Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches

Sustainable materials and energy from pine needle waste - a review

The accumulation of pine needle waste on the floor of a large pine forest is a severe problem. Dry pine needle waste acts as a fuel for forest fires which release harmful compounds into the atmosphere. The particulate matter in the smoke, released during forest fires, adversely affects human health. The top layer of fertile ground is harmed by unburned bioresidue. Moreover, pine needles provide the ground for pests' growth, creating a threat to nearby vegetation and structures. Managing pine needle waste through conversion into sustainable materials and energy will help reduce environmental pollution and health risks. The biosorbents from pine needle waste can be used to remove heavy metals and dyes from wastewater. The remote forest areas may be supplied with electricity obtained through the gasification of pine needles. The extracts from pine needles offer a variety of benefits such as anti-inflammatory, antioxidant, and antimicrobial. Currently, laws and subsidies promote the use of forest biomass to create biofuels. The present paper reviews the literature, provides the status and prospects, and analyses the literature data on the synthesis of bio briquettes, using the analysis of variance tool of Microsoft Excel®.

Forest fire emission estimates over South Asia using Suomi-NPP VIIRS-based thermal anomalies and emission inventory

Emission estimates of carbon-containing greenhouse gases (CO2, CH4) and aerosols (PM2.5) were made from forest fire across South Asia using Visible Infrared Imaging Radiometer Suite (VIIRS) based thermal anomalies and fire products. VIIRS 375 m I-band active fire product was selectively retrieved for the years 2012-2021 over forest cover across South Asia. Annual incidence of fire events across South Asia was 0.17 (±0.05) million (M) with robust spatio-temporal variation. Fire occurrences were mainly concentrated over the forest across Hindu Kush Himalayan region (HKH; 56%), Deccan Plateau (DP) and Central Highlands (CH; 34%). Monthly mean fire incidences emphasize February to May as a typical forest fire season, accounting 90% of annual fire counts. The highest fire pixel density (>1.5 km -2 yr-1) was noted over the tropical dry/moist deciduous and tropical semi-evergreen forests. Strong diurnal nature of fire radiative power (FRP) was evident with >85% of FRP linked to daytime retrieval. VIIRS based Fire Emission Inventory (VFEI, Version 0) was followed to constitute regional emissions of PM2.5 and green house gases from forest fire. Forest fire accounted a yearly emission of 91.58 (±14.76) and 0.25 (±0.04) Tg yr-1 CO2 and CH4 respectively, with 25.14 (±3.94) Tg of cumulative carbon release per year, i.e., roughly 1.3% of global fire-related carbon emission. Fire associated PM2.5 emission rate was 0.60 (±0.10) Tg yr-1, 95% of which emitted during peak fire season as was the case for carbon-containing gases. Forest fire across HKH (75%) and DP + CH (20%) predominately contribute to the regional carbon emission, while also accounting 68% (HKH) and 27% (DP + CH) of fire associated PM2.5 emission budget. With >70% of forest fires within South Asia being typically anthropogenic, forest fire appears to be a major sector of greenhouse gas and aerosols emissions, and necessitate planning and strict legalities to reduce emission load.

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.

Pine needle gasification-based electricity production: Understanding the effect of supply chain

