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Padalia, Hitendra

Monitoring of forest Cover in India: Imaging Spectroscopy Perspective

Abstract Views :253 | PDF Views:105

Authors

N. S. R. Krishnayya ¹, Binal Christian ¹, Dhaval Vyas ¹, Manjit Saini ¹, Nikita Joshi ¹, K. R. Manjunath ², Tanumi Kumar ², Hitendra Padalia ³, Rajee George ⁴, S. P. S. Kushwaha ³

Affiliations
1 Ecology Laboratory, Department of Botany, The M.S. University of Baroda, Vadodara 390 002, IN
2 Space Applications Centre, India Space Research Organization (ISRO), Ahmedabad 380 015, IN
3 Forest and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
4 Department of Environment and Forest, Andaman and Nicobar Islands, Port Blair 744 102, IN

Source

Current Science, Vol 108, No 5 (2015), Pagination: 869-878

Abstract

Tropical forests are the most diverse and complex terrestrial systems. India is one of the mega diverse countries supporting rich floral diversity coming from diverse climatic conditions spread across the length and breadth of the country. Unique characteristics of these forest covers coupled with immense pressure of human activities make their monitoring essential so as to ensure their long-term sustainability. More reliable evaluation of forest cover can give better inputs to the National Mission for a Green India. Imaging spectroscopy is an appropriate technique to address some of these vital issues. This technique has seen an exponential growth in the past two decades, addressing various forestry applications such as tree species identification, invasive species mapping, monitoring phenology, biophysical and biochemical characterization, to name a few. Data acquisition through imaging spectroscopy can be done across different spatial and spectral ranges according to the needs of the user. The review highlights important measures to be taken in using imaging spectroscopy for forestry studies, specifically in the Indian context. It emphasizes future outlook of the technology for a sustained assessment of tropical forest cover.

Keywords

Forest Cover, Imaging Spectroscopy, Parameter Estimation, Sustained Assessment.

Full Text

Space-Borne Sun-Induced Fluorescence:An Advanced Probe to Monitor Seasonality of Dry and Moist Tropical Forest Sites

Abstract Views :211 | PDF Views:74

Authors

Sanjiv K. Sinha ¹, Hitendra Padalia ¹, A. Senthil Kumar ¹

Affiliations
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN

Source

Current Science, Vol 113, No 11 (2017), Pagination: 2180-2183

Abstract

Space-borne sun-induced fluorescence (SIF) is the latest breakthrough in remote sensing of physiological response of plants. We studied the seasonality of sal (Shorea robusta) forest canopies analysing space-borne SIF and reflectance data collected over moist and dry sites in central India. Results indicate that the monthly response of OCO-2 SIF, MODIS NDVI and GPP differs significantly across the wet and dry forest sites. SIF explained higher seasonal variations and was also better correlated to rainfall across sites compared to NDVI.

Keywords

Fluorescence, Remote Sensing, Seasonal Variations, Tropical Forests, Vegetation Index.

Full Text

References

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Estimating Net Primary Productivity of Croplands in Indo-Gangetic Plains Using GOME-2 Sun-Induced Fluorescence and MODIS NDVI

Abstract Views :216 | PDF Views:83

Authors

N. R. Patel ¹, Hitendra Padalia ¹, R. Devadas ², A. Huete ², A. Senthil Kumar ¹, Y. V. N. Krishna Murthy ³

Affiliations
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
2 University of Technology Sydney, Sydney, AU
3 National Remote Sensing Centre, Hyderabad 500 072, IN

Source

Current Science, Vol 114, No 06 (2018), Pagination: 1333-1337

Abstract

Recently evolved satellite-based sun-induced fluorescence (SIF) spectroscopy is considered as a direct measure of photosynthetic activity of vegetation. We have used monthly averages of satellite-based SIF retrievals for three agricultural year cycles, i.e. May to April for each of the three years, viz. 2007–08, 2008–09 and 2009–10 to assess comparative performance of SIF and normalized difference vegetation index (NDVI) for predicting net primary productivity (NPP) over the Indo-Gangetic Plains, India. Results show that SIF values for C4 crop-dominated districts were higher than C3 crop-dominated districts during summer and low during winter for all three years. SIF explained more or less above 70% of variance in NPP. The variance explained by integrated NDVI ranged from 60% to 67%. Thus the present study has shown the potential of SIF data for improved modelling of agricultural productivity at a regional scale.

