Your search found 5 records
1 Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, A.; Singh, S.; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, S.; Gupta, S.; Pujar, G.; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, P.; Singh, J. S.; Chitale, V.; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, D.; Karnatak, H.; Saran, S.; Amarnath, Giriraj; Padalia, H.; Kale, M.; Nandy, S.; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, C.; Singh, D. K.; Devagiri, G. M.; Talukdar, G.; Panigrahy, R. K.; Singh, H.; Sharma, J. R.; Haridasan, K.; Trivedi, S.; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, M.; Nagabhatla, N.; Prasad, N.; Tripathi, O. P.; Prasad, P. R. C.; Dash, P.; Qureshi, Q.; Tripathi, S. K.; Ramesh, B. R.; Gowda, B.; Tomar, S.; Romshoo, S.; Giriraj, S.; Ravan, S. A.; Behera, S. K.; Paul, S.; Das, A. K.; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, U.; Menon, A. R. R.; Srivastava, G.; Sharma, N. S.; Mohapatra, U. B.; Peddi, A.; Rashid, H.; Salroo, I.; Krishna, P. H.; Hajra, P. K.; Vergheese, A. O.; Matin, S.; Chaudhary, S. A.; Ghosh, S.; Lakshmi, U.; Rawat, D.; Ambastha, K.; Malik, A. H.; Devi, B. S. S.; Gowda, B.; Sharma, K. C.; Mukharjee, P.; Sharma, A.; Davidar, P.; Raju, R. R. V.; Katewa, S. S.; Kant, S.; Raju, V. S.; Uniyal, B. P.; Debnath, B.; Rout, D. K.; Thapa, R.; Joseph, S.; Chhetri, P.; Ramachandran, R. M. 2015. New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39:142-159. [doi: https://doi.org/10.1016/j.jag.2015.03.003]
Satellite imagery ; Remote sensing ; Vegetation ; Climate change ; Temperature ; Precipitation ; Scrublands ; Grasslands ; Ecology ; Global positioning systems ; Land cover ; Assessment ; Cultivation / India
(Location: IWMI HQ Call no: e-copy only Record No: H047008)
(2.48 MB)
A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. Weused 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).
2 Ghosh, S.; Thakur, P. K.; Sharma, R.; Nandy, S.; Garg, V.; Amarnath, Giriraj; Bhattacharyya, S. 2017. The potential applications of satellite altimetry with SARAL [Satellite with ARGOS and ALTIKA]/AltiKa for Indian inland waters. Proceedings of the National Academy of Sciences India Section A-Physical Sciences, 19p. (Online first). [doi: https://doi.org/10.1007/s40010-017-0463-5]
Earth observation satellites ; Inland waters ; Surface water ; Monitoring ; Satellite observation ; Radar ; Water levels ; River basins ; Flow discharge ; Reservoirs ; Sedimentation ; Measurement ; Hydrology ; Models ; Calibration / India
(Location: IWMI HQ Call no: e-copy only Record No: H048445)
(1.39 MB)
The satellite radar altimetry datasets are now extensively used for continental water monitoring although it was primarily designed for oceanic surface and ice cap studies. Water level estimated from satellite altimetry can help to assess many hydrological parameters like river discharge and reservoir volume. These parameters can be employed for calibration and validation purposes of hydrological and hydrodynamic models, rating curve (stage-discharge relationship) generation, near real-time flood forecasting, reservoir operations and transboundary water related issues. Satellite with Argos and AltiKa (SARAL/AltiKa), a joint venture of Indian Space Research Organisation and Centre National d’Etudes Spatiales, is one of the pioneer missions in the history of satellite radar altimetry. It is first high-frequency (Ka-band, 35.75 GHz) mission with the highest sampling rate (40 Hz). The applications of radar altimetry to inland hydrology have been significantly increased in recent years in India. Major studies have been carried out in Ganga, Brahmaputra, Tapi and Godavari river basins with AltiKa data. AltiKa datasets have been successfully used for retrieving water level in reservoir and river, estimating river discharge and calculating reservoir sedimentation. Considering the stress on India’s fresh water resources and the importance of SARAL/AltiKa mission, this work was carried out. The present review paper may be helpful to understand the working principle of altimetry, altimetry waveform, waveform retracking methods, water stage, river discharge and changes in reservoir’s water storage calculation, and the status of altimetry applications to inland hydrology, specifically solicitation of SARAL/AltiKa in the Indian context.
