1,720,963 research outputs found
Landsat-8 and sentinel-2 based forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya
No full textThe accurate quantitative and qualitative estimation of burn-area are crucial to analyze the impact of fire on forest. The medium resolution optical-satellite imagery of Landsat-8 and Sentinel-2 are employed covering the period 2016 to 2019 for forest fire patches identification on Google Earth Engine (GEE). The most indispensable season of Forest Fire (FF) is pre-monsoon in Uttarakhand, western Himalaya, India. Bi-temporal (pre and post fire) reflectance contrast of burn-sensitive spectral bands was used to compute differential spectral indices, namely, Normalized Burn Ratio (dNBR), Normalized Difference Vegetation Index (dNDVI), Normalized Difference Water Index (dNDWI), and Short-Wave Infrared (dSWIR). The differential spectral-indices composite is further used as an input to unsupervised Weka clustering algorithms for capturing the shape and pattern of fire patches. Sample training-data of burn and unburn classes were collected with reference to thermal and optical spectral principle. Classification Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM) algorithms have been employed to identify FF. The key findings revealed that CART and RF algorithms displayed similar forest fire patches with an overall accuracy of 97–100%. The classification accuracy is slightly lower in SVM and its underestimating forest fire patches detections. Landsat-8 OLI derived burn area was fitted better with fire product of Climate Change Initiative (Fire-CCI of ESA) and MCD64A1 of MODIS burn area product with R-square of 0.71–0.93 and 0.62–0.91, respectively which attributed to better spectral bands of Landsat-8 than the Sentinel-2. However, Sentinel-2 bands have the potential to capture fire patches during post-fire events. This study has demonstrated the potential utilities of combined effort of unsupervised and supervised algorithms on Landsat-8 and Sentinel-2 on GEE to identify fire patches
Estimating forest biophysical and biochemical parameters in Behali Reserve Forest (Assam) using proximal and remote sensing techniques
No full textForest biophysical and biochemical parameters are critical for assessing forest health. The integration of proximal and remote sensing approaches is becoming more prevalent for plant characterization because of the benefits associated with multi-dimensional data collection and interpretation. This study aims to deduce the biophysical and biochemical parameters of forests in the Behali Reserve Forest (BRF) located in the Eastern Himalayas. Specifically, the red-edge spectral bands of the Sentinel-2A sensor were deployed to derive the Leaf Area Index (LAI), Enhanced Vegetation Index (EVI), and Normalized Difference Red-Edge (NDRE). Furthermore, the Normalized Area Over Reflectance Curve (NAOC) is used to deduce leaf chlorophyll content and leaf nitrogen content. The biophysical parameters analysis showed that the LAI ranged from 0 to 5.5 m 2/m 2. The healthy dense forests showed an LAI of more than 4.5 that comprised 37.5% of the area. The satellite-derived NDRE has a significant positive association with measured leaf chlorophyll and nitrogen contents that exhibited coefficient of determination (R 2) of 0.88 and 0.89, respectively. The NAOC-based empirical model leaf chlorophyll content of dense forests ranges between 30 and 45 μg/cm 2. The leaf nitrogen content of dense forest as demonstrated by the Nitrogen Balance Index (NBI) was estimated between 40 and 70 (unitless). The synergy of near-proximal and remote sensing data has demonstrated a robust and efficient method of monitoring the health of forests in reserve forests. The retrieved biophysical and biochemical parameters have supplied crucial information on forest health which is vital for forest conservation, plantation, monitoring and management.</p
A short-term decline in anthropogenic emission of CO<sub>2</sub> in India due to COVID-19 confinement
No full textTo curb the spread of novel coronavirus (COVID-19), confinement measures were undertaken, which altered the pattern of energy consumption and India’s anthropogenic CO2 emissions during the effective lockdowns periods (January to June 2020). Such changes are being analyzed using data of energy generated from coal and renewable sources and fossil-based daily CO2 emissions. Results revealed that coal-fired (fossil-based) energy generation fell by –13% in March, –29% in April, and –20% in May, and –16.6% in mid-June 2020 as compared with the same period in 2018–2019. Conversely, the renewable energy generation increased by 19% in March, 12% in April, 17% in May, and 7% in June 2020. The share of fossil-based energy fell by –6.55% in 2020 compared with