1,720,978 research outputs found
Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data
No full textDespite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (C
ab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the C
ab and nitrogen contents. The C
ab of healthy tea plants varied from 25 to 35 µg/cm
2. Pearson correlation among satellite-derived C
ab and nitrogen with field measurements showed R
2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.
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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
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
Unfolding the contribution of environmental and anthropogenic variables in forest fire over western Himalayan fire regime
No full textIn last few decades, a surge of uncontrolled wild and forest fire has been observed over biomes, mostly from tropical and subtropical regions. The present study has disentangled the contribution of different environmental and anthropogenic factors in forest fire over the western Himalayan (Uttarakhand and Himachal Pradesh) fire regime, which is an active fire hotspot in India. Fire-CCI v5.1 data was used to labelled fire and non-fire pixel. The climatic (e.g. maximum and minimum temperature, precipitation, solar radiation, vapour pressure, wind speed, water vapour deficit, soil moisture and palmer drought index), physiographic (elevation, slope, aspect and roughness), anthropogenic (population density and human modification) and locational (latitude and longitude) variables were utilized to unfold their contribution in forest fire by the aid of Random Forest (RF) a machine learning technique. After parameterization, a 10-fold cross-validation RF model was built over the whole dataset and the average overall accuracy, precision, recall, F-1 score and overall accuracy were estimated as 0.94 (±0.002), 0.86 (±0.003), 0.91 (±0.002) and 0.91 (±0.002), respectively. Furthermore, the whole dataset (2005-2018) was divided into two parts, training set (2005-2017) and testing (2018), to get a robust model. The testing accuracy (overall accuracy = 0.82, precision =0.79, recall = 0.95, F1 score = 0.86 and area under curve (AUC) = 0.95) suggested a reliable performance of RF model in forest fire classification (fire and non-fire). The contributions of the selected variables were retrieved from the feature importance of the RF model. The maximum temperature exhibited the highest importance, followed by elevation, minimum temperature and location variable (latitude and longitude). The population density and human modification (gHM) are moderately contributing to western Himalayan forest fire. Keywords: Forest fire; Western Himalaya; Random Forest </p
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
Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic
No full textThe SARS CoV-2 (COVID-19) pandemic and the enforced lockdown have reduced the use of surface and air transportation. This study investigates the impact of the lockdown restrictions in India on atmospheric composition, using Sentinel–5Ps retrievals of tropospheric NO2 concentration and ground-station measurements of NO2 and PM2.5 between March–May in 2019 and 2020. Detailed analysis of the changes to atmospheric composition are carried out over six major urban areas (i.e. Delhi, Mumbai, Kolkata, Chennai, Bangalore, and Hyderabad) by comparing Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and land surface temperature (LST) measurements in the lockdown year 2020 and pre-lockdown (2015–2019). Satellite-based data showed that NO2 concentration reduced by 18% (Kolkata), 29% (Hyderabad), 32-34% (Chennai, Mumbai, and Bangalore), and 43% (Delhi). Surface-based concentrations of NO2, PM2.5, and AOD also substantially dropped by 32–74%, 10–42%, and 8–34%, respectively over these major cities during the lockdown period and co-located with the intensity of anthropogenic activity. Only a smaller fraction of the reduction of pollutants was associated with meteorological variability. A substantial negative anomaly was found for LST both in the day (–0.16 °C to –1 °C) and night (–0.63 °C to –2.1 °C) across select all cities, which was also consistent with air temperature measurements. The decreases in LST could be associated with a reduction in pollutants, greenhouse gases and water vapor content. Improvement in air quality with lower urban temperatures due to lockdown may be a temporary effect, but it provides a crucial connection among human activities, air pollution, aerosols, radiative flux, and temperature. The lockdown for a shorter-period showed a significant improvement in environmental quality and provides a strong evidence base for larger scale policy implementation to improve air quality
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
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