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Meghalaya, 'the abode of clouds' is one of the states of North East India. The state witnesses several forest fire incidents every year. In this study, MODIS-based forest fire counts from 2003 to 2023 have been used for the generation of forest fire-prone and vulnerability maps. The forest fire vulnerability map has been generated at 1: 10 k for the entire state and at 1: 5 k for Reserve Forests, Community Reserve Forests and other Protected Areas. Ri Bhoi and West Khasi Hills districts witness high forest fire incidents compared to all the other districts. Maximum number of forest fire occurs in moderately dense and very dense forests. The eForest Fire Information System, viz. 'Meghalaya Forest Fire Information System' has a dashboard with geospatial forest fire information and a forest fire incident reporting android based app developed for fire managers both at Government and community level. Shifting cultivation (jhum) is a predominant land-use pattern; therefore the framework has embedded jhum area information for improving forest fire management strategies to match the local scenario. [ABSTRACT FROM AUTHOR]

It is important to assess the rapid land-use/land-cover change (LULCC) and its impact on ecosystem service value (ESV), affecting the socio-economic and climatic stabilization of wetlands. In this study, a multi-resolution image segmentation and k-nearest neighbours algorithm were used for the thematic classification of satellite images of 2003 and 2018 in the Deepor Beel wetland, a lake of international significance. A notable increase in built-up areas was observed from 9.07 to 32.19 sq. km during this period. Proximity analysis in the 500 m buffer from the Deepor Beel boundary revealed an increasing built-up areas in close proximity. The ESV evaluation estimated a net decrease in the monetary value of 4.85 × 104 USD of the wetland for the respective years. The study recommends strict demarcation of zones of influence on the ground and regular monitoring of LULCC to protect important wetlands and preserve their ecosystem services. [ABSTRACT FROM AUTHOR]

The name Geodorum laxiflorum Griff. (Orchidaceae) is re‐lectotypified by designating a specimen in CAL as a new lectotype, thus superseding the earlier lectotypification which was found inadmissible. In addition, morphological variation and an overlooked character of the labellum of this Indian endemic and vulnerable species of orchid are presented based on studies of living plants collected in the wild from Tawi Wildlife Sanctuary, Mizoram and Garh Jungle, West Bengal. [ABSTRACT FROM AUTHOR]

North Eastern Region (NER) of India comprising of eight states considered to be most unique and one of the most challenging regions to govern due to its unique physiographic condition, rich biodiversity, disaster prone and diverse socio-economic characteristics. Operational Remote Sensing services increased manifolds in the region with the establishment of North Eastern Space Applications Centre (NESAC) in the year 2000. Since inception, NESAC has been providing remote sensing services in generating inventory, planning and developmental activities, and management of natural resources, disasters and dissemination of information and services through geo-web services for NER. The operational remote sensing services provided by NESAC can be broadly divided into three categories viz. natural resource planning and developmental services, disaster risk reduction and early warning services and information dissemination through geo-portal services. As a apart of natural resources planning and developmental services NESAC supports the state forest departments in preparing the forest working plans by providing geospatial inputs covering entire NER, identifying the suitable culturable wastelands for cultivation of silkworm food plants, mapping of natural resources such as land use/land cover, wastelands, land degradation etc. on temporal basis. In the area of disaster risk reduction, NESAC has initiated operational services for early warning and post disaster assessment inputs for flood early warning system (FLEWS) using satellite remote sensing, numerical weather prediction, hydrological modeling etc.; forest fire alert system with actionable attribute information; Japanese Encephalitis Early Warning System (JEWS) based on mosquito vector abundance, pig population and historical disease intensity and agriculture drought monitoring for the region. The large volumes of geo-spatial databases generated as part of operational services are made available to the administrators and local government bodies for better management, preparing prospective planning, and sustainable use of available resources. The knowledge dissemination is being done through online web portals wherever the internet access is available and as well as offline space based information kiosks, where the internet access is not available or having limited bandwidth availability. This paper presents a systematic and comprehensive study on the remote sensing services operational in NER of India for natural resources management, disaster risk reduction and dissemination of information and services, in addition to outlining future areas and direction of space applications for the region. [ABSTRACT FROM AUTHOR]

The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and allweather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of aboveground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB ≤ 80 t/ha. The overall RMSE for all vegetation with AGB ≤ 80 t/ha was 15.3 t/ha (R² – 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0–245 t/ha (RMSE – 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands. [ABSTRACT FROM AUTHOR]