mean levels, which was further offset by increases of renewable energy. India’s daily fossil-based CO2 emissions fell by –11.6% (–5 to –25.7%) by mid-June 2020 compared with mean levels of 2017–2019 with total change in fossil-based CO2 emission by –139 (–62 to –230) MtCO2, with the largest reduction in the industry (–41%), transport (–28.5%), and power (–21%) followed by the public (–5.4%), and aviation (–4%) sectors. If some levels of lockdown persist until December 2020, both energy consumption and CO2 emissions patterns would be below the 2019 level. The nationwide lockdown has led to a reduction in anthropogenic CO2 emissions and, subsequently, improved air quality and global environment and has also helped in reducing atmospheric CO2 concentrations at the local level but not on the global level. With suitable government policies, switching to a cleaner mode of energy generation other than fossil fuels could be a viable option to minimize CO2 emissions under increasing demand for energy.</p
Changing forest fire regime in relation to climatic conditions over Western and Eastern Himalaya, India
No full textThe forest fire regime has been altering due to changing climatic patterns and the increasing human footprint. The present study examined changes in the forest fire regime (e.g., spatio-temporal distribution, trend, peak fire time, and size of burn spots) and its connections with regional climatic conditions over Himalaya (India, Nepal, and Bhutan) in the last two decades. A moderate resolution imaging spectroradiometer (MODIS)–derived MCD64A1 burn area dataset was used to extract the fire information (i.e., burn area and date). For the climatic variables (i.e., maximum temperature, minimum temperature, precipitation, and Palmer Drought Severity Index), data from TerraClimate were used to quantify their trend and variability and their connections with changing forest fire regimes. Over the last two decades, the highest annual average burn area was 3156 (σ = 1958) km2 in Eastern Himalaya (including Bhutan). We observed an increasing trend in burn area (837.82km2 year−1) in the first decade (2001–2010) and a decreasing trend (–297.22km2 year−1) in the last decade (2011–2020), particularly over Eastern Himalaya (257.82km2 year−1). The peak fire has a wide variation over the Himalayas; mainly peak fire time is concentrated between March and May. In the last decade, the average peak fire time was delayed by 7 to 24 days from the first decade. The size (km2) of the fire spots varies from Western to Eastern Himalaya. The largest fire spot was found over Nepal (1.91km2), followed by Western Himalaya (1.50km2) and Eastern Himalaya (1.12km2). The burn area trend and changes in the size of fire spots exhibited a correspondence with decadal scale trend of climatic components (specifically, temperatures and precipitation). The annual burn area climatic variables showed a moderate to weak association (r = 0.6 to −0.47); the weak relation could be explained by other affecting factors.<br/
Monitoring land use/land cover change and high-altitude vegetation trends along with their climatic controls across the Central and Eastern Himalayas
No full textMonitoring the spatial pattern of vegetation growth trends is important in the Central and Eastern Himalayas as many ecosystems in the Himalayas are sensitive to climatic change. The human-induced land use/land cover (LULC) changes are the potential driving forces for changes in ecosystems. This study employed MODIS (MCD12Q1) product to quantify the spatial pattern of LULC from 2001 to 2019. The long-term vegetation datasets (NDVI3g) (1982-2015) were utilized to estimate vegetation trends and climatic variables (e.g., precipitation, soil moisture, temperature, solar radiation) trends. The Mann-Kendall (τ) test and Theil-Sen’s slope were deployed for computing trends over vegetation (e.g., forests, shrublands, savannas, croplands, and grassland). The results showed a prominent large-scale greening trend of croplands (77% of area) and forests including shrublands, savannas, and grassland (42% of area), mostly across the Central (Nepal) Himalayas. The browning trends of forests were also evident, especially over the Eastern Himalaya (Bhutan). The greening trends of vegetation were mainly associated with climatic factors like precipitation and soil moisture, and the corresponding correlation coefficients (r) were 0.69 and 0.28, respectively at p-value ̼ 0.001. Additionally, temperature control on vegetation was found at higher elevation zones of the Central and Eastern Himalayas (r = 0.93, p-value ̼ 0.001), whereas browning trends of vegetation occurred due to temperature-induced moisture stress along with the decreasing trends of solar radiation, and a profound impact was seen over Bhutan. Human-induced land-use change (e.g., shifting cultivation, deforestation) was also attributed to declining vegetation growth since an increase in built-up area was noticed that mainly replaced the croplands and barren land over the study regions. Therefore, the quantification of vegetation trends is important for understanding and managing agriculture and forests ecosystems located in the high-altitude zone, and attention from ecologists and policymakers is required to monitor and manage vegetation in the Himalayas
