scholarly journals High-Resolution Spatio-Temporal Estimation of Net Ecosystem Exchange in Ice-Wedge Polygon Tundra Using In Situ Sensors and Remote Sensing Data

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 722
Author(s):  
Haruko M. Wainwright ◽  
Rusen Oktem ◽  
Baptiste Dafflon ◽  
Sigrid Dengel ◽  
John B. Curtis ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Weighted Average ◽  
Net Ecosystem Exchange ◽  
Remote Sensing Data ◽  
Carbon Exchange ◽  
Flux Chamber ◽  
Flux Tower ◽  
Spatial Coverage ◽  
Ice Wedge ◽  
Spatio Temporal

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

scholarly journals A MANY-TO-MANY FULLY CONVOLUTIONAL RECURRENT NETWORK FOR MULTITEMPORAL CROP RECOGNITION

2019 ◽  
Vol IV-2/W7 ◽  
pp. 25-32 ◽  
Author(s):  
J. A. Chamorro ◽  
J. D. Bermudez ◽  
P. N. Happ ◽  
R. Q. Feitosa
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Temporal Dynamics ◽  
Agricultural Practices ◽  
Remote Sensing Data ◽  
Recurrent Network ◽  
Data Sets ◽  
Tropical Regions ◽  
Spatio Temporal ◽  
Multitemporal Remote Sensing

<p><strong>Abstract.</strong> Recently, recurrent neural networks have been proposed for crop mapping from multitemporal remote sensing data. Most of these proposals have been designed and tested in temperate regions, where a single harvest per season is the rule. In tropical regions, the favorable climate and local agricultural practices, such as crop rotation, result in more complex spatio-temporal dynamics, where the single harvest per season assumption does not hold. In this context, a demand arises for methods capable of recognizing agricultural crops at multiple dates along the multitemporal sequence. In the present work, we propose to adapt two recurrent neural networks, originally conceived for single harvest per season, for multidate crop recognition. In addition, we propose a novel multidate approach based on bidirectional fully convolutional recurrent neural networks. These three architectures were evaluated on public Sentinel-1 data sets from two tropical regions in Brazil. In our experiments, all methods achieved state-of-the-art accuracies with a clear superiority of the proposed architecture. It outperformed its counterparts in up to 3.8% and 7.4%, in terms of per-month overall accuracy, and it was the best performing method in terms of F1-score for most crops and dates on both regions.</p>

scholarly journals Prioritization of the vector factors controlling <i>Emiliania huxleyi</i> blooms in subarctic and arctic seas: A multidimensional statistical approach

2019 ◽  
Author(s):  
Dmitry Kondrik ◽  
Eduard Kazakov ◽  
Svetlana Chepikova ◽  
Dmitry Pozdnyakov
Keyword(s):  
Statistical Approach ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Marine Ecology ◽  
Arctic Seas ◽  
Prediction Ability ◽  
The North ◽  
Spatio Temporal ◽  
The Individual ◽  
The Impact

Abstract. Producing very extensive blooms in the world's oceans in both hemispheres, a coccolithophore E. huxleyi is capable of affecting both the marine ecology and carbon fluxes at the atmosphere-ocean interface. At the same time, it is subject to the impact of multiple co-acting environmental forcings, which determine the spatio-temporal dynamics of E. huxleyi blooming phenomenon. To reveal the individual importance of each forcing factor (FF) that is known to significantly control the extent and intensity of E. huxleyi blooms and can be retrieved from remote sensing data, we used long-term spatial time series (1998–2016) of sea surface temperature and salinity, incident photosynthetically active radiation, and Ekman layer depth relevant to the marine environments located in the North Atlantic, Arctic and North Pacific oceans, namely the North, Norwegian, Greenland, Labrador, Barents and Bering seas. The FFs retrieved were subjected to statistical analyses. The descriptive statistical approach has shown that E. huxleyi phytoplankton were highly adaptive to the environmental conditions and capable of arising and developing within wide FFs ranges, which proved to be expressly sea-specific. It was also found that there were FFs optimal ranges (also sea-specific), within which the blooms were particularly extensive. The application of the Random Forest Classifier (RFC) approach to each target sea allowed to reliably rank the FFs considered in terms of their role in the spatio-temporal dynamics of E. huxleyi blooms. With the only exception of the Bering Sea, allegedly due to temporally established untypical hydrological conditions, the prediction ability of RFC modeling characterized in terms of precision, recall, and f1-score generally was in excess of 70 %, thus indicating the adequacy of the developed models for FFs prioritization with regard to E. huxleyi blooms.

Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

2020 ◽  
Vol 637 ◽  
pp. 117-140 ◽  
Author(s):  
DW McGowan ◽  
ED Goldstein ◽  
ML Arimitsu ◽  
AL Deary ◽  
O Ormseth ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Marine Ecosystem ◽  
Gulf Of Alaska ◽  
Data Series ◽  
Limited Information ◽  
Important Species ◽  
Multiple Data ◽  
Spatial And Temporal Dynamics ◽  
Spawning Areas ◽  
Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

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