3 Srinet, R.; Nandy, S.; Padalia, H.; Ghosh, Surajit; Watham, T.; Patel, N. R.; Chauhan, P. 2020. Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine. International Journal of Remote Sensing, 41(18):7296-7309. [doi: https://doi.org/10.1080/01431161.2020.1766147]
Forests ; Highlands ; Normalized difference vegetation index ; Ecosystems ; Time series analysis ; Moderate resolution imaging spectroradiometer ; Digital elevation models ; Climatic factors ; Mapping / India / Himalayan Foothills
(Location: IWMI HQ Call no: e-copy only Record No: H050791)
(6.51 MB)
Plant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with the surrounding environment in various earth system models. The present study aims to generate a PFT map for the Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, and climatic information from various satellite data and products using Random Forest (RF) algorithm in Google Earth Engine (GEE) platform. The seasonality information was extracted by carrying out a harmonic analysis of Normalized Difference Vegetation Index (NDVI) time-series (2008 to 2018) from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance 8 day 500 m data (MOD09A1). For topographic information, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived aspect and Multi-Scale Topographic Position Index (MTPI) were used, whereas, for climatic variables, WorldClim V2 Bioclimatic (Bioclim) variables were used. RF, a machine learning classifier, was used to generate a PFT map using these datasets. The overall accuracy of the resulting PFT map was found to be 83.33% with a Kappa coefficient of 0.71. The present study provides an effective approach for PFT classification using different well-established, freely available satellite data and products in the GEE platform. This approach can also be implemented in different ecological settings by using various meaningful variables at varying resolutions.
4 Nandy, S.; Ghosh, Surajit; Singh, S. 2021. Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India. Environmental Monitoring and Assessment, 193(9):616. [doi: https://doi.org/10.1007/s10661-021-09356-9]
Forests ; Phenology ; Climatic factors ; Shorea robusta ; Moderate resolution imaging spectroradiometer ; Time series analysis ; Remote sensing ; Temperature ; Rain ; Vegetation index / India / Assam / Chhattisgarh / Jharkhand / Madhya Pradesh / Meghalaya / Uttarakhand / West Bengal
(Location: IWMI HQ Call no: e-copy only Record No: H050795)
(2.27 MB)
Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between phenology metrics and climatic parameters. Sal, one of the main timber-producing species of India, can be categorized into dry, moist, and very moist sal. The phenological metrics of different types of sal forests were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Enhanced Vegetation Index (EVI) time series data (2002–2015). During the study period, the average start of season (SOS) was found to be 16 May, 17 July, and 29 June for very moist, moist, and dry sal forests, respectively. The spatial distribution of mean SOS was mapped as well as the impact of climatic variables (temperature and rainfall) on SOS was investigated during the study period. In relation to the rainfall, values of the coefficient of determination (R2) for very moist, moist, and dry sal forests were 0.69, 0.68, and 0.76, respectively. However, with temperature, R2 values were found higher (R2 = 0.97, 0.81, and 0.97 for very moist, moist, and dry sal, respectively). The present study concluded that MODIS EVI is well capable of capturing the phenological metrics of different types of sal forests across different biogeographic provinces of India. SOS and length of season (LOS) were found to be the key phenology metrics to distinguish the different types of sal forests in India and temperature has a greater influence on SOS than rainfall in sal forests of India.
5 Pandey, S. K.; Chand, N.; Nandy, S.; Muminov, A.; Sharma, A.; Ghosh, Surajit; Srinet, R. 2020. High-resolution mapping of forest carbon stock using Object-Based Image Analysis (OBIA) technique. Journal of the Indian Society of Remote Sensing, 48(6):865-875. [doi: https://doi.org/10.1007/s12524-020-01121-8]
Forests ; Carbon stock assessments ; Mapping ; Satellite imagery ; Image analysis ; Techniques ; Estimation / India / Uttarakhand / Barkot Forest
(Location: IWMI HQ Call no: e-copy only Record No: H050799)
(6.21 MB)
This study assessed and mapped the aboveground tree carbon stock using very high-resolution satellite imagery (VHRS)—WorldView-2 in Barkot forest of Uttarakhand, India. The image was pan-sharpened to get the spectrally and spatially good-quality image. High-pass filter technique of pan-sharpening was found to be the best in this study. Object-based image analysis (OBIA) was carried out for image segmentation and classification. Multi-resolution image segmentation yielded 74% accuracy. The segmented image was classified into sal (Shorea robusta), teak (Tectona grandis) and shadow. The classification accuracy was found to be 83%. The relationship between crown projection area (CPA) and carbon was established in the field for both sal and teak trees. Using the relationship between CPA and carbon, the classified CPA map was converted to carbon stock of individual trees. Mean value of carbon stock per tree for sal was found to be 621 kg, whereas for teak it was 703 kg per tree. The study highlighted the utility of OBIA and VHRS imagery for mapping high-resolution carbon stock of forest.
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