Spatio-temporal analysis of remotely-sensed forest mortality associated with road de-icing salts

2014 ◽  
Vol 472 ◽  
pp. 929-938 ◽  
Author(s):  
Yuanchao Fan ◽  
Peter J. Weisberg ◽  
Robert S. Nowak
Keyword(s):  
Temporal Analysis ◽  
Remotely Sensed ◽  
Forest Mortality ◽  
Spatio Temporal

scholarly journals Spatio-temporal analysis of remote sensing images provides early warning signals of forest mortality

2021 ◽  
Author(s):  
Sara Alibakhshi
Keyword(s):  
Spatial Autocorrelation ◽  
Early Warning ◽  
Temporal Analysis ◽  
Warning Signals ◽  
Early Warning Signals ◽  
New Approach ◽  
Forest Mortality ◽  
Spatio Temporal ◽  
Study Sites ◽  
Local Spatial Autocorrelation

AbstractEcosystems are under unprecedented pressures, reflected in rapid changes in the regime of disturbances that may cause negative impacts on them. This highlights the importance of characterizing the state of an ecosystem and its response to disturbances, which is known as a notoriously difficult task. The state-of-the-art knowledge has been tested rarely in real ecosystems for a number of reasons such as mismatches between the time scale of ecosystem processes and data accessibility as well as weaknesses in the performance of available methods. This study aims to use remotely sensed spatio-temporal data to identify early warning signals of forest mortality using satellite images. For this purpose, I propose a new approach that measures local spatial autocorrelation (using local Moran’s I and local Geary’s c method) at each time, which proved to produce robust results in multiple different study sites examined in this article. This new approach successfully generates early warning signals from time series of local spatial autocorrelation values in unhealthy study sites 2 years prior to forest mortality occurrence. Furthermore, I develop a new R package, called “stew”, that enables users to explore spatio-temporal analysis of ecosystem state changes. This work corroborates the suggestion that spatio-temporal indicators have the potential to diagnose early warning signals to identify upcoming climate-induced forest mortality, up to two years before its occurrence.

scholarly journals Spatio-temporal analysis of remotely sensed rainfall datasets retrieved for the transboundary basin of the Madeira River in Amazonia

Atmósfera ◽  
2020 ◽  
Author(s):  
Vinicius Alexandre Sikora de Souza ◽  
Daniel Medeiros Moreira ◽  
Otto Corrêa Rotunno Filho ◽  
Anderson Paulo Rudke ◽  
Claudia Daza Andrade ◽  
...  
Keyword(s):  
River Basin ◽  
Rainfall Variability ◽  
Temporal Analysis ◽  
Remotely Sensed ◽  
Rain Gauges ◽  
Madeira River ◽  
Satellite Rainfall ◽  
Climate Hazards ◽  
Spatio Temporal ◽  
Madeira River Basin

Rainfall is recognized as the most important driving force of the hydrologic cycle. To accurately represent the spatio-temporal rainfall variability continues to be an enormous hydrological task when using commonly sparse, if available, rain gauges networks. Therefore, the present study devoted a special effort to analyze the robustness of some satellite rainfall products, notably the datasets hereafter named as (i) CHIRP (Climate Hazards Group InfraRed Precipitation), (ii) CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), (iii) 3B42, and (iv) 3B42RT of the Tropical Rainfall Measuring Mission (TRMM), to adequately represent the pluviometric regime in the Madeira river basin. To assess the accuracy of acquired remotely sensed rainfall products, comparisons to observational available rain gauges usually taken as ground-truth in the literature, despite their well-known limitations, were performed. Wavelet analysis was also used to validate the performance of the referred satellite products by means of extracting the corresponding cycles, frequencies, and tendencies along the available time series across the studied basin. The results showed that the data sources CHIRPS and CHIRP better represent the pluviometric phenomenon by means of their monthly accumulated rainfall in the Madeira river basin when compared to the 3B42 and 3B42RT products taking into account rain gauges as baseline information. The CHIRPS product performed the best among the selected rainfall estimators for the Madeira river basin. Further analysis brought up also another very interesting result related to non-rainfall periods, which is usually not reported. However, such evaluation is quite important in hydrology when examining run sequences of droughts and consequent effects in the water balance at the watershed scale. Highly accurate estimates in the sense of identifying non-rainfall periods by remotely sensed information was achieved, which represents an additional and valuable asset of satellite rainfall products. It is worthwhile to say that this perspective deserves to receive much more attention in the literature in order to deeply discuss the water-energy-food nexus.

scholarly journals Spatio-Temporal Analysis of Gyres in Oriented Lakes on the Arctic Coastal Plain of Northern Alaska Based on Remotely Sensed Images

2014 ◽  
Vol 6 (10) ◽  
pp. 9170-9193 ◽  
Author(s):  
Shengan Zhan ◽  
Richard Beck ◽  
Kenneth Hinkel ◽  
Hongxing Liu ◽  
Benjamin Jones
Keyword(s):  
Coastal Plain ◽  
Temporal Analysis ◽  
Remotely Sensed ◽  
The Arctic ◽  
Arctic Coastal Plain ◽  
Remotely Sensed Images ◽  
Spatio Temporal ◽  
Northern Alaska

scholarly journals Spatio-temporal analysis of remotely sensed rainfall datasets retrieved for the transboundary basin of the Madeira river in Amazonia

2020 ◽  
Author(s):  
Vinicius Alexandre Sikora de Souza ◽  
Daniel Medeiros Moreira ◽  
Otto Corrêa Rotunno Filho ◽  
Anderson Paulo Rudke ◽  
Claudia Daza Andrade ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Remotely Sensed ◽  
Transboundary Basin ◽  
Madeira River ◽  
Spatio Temporal

Spatio-Temporal Analysis of Frontal Midline Theta Rhythm in EEG during 3-D Maze Task

2009 ◽  
Vol 129 (10) ◽  
pp. 1778-1784
Author(s):  
Yasuaki Uehara ◽  
Keita Tanaka ◽  
Yoshinori Uchikawa ◽  
Bong-Soo Kim
Keyword(s):  
Theta Rhythm ◽  
Temporal Analysis ◽  
Maze Task ◽  
Frontal Midline Theta ◽  
Spatio Temporal

Spatio-temporal analysis and prediction of grain security in Shaanxi Province

2010 ◽  
Vol 17 (4) ◽  
pp. 770-775
Author(s):  
Ren YANG ◽  
Zhi-Yuan REN ◽  
Qian XU ◽  
Mei-Xia WANG
Keyword(s):  
Temporal Analysis ◽  
Shaanxi Province ◽  
Spatio Temporal ◽  
Grain Security
Sign in / Sign up

Export Citation Format

Share Document