The European Forest Ecosystem Research Network (FERN)

1987 ◽  
Vol 2 (8) ◽  
pp. 236-237 ◽  
Author(s):  
Ph. Bourdeau
Keyword(s):  
Forest Ecosystem ◽  
Research Network ◽  
Ecosystem Research ◽  
European Forest

The Forest Ecosystem Research Network of Sites (FERNS)

1999 ◽  
Vol 75 (3) ◽  
pp. 481-482 ◽  
Author(s):  
A. K. Mitchell ◽  
C. Lee
Keyword(s):  
Forest Management ◽  
Forest Ecosystem ◽  
Information Exchange ◽  
Management Practices ◽  
Sustainable Forest Management ◽  
Ecosystem Processes ◽  
Research Network ◽  
Ecosystem Research ◽  
Research Investments

The Canadian Forest Service (CFS) has organized a National Forest Ecosystem Research Network of Sites (FERNS). These sites are focussed on the study of sustainable forest management practices and ecosystem processes at the stand level. Network objectives are to promote this research nationally and internationally, provide linkages among sites, preserve the long-term research investments already made on these sites and provide a forum for information exchange and data sharing. The 17 individual sites are representative of six ecozones across Canada and address the common issue of silvicultural solutions to problems of sustainable forest management. While the CFS coordinates and promotes FERNS, the network consists of local autonomous partners nationwide who benefit from the FERNS affiliation through increased publicity for their sites. Key words: long-term, silviculture, network, interdisciplinary, ecozone, ecosystem processes

scholarly journals National Forest Ecosystem Inventory System of China: Methodology and Applications

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 732
Author(s):  
Bing Wang ◽  
Xiang Niu ◽  
Wenjun Wei
Keyword(s):  
Ecosystem Services ◽  
Forest Ecosystem ◽  
Forest Ecosystems ◽  
Ecological Indicators ◽  
Indicator System ◽  
Inventory System ◽  
Research Network ◽  
Ecosystem Research ◽  
Forest Ecosystem Services ◽  
The World

The technical framework of China’s Forest Ecosystem Inventory System (CFEIS) was recently developed based on ecological indicators assessed continuously in the field at forest ecosystem research stations and China’s Forest Resource Inventory (CNFRI) conducted every 5 years. The CFEIS consists of Field Observations (FOs)of ecological indicators and Distributed Valuations (DVs)of forest ecosystem services. The CFEIS can be used with the CNFRI to observe and monitor the ecological status of forests in China. This paper provides a brief review of the CFEIS by introducing its establishment and summarizing its application coupled with the CNFRI. For the FOs, the principles of the monitoring system layout are provided. The Chinese Forest Ecosystem Research Network (CFERN) was set up, which was the largest nationwide network of forest ecological stations in the world. The facilities and equipment were systematically assembled. The national forestry standards were drawn up for describing and measuring the ecological indicators of forest ecosystems, and these standards were used to specify data collection and transmission. For DVs, a distributed measurement method was created, and an indicator system of evaluation was studied and established, with the CNFRI integrated; a series of evaluation formulas and a package of models were also integrated with the DVs. The CFEIS integrated with the CNFRI estimates forest ecosystem services in China and the ecological benefits derived from the Grain for Green program, and a green national economic accounting system will provide an important case for monitoring and inventorying forest ecosystems at a national scale. The CFEIS can provide important experiences for forest ecosystem inventory systems in China and many other parts of the world.

scholarly journals Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Honglin He ◽  
Rong Ge ◽  
Xiaoli Ren ◽  
Li Zhang ◽  
Qingqing Chang ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Global Climate ◽  
Meteorological Data ◽  
C Sequestration ◽  
Research Network ◽  
Soil Biology ◽  
Area Index ◽  
Ecosystem Research ◽  
Organic C ◽  
C Cycle

