽

The Impact ◽

Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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