Pine needles (pine tree leaves), found abundantly across continents such as North America, Asia, Europe, South America, Africa, and parts of the Southern Hemisphere, are a significant global concern due to their high susceptibility to catching fire, especially in dry and hot climates. The same issue persists in the Uttarakhand state of India, which boasts ample pine forests, yielding a substantial 1.67 × 109 kg of pine needles annually. In the present study, the annual potential emissions from forest fires in Uttarakhand were estimated to be 58.37 × 109 kg of CO2 equivalent. Therefore, the present research aims to unlock pine needles' potential via gasification for green electricity and biochar production, offering an alternative to coal-based plants while reducing forest fire frequencies. Nevertheless, obstacles hindering pine needle gasification include an unsteady supply chain, limited collection windows (100 days), and plant expenses, including transportation and operational costs. The primary focus of the research is to design and assess the performance of a gasification-based supply chain for pine needles in the Almora District of Uttarakhand. Ten plant capacity scenarios were considered, ranging from 25 to 250 kW. The study incorporated critical factors, encompassing diverse losses within the supply chain, selecting potential plant sites, minimizing transportation distance, and evaluating the supply chain's economic and environmental performance. The economic analysis revealed that the 250-kW plant scenario exhibited a minimum discounted payback period (DPP) of 3.93 years, alongside an internal rate of return (IRR) of 19% and a net present value (NPV) of 653.32 million INR without government subsidies. With subsidies included, the DPP decreased to 1.30 years, improving the IRR to 69% with an NPV of 916.17 million INR. The emission analysis indicated that gasification plant capacity scenarios could potentially reduce 44.63 × 106 to 46.16 × 106 kg of CO2 equivalent emissions annually compared to grid electricity while meeting nearly 5.5% of the electricity demand of Almora district. The present study aligns with SDG-7 (Affordable and Clean Energy), SDG-13 (Climate Action), SDG-9 (Industry, Innovation, and Infrastructure), SDG-11 (Sustainable Cities and Communities), SDG-3 (Good Health and Well-being), and SDG-15 (Life on Land).

Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality

In evaluating the integrated remote sensing-based ecological index (RSEIPCA), principal component analysis (PCA) has been extensively utilized. However, the conventional PCA-based RSEI (RSEIPCA) cannot accurately evaluate component indicators' spatially shifting relative significance. This study presented a novel RSEI evaluation strategy based on geographically weighted principal component analysis (RSEIGWPCA) to address this deficiency. Second, compared to the classic RSEIPCA, RSEIGWPCA was tested at English Bazar and surrounding areas using two-fold validation. In this regard, the Jaccard test from a different setting and correlation analysis were utilized to examine the geographical distribution of RSEI derived by PCA and GWPCA. The validation output revealed better effectiveness of GWPCA over PCA in assessing the RSEI. The findings revealed that (i) in RSEI assessment, the spatial heterogeneity of the dataset helped to formulate individual weights by GWPCA that was not performed by PCA; and (ii) the areas having higher RSEI were primarily located around the Chatra wetland of this study area, and the areas with lower RSEI were located mainly in the industrial part. It has been concluded that RSEIGWPCA is a helpful approach in the RSEI evaluating for the regional and local scale like English bazaar city and its neighbourhood.

Projected decline in the strength of vegetation carbon sequestration under climate change in India

Tropical vegetation plays a critical role in terrestrial carbon budget and supply many ecological functions such as carbon sequestration. In recent decades, India has witnessed an increase in net primary productivity (NPP), an important measure of carbon sequestration. However, uncertainties persist regarding the sustainability of these land carbon sinks in the face of climate change. The enhanced NPP is driven by the strong CO2 fertilization effect (CFE), but the temporal patterns of this feedback remain unclear. Using the carbon flux data from the Earth System Models (ESMs), an increasing trend in NPP was observed, with projections of NPP to 2.00 ± 0.12 PgCyr-1 (25 % increase) during 2021-2049, 2.36 ± 0.12 PgCyr-1 (18 % increase) during 2050-2079, and 2.67 ± 0.07 PgCyr-1 (13 % increase) during 2080-2099 in Indian vegetation under SSP585 scenario. This suggests a significant decline in the NPP growth rate. To understand the feedback mechanisms driving NPP, the relative effects of CFE and warming were analyzed. Comparing simulations from the biogeochemically coupled model (BGC) with the fully coupled model, the BGC model projected a 74.7 % increase in NPP, significantly higher than the 55.9 % increase projected by the fully coupled model by the end of the century. This indicates that the consistent increase in NPP was associated with CO2 fertilization. More importantly, results reveal that the decrease in the NPP growth rate was due to the declining contribution of CFE at a rate of -0.62 % per 100 ppm CO2 increase. This decline could be attributed to factors such as nutrient limitations and high temperatures. Additionally, significant shifts in the strength of carbon sinks in offsetting the CO2 emissions were identified, decreasing at a rate of -1.15 % per decade. This decline in the strength of vegetation carbon sequestration may increase the societal dependence on mitigation measures to address climate change.