AbstractChinese forests cover most of the representative forest types in the Northern Hemisphere and function as a large carbon (C) sink in the global C cycle. The availability of long-term C dynamics observations is key to evaluating and understanding C sequestration of these forests. The Chinese Ecosystem Research Network has conducted normalized and systematic monitoring of the soil-biology-atmosphere-water cycle in Chinese forests since 2000. For the first time, a reference dataset of the decadal C cycle dynamics was produced for 10 typical Chinese forests after strict quality control, including biomass, leaf area index, litterfall, soil organic C, and the corresponding meteorological data. Based on these basic but time-discrete C-cycle elements, an assimilated dataset of key C cycle parameters and time-continuous C sequestration functions was generated via model-data fusion, including C allocation, turnover, and soil, vegetation, and ecosystem C storage. These reference data could be used as a benchmark for model development, evaluation and C cycle research under global climate change for typical forests in the Northern Hemisphere.

Building Australia’s Scalable Drone Cloud

2021 ◽  
Author(s):  
Jens Klump ◽  
Tim Brown ◽  
Rohan Clarke ◽  
Robert Glasgow ◽  
Steve Micklethwaite ◽  
...  
Keyword(s):  
Data Management ◽  
Best Practice ◽  
Management Practices ◽  
Agricultural Research ◽  
Terrestrial Ecosystem ◽  
Large Data ◽  
Sensor Data ◽  
Research Network ◽  
Ecosystem Research ◽  
Plant Phenomics

<p>Remotely Piloted Aircraft (RPA), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground- and satellite-based observations. Their versatility allows researchers to deliver near-real-time information for society.</p><p>Key to delivering RPA information is the capacity to enable researchers to systematically collect, process, manage and share RPA-borne sensor data. Importantly, this should allow vertical integration across scales and horizontal integration across different RPA deployments. However, as an emerging technology, the best practice and standards are still developing and the large data volumes collected during RPA missions can be challenging.</p><p>Australia’s Scalable Drone Cloud (ASDC) aims to coordinate and standardise how scientists from across earth, environmental and agricultural research manage, process and analyse data collected by RPA-borne sensors, by establishing best practices in managing 3D-geospatial data and aligned with the FAIR data principles.</p><p>The ASDC is building a cloud-native platform for research drone data management and analytics, driven by exemplar data management practices, data-processing pipelines, and search and discovery of drone data. The aim of the platform is to integrate sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools (including computer vision / deep learning techniques) to establish a national ecosystem for drone data management.</p><p>The ASDC is a partnership of the Monash Drone Discovery Platform, CSIRO and key National Collaborative Research Infrastructure (NCRIS) capabilities including the Australian Research Data Commons (ARDC), Australian Plant Phenomics Facility (APPF), Terrestrial Ecosystem Research Network (TERN), and AuScope.</p><p>This presentation outlines the roadmap and first proof-of-concept implementation of the ASDC.</p>

scholarly journals A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Zhongen Niu ◽  
Honglin He ◽  
Gaofeng Zhu ◽  
Xiaoli Ren ◽  
Li Zhang ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Terrestrial Ecosystem ◽  
Research Network ◽  
Energy Budgets ◽  
Climate Data ◽  
Area Index ◽  
Large Variability ◽  
Ecosystem Research ◽  
Insight Into

Abstract The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, several studies have documented a large variability in T/ET. In this paper, we present a new T/ET dataset (also including transpiration, evapotranspiration data) for China from 1981 to 2015 with spatial and temporal resolutions of 0.05° and 8 days, respectively. The T/ET dataset is based on a model-data fusion method that integrates the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration). The dataset is driven by satellite-based leaf area index (LAI) data from GLASS and GLOBMAP, and climate data from the Chinese Ecosystem Research Network (CERN). Observational annual T/ET were used to validate the model, with R2 and RMSE values were 0.73 and 0.07 (12.41%), respectively. The dataset provides significant insight into T/ET and its changes over the Chinese terrestrial ecosystem and will be beneficial for understanding the hydrological cycle and energy budgets between the land and the atmosphere.

scholarly journals Post-disturbance recovery of forest cover and tree height differ with management in Central Europe

