Integrating Remote Sensing and Ecosystem Process Models for Landscape- to Regional-Scale Analysis of the Carbon Cycle

BioScience ◽  
2004 ◽  
Vol 54 (6) ◽  
pp. 573 ◽  
Author(s):  
DAVID P. TURNER ◽  
SCOTT V. OLLINGER ◽  
JOHN S. KIMBALL
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Regional Scale ◽  
Process Models ◽  
Scale Analysis ◽  
Ecosystem Process

scholarly journals Regional-scale analysis of lake outburst hazards in the southwestern Pamir, Tajikistan, based on remote sensing and GIS

2011 ◽  
Vol 11 (5) ◽  
pp. 1447-1462 ◽  
Author(s):  
M. Mergili ◽  
J. F. Schneider
Keyword(s):  
Remote Sensing ◽  
Risk Mitigation ◽  
Regional Scale ◽  
Remote Sensing And Gis ◽  
Scale Analysis ◽  
Empirical Modelling ◽  
Medium Resolution ◽  
Area 6 ◽  
Rating Scheme ◽  
Selection Of

Abstract. This paper presents an analysis of the hazards emanating from the sudden drainage of alpine lakes in South-Western Tajik Pamir. In the last 40 yr, several new lakes have formed in the front of retreating glacier tongues, and existing lakes have grown. Other lakes are dammed by landslide deposits or older moraines. In 2002, sudden drainage of a glacial lake in the area triggered a catastrophic debris flow. Building on existing approaches, a rating scheme was devised allowing quick, regional-scale identification of potentially hazardous lakes and possible impact areas. This approach relies on GIS, remote sensing and empirical modelling, largely based on medium-resolution international datasets. Out of the 428 lakes mapped in the area, 6 were rated very hazardous and 34 hazardous. This classification was used for the selection of lakes requiring in-depth investigation. Selected cases are presented and discussed in order to understand the potentials and limitations of the approach used. Such an understanding is essential for the appropriate application of the methodology for risk mitigation purposes.

scholarly journals Detecting regional patterns of changing CO2 flux in Alaska

2016 ◽  
Vol 113 (28) ◽  
pp. 7733-7738 ◽  
Author(s):  
Nicholas C. Parazoo ◽  
Roisin Commane ◽  
Steven C. Wofsy ◽  
Charles D. Koven ◽  
Colm Sweeney ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Regional Scale ◽  
Co2 Flux ◽  
Cold Season ◽  
The Arctic ◽  
Systematic Sampling ◽  
Regional Patterns ◽  
Near Surface ◽  
Spatial Coverage

With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.

Implementation of multispecies ecological networks at the regional scale: analysis and multi-temporal assessment

2021 ◽  
Vol 289 ◽  
pp. 112494
Author(s):  
Giuseppe Modica ◽  
Salvatore Praticò ◽  
Luigi Laudari ◽  
Antonio Ledda ◽  
Salvatore Di Fazio ◽  
...  
Keyword(s):  
Regional Scale ◽  
Ecological Networks ◽  
Scale Analysis ◽  
Multi Temporal ◽  
Temporal Assessment

scholarly journals Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model

2021 ◽  
Vol 13 (8) ◽  
pp. 1516
Author(s):  
Boyang Li ◽  
Yaokui Cui ◽  
Xiaozhuang Geng ◽  
Huan Li
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Energy Exchange ◽  
Vegetation Index ◽  
Regional Scale ◽  
Reconstruction Algorithm ◽  
Heihe River ◽  
Main Process ◽  
In Situ Data

Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.

scholarly journals Regional-scale phenology modeling based on meteorological records and remote sensing observations

2012 ◽  
Vol 117 (G3) ◽  
pp. n/a-n/a ◽  
Author(s):  
Xi Yang ◽  
John F. Mustard ◽  
Jianwu Tang ◽  
Hong Xu
Keyword(s):  
Remote Sensing ◽  
Regional Scale
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