Towards food security: Exploring the spatio-temporal dynamics of soybean in India.

India is the world's largest edible oil importer, and soybean oil accounts for a major portion of those imports, with implications for the Indian economy. Despite being the 4th largest globally in terms of harvested soybean area and 5th largest in terms of production, India is still heavily dependent on imports to meet the vegetable oil requirement for its population. It is therefore imperative to understand the dynamics and trends in India's soybean production to help the country achieve self-sufficiency in edible oils. This study provides the first spatially explicit analysis of soybean in India, using long-term spatial and temporal statistics at national and subnational levels, using spatial and temporal statistical analysis models to examine the historical trends and its future prospects. Our analysis details the overall soybean expansion across the country and the increase in production but we also note that the annual growth rate has declined in each consecutive decade even though the area continues to expand. The average national yield has been stagnant at around 1 T/Ha but for some of the low-producing districts, a higher yield of more than 3 T/ha is reported. For most major producing districts, soybean yields are below 1.5 T/Ha. The state of Madhya Pradesh which was the major soybean producer is now matched by the state of Maharashtra in terms of production, however, Madhya Pradesh still has the largest area under soybean. We analyzed soybean hotspot expansion in India and found that the mean center of the soybean area and production has shifted approximately 93 km towards the south and 24 km to the west as the crop is rapidly being adopted in the southern and western parts of India expanding the hotspot in these parts. District-level analysis showed that the total number of districts constituting hotspots of soybean cultivation in India has increased from 29 to 42 in three decades. Furthermore, analysis of soybean oil and meal consumption with respect to the national population, import, export, domestic production, GDP per capita, and price of soybean oil and meal suggests that soybean oil and meal are highly correlated with GDP per capita and population, indicating that consumption of soybean oil and meal is likely to increase as GDP per capita increases, and future demand is expected to rise with the anticipated growth in the Indian population. Increased soybean production can play a significant role in increasing national food security for India and reducing dependence on foreign oil imports and also help the economy with soy meal exports. Understanding the spatiotemporal variability in area and yield will help target interventions to increase production. Given the overall low yields with high variability in production, particularly in recent years primarily due to successive extreme rains and droughts in major producing districts and the overall need to increase production to meet the country's demand, there is a pressing need for government policies and research aimed at narrowing the yield gap and developing soybean varieties that are more productive and resilient to climate change.

Contemporary forest loss in Myanmar: Effect of democratic transition and subsequent timber bans on landscape structure and composition.

This study addresses the effect of political transition and subsequent timber bans on forest loss in Myanmar, in the context of identified drivers. Cook's Distance (CD) was applied to remotely sensed time-series forest loss dataset to measure the effect of the events. Forest loss derived fragmentation metrics were linked to drivers at a landscape scale. Results show that at the national level, the political transition in 2011 had maximum effect (CD 0.935) on forest loss while the timber bans decreased forest loss by 612.04 km2 and 213.15 km2 in 2015 and 2017 (CD 0.146 and 0.035), respectively. The effect of the events varied for different States/Regions. The dominant drivers of change shifted from plantations in 2011 to infrastructure development in 2015. This study demonstrates the effects of policy on forest loss at various scales and can inform decision-makers for forest conservation, planning and development of mitigation measures.

Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period.

In this study, we characterize the impacts of COVID-19 on air pollution using NO2 and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO2 reduction during the lockdown (March 25-May 3rd, 2020) compared to the pre-lockdown (January 1st-March 24th, 2020) period. Also, a 19% reduction in NO2 was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO2 reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO2 for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO2 levels decreased exponentially. In contrast, to the decreased NO2 observed for most of the cities, we observed an increase in NO2 for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO2 temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO2, and the results can inform pollution mitigation efforts across different cities of India.

Trends in Vegetation fires in South and Southeast Asian Countries.

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012-2016) in South/Southeast Asia (S/SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries.

Correction: Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets.

[This corrects the article DOI: 10.1371/journal.pone.0196629.].

Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam.