Characterizing the regional XCO2 variability and its association with ENSO over India inferred from GOSAT and OCO-2 satellite observations

India is primarily concerned with comprehending regional carbon source-sink response in the context of changes in atmospheric CO2 concentrations or anthropogenic emissions. Recent advancements in high-resolution satellite's fine-scale XCO2 measurements provide an opportunity to understand unprecedented details of source-sink activity on a regional scale. In this study, we investigated the long-term variations of XCO2 concentration and growth rates as well as its covarying relationship with ENSO and regional climate parameters (temperature, precipitation, soil moisture, and NDVI) over India from 2010 to 2021 using GOSAT and OCO-2 retrievals. The results show since the launch of OCO-2 in 2014, the number of monthly high-quality XCO2 soundings over India has grown nearly 100-fold compared to GOSAT, launched in 2009. Also, the discrepancy in XCO2 increase of 2.54(2.43) ppm/yr was observed in GOSAT (OCO-2) retrieval during an overlapping measurement period (2015-2021). Additionally, wavelet analysis indicated that the OCO-2 retrieval is able to capture a better frequency of local-scale XCO2 variability compared to GOSAT, owing to its high-resolution cloud-free XCO2 soundings, providing more well-defined regional-scale source-sink features. Furthermore, dominant spatial pattern of XCO2 variability observed over south and southeast of India in both satellites, with XCO2 semi-annual and annual variability more distinctly present in OCO-2 compared to GOSAT. A cross-correlation analysis suggested GOSAT XCO2 growth rate positively correlates with ENSO in different homogeneous monsoon regions of India, with ENSO leading the GOSAT XCO2 growth rate in all homogeneous regions by 3-9 months. The South Peninsular region sensitive to ENSO changes, especially during 2015-2016 ENSO event, where a decrease in CO2 uptake was observed is closely linked with precipitation, soil moisture, and temperature anomalies. However, regional climate parameters show a low correlation with XCO2 growth since CO2 is a long-lived well-mixed gas primarily having an imprint of large-scale transport in column CO2.

Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India

Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.

Browning of vegetation in efficient carbon sink regions of India during the past two decades is driven by climate change and anthropogenic intrusions

Accurate estimation of carbon cycle is a challenging task owing to the complexity and heterogeneity of ecosystems. Carbon Use Efficiency (CUE) is a metric to define the ability of vegetation to sequester carbon from the atmosphere. It is key to understand the carbon sink and source pathways of ecosystems. Here, we quantify CUE using remote sensing measurements to examine its variability, drivers and underlying mechanisms in India for the period 2000-2019, by applying the principal component analyses (PCA), multiple linear regression (MLR) and causal discovery. Our analysis shows that the forests in the hilly regions (HR) and northeast (NE), and croplands in the western areas of South India (SI) exhibit high (>0.6) CUE. The northwest (NW), Indo-Gangetic plain (IGP) and some areas in Central India (CI) show low (<0.3) CUE. In general, the water availability as soil moisture (SM) and precipitation (P) promote higher CUE, but higher temperature (T) and air organic carbon content (AOCC) reduce CUE. It is found that SM has the strongest relative influence (33%) on CUE, followed by P. Also, SM has a direct causal link with all drivers and CUE; reiterating its importance in driving vegetation carbon dynamics (VCD) for the cropland dominated India. The long-term analysis reveals that the low CUE regions in NW (moisture induced greening) and IGP (irrigation induced agricultural boom) have an increasing trend in productivity (greening). However, the high CUE regions in NE (deforestation and extreme events) and SI (warming induced moisture stress) exhibit a decreasing trend in productivity (browning), which is a great concern. Our study, therefore, provides new insights on the rate of carbon allocation and the need of proper planning for maintaining balance in the terrestrial carbon cycle. This is particularly important in the context of drafting policy decisions for the mitigation of climate change, food security and sustainability.

