Investigation of fire regime dynamics and modeling of burn area over India for the twenty-first century.

The characteristics of the vegetation fire (VF) regime are strongly influenced by geographical variables such as regional physiographic settings, location, and climate. Understanding the VF regime is extremely important for managing and mitigating the impacts of fires on ecosystems, communities, and human activities in forest fire-prone regions. The present study thereby aimed to explore the potential effects of the confounding factors on VF in India to offer actionable and achievable solutions for mitigating this concurring environmental issue sustainably. A global burn area (250 m) data (Fire-CCIv5.1) and fire radiative power (FRP) were used to investigate the dynamics of VF across seven different divisions in India. The study also used the maximum and minimum temperatures, precipitation, population density, and intensity of human modification to model forest burn areas (including grassland). The Coupled Model Intercomparison Project-6 (CMIP6) was used to predict the burn area for 2030 and 2050 future climate scenarios. The present study accounted for a sizable increasing trend of VF during 2001-2019 period. The highest increasing trend was found in central India (513 and 343 km2 year-1 in the forest and crop fire, respectively), followed by southern India (364 km2 year-1 in forest fire), and upper Indo-Gangetic plain (128 km2 year-1 in crop fire). The FRP has varied significantly across the divisions, with the north-eastern Himalayas exhibiting the highest FRP hotspot. The maximum and minimum temperatures have the greatest influence on forest fires, according to Random Forest (RF) modeling. The estimated pre-monsoonal burn area for 2050 and 2050 future scenarios suggested a more frequent forest fire occurrence across India, particularly in southern and central India. A comprehensive forest fire control policy is therefore essential to safeguard and conserve forest cover in the regions, affected by forest fire periodically.

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.

Despite 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 (Cab) 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 Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35 µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 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.

Monitoring tea plantations during 1990-2022 using multi-temporal satellite data in Assam (India).

Background: Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives: The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods: A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results: The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45-89%. During 32 years (1990-2022), about 1280.47 km2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions: This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.

Forest Fire Characterization Using Landsat-8 Satellite Data in Dalma Wildlife Sanctuary.

A forest fire has caused a loss of biodiversity and forest heterogeneity and resulted in forest degradation and fragmentation. Remote sensing techniques have been widely used for locating and delineating forest fires. The present study has employed Landsat-8 satellite data during 2014-2020 for spatiotemporal analysis of forest fire in Dalma Wildlife Sanctuary (DWS). Normalized burn ratio (NBR) has been used to delineate forest fire-affected locations along with visual interpretation techniques. The results showed that an extensive area was burnt and deforested due to forest fire in DWS during 2014-2020. The burned areas due to forest fires within the notified forest boundary in DWS were 12.11 km2, 25.5 km2, 22.45 km2, 9.11 km2, 24.44 km2, 10.09 km2, and 1 km2 during 2014, 2015, 2016, 2017, 2018, 2019, and 2020, respectively, whereas burned areas outside notified boundary were 2.24 km2, 4.15 km2, 1.48 km2, 3.29 km2, 3.31 km2, 1.9 km2, and 0.1 km2. According to visual image interpretation, the highest burned area was found in 2015 (25.5 km2), whilst the least affected was found in 2020 (1 km2), and fires were mainly seen in the degraded forests and open forest regions. The present study revealed that forest fire is more dominant in Asanbani, Pardih, Bhelaipahari, Gobargushi, Bamri, Andharjhor, Somadih, Koira, Tetla, Bochkamkocha, Sah, Rbera, and Jamdih locations/beats in DWS. Thereby, these forest beats need attention from forest managers to control fire-mediated forest degradation for the conservation and restoration of forests in DWS.

Extreme rainfall-induced urban flood monitoring and damage assessment in Wuhan (China) and Kumamoto (Japan) cities using Google Earth Engine.

Floods in urban areas result in a detrimental impact on the natural environment and human health and pose major risks to assets and communication systems. In cities with high population density, the magnitude of flood damage largely depends upon flood inundation as well as floodwater depths. The present study compared recent flood inundation extent, damages caused, and possible floodwater depth in two highly developed metropolises of China and Japan, i.e., Wuhan and Kumamoto cities, for the year 2020. Sentinel-1 satellite data-driven change detection algorithm in Google Earth Engine (GEE) was applied to identify potentially flooded regions. Major land use land cover classes such as urban areas and croplands affected by the flood were mapped in conjunction with the exposed population. ALOS PALSAR digital elevation model (DEM) was used to study the inundation depth. The study revealed that 322 km2 of the area has been inundated by floodwater in Wuhan city with 230 km2 and 140 km2 areas under damaged croplands and urban regions. Around 817,095 people were exposed to this natural catastrophe in Wuhan. The city Kumamoto has witnessed an inundation area of about 505 km2 with damaged cropland of 350 km2 and an urban area of 83 km2.

