scholarly journals Orographic Factors as a Predictor of the Spread of the Siberian Silk Moth Outbreak in the Mountainous Southern Taiga Forests of Siberia

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 115
Author(s):  
Svetlana M. Sultson ◽  
Andrey A. Goroshko ◽  
Sergey V. Verkhovets ◽  
Pavel V. Mikhaylov ◽  
Valery A. Ivanov ◽  
...  
Keyword(s):  
Large Scale ◽  
Thermal Emission ◽  
Coniferous Forest ◽  
Southern Taiga ◽  
Slope Aspect ◽  
Landsat 8 ◽  
Silk Moth ◽  
Pest Outbreaks ◽  
Taiga Forests ◽  
Siberian Silk Moth

This research is dedicated to solving an urgent problem associated with the large-scale destruction of taiga forests by Siberian silk moth (Dendrolimus sibiricus) outbreaks. The dynamics of the damage to dark coniferous forest stands induced by the Siberian silk moth outbreaks in mid-altitude mountains were studied. A hypothesis was formulated based on the fundamental influence of the orography on the phytophage’s dispersal within the landscape, along with the climate, which acts as a secondary predictor—a catalyst for outbreaks. The study was carried out using Landsat−8 satellite imagery time-series (from 2018 to 2020). The data were verified using a field forest pathological survey of the territory. An assessment of the defoliated forest area and damage association with the landscape was carried out using an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model. The assessment was aimed to detail the forecast parameters for an outbreak development in mid-altitude mountains using the orographic features—altitude, terrain slope, and slope aspect. Early warnings of phytophagous insect outbreaks in mountain southern taiga should be focused on the permanent monitoring of dark coniferous stands of the mossy group of forest types, covering altitude levels from 400 to 600 m, located on gentle terrains and slopes of up to 15 degrees. The greatest vulnerability to phytophage impacts was characterized as areas located at altitudes from 400 to 600 m. The upper limit of D. sibiricus distribution was 900 m above sea level. The results obtained provide comprehensive information on the Siberian silk moth potential reserves within the study area with the possibility of extrapolation to similar territories. The data will make it possible to model pest outbreaks based on orography and improve the forest pathological monitoring methods at the regional level.

scholarly journals CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 855-862 ◽  
Author(s):  
Y. T. Guo ◽  
X. M. Zhang ◽  
T. F. Long ◽  
W. L. Jiao ◽  
G. J. He ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Forest Cover ◽  
Survey Methods ◽  
Coniferous Forest ◽  
Google Earth ◽  
Forest Area ◽  
Landsat 8 ◽  
Forest Change ◽  
Google Earth Engine

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

Spatial and temporal dynamics of Siberian silk moth large-scale outbreak in dark-needle coniferous tree stands in Altai

2016 ◽  
Vol 9 (6) ◽  
pp. 711-720 ◽  
Author(s):  
V. I. Kharuk ◽  
D. A. Demidko ◽  
E. V. Fedotova ◽  
M. L. Dvinskaya ◽  
U. A. Budnik
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Coniferous Tree ◽  
Silk Moth ◽  
Spatial And Temporal Dynamics ◽  
Siberian Silk Moth

scholarly journals The Greater Caucasus Glacier Inventory (Russia, Georgia and Azerbaijan)

2018 ◽  
Vol 12 (1) ◽  
pp. 81-94 ◽  
Author(s):  
Levan G. Tielidze ◽  
Roger D. Wheate
Keyword(s):  
Surface Area ◽  
Large Scale ◽  
Thermal Emission ◽  
Landsat 8 ◽  
Mountain Range ◽  
Height Distribution ◽  
Glacier Surface ◽  
Data Set ◽  
Glacier Inventory ◽  
Greater Caucasus