The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China

Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃). A positive correlation might be indicated between air pollutants (PM_2.5, PM₁₀, and NO₂) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis

Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its driving mechanism is key to success in China's pursuit of low-carbon development, and it is also the scientific basis for the formulation and implementation of regional carbon emissions strategies. Based on fossil fuel carbon emissions raster data (published by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) platform) and land use data, this manuscript selects the Yellow River Delta as the study area and uses an improved LCE measurement model, exploratory spatial data analysis, multiscale geographical weighting regression (MGWR), and other models to explore the spatiotemporal heterogeneity and driving mechanisms of LCE at the grid level. The results showed the following: ① The total amount of LCE in the study area continued to increase from 2000 to 2019, the growth rate decreased, but the peak of LCE had not yet been reached. ② The LCE of the study area showed a significant positive global autocorrelation. The H-H aggregation region showed a relatively stable spatial distribution range; the L-L aggregation region showed wide distribution characteristics that covered the entire study area; and the aggregation regions of H-L and L-H, which have not yet reached the scale. ③ At the global dimension, the mean correlation coefficients between LCE and driving factors (net primary productivity (NPP), nighttime light (NTL), and population density (PD)) from 2000 to 2019 were -0.11, 0.28, and 0.12; at the local dimension, the strength (from strong to weak) of the effect of each factor on LCE was PD, NTL, NPP (2000) and NTL, PD, NPP (2019). The research results provide a scientific basis and basic guarantee for the development, and implementation of regional carbon emission strategies.

Variabilities of δ13C and carbonaceous components in ambient PM2.5 in Northeast India: Insights into sources and atmospheric processes

A year-long sampling campaign of ambient PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 mm) at a regional station in the North-Eastern Region (NER) of India was performed to understand the sources and formation of carbonaceous aerosols. Mass concentration, carbon fractions (organic and elemental carbon), and stable carbon isotope ratio (δ¹³C) of PM_2.5 were measured and studied along with cluster analysis and Potential Source Contribution Function (PSCF) modelling. PM_2.5 mass concentration was observed to be highest during winter and post-monsoon seasons when the meteorological conditions were relatively stable compared to other seasons. Organic carbon (OC) concentration was more than two times higher in the post-monsoon and winter seasons than in the pre-monsoon and monsoon seasons. Air mass back trajectory cluster analysis showed the dominance of local and regional air masses during winter and post-monsoon periods. In contrast, long-range transported air masses influenced the background site in pre-monsoon and monsoon. Air mass data and PSCF analysis indicated that aerosols during winter and post-monsoon are dominated by freshly generated emissions from local sources along with the influence from regional transport of polluted aerosols. On the contrary, the long-range transported air masses containing aged aerosols were dominant during pre-monsoon. No significant variability was observed in the range of δ¹³C values (-28.2‰ to -26.4‰) during the sampled seasons. The δ¹³C of aerosols indicates major sources to be combustion of biomass/biofuels (C3 plant origin), biogenic aerosols, and secondary aerosols. The δ¹³C variability and cluster/PSCF modelling suggest that aged aerosols (along with enhanced photo-oxidation derived secondary aerosols) influenced the final δ¹³C during the pre-monsoon. On the other hand, lower δ¹³C in winter and post-monsoon is attributed to the freshly emitted aerosols from biomass/biofuels.

Inventory of China’s Net Biome Productivity since the 21st Century

Net biome productivity (NBP), which takes into account abiotic respiration and metabolic processes such as fire, pests, and harvesting of agricultural and forestry products, may be more scientific than net ecosystem productivity (NEP) in measuring ecosystem carbon sink levels. As one of the largest countries in global carbon emissions, in China, however, the spatial pattern and evolution of its NBP are still unclear. To this end, we estimated the magnitude of NBP in 31 Chinese provinces (except Hong Kong, Macau, and Taiwan) from 2000 to 2018, and clarified its temporal and spatial evolution. The results show that: (1) the total amount of NBP in China was about 0.21 Pg C/yr1. Among them, Yunnan Province had the highest NBP (0.09 Pg C/yr1), accounting for about 43% of China’s total. (2) NBP increased from a rate of 0.19 Tg C/yr1 during the study period. (3) At present, NBP in China’s terrestrial ecosystems is mainly distributed in southwest and south China, while northwest and central China are weak carbon sinks or carbon sources. (4) The relative contribution rates of carbon emission fluxes due to emissions from anthropogenic disturbances (harvest of agricultural and forestry products) and natural disturbances (fires, pests, etc.) were 70% and 9.87%, respectively. This study emphasizes the importance of using NBP to re-estimate the net carbon sink of China’s terrestrial ecosystem, which is beneficial to providing data support for the realization of China’s carbon neutrality goal and global carbon cycle research.

