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Co-Authors
- P. Hari Krishna
- K. R. L. Saranya
- C. Sudhakar Reddy
- V. K. Dadhwal
- Kalloli Dutta
- Subrat Sharma
- G. Manaswini
- V. V. L. Padma Alekhya
- S. Vazeed Pasha
- K. V. Satish
- P. G. Diwakar
- R. Suraj Reddy
- Rakesh
- K. S. Rajan
- J. Singhal
- C. S. Reddy
- G. Rajashekar
- S. Maity
- C. Patnaik
- Anup Das
- Arundhati Misra
- C. P. Singh
- Jakesh Mohapatra
- N. S. R. Krishnayya
- Sandhya Kiran
- Phil Townsend
- Margarita Huesca Martinez
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Jha, C. S.
- Arbor Day in Hazaribagh
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Indian Forester, Vol 66, No 11 (1940), Pagination: 655-658Abstract
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- Assessment and Monitoring of Deforestation from 1930 to 2011 in Andhra Pradesh, India Using Remote Sensing and Collateral Data
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Authors
Affiliations
1 National Remote Sensing Centre, ISRO, Balanagar, Hyderabad 500 037, IN
1 National Remote Sensing Centre, ISRO, Balanagar, Hyderabad 500 037, IN
Source
Current Science, Vol 107, No 5 (2014), Pagination: 867-875Abstract
Deforestation is one of the greatest threats to the world's forest ecosystems. The present study has utilized remote sensing and GIS techniques to quantify changes in forest cover and to map patterns of deforestation in Andhra Pradesh, India during 1930-2011. Andhra Pradesh has the second largest recorded forest area and ranks sixth with an actual forest cover amongst all Indian states. Forest cover maps from seven temporal datasets were prepared based on interpretation of multi-source topographical maps and satellite data. A representative set of landscape indices has been used to study landscape-level changes over time. The mapping for the period of 1930, 1960, 1975, 1985, 1995, 2005 and 2011 indicates that the forest cover accounts for 85,392, 68,063, 46,940, 45,520, 44,409, 43,577 and 43,523 sq. km of the study area respectively. The study found the net forest cover declined as 49% of the total forest area during the last eight decades. The annual rate of estimated deforestation during 2005-2011 was 0.02%. Annual rate of deforestation of teak mixed forests was relatively higher (0.76) followed by mangroves (0.58%), semi-evergreen forests (0.43%), dry deciduous forests (0.21%), moist deciduous forests (0.09%) and dry evergreen forests (0.07%) during 1975-2011. The landscape analysis shows that the number of forest patches was 3,981 in 1930, 5,553 in 1960, 8,760 in 1975, 9,412 in 1985, 9,646 in 1995 and 10,597 in 2011, which indicates ongoing anthropogenic pressure on the forests. The mean patch size (sq. km) of forest decreased from 21.5 in 1930 to 12.3 in 1960 and reached 3.9 by 2011. The analysis of historical forest cover changes provides a basis for management effectiveness and future research on various components of biodiversity, climate change and accounting of carbon.Keywords
Collateral Data, Deforestation, Landscape Metrics, Remote Sensing.Full Text
- Quantification and Monitoring of Forest Cover Changes in Agasthyamalai Biosphere Reserve, Western Ghats, India (1920-2012)
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Authors
Affiliations
1 Forestry and Ecology Group, National Remote Sensing Centre, ISRO, Balanagar, Hyderabad 500 037, IN
2 G.B. Pant Institute of Himalayan Environment and Development, Kosi-Katarmal, Almora 263 643, IN
1 Forestry and Ecology Group, National Remote Sensing Centre, ISRO, Balanagar, Hyderabad 500 037, IN
2 G.B. Pant Institute of Himalayan Environment and Development, Kosi-Katarmal, Almora 263 643, IN
Source
Current Science, Vol 110, No 4 (2016), Pagination: 508-520Abstract
Protected areas need to be monitored regularly to realize the effectiveness of conservation measures. In this study, Agasthyamalai Biosphere Reserve of Western Ghats has been monitored for deforestation in a historic time frame. The study attempted to identify the changes that occurred within the Biosphere Reserve from the early 1920s to the recent by mapping the land use/land cover and quantifying the forest cover changes that have occurred in the Biosphere Reserve individually for each conservation zone and protected area. Multi-temporal satellite datasets and topographical maps were used for mapping the forest cover of the study area. Visual interpretation technique involving on screen digitization was used for mapping and post-classification comparison method was used for carrying out change detection process. In addition, grid wise spatial tracking was carried out for the periods of 1920-1973 and 1973-2012 to prioritize change areas. Results showed that 747.1 km2 of forests have been lost during the period of 1920-2012. The present study demonstrates the importance of long-term land use/land cover information to examine conservation effectiveness by utilizing remote sensing and GIS techniques to carry out best management practices.Keywords
Conservation, Deforestation, Land Use/Land Cover, Long-Term Study, Protected Area.Full Text
References
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- Nationwide Assessment of Forest Burnt Area in India Using Resourcesat-2 AWiFS Data
Abstract Views :240 |
PDF Views:92
Authors
C. Sudhakar Reddy
1,
C. S. Jha
1,
G. Manaswini
1,
V. V. L. Padma Alekhya
1,
S. Vazeed Pasha
1,
K. V. Satish
1,
P. G. Diwakar
1,
V. K. Dadhwal
1
Affiliations
1 National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, IN
1 National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, IN
Source
Current Science, Vol 112, No 07 (2017), Pagination: 1521-1532Abstract
