A rapid assessment of stubble burning and air pollutants from satellite observations.

For the last several years, the air quality of India's capital Delhi and surrounding region (NCR) has been degrading to a very poor and severe category during the autumn season. In addition to the various sources of air pollutants within the NCR region, the stubble burning in Punjab and Haryana states contributes to the poor air quality in this region. The current study employs the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products and TROPOspheric Monitoring Instrument (TROPOMI) products on carbon monoxide (CO) and nitrogen dioxide (NO2) concentrations for spatio-temporal assessment of stubble burning and associated emissions. The analysis performed in the Google Earth Engine (GEE) platform indicated a nearly threefold rise in crop residue burning in November than in October, with 92.58% and 7.42% reported from Punjab and the Haryana states in November, respectively. The study highlights the availability of near-real-time remote sensing observations and the utility of the GEE platform for rapid assessment of stubble burning and emissions thereof, having the potential for developing mitigation strategies.

A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India.

Mangroves can play a crucial part in climate change mitigation policies due to their high carbon-storing capacity. However, the carbon sequestration potential of Indian mangroves generally remained unexplored to date. In this study, multi-temporal Sentinel-1 and 2 data-derived variables were used to estimate the AGB of a tropical carbon-rich mangrove forest of India. Ensemble prediction of multiple machine learning algorithms, including Random Forest (RF), Gradient Boosted Model (GBM), and Extreme Gradient Boosting (XGB), were used for AGB prediction. The multi-temporal dataset was used in two different ways to find the most suitable method of using them. The results of the analysis showed that the modeling field measured AGB with individual date data values results in estimates with root mean square errors (RMSE) ranging from 149.242 t/ha for XGB to 151.149 t/ha for the RF. Modeling AGB with the average and percentile metrics of the multi-temporal image stack improves the prediction accuracy of AGB, with RMSE ranging from 81.882 t/ha for the XGB to 74.493 t/ha for the RF. The AGB modeling using ensemble prediction showed further improvement in accuracy with an RMSE of 72.864 t/ha and normalized RMSE of 11.38%. In this study, the intra-seasonal variation of Sentinel-1 and 2 data for mangrove ecosystems was explored for the first time. The variations in remotely sensed variables could be attributed mainly to soil moisture availability and rainfall in the mangrove ecosystem. The efficiency of Sentinel-1 and 2 data-derived variables and ensemble prediction of machine learning models for Indian mangroves were also explored for the first time. The methodologies established in this study can be used in the future for accurate prediction and repeated monitoring of AGB for mangrove ecosystems.

Evaluating the applicability of ESM (Ecotourism Sustainability Maximization) model to assess, monitor, and manage the ecotourism sustainability in mountain ecosystem (Mt. Kangchendzonga Base Camp Trek, India).

Ecotourism is the greener variant of tourism which advocates conservation of biodiversity and acts as a development strategy to build a self-sustainable system to help protect and further enhance the ecosystem through the income generated by ecotourism activities. There is a strong linkage between biodiversity conservation and ecotourism which has also been recognized by the UN and finds its place under the technical note on "Biodiversity and the 2030 Agenda for Sustainable Development". But, are the stakeholders of the ecotourism destinations religiously following practices that will ensure biodiversity conservation at all times is something that needs continuous evaluation and validation. The authors have worked in the past on developing multi-stage methodology (ESA Framework, ESM Model and their validation) using Qualitative and Quantitative techniques and successfully developed Ecotourism Sustainability Assessment Method (ESAM) for such an appraisal. The present paper aims at devising a process which will show the applicability of ESM model in identifying the biodiversity related and other environmental factors, adversely impacting the ecotourism destination at present, or may impact it in times to come. In addition, it also offers the prescription to solve these issues and achieve the goal of ecotourism sustainability at the operational level. This proposed process initially will be defined with the help of a site-level case study of Mt. Kangchendzonga Base Camp Trek, Sikkim. Later, this would be extended to other ecologies and geographies so that in the future, a robust and useful model, applicable for most of the ecotourism destinations, can be developed.

Advances in terrestrial and ocean dynamics studies in India.

