scholarly journals Burn severity and regeneration in large forest fires: an analysis from Landsat time series

2017 ◽  
pp. 17 ◽  
Author(s):  
S. Martínez ◽  
E. Chuvieco ◽  
I. Aguado ◽  
J. Salas
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Sustainable Forest Management ◽  
The Other ◽  
Burn Severity ◽  
Effective Strategies ◽  
Recovery Processes ◽  
Fire Condition ◽  
Spectral Profiles ◽  
Disturbance And Recovery

<p>The main objective of this study is to take a close look at post-fire recovery patterns in forestry areas under different burn severity conditions. We also investigate the time that forestry ecosystems take to recover their pre-fire condition. In this context, this study analyses both the level of severity in Uncastillo forest wildfire (7.664ha), one of the greatest occurred in Spain in 1994, and the pattern of natural recovery in the following decades (until 2014) using annual Landsat time series (sensors TM&amp;ETM+). Burn severity has been estimated by means of PROSPECT and GeoSAIL radiative transfer models following methodologies described in De Santis and Chuvieco (2009). On the other hand, recovery processes have been assessed from spectral profiles using the LandTrendr model (Landsat-based Detection of Trends in Disturbance and Recovery) (Kennedy et al., 2010). Results contribute to a further understanding of the post-fire evolution in forestry areas and to develop effective strategies for sustainable forest management.</p>

Time series of high-resolution images enhances efforts to monitor post-fire condition and recovery, Waldo Canyon fire, Colorado, USA

2018 ◽  
Vol 27 (10) ◽  
pp. 699 ◽  
Author(s):  
Melanie K. Vanderhoof ◽  
Clifton Burt ◽  
Todd J. Hawbaker
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Vegetation Index ◽  
Burn Severity ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Object Based ◽  
Fire Condition ◽  
Normalised Difference Vegetation Index ◽  
High Resolution Images

Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011–2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, Worldview-3 and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery, resprouting vigorously within 1 year, whereas 4 years post-fire, areas previously dominated by conifers were divided approximately equally between being classified as dominated by quaking aspen saplings with herbaceous species in the understorey or minimally recovered. Relative to using a pixel-based Normalised Difference Vegetation Index (NDVI), our object-based approach showed higher rates of revegetation. High-resolution imagery can provide an effective means to monitor post-fire site conditions and complement more prevalent efforts with moderate- and coarse-resolution sensors.

scholarly journals Tracking Rates of Forest Disturbance and Associated Carbon Loss in Areas of Illegal Amber Mining in Ukraine Using Landsat Time Series

2020 ◽  
Vol 12 (14) ◽  
pp. 2235
Author(s):  
Viktor Myroniuk ◽  
Andrii Bilous ◽  
Yevhenii Khan ◽  
Andrii Terentiev ◽  
Pavlo Kravets ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Sustainable Forest Management ◽  
Quality Management System ◽  
Forest Disturbance ◽  
Google Earth ◽  
Forest Stewardship Council ◽  
Carbon Loss ◽  
Disturbed Area ◽  
Disturbance And Recovery

Mapping forest disturbance is crucial for many applications related to decision-making for sustainable forest management. This study identified the effect of illegal amber mining on forest change and accumulated carbon stock across a study area of 8125.5 ha in northern Ukraine. Our method relies on the Google Earth Engine (GEE) implementation of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm of Landsat time-series (LTS) to derive yearly maps of forest disturbance and recovery in areas affected by amber extraction operations. We used virtual reality (VR) 360 interactive panoramic images taken from the sites to attribute four levels of forest disturbance associated with the delta normalized burn ratio (dNBR) and then calculated the carbon loss. We revealed that illegal amber extraction in Ukraine has been occurring since the middle of the 1990s, yielding 3260 ha of total disturbed area up to 2019. This study indicated that the area of forest disturbance increased dramatically during 2013–2014, and illegal amber operations persist. As a result, regrowth processes were mapped on only 375 ha of total disturbed area. The results were integrated into the Forest Stewardship Council® (FSC®) quality management system in the region to categorize Forest Management Units (FMUs) conforming to different disturbance rates and taking actions related to their certification status. Moreover, carbon loss evaluation allows the responsible forest management systems to be streamlined and to endorse ecosystem service assessment.