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America.

The 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.

Impacts of partial to complete COVID-19 lockdown on NO2 and PM2.5 levels in major urban cities of Europe and USA.

SARS CoV-2 (COVID-19) coronavirus has been causing enormous suffering, death, and economic losses worldwide. There are rigorous containment measures on industries, non-essential business, transportation, and citizen mobility to check the spread. The lockdowns may have an advantageous impact on reducing the atmospheric pollutants. This study has analyzed the change in atmospheric pollutants, based on the Sentinel-5Ps and ground-station observed data during partial to complete lockdown period in 2020. Results revealed that the mean tropospheric NO2 concentration substantially dropped in 2020 due to lockdown against the same period in 2019 by 18-40% over the major urban areas located in Europe (i.e. Madrid, Milan, Paris) and the USA (i.e. New York, Boston, and Springfield). Conversely, urban areas with partial to no lockdown measures (i.e. Warsaw, Pierre, Bismarck, and Lincoln) exhibited a relatively lower dropdown in mean NO2 concentration (3 to 7.5%). The role of meteorological variability was found to be negligible. Nevertheless, the reduced levels of atmospheric pollutants were primarily attributed to the shutdown of vehicles, power plants, and industrial emissions. Improvement in air quality during COVID-19 may be temporary, but regulatory bodies should learn to reduce air pollution on a long-term basis concerning the trade-offs between the environment, society, and economic growth. The intersection of urban design, health, and environment should be addressed by policy-makers to protect public health and sustainable urban policies could be adopted to build urban resilience against any future emergencies.

Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic.

The 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.

Imaging Findings in a Rare Case of Leiomyomatosis Peritonealis Disseminata with Malignant Transformation.

BACKGROUND: Leiomyomatosis peritonealis disseminata (LPD) is a rare condition characterized by formation of multiple leiomyomas in the abdominal and pelvic peritoneum. Malignant transformation of LPD is an extremely rare occurrence. We hereby report a case of a middle-aged female who was subsequently diagnosed with leiomyomatosis peritonealis disseminata with a few of the lesions showing malignant transformation. CASE REPORT: A forty-year-old female with a history of hysterectomy presenting with vague abdominal pain was completely evaluated with ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI) and was subsequently diagnosed with leiomyomatosis peritonealis disseminata. The imaging findings of leiomyomatosis peritonealis disseminata were correlated with and confirmed by a histopathologic examination of the excision biopsy specimen. CONCLUSIONS: The imaging findings were found to be specific for leiomyomatosis peritonealis disseminata with a malignant transformation.

Extremely Rare Case of Bilateral Pure Primary Non-Gestational Ovarian Choriocarcinoma.

BACKGROUND: Germ cell tumors of the ovary constitute less than one percent of ovarian tumors worldwide. Choriocarcinoma arising de novo from the ovary is very rare and only occasionally reported in the literature. Herein, we report a case of bilateral non-gestational pure primary ovarian choriocarcinoma that was confirmed by beta human chorionic gonadotropin (ß-HCG) levels and histopathology. CASE REPORT: Our case is of a middle-aged multiparous female who presented with amenorrhea for three months. She underwent an evaluation with ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), which revealed bilateral bulky solid adnexal masses. Based on an increased blood level of the beta human chorionic gonadotropin and a histopathological examination, the diagnosis of bilateral non-gestational pure primary ovarian choriocarcinoma was made. CONCLUSIONS: The imaging findings were found to be specific for bilateral non-gestational pure primary ovarian choriocarcinoma.

Detailed Imaging Findings in a Rare Case of Kimura Disease, with Special Mention on Diffusion Weighted Imaging.

BACKGROUND: Kimura disease is an uncommon, benign, chronic, idiopathic disease that is seen mainly in the Asian population, particularly in females. It mainly affects the salivary glands and in particular parotid and cervical lymph nodes. So far, the diagnosis of Kimura disease has been based on histopathology. We hereby report of a confirmed case of Kimura disease and provide a detailed explanation of its imaging features with a special emphasis on diffusion-weighted imaging, as diffusion sequences may help distinguish Kimura disease from malignancy. CASE REPORT: A middle-aged female patient presenting with a history of multiple chronic neck swellings was thoroughly evaluated by ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). The imaging findings included gross enlargement of the left parotid gland, a focal lesion in the right parotid gland, and cervical lymphadenopathy. These features correlated with histopathological findings, and the diagnosis of Kimura disease was made. CONCLUSIONS: The imaging findings were found to be specific for Kimura disease.