The land, oceans, and atmosphere are tightly linked and form the most dynamic component of the climate system. Studies on terrestrial and ocean science enhance the understanding on the impacts of climate change. Across India and the world over, human-driven land use and climate changes are altering the structure, function, and extent of natural terrestrial ecosystems and in turn regional biogeochemical feedbacks. In this special issue, we present 29 manuscripts; those discuss wide-ranging aspects of terrestrial and oceanic characterization and dynamics. These contributions are based on selected presentations made at the 2nd International Workshop on Biodiversity and Climate Change (BDCC-2018) held on 24-27 February 2018 at the Indian Institute of Technology Kharagpur, India. The manuscripts are arranged in five sections such as Ecological Assessment, Plant Invasion, Carbon Dynamics, Ecosystem Characterization, and Ocean Dynamics. We realized that the utility of satellite remote sensing data has been emerging as a dominant trend in environmental monitoring and assessment studies in India.

Developing quantifiable approaches for delineating suitable options for irrigating fallow areas during dry season-a case study from Eastern India.

Harvesting surface runoff during monsoon season for further utilization in crop production during the post-monsoon season is now becoming an effective solution to mitigate water scarcity problems. In this study, multi-criteria analysis-analytic hierarchy process (MCA-AHP)-based approach was envisaged for rainwater harvesting (RWH) zoning for a case study area, i.e., two districts of Odisha state situated in Eastern India. In spite of having a large irrigation network in the study area, major portion of these two densely populated and agriculture dominated districts remains fallow during dry seasons. Suitable locations for RWH structures such as farm pond, check dam, and percolation tanks were identified through Boolean conditions. RWH potential map was generated using different thematic layers namely land use/land cover (LU/LC), geomorphology, slope, stream density, soil type, and surface runoff. AHP-based MCA technique was used to integrate these thematic layers by assigning weights to the thematic layers and ranks to the individual theme features on 1-9 AHP Saaty's scale, considering their relative importance on RWH potential of the study area. The Natural Resources Conservation Service-Curve Number method was used to derive surface runoff using Climate Hazards Group Infra-Red Precipitation with Station rainfall data, satellite-derived LU/LC and FAO soil maps. In comparison to single cropped areas in 48% of the total study area, only 4% area was under double and triple cropped areas during 2016-2017. Moderate runoff was observed in > 50% of the study area dominated by agricultural landscape. Nearly 40%, 25.11%, and 32.45% of the study area indicated very high, high, and moderate RWH potentials, respectively. Particularly, very high RWH potential is observed in the eastern and central portion of the study area. The use of appropriate RWH structures in less irrigated areas will facilitate multiple cropping and will substitute the use of sub-surface water harvesting practices. In these two districts, 73 check dams and 153 percolation tanks are prescribed along the 2nd- and 3rd-order streams. In coarser textured soil, nearly 306 km2 and 608 km2 areas are identified as moderate and highly suitable zones for percolation tank construction on ground, while in fine soil, around 786 km2 area is identified as suitable for farm pond construction. Majority of the suitable zones for percolation tanks is found in Jajpur district, while suitability for adoption of farm pond and check dam is more in Bhadrak district. It is expected that implementation of the prescribed RWH structures can mitigate the threats of flood, drought, soil erosion, and enhance the soil moisture and cropping intensity significantly. The use of GIS platform with the spatial layers and the methodology adopted can be updated and replicated in larger regions in a shorter time. The spatially explicit maps are offering insights to different themes, providing useful information to the water resource managers, and may improve the decision-making process.

Studying land use dynamics using decadal satellite images and Dyna-CLUE model in the Mahanadi River basin, India.

Population growth rate indicates the proportional rate of settlement expansion and landscape modification in any river basin. The Mahanadi River basin (MRB), which is a densely populated, cropland and forest-dominated landscape, is selected as a case study area for studying the nature of built-up expansion and the corresponding land cover modifications. Satellite data-derived land use/land cover (LU/LC) maps for the years 1995, 2005, and 2015 were used for identification of landscape changes during the past three decades. One of the major LU/LC changes are observed in terms of increase in the water, which may be attributed to construction of new dams at the cost of the croplands and forest areas. Conversion of forest to cropland and expansion and densification of built-up areas in and around the existing built-up areas are also identified as a major LU/LC change. The geostatistical analysis was performed to identify the relationship between LU/LC classes with drivers, which showed that built-up areas were more in topographically flat terrain with higher soil depth, and expanded more around the existing built-up areas; cropland areas were more at lower elevation and less sloppy terrain, and forest areas were more at higher elevation. The LU/LC scenario of 2025 was projected using a spatially explicit dynamic conversion of land use and its effects (Dyna-CLUE) modeling platform with the LU/LC change trends of past 10 years (2005-2015) and 20 years (1995-2015). The major LU/LC changes observed during 2005-2015 were built-up expansion by 36.53% and deciduous forest and cropland reduction by 0.35% and 0.45%, respectively. Thus, the corresponding predicted change during 2015-2025 estimated built-up expansion by 25.70% and deciduous forest and croplands loss by 0.43% and 0.35%, respectively. On the other hand, during 1995 to 2015, the total built-up expansion and deciduous forest and cropland reduction were observed 50.79%, 0.45%, and 0.73%, respectively. Thus, the predicted changes during 2015-2025 were estimated as 18.48% built-up expansion and 0.22% and 0.21% deciduous forest and cropland loss. However, with the conditions of restricted deforestation and less landscape modification, the LU/LC projections show less built-up area expansion, reducing the cropland, fallow land, plantation, and waste land. The reduced numbers of land cover conversions types during 2005-2015 compared with 1995-2005 indicate more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.

Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India.

Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield-based, remote sensing data-driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m-2 year-1, with a mean value of 1507.32 g C m-2 year-1. The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.

Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985.

Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanization, industrialization etc. We used on-screen digital interpretation technique to derive LULC maps from Landsat images at three decadal intervals i.e., 1985, 1995 and 2005 of two major river basins of India. Rain-fed, Mahanadi river basin (MRB) attributed to 55% agricultural area wherein glacier-fed, Brahmaputra river basin (BRB) had only 16% area under agricultural land. Though conversion of forest land for agricultural activities was the major LULC changes in both the basins, the rate was higher for BRB than MRB. While water body increased in MRB could be primarily attributed to creation of reservoirs and aquaculture farms; snow and ice melting attributed to creation of more water bodies in BRB. Scrub land acted as an intermediate class for forest conversion to barren land in BRB, while direct conversion of scrub land to waste land and crop land was seen in MRB. While habitation contributed primarily to LULC changes in BRB, the proximity zones around habitat and other socio-economic drivers contributed to LULC change in MRB. Comparing the predicted result with actual LULC of 2005, we obtained >97% modelling accuracy; therefore it is expected that the Dyna-CLUE model has very well predicted the LULC for the year 2025. The predicted LULC of 2025 and corresponding LULC changes in these two basins acting as early warning, and with the past 2-decadal change analysis this study is believed to help the land use planners for improved regional planning to create balanced ecosystem, especially in a changing climate.

Use of satellite remote sensing as a monitoring tool for land and water resources development activities in an Indian tropical site.

With the availability of satellite data from free data domain, remote sensing has increasingly become a fast-hand tool for monitoring of land and water resources development activities with minimal cost and time. Here, we verified construction of check dams and implementation of plantation activities in two districts of Tripura state using Landsat and Sentinel-2 images for the years 2008 and 2016-2017, respectively. We applied spectral reflectance curves and index-based proxies to quantify these activities for two time periods. A subset of the total check dams and plantation sites was chosen on the basis of site condition, nature of check dams, and planted species for identification on satellite images, and another subset was randomly chosen to validate identification procedure. The normalized difference water index (NDWI) derived from Landsat and Senitnel-2 were used to quantify water area evolved, qualify the water quality, and influence of associated tree shadows. Three types of check dams were observed, i.e., full, partial, and fully soil exposed on the basis of the presence of grass or scrub on the check dams. Based on the nature of check dam and site characteristics, we classified the water bodies under 11-categories using six interpretation keys (size, shape, water depth, quality, shadow of associated trees, catchment area). The check dams constructed on existing narrow gullies totally covered by branches or associated plants were not identified without field verification. Further, use of EVI enabled us to approve the plantation activities and adjudge the corresponding increase in vegetation vigor. The plantation activities were established based on the presence and absence of existing vegetation. Clearing on the plantation sites for plantation shows differential increase in EVI values during the initial years. The 403 plantation sites were categorized into 12 major groups on the basis of presence of dominant species and site conditions. The dominant species were Areca catechu, Musa paradisiaca, Ananas comosus, Bambusa sp., and mix plantation of A. catechu and M. paradisiaca. However, the highest maximum increase in average EVI was observed for the pine apple plantation sites (0.11), followed by Bambussa sp. (0.10). These sites were fully covered with plantation without any exposed soil. The present study successfully demonstrates a satellite-based survey supplemented with ground information evaluating the changes in vegetation profile due to plantation activities, locations of check dams, extent of water bodies, downstream irrigation, and catchment area of water bodies.