Test of a Benthic Macroinvertebrate Habitat-Flow Time Series Model Incorporating Disturbance and Recovery Processes

2014 ◽  
Vol 31 (7) ◽  
pp. 785-797 ◽  
Author(s):  
J. W. Hayes ◽  
K. A. Shearer ◽  
E. O. Goodwin ◽  
J. Hay ◽  
C. Allen ◽  
...  
Keyword(s):  
Time Series ◽  
Flow Time ◽  
Time Series Model ◽  
Benthic Macroinvertebrate ◽  
Recovery Processes ◽  
Disturbance And Recovery

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

scholarly journals The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4024
Author(s):  
Krzysztof Dmytrów ◽  
Joanna Landmesser ◽  
Beata Bieszk-Stolorz
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Dynamic Time Warping ◽  
Global Economy ◽  
Price Change ◽  
Commodity Prices ◽  
The Other ◽  
Time Warping ◽  
Heating Oil ◽  
Dynamic Time

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

Demographic changes and savings behavior: the experience of a developing country

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ebrahim Rezaei
Keyword(s):  
Time Series ◽  
Demographic Data ◽  
Calculated Data ◽  
The Other ◽  
Demographic Changes ◽  
Savings Behavior ◽  
Content Type ◽  
Household Level ◽  
Savings Rate ◽  
The Government

PurposeThis paper aims to disclose the savings behavior of Iran's economy in the context of demographic transition.Design/methodology/approachEmploying a version of Ramsey-Cass-Koopmans growth model, this paper benefits from a broad range of data and variables which are mainly taken from the Central Bank of Iran's database. The study uses actual and calculated data to produce analogous simulated data. The data cover the 1970–2015 period. This long period provides an opportunity to simulate more valid time series. It is worth noting that due to the severe economic sanctions imposed on the Iran's economy, particularly after 2017, some most recent data have been obliterated from the sample.FindingsThe results, stemming from the simulated model, hint that; firstly, the population variable is a notable determinant of the savings rate. Secondly, the effects of a slump in the population growth rate would attenuate the savings level significantly. Thirdly, other pragmatic steps could be taken to redress the fallout of the demographic changes.Research limitations/implicationsThere are some limitations in providing broad data related to economic sectors in Iran. The savings data, for instance, are available as an aggregated time series, and if the authors had wide data of household level, they would have been able to build more detail-based model. Similar to this issue of lack of households’ income-based data, some measures such as high or low levels as well as detailed demographic data could be helpful in sophisticated household level resulting. In addition, the complex relationship between the government and social security (pension) funds, in terms of financing part of government's budget deficit by these funds, thwarts a typical researcher in using comprehensive and transparent government expenditure data in their research. In other words, the possible positive or negative role of the funds, as a related issue to the demographic changes, cannot simply be determined in the model. It might be possible after necessary corrections are carried out in the mentioned relations.Originality/valueIn fact, the problem statement in this paper is to discern how the population aging can impact the saving rates on the one hand, and to what extent its repercussion can be modified by the other theoretical-based determinants on the other. In fact, the underlying argument of the present research arises from the stylized facts concerning prognosticates of the future evolutions of the world's population. To that end, the study will use Iran's economic and demographic data.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

scholarly journals On the volatility of sensex

2012 ◽  
Vol 1 (1) ◽  
pp. 10-22
Author(s):  
Nateson C ◽  
Suganya D
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The Other ◽  
Stock Index ◽  
Series Data ◽  
Closing Time ◽  
Market Activity ◽  
Year 2000 ◽  
Daily Returns

The present study seeks to analyse Volatility of popular stock index SENSEX. The present study is based on the closing time series data of SENSEX covering the period from 3rd January 2000, to 30th June 2011. The year 2008 has recorded higher Volatility compared to the other years of the study. Volatility fell in the year 2009 from the high of 2008. The years after were comparatively calmer. In the year 2000, the Volatility was higher signifying enhance market activity. The overall daily Volatility for SENSEX was approximately 1.70 % while the annualized value was approximately 25%-26%. Events Reported around Daily Returns in Excess of +/-5%have also been identified.

scholarly journals Analysis PM10 and Visibility During Forest Fire in Palangka Raya

2021 ◽  
Vol 893 (1) ◽  
pp. 012002
Author(s):  
A. Indrawati ◽  
D. F. Andarini ◽  
N. Cholianawati ◽  
Sumaryati
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Pm10 Concentration ◽  
The Other ◽  
Data Filtering ◽  
Coefficient Corrélation ◽  
High Bias ◽  
R Value ◽  
The Relationship ◽  
Regression Pattern

Abstract Forest fires have an impact on air quality and visibility. Visibility can be associated with a highly visual indicator of air pollution. This research aims to analyze the relationship between the PM10 concentration and visibility during the forest firest events and normal conditions in Palangkaraya from 2000 to 2014 by using a regression method. The relative humidity data was used to filter the PM10 and visibility. Furthermore, the equation resulted from the regression analysis was used to predict PM10 concentration in Palangka Raya. The result showed that the regression pattern tends to form a logarithmic function. Specifically, without filtering data, the coefficient correlation (r-value) during the forest fire events and normal conditions are 0.69 and 0.5, respectively. Meanwhile, a data filtering method gives a higher relationship between PM10 and visibility, with the r-value of 0.7 for the forest fire events and 0.68 for the normal condition. On the other hand, the prediction of PM10 concentration indicates a high bias value due to the other influenced factors that have not been included in this study.

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