Paddy Rice is the prevalent land cover in the mosaicked landscape of the Hanoi Capital Region, Vietnam. In this study, we map double and single crop rice in Hanoi using a random forest algorithm and a time-series of Sentinel-1 SAR imagery at 10 and 20 m resolution using VV-only, VH-only, and both polarizations. We compare spatial and areal variation and quantify input band importance, estimate crop growth stages, estimate rice field/collective metrics using Fragstats with image segmentation, and highlight the importance of the results for land use and land cover. Results suggest double crop rice ranged from 208 000 to 220 000 ha with 20-m resolution imagery accounting for the most area in all polarizations. Based on accuracy assessment, we found 10 m data for VV/VH to have highest overall accuracy (93.5%, ±1.33%), while VV at 10 and 20 m had lowest overall accuracies (90.9%, ±1.57; 91.0%, ±2.75). Mean decrease in accuracy suggests for all but VV at 10 m, data from harvest and flooding stages are most critical for classification. Results suggest 20 m data for both VV and VH overestimates rice land cover, however 20 m data may be indicative of rice land use. Analysis of growing season suggests average estimated length of 93-104 days for each season. Commune-level results suggest up to 20% coefficient of variation between VV10m and VH10m with significant spatial variation in rice area. Landscape metrics show rice fields are typically plantedin groups of 3-4 fields with over 796 000 collectives and 2.69 millionfields estimated in the study area.

Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets.

Air pollution is one of the major environmental concerns in Vietnam. In this study, we assess the current status of air pollution over Hanoi, Vietnam using multiple different satellite datasets and weather information, and assess the potential to capture rice residue burning emissions with satellite data in a cloud-covered region. We used a timeseries of Ozone Monitoring Instrument (OMI) Ultraviolet Aerosol Index (UVAI) satellite data to characterize absorbing aerosols related to biomass burning. We also tested a timeseries of 3-hourly MERRA-2 reanalysis Black Carbon (BC) concentration data for 5 years from 2012-2016 and explored pollution trends over time. We then used MODIS active fires, and synoptic wind patterns to attribute variability in Hanoi pollution to different sources. Because Hanoi is within the Red River Delta where rice residue burning is prominent, we explored trends to see if the residue burning signal is evident in the UVAI or BC data. Further, as the region experiences monsoon-influenced rainfall patterns, we adjusted the BC data based on daily rainfall amounts. Results indicated forest biomass burning from Northwest Vietnam and Laos impacts Hanoi air quality during the peak UVAI months of March and April. Whereas, during local rice residue burning months of June and October, no increase in UVAI is observed, with slight BC increase in October only. During the peak BC months of December and January, wind patterns indicated pollutant transport from southern China megacity areas. Results also indicated severe pollution episodes during December 2013 and January 2014. We observed significantly higher BC concentrations during nighttime than daytime with peaks generally between 2130 and 0030 local time. Our results highlight the need for better air pollution monitoring systems to capture episodic pollution events and their surface-level impacts, such as rice residue burning in cloud-prone regions in general and Hanoi, Vietnam in particular.

Factors controlling vegetation fires in protected and non-protected areas of myanmar.

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.

NASA Land Cover and Land Use Change (LCLUC): an interdisciplinary research program.

Understanding Land Cover/Land Use Change (LCLUC) in diverse regions of the world and at varied spatial scales is one of the important challenges in global change research. In this article, we provide a brief overview of the NASA LCLUC program, its focus areas, and the importance of satellite remote sensing observations in LCLUC research including future directions. The LCLUC Program was designed to be a cross-cutting theme within NASA's Earth Science program. The program aims to develop and use remote sensing technologies to improve understanding of human interactions with the environment. Since 1997, the NASA LCLUC program has supported nearly 280 research projects on diverse topics such as forest loss and carbon, urban expansion, land abandonment, wetland loss, agricultural land use change and land use change in mountain systems. The NASA LCLUC program emphasizes studies where land-use changes are rapid or where there are significant regional or global LCLUC implications. Over a period of years, the LCLUC program has contributed to large regional science programs such as Land Biosphere-Atmosphere (LBA), the Northern Eurasia Earth Science Partnership Initiative (NEESPI), and the Monsoon Area Integrated Regional Study (MAIRS). The primary emphasis of the program will remain on using remote sensing datasets for LCLUC research. The program will continue to emphasize integration of physical and social sciences to address regional to global scale issues of LCLUC for the benefit of society.