This study provides application of Resourcesat-2 AWiFS satellite imagery for forest burnt area assessment in India. AWiFS datasets covering peak forest fire months of 2014 have been analysed. The total burnt area under vegetation cover (forest, scrub and grasslands) of India was estimated as 57,127.75 sq. km. In 2014, 7% of forest cover of India was affected by fires. Of the major forest types, dry deciduous forests are affected by the highest burnt area, followed by moist deciduous forests. Among the biogeographic zones, the highest forest burnt area was recorded in Deccan followed by North East and Western Ghats. The highest burnt area was recorded in Odisha followed by Andhra Pradesh, Maharashtra, Chhattisgarh, Tamil Nadu, Madhya Pradesh, Telangana, Jharkhand, Manipur and Karnataka. Spatial analysis shows that 232 grid cells in India have a burnt area greater than 20 sq. km. The database generated would be useful in ecological damage assessment, fire risk modelling, carbon emissions accounting and biodiversity conservation.Keywords
AWiFS, Forest Fire, Forest Type, India, Remote Sensing.Full Text
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- Engineering Education in the 21st Century
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Journal of Engineering Education Transformations, Vol 10, No 1 (1996), Pagination: 18-28Abstract
Forecasting the future has always been a risky business. Although futurology is now being considered a branch of Science, the accuracy of prediction depends on a large number of fuzzy parameters, the growth pattern of which is very difficult to estimate.- Automatic Estimation of Tree Stem Attributes Using Terrestrial Laser Scanning in Central Indian Dry Deciduous Forests
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Authors
Affiliations
1 Forestry and Ecology Group, National Remote Sensing Centre, Hyderabad 500 037, IN
2 Lab of Spatial Informatics, Indian Institute of Information Technology, Hyderabad 500 032, IN
1 Forestry and Ecology Group, National Remote Sensing Centre, Hyderabad 500 037, IN
2 Lab of Spatial Informatics, Indian Institute of Information Technology, Hyderabad 500 032, IN
Source
Current Science, Vol 114, No 01 (2018), Pagination: 201-206Abstract
Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level (e.g. tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single- and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betul, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single- and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R2 = 0.96) and multiple scans (R2 = 0.99). The DBH estimates from multi-scan TLS data resulted in low ischolar_main-meansquare error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R2 = 0.98; N = 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with reliable accuracy at individual tree level.Keywords
DBH, Forest Inventory Parameters, Multiscan, Single-Scan, Terrestrial Laser Scanner.Full Text
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- Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data
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Authors
C. S. Jha
1,
Rakesh
1,
J. Singhal
1,
C. S. Reddy
1,
G. Rajashekar
1,
S. Maity
2,
C. Patnaik
2,
Anup Das
2,
Arundhati Misra
2,
C. P. Singh
2,
Jakesh Mohapatra
2,
N. S. R. Krishnayya
3,
Sandhya Kiran
3,
Phil Townsend
4,
Margarita Huesca Martinez
5
Affiliations
1 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
2 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
3 MS University of Baroda, Vadodara 390 002, IN
4 University of Wisconsin, Madison 53706, US
5 University of California, Davis 95616, US
1 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
2 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
3 MS University of Baroda, Vadodara 390 002, IN
4 University of Wisconsin, Madison 53706, US
5 University of California, Davis 95616, US
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1124-1135Abstract
Species diversity and vegetation health are two critical components to be monitored for sustainable forest management and conservation of biodiversity. The present study characterizes species dominance and α -diversity of a forest for the selected region in Mudumalai Wildlife Sanctuary (MWS), Western Ghats, which represents one of the most economically important forest types in India – the tropical dry deciduous forest. NASA’s Next-Generation Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-NG) data at spectral resolution of 5 nm and spatial resolution of 5 m were used to analyse the forest matrix. Biodiversity (α -diversity) map thus generated from airborne platform over 14.5 sq. km area mostly represents the forest tree species diversity. Dominant tree species in the study area were also mapped using AVIRIS data for 21.7 sq. km. Canopy emergent dominant species, viz. Anogeissus latifolia, Tectona grandis, Terminalia alata, Grewia tiliifolia, Syzygium cumini and Shorea roxburghii were classified using spectral angle mapper technique and image-based spectra in the MWS study site. The study shows that nearly 40% area is dominated by A. latifolia and 27.5% by T. grandis in the study site. This study concludes that AVIRIS data can be used in the delineation of species and α -diversity mapping at community level; however, the accuracy achieved for species classification is moderate (60%) due to intermixing of species in the study area. For the Shimoga study site in Karnataka, the field spectra were collected using a spectroradiometer and used for the classification for the three dominant tree species using absorption peak decomposition technique. Fieldcollected pure spectra were analysed and species-wise absorption peaks (Gaussian) with central wavelength, peak amplitude and dispersion were used as the endmembers for classification. AVIRIS-NG data over Shoolpaneshwar Wildlife Sanctuary (SWS) study site used for fuel load estimation with narrow band indices calculated from AVIRIS-NG datasets. AVIRIS-NG data for MWS and Shimoga study site were collected during 2 and 5 January 2016, while for SWS site data were collected on 8 February 2016.Keywords
Airborne Sensors, Forest Health, Hyperspectral Imaging, Species Diversity.Full Text
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