scholarly journals A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 363 ◽  
Author(s):  
Moreno-Ruiz ◽  
García-Lázaro ◽  
Arbelo
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
Spatial Resolution ◽  
Reference Data ◽  
Global Climate ◽  
Fire Service ◽  
Burned Area ◽  
Spatial And Temporal Patterns ◽  
Modis Data

Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

scholarly journals Monitoring Changes to Arctic Vegetation and Glaciers at Ny-Ålesund, Svalbard, Based on Time Series Remote Sensing

2021 ◽  
Vol 13 (19) ◽  
pp. 3845
Author(s):  
Guangbo Ren ◽  
Jianbu Wang ◽  
Yunfei Lu ◽  
Peiqiang Wu ◽  
Xiaoqing Lu ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
High Latitude ◽  
Global Climate ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Coastal Regions ◽  
Average Annual Temperature

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.

scholarly journals Automated mapping of burned areas in semi-arid ecosystems using modis time-series imagery

2015 ◽  
Vol XL-7/W3 ◽  
pp. 811-814 ◽  
Author(s):  
L. A. Hardtke ◽  
P. D. Blanco ◽  
H. F. del Valle ◽  
G. I. Metternicht ◽  
W. F. Sione
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Regional Scale ◽  
Arid Ecosystems ◽  
Burned Area ◽  
Second Phase ◽  
Commission Error ◽  
Spatial And Temporal Patterns ◽  
Burned Areas ◽  
Semi Arid

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R<sup>2</sup> = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R<sup>2</sup> = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

scholarly journals Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Stefan Erasmi ◽  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Florian Schneider ◽  
Markus Hauck
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Climate Change ◽  
Global Climate ◽  
Siberian Larch ◽  
Growing Season ◽  
Forest Productivity ◽  
Forest Steppe ◽  
Fragmented Forest ◽  
Southern Fringe

AbstractThe monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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.

Landscape Biodiversity Characterization in Ecoregion 29 Using MODIS

2013 ◽  
pp. 815-831
Author(s):  
Nitin Kumar Tripathi ◽  
Aung Phey Khant
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Global Warming ◽  
Forest Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Modis Data ◽  
Moderate Resolution ◽  
Landscape Biodiversity

Biodiversity conservation is a challenging task due to ever growing impact of global warming and climate change. The chapter discusses various aspects of biodiversity parameters that can be estimated using remote sensing data. Moderate resolution satellite (MODIS) data was used to demonstrate the biodiversity characterization of Ecoregion 29. Forest type map linked to density of the study area was also developed by MODIS data. The outcome states that remote sensing and geographic information systems can be used in combination to derive various parameters related to biodiversity surveillance at a regional scale.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

Sign in / Sign up

Export Citation Format

Share Document