Vegetation fires and air pollution in Vietnam.

Forest fires are a significant source of air pollution in Asia. In this study, we integrate satellite remote sensing data and ground-based measurements to infer fire-air pollution relationships in selected regions of Vietnam. We first characterized the active fires and burnt areas at a regional scale from MODIS satellite data. We then used satellite-derived active fire data to correlate the resulting atmospheric pollution. Further, we analyzed the relationship between satellite atmospheric variables and ground-based air pollutant parameters. Our results show peak fire activity during March in Vietnam, with hotspots in the Northwest and Central Highlands. Active fires were significantly correlated with UV Aerosol Index (UVAI), aerosol extinction absorption optical depth (AAOD), and Carbon Monoxide. The use of satellite aerosol optical thickness improved the prediction of Particulate Matter (PM) concentration significantly.

Analysis of Southeast Asian pollution episode during June 2013 using satellite remote sensing datasets.

In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.

MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India.

Agricultural residue burning is one of the major causes of greenhouse gas emissions and aerosols in the Indo-Ganges region. In this study, we characterize the fire intensity, seasonality, variability, fire radiative energy (FRE) and aerosol optical depth (AOD) variations during the agricultural residue burning season using MODIS data. Fire counts exhibited significant bi-modal activity, with peak occurrences during April-May and October-November corresponding to wheat and rice residue burning episodes. The FRE variations coincided with the amount of residues burnt. The mean AOD (2003-2008) was 0.60 with 0.87 (+1σ) and 0.32 (-1σ). The increased AOD during the winter coincided well with the fire counts during rice residue burning season. In contrast, the AOD-fire signal was weak during the summer wheat residue burning and attributed to dust and fossil fuel combustion. Our results highlight the need for 'full accounting of GHG's and aerosols', for addressing the air quality in the study area.

A comparison of Spectral Angle Mapper and Artificial Neural Network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping.

Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ≈ 1% for ANN and ≈ 6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

Fire risk evaluation using multicriteria analysis--a case study.

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential "hotspots" of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing "where and when" forest fires will most likely occur.

Spatial patterns in vegetation fires in the Indian region.

In this study, we used fire count datasets derived from Along Track Scanning Radiometer (ATSR) satellite to characterize spatial patterns in fire occurrences across highly diverse geographical, vegetation and topographic gradients in the Indian region. For characterizing the spatial patterns of fire occurrences, observed fire point patterns were tested against the hypothesis of a complete spatial random (CSR) pattern using three different techniques, the quadrat analysis, nearest neighbor analysis and Ripley's K function. Hierarchical nearest neighboring technique was used to depict the 'hotspots' of fire incidents. Of the different states, highest fire counts were recorded in Madhya Pradesh (14.77%) followed by Gujarat (10.86%), Maharastra (9.92%), Mizoram (7.66%), Jharkhand (6.41%), etc. With respect to the vegetation categories, highest number of fires were recorded in agricultural regions (40.26%) followed by tropical moist deciduous vegetation (12.72), dry deciduous vegetation (11.40%), abandoned slash and burn secondary forests (9.04%), tropical montane forests (8.07%) followed by others. Analysis of fire counts based on elevation and slope range suggested that maximum number of fires occurred in low and medium elevation types and in very low to low-slope categories. Results from three different spatial techniques for spatial pattern suggested clustered pattern in fire events compared to CSR. Most importantly, results from Ripley's K statistic suggested that fire events are highly clustered at a lag-distance of 125 miles. Hierarchical nearest neighboring clustering technique identified significant clusters of fire 'hotspots' in different states in northeast and central India. The implications of these results in fire management and mitigation were discussed. Also, this study highlights the potential of spatial point pattern statistics in environmental monitoring and assessment studies with special reference to fire events in the Indian region.