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Sudhakar Reddy, C.

Assessment and Monitoring of Deforestation from 1930 to 2011 in Andhra Pradesh, India Using Remote Sensing and Collateral Data

Abstract Views :247 | PDF Views:95

Authors

P. Hari Krishna ¹, K. R. L. Saranya ¹, C. Sudhakar Reddy ¹, C. S. Jha ¹, V. K. Dadhwal ¹

Affiliations
1 National Remote Sensing Centre, ISRO, Balanagar, Hyderabad 500 037, IN

Source

Current Science, Vol 107, No 5 (2014), Pagination: 867-875

Abstract

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.

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Quantification and Monitoring of Forest Cover Changes in Agasthyamalai Biosphere Reserve, Western Ghats, India (1920-2012)

Abstract Views :347 | PDF Views:133

Authors

Kalloli Dutta ¹, C. Sudhakar Reddy ¹, Subrat Sharma ², C. S. Jha ¹

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

Source

Current Science, Vol 110, No 4 (2016), Pagination: 508-520

Abstract

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

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Assessment and Monitoring of Deforestation and Land-Use Changes (1976-2014) in Andaman and Nicobar Islands, India Using Remote Sensing and GIS

Abstract Views :285 | PDF Views:93

Authors

C. Sudhakar Reddy ¹, K. V. Satish ¹, S. Vazeed Pasha ¹, C. S. ¹, V. K. Dadhwal ¹

Affiliations
1 National Remote Sensing Centre, Indian Space Research Organization, Balanagar, Hyderabad 500 037, IN

Source

Current Science, Vol 111, No 9 (2016), Pagination: 1492-1499

Abstract

Andaman and Nicobar Islands are part of Indo- Burma and Sundaland global biodiversity hotspots. This study provides spatial information on forest types, deforestation and associated land-use changes in Andaman and Nicobar Islands during 1976 to 2014. Satellite remote sensing and geographical information system (GIS) techniques have been used to analyse forest cover changes, rate of deforestation and to map patterns of forest cover distribution in Andaman and Nicobar Islands. Classified maps prepared for 1976, 1989, 1993, 2000, 2006 and 2014 indicate that the forest cover accounts for an area of 7086.1 (85.9%), 6969.2 (84.5%), 6941.1 (84.1%), 6934.6 (84.1%), 6617.8 (80.2%) and 6407.3 sq. km (77.7%) respectively. It was found that the area occupied by evergreen forests is very high, consisting of 3065.1 sq. km (32.2%) followed by semi-evergreen (1531.6 sq. km), moist deciduous (1133.4 sq. km) and mangrove forest (677.2 sq. km) in 2014. There is large-scale deforestation in Andaman and Nicobar Islands which has been estimated as 678.8 sq. km during the last four decades. The loss of forest cover is high in moist deciduous forests which has been estimated as 312.2 sq. km in Andaman Islands; whereas in Nicobar Islands, the highest loss was found in evergreen forests (244.6 sq. km). The rate of deforestation in Andaman and Nicobar Islands was high during 2000-2006 (0.78) indicating major influence of the tsunami of 26 December 2004. The annual rate of deforestation from 2006 to 2014 was 0.40. The geospatial analysis of areas of forest cover change provides baseline information for restoration and conservation planning.

Keywords

Andaman and Nicobar Islands, Deforestation, Forest, GIS, Remote Sensing, Land Use.

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Monitoring of forest Fires from Space-ISRO's Initiative for near Real-Time Monitoring of the Recent forest Fires in Uttarakhand, India

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

Source

Current Science, Vol 112, No 07 (2017), Pagination: 1521-1532

Abstract

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.

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Micro Hotspots of New Species Discoveries in India: Flora and Fauna

Deciphering Tropical Tree Communities Using Earth Observation Data and Machine Learning

Abstract Views :120 | 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

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