2019 ◽  
Vol 34 (12) ◽  
pp. 2837-2850 ◽  
Author(s):  
Cornelius Senf ◽  
Jörg Müller ◽  
Rupert Seidl
Keyword(s):  
Central Europe ◽  
Forest Ecosystem ◽  
Forest Structure ◽  
Forest Ecosystems ◽  
Tree Cover ◽  
Managed Forests ◽  
Central European ◽  
Stand Height ◽  
European Forest ◽  
Disturbance Recovery

Abstract Context Recovery from disturbances is a prominent measure of forest ecosystem resilience, with swift recovery indicating resilient systems. The forest ecosystems of Central Europe have recently been affected by unprecedented levels of natural disturbance, yet our understanding of their ability to recover from disturbances is still limited. Objectives We here integrated satellite and airborne Lidar data to (i) quantify multi-decadal post-disturbance recovery of two indicators of forest structure, and (ii) compare the recovery trajectories of forest structure among managed and un-managed forests. Methods We developed satellite-based models predicting Lidar-derived estimates of tree cover and stand height at 30 m grain across a 3100 km2 landscape in the Bohemian Forest Ecosystem (Central Europe). We summarized the percentage of disturbed area that recovered to > 40% tree cover and > 5 m stand height and quantified the variability in both indicators over a 30-year period. The analyses were stratified by three management regimes (managed, protected, strictly protected) and two forest types (beech-dominated, spruce-dominated). Results We found that on average 84% of the disturbed area met our recovery threshold 30 years post-disturbance. The rate of recovery was slower in un-managed compared to managed forests. Variability in tree cover was more persistent over time in un-managed forests, while managed forests strongly converged after a few decades post-disturbance. Conclusion We conclude that current management facilitates the recovery of forest structure in Central European forest ecosystems. However, our results underline that forests recovered well from disturbances also in the absence of human intervention. Our analysis highlights the high resilience of Central European forest ecosystems to recent disturbances.

scholarly journals First Effort at Constructing a High-Density Photosynthetically Active Radiation Dataset during 1961–2014 in China

2019 ◽  
Vol 32 (10) ◽  
pp. 2761-2780 ◽  
Author(s):  
Wenmin Qin ◽  
Lunche Wang ◽  
Ming Zhang ◽  
Zigeng Niu ◽  
Ming Luo ◽  
...  
Keyword(s):  
Photosynthetically Active Radiation ◽  
Back Propagation ◽  
Control Process ◽  
High Density ◽  
Research Network ◽  
Bias Error ◽  
Ecosystem Research ◽  
Vegetation Growth ◽  
Active Radiation ◽  
Quality Control Process

Abstract Photosynthetically active radiation (PAR) is a key factor for vegetation growth and climate change. Different types of PAR models, including four physically based models and eight artificial intelligence (AI) models, were proposed for predicting daily PAR. Multiyear daily meteorological parameters observed at 29 Chinese Ecosystem Research Network (CERN) stations and 2474 Chinese Meteorological Administration (CMA) stations across China were used for testing, validating, and comparing the above models. The optimized back propagation (BP) neural network based on the mind evolutionary algorithm (MEA-BP) was the model with highest accuracy and strongest robustness. The correlation coefficient R, mean absolute bias error (MAE), and RMSE for MEA-BP were 0.986, 0.302 MJ m−2 day−1 and 0.393 MJ m−2 day−1, respectively. Then, a high-density PAR dataset was constructed for the first time using the MEA-BP model at 2474 CMA stations of China. A quality control process and homogenization test (using RHtestsV4) for the PAR dataset were further conducted. This high-density PAR dataset would benefit many climate and ecological studies.

scholarly journals Passive nighttime warming facility for forest ecosystem research

1998 ◽  
Vol 18 (8-9) ◽  
pp. 615-623 ◽  
Author(s):  
R. J. Luxmoore ◽  
P. J. Hanson ◽  
J. J. Beauchamp ◽  
J. D. Joslin
Keyword(s):  
Forest Ecosystem ◽  
Ecosystem Research ◽  
Nighttime Warming
Sign in / Sign up

Export Citation Format

Share Document