Keywords

Crop Lands, Net Primary Productivity, Photosynthetic Activity, Sun-Induced Fluorescence.

Full Text

References

Smorenburg, K. et al., Remote sensing of solar induced fluorescence of vegetation. SPIE Proc.: Remote Sensing Agric., Ecosyst. Hydrol. III, 2000, p. 4542.

Meroni, M., Rossini, M., Guanter, L., Alonso, L., Rascher, U., Colombo, R. and Moreno, J., Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sensing Environ., 2009, 113, 2037–2051.

Porcar-Castell, A. et al., Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot., 2014, 65, 4065–4095.

Frankenberg, C. et al., New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett., 2011, 38, L03801; doi:10.1029/2010GL045896.

Guanter, L. et al., Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sensing Environ., 2012, 121, 236–251.

Joiner, J., Yoshida, Y., Vasilkov, A. P., Yoshida, Y., Corp, L. A. and Middleton, E. M., First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 2011, 8, 637–651.

Joiner, J. et al., Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech., 2013, 6, 2803–2823.

Guanter, L. et al., Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA, 2014, 111(14), E1327–E1333.

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Nayak, R. K., Patel, N. R. and Dadhwal, V. K., Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environ. Monit. Assess., 2010, 1, 195–213.

Citizen-centric Tool for Near Real-Time Mapping of Active Forest Fires

Abstract Views :251 | PDF Views:78

Authors

Sameer Saran ¹, Priyanka Singh ¹, Hitendra Padalia ¹, Arshdeep Singh ², Vishal Kumar ¹, Prakash Chauhan ¹

Affiliations
1 Indian Institute of Remote Sensing (ISRO), #4 Kalidas Road, Dehradun 248 001, IN
2 Jammu and Kashmir Forest Department, Sheikh Bagh, Near Lal Chowk, Srinagar 180 001, IN

Source

Current Science, Vol 119, No 5 (2020), Pagination: 780-789

Abstract

In this study, a mobile app is presented as a citizencentric geospatial solution to record real-time forest fire incidents. This tool fetches accurate geographical coordinates and captures forest fire images, along with relevant fields related to the event such as cause of fire, fire type, species affected, etc. in both online and offline mode. The background, application foundation, system design and main features are also described. Evaluation of robustness of the application and a case study are presented to show the potential use of this participatory sensing-based geospatial tool.

Keywords

Citizen Science, Forest Fire, Geospatial Tool, Mobile Application, Real-time Mapping.

Full Text

Deciphering Tropical Tree Communities Using Earth Observation Data and Machine Learning

Abstract Views :118 | PDF Views:76

Authors

Rahul Bodh ¹, Hitendra Padalia ¹, Divesh Pangtey ¹, Ishwari Datt Rai ¹, Subrata Nandy ¹, C. Sudhakar Reddy ²

Affiliations
1 Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN

Source

Current Science, Vol 124, No 6 (2023), Pagination: 704-712

Abstract

Publicly available EO datasets offer new possibilities to generate biodiversity information at the community composition level, an essential biodiversity variable, beyond forest type. We demonstrated the potential of Sentinel-2, GEDI LiDAR canopy height and ALOSDEM in discriminating and classifying tropical tree communities in the Western Himalayas, India. For this, tree communities were first identified based on the ordination of field data and subsequently classified using satellite data applying machine learning, i.e. random forest (RF). From the three forest types in the study area, eight distinct tree communities were identified for which classification accuracy increased from single date (75.17%) to multi-date images (85.33%) and further by applying feature selection (88.17%). Whereas the best classification accuracy of 94.66% was achieved when canopy height and topographic variables were also considered. The findings suggest that RF is suitable for mapping tree communities by combining Sentinel-2 with GEDI and DEM parameters.

Keywords

Biodiversity, Canopy Height, Machine Learning, Remote Sensing, Tropical Forest.

Full Text

References

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