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

The accurate detection of burned forest area is essential for post-fire management and assessment, and for quantifying carbon budgets. Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB’s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF’s, BASM-BPNN’s, BASM-SVM’s, and BASM-notra’s, even though BASM-RF’s and BASM-notra’s average PA is higher than BASM-FERB’s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.

Forest Fire Monitoring and Positioning Improvement at Subpixel Level: Application to Himawari-8 Fire Products

Forest fires are among the biggest threats to forest ecosystems and forest resources, and can lead to ecological disasters and social crises. Therefore, it is imperative to detect and extinguish forest fires in time to reduce their negative impacts. Satellite remote sensing, especially meteorological satellites, has been a useful tool for forest-fire detection and monitoring because of its high temporal resolution over large areas. Researchers monitor forest fires directly at pixel level, which usually presents a mixture of forest and fire, but the low spatial resolution of such mixed pixels cannot accurately locate the exact position of the fire, and the optimal time window for fire suppression can thus be missed. In order to improve the positioning accuracy of the origin of forest fire (OriFF), we proposed a mixed-pixel unmixing integrated with pixel-swapping algorithm (MPU-PSA) model to monitor the OriFFs in time. We then applied the model to the Japanese Himawari-8 Geostationary Meteorological Satellite data to obtain forest-fire products at subpixel level. In this study, the ground truth data were provided by the Department of Emergency Management of Hunan Province, China. To validate the positioning accuracy of MPU-PSA for OriFFs, we applied the model to the Himawari-8 satellite data and then compared the derived fire results with fifteen reference forest-fire events that occurred in Hunan Province, China. The results show that the extracted forest-fire locations using the proposed method, referred to as forest fire locations at subpixel (FFLS) level, were far closer to the actual OriFFs than those from the modified Himawari-8 Wild Fire Product (M-HWFP). This improvement will help to reduce false fire claims in the Himawari-8 Wild Fire Product (HWFP). We conducted a comparative study of M-HWFP and FFLS products using three accuracy-evaluation indexes, i.e., Euclidean distance, RMSE, and MAE. The mean distances between M-HWFP fire locations and OriFFs and between FFLS fire locations and OriFFs were 3362.21 m and 1294.00 m, respectively. The mean RMSEs of the M-HWFP and FFLS products are 1225.52 m and 474.93 m, respectively. The mean MAEs of the M-HWFP and FFLS products are 992.12 m and 387.13 m, respectively. We concluded that the newly proposed MPU-PSA method can extract forest-fire locations at subpixel level, providing higher positioning accuracy of forest fires for their suppression.

India’s Greening Trend Seems to Slow Down. What Does Aerosol Have to Do with It?

Multiple drivers perturb the terrestrial carbon cycle, which ultimately reshapes the fertilization of carbon dioxide (CO2) and reorientates the climate. One such driver is atmospheric aerosols, which cascade the ecosystem’s productivity in a large proportionality. Investigating this relation is non-conventional and limited across the globe. With the abundance of heterogenetic terrestrial ecosystems, India’s primary productivity has a large proportion of the global carbon balance. Under climate change stress, India’s unique spatial and climatological features perturb atmospheric aerosols from natural sources to anthropogenic sources. In light of that, this study utilizes the Carnegie–Ames Stanford Approach (CASA) model to elucidate the consequence by examining the potential effect of aerosol load on the ecosystem productivity (Net Primary Production; NPP) for various agroclimatic zones of India from 2001–2020. CASA reveals a negative decadal amplitude with an overall increase in the NPP trend. In contrast, aerosol loadings from MODIS highlight the increasing trend, with definite seasonal intensities. Employing the CASA model and earth observations, the study highlights the increase in NPP in forest-based ecosystems due to relatively lower aerosols and higher diffuse radiation. Critically, strong dampening of NPP was observed in the agroecological and sparse vegetation zones inferring that the aerosol loadings affect the primary productivity by affecting the photosynthesis of canopy architecture. Spatial sensitivity zones across different ecological regions result in a non-homogenous response because of different phenological and canopy architecture that is mediated by the radiation intensities. Based on the analysis, the study infers that AOD positively influences the canopy-scale photosynthesis by diffuse radiation, which promotes NPP but is less likely for the crop canopy ecosystems. Barring the limitations, enhancement of NPP in the forest ecosystems offset the demand for carbon sink in the agroecosystems. Findings from this study reveal that a more precise provenance of aerosol effects on carbon fluxes is required to understand the uncertainties in the terrestrial carbon cycle.