Abstract. There have been numerous studies of glaciers in the Greater Caucasus, but none that have generated a modern glacier database across the whole mountain range. Here, we present an updated and expanded glacier inventory at three time periods (1960, 1986, 2014) covering the entire Greater Caucasus. Large-scale topographic maps and satellite imagery (Corona, Landsat 5, Landsat 8 and ASTER) were used to conduct a remote-sensing survey of glacier change, and the 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM; 17 November 2011) was used to determine the aspect, slope and height distribution of glaciers. Glacier margins were mapped manually and reveal that in 1960 the mountains contained 2349 glaciers with a total glacier surface area of 1674.9 ± 70.4 km2. By 1986, glacier surface area had decreased to 1482.1 ± 64.4 km2 (2209 glaciers), and by 2014 to 1193.2 ± 54.0 km2 (2020 glaciers). This represents a 28.8 ± 4.4 % (481 ± 21.2 km2) or 0.53 % yr−1 reduction in total glacier surface area between 1960 and 2014 and an increase in the rate of area loss since 1986 (0.69 % yr−1) compared to 1960–1986 (0.44 % yr−1). Glacier mean size decreased from 0.70 km2 in 1960 to 0.66 km2 in 1986 and to 0.57 km2 in 2014. This new glacier inventory has been submitted to the Global Land Ice Measurements from Space (GLIMS) database and can be used as a basis data set for future studies.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

SCIENTIFIC RESEARCH IN THE VISIM NATURE RESERVE: FROM THE VISIM RESERVE TO THE BIOSPHERE RESERVE

2021 ◽  
Author(s):  
M.Y. FEDOROV ◽  
◽  
I.A. KUZNETSOVA ◽  
Keyword(s):  
Nature Reserve ◽  
Historical Analysis ◽  
Biosphere Reserve ◽  
Metallurgical Industry ◽  
Southern Taiga ◽  
Joint Research ◽  
Educational Tourism ◽  
Nature Preservation ◽  
Recreational Pressure ◽  
Taiga Forests

This article presents a historical analysis of human impact and further step-by-step nature reserve creation within the territory of the current Visimsky State Biosphere Reserve. From the end of XVII to the middle of XX centuries the ecosystem of low-mountain southern taiga forests in the Middle Ural region was strongly exploited by a local mining and metallurgical industry. The establishment of the Visim State Reserve in 1946 did not provide sustainable results but initiated research studies that laid a foundation for a subsequent preservation and the studies of the endemic taiga forests of the Middle Ural in the framework of the modern Visimsky State Biosphere Reserve. Since 1975 the science department of the reserve has conducted regular observations of the nature processes. The reserve has established long-term and efficient collaboration with the Institute of Ecology of Animals and Plants, Ural Branch of the Russian Academy of Sciences. The joint research findings are used in practical solutions of the nature preservation in the region. This collaboration is also focused on the monitoring of the recreational pressure caused by the educational tourism.

Migration of the Northern Boundary of the Siberian Silk Moth

2018 ◽  
Vol 11 (1) ◽  
pp. 26-34 ◽  
Author(s):  
V. I. Kharuk ◽  
S. T. Im ◽  
M. N. Yagunov
Keyword(s):  
Northern Boundary ◽  
Silk Moth ◽  
Siberian Silk Moth

scholarly journals Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite

2018 ◽  
Vol 10 (9) ◽  
pp. 1379 ◽  
Author(s):  
Simon Plank ◽  
Michael Nolde ◽  
Rudolf Richter ◽  
Christian Fischer ◽  
Sandro Martinis ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Thermal Emission ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Villarrica Volcano ◽  
Radiant Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
German Aerospace ◽  
Active Volcanoes

Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at subpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash coverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

scholarly journals A Novel Index for Impervious Surface Area Mapping: Development and Validation

2018 ◽  
Vol 10 (10) ◽  
pp. 1521 ◽  
Author(s):  
Yugang Tian ◽  
Hui Chen ◽  
Qingju Song ◽  
Kun Zheng
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
Impervious Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Capital Cities ◽  
Heat Islands ◽  
Surface Areas ◽  
Hydrological Cycles

The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images.

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