Pixel-Based Long-Term (2001–2020) Estimations of Forest Fire Emissions over the Himalaya
Full text linkForest/wildfires have been one of the most notable severe catastrophes in recent decades across the globe, and their intensity is expected to rise with global warming. Forest fire contributes significantly to particulate and gaseous pollution in the atmosphere. This study has estimated the pixel-based emissions (CO, CO2, CH4, NOx, SO2, NH3, PM2.5, PM10, OC, and BC) from forest fires over the Himalaya (including India, Nepal, and Bhutan). The MODIS-based burned area (MCD64A1), Land Use Land Cover (LULC; MCD12A1), NDVI (MOD13A2), percentage tree cover (MOD44A6), gridded biomass, and species-wise emissions factors were used to estimate the monthly emissions from forest fires over the last two decades (2001–2020). A bottom-up approach was adopted to retrieve the emissions. A substantial inter-annual variation of forest burn area was found over the western, central (Nepal), and eastern Himalaya (including Bhutan). The eastern Himalaya exhibited the highest average annual CO2 emission, i.e., 20.37 Tg, followed by Nepal, 15.52 Tg, and the western Himalaya, 4.92 Tg. Spatially, the higher CO2 (0.01–0.02 Tg year−1/km2) and CO (0.007–0.002 Tg year−1/km2) emissions were detected along the south-eastern parts of the eastern Himalaya, southern regions of Nepal, and south-eastern parts of the western Himalaya. The trend of forest fire emissions in 2001–2010 was significantly positive, while in the next decade (2011–2020) a negative trend was recorded. The estimated pixel-based emission and Global Fire Emission Dataset (GFEDv4.1s) data demonstrated a promising association with a correlation coefficient (r) between 0.80 and 0.93. An inventory of forest fire emissions over long-term periods can be helpful for policymakers. In addition, it helps to set guidelines for air quality and atmospheric transport modelling and to better understand atmospheric pollution over the Himalayan and associated regions
Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America
No full textThe outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (-0.11 °C to -2.6 °C), USA (-0.70 °C) and Canada (-0.27 °C) in March-May of the pandemic year 2020 compared to the mean of 2015-2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. -0.46 °C to -0.96 °C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1-2 °C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapor content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation
Disentangle the short-term forest degradation over most fire-affected parts of Western Himalaya, India
No full textThe tropical forest contributes around 5% to 15% of atmospheric carbon emissions, which are mostly anthropogenic. But there are large uncertainties in the quantification of these emissions from its sources. The remote-sensing data offers a practical opportunity to monitor and assess different forest disturbances. Western Himalayan forest is often affected by fire events, mostly during (pre-monsoon) dry and warm periods. In this study, we present a way to monitor the forest degradation condition using spectral mixture analysis (SMA) and surface reflectance of Landsat-8 data from 2014 to 2019. The Normalized Degradation Fraction Index (NDFI) has been performed by using spectral end member fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade in the Google Earth Engine (GEE) cloud platform. The NDFI shows considerable spatial correspondences with clusters of fire spots during the pre-monsoon period. Around 3% to 9% of the forest burned area transformed to partially to highly degraded forest. The overall trend of degradation fraction (NDFI) over total forest cover shows a significant negative trend over a considerable area. Thus, Landsat-8-based SMA and NDFI demonstrate a potential way to identify forest degradation mediated by forest fires, although remote sensing-based approaches are limited in their capacity to accurately detect forest disturbances. Furthermore, field-based studies are needed to monitor the potentialities of the NDFI approach in forest degradation identification
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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