Estimating Long-Term Average Carbon Emissions from Fires in Non-Forest Ecosystems in the Temperate Belt

Research into pyrogenic carbon emissions in the temperate belt of the Russian Federation has traditionally focused on the impact of forest fires. Nevertheless, ecosystems in which wildfires also make a significant contribution to anthropogenic CO2 emissions are poorly studied. We evaluated the carbon emissions of fires in the non-forest ecosystems of the Middle Amur Lowland, in the Khabarovsk Territory of the Russian Federation. Our study is based on long-term Earth remote sensing data of medium spatial resolution (Landsat 5, 7, and 8) and expeditionary studies (2018–2021). The assessment of carbon directly emitted from wildfires in meadow and meadow–mire temperate ecosystems in the Middle Amur lowland shows that specific emissions from such ecosystems vary, from 1.09 t/ha in dwarf shrub–sphagnum and sphagnum–ledum and sedge–reed fens to 6.01 t/ha in reed–forb, forb, reed, and sedge meadows. Meanwhile, carbon emissions specifically from fires in meadow and meadow–mire ecosystems are less significant—often an order of magnitude less than carbon emissions from forest fires (which reach 37 tC/ha). However, due to their high frequency and the large areas of land burned annually, the total carbon emissions from such fires are comparable to annual emissions from fires in forested areas. The results obtained show that the inadequacy of the methods used in the automatic mapping of burns leads to a significant underestimation of the area of grassland fires and carbon emissions from non-forest fires.

Short-Term Recovery of the Aboveground Carbon Stock in Iberian Shrublands at the Extremes of an Environmental Gradient and as a Function of Burn Severity

The degree to which burn severity influences the recovery of aboveground carbon density (ACD) of live pools in shrublands remains unclear. Multitemporal LiDAR data was used to evaluate ACD recovery three years after fire in shrubland ecosystems as a function of burn severity immediately after fire across an environmental and productivity gradient in the western Mediterranean Basin. Two large mixed-severity wildfires were assessed: an Atlantic site, dominated by resprouter shrubs and located at the most productive extreme of the gradient, and a Mediterranean site, dominated by obligate seeders and located at the less productive extreme. Initial assessment of burn severity was performed using the differenced Normalized Burn Ratio index computed from Landsat imagery. Thresholds for low and high burn severity categories were established using the Composite Burn Index (CBI). LiDAR canopy metrics were calibrated with field measurements of mean shrub height and cover at plot level in a post-fire situation. Pre-fire and post-fire ACD estimates, and their ratio (ACDr) to calculate carbon stock recovery, were computed from the predictions of LiDAR grid metrics at landscape level using shrubland allometric relationships. Overall, ACDr decreased both with high burn severity and low productivity, although the burn severity impact was not homogeneous within the gradient. In the Atlantic site, ACDr was similar under low and high burn severity, whereas it decreased with burn severity in the Mediterranean site. These results suggest that carbon cycling models could be biased by not accounting for both fire severity and species composition of shrublands under different environmental conditions.

Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India

The information on land use and land cover (LULC) plays a critical role in understanding the interactions between human activities and the natural environment. The changes in LULC have a significant impact on the ecological integrity of forests, biodiversity, and natural resources, which in turn trigger global environmental change. Forest fragmentation is an important conservation challenge that includes interdependent forest loss components and spatial shift patterns. Over the years, Northeast India has experienced major changes in LULC and forest fragmentation. There are limited information and data regarding the change in LULC patterns and causes of forest fragmentation. The present study was carried out with an attempt to analyze the change in LULC and forest fragmentation using satellite data of three different time series: 1991, 2005, and 2020 for Ukhrul district, Manipur, Northeast India. Different LULC classes were classified using the supervised method, viz., maximum likelihood algorithm in ERDAS Imagine 2014 and generated thematic maps in ArcGIS 10.4 software. Considering the classified forest class, fragmentation in the forest area was grouped into different categories of fragmentation using the Landscape Fragmentation Tool (LFT v 2.0). The distribution of the perforated category has tremendously increased in 2020 from 1991. The outcome of the present study will help to understand the inherent forest vulnerability and to adopt sustainable management strategies for forest and agriculture ecosystems in the hill landscape.

SS, M. R, Madurakavi K, I. JR, J. BE, R. RS, R. K. Assessing nitrogen dioxide (NO2) impact on health pre- and post-COVID-19 pandemic using IoT in India

Purpose

Corona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2; it affects the respiratory system of the human and sometimes leads to death. The COVID-19 mainly attacks the person with previous lung diseases; the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration. NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes. Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality. This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown. The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19.

Design/methodology/approach

Spatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things. Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship. Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data. Moreover, aerosol optical depth analysis has been carried out based on NASA’s Earth Observing System data.

Findings

The results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown. The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2.

Originality/value

Using the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities. The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body.

Spatiotemporal regularity and spillover effects of carbon emission intensity in China's Bohai Economic Rim

The Bohai Economic Rim (BER) is a momentous economic growth district with rapid development in northern China, but the environmental problems there have also become prominent. In 2017 the BER's carbon emission intensity outclassed the national average, the emission reduction situation was also grim. For clarifying the influence mechanism of the economy on carbon emission intensity, this paper explores the spatiotemporal regularity, the spatial correlation, and the spillover effect in carbon emission intensity employing the Moran index and the spatial Durbin model. The results indicate that the carbon emission intensity in the BER decreased year-by-year from 2006 to 2017. Shanxi and Inner Mongolia were emission hot spots, whereas Beijing and Tianjin were emission cold spots. And the Moran's I values all passed the significance test, which verified the spatial correlation of the carbon emission intensity in the BER is significant. Urbanization, energy intensity, population density, and industry structure have a biggish impact on such spatial distribution of the carbon emission intensity. The direct effect coefficient of the energy intensity is the highest, and the spillover effect of the industry structure is the most significant. Finally, this paper puts forward suggestions on the formulation of regional coordinated carbon reduction programs in the BER.

A 13-Year Approach to Understand the Effect of Prescribed Fires and Livestock Grazing on Soil Chemical Properties in Tivissa, NE Iberian Peninsula

The high density of fuel accumulated in the Mediterranean ecosystems due to land abandonment results in high severity fires. Traditional fire practices and livestock grazing have played an important role in shaping the structure and composition of Mediterranean landscapes, and both can be efficient tools to manage them now that land abandonment is widespread. Attempts at controlling forest fires are essential for landscape management practices that, in their turn, seek to maintain a specific species composition. Against this backdrop, this study aims to determine the short- and long-term effects of the combined management practices of prescribed fires and goat grazing on the chemical properties of soils in Tivissa, Tarragona (NE Iberian Peninsula). Forty-two samples were collected in a 4 × 18 m plot before the prescribed fire of 2002 (1), immediately after the 2002 prescribed fire (PF) (2), one year after the 2002 PF (3), three years after the 2002 PF (4), and thirteen years after the 2002 PF (5). Soil samples were taken at each sampling point from the top layer (0–5 cm), sieved to obtain a <2 mm fraction, and soil pH, EC, Total C, total N, available P, K+, Ca2+, and Mg2+ were determined. The results indicate that the short-term effects of fire are more relevant than those attributable to the livestock over the long term due to the low grazing intensity of less than one goat per ha. The long-term effects of prescribed fires were not visible in the research, suggesting that they recovered after burning with all their functions intact and with enhanced levels of natural fertility. Combined land management practices of prescribed fire and livestock grazing did not affect soil chemical properties. The applied management enhanced soil fertility and boosted the ecosystem’s resilience.