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ID: 66320
Title: Lake 2024: Wetlands for Human well-being
Author: T V Ramachandra Vinay S M D Subash Chandran Abhishek Baghel Asulabha K S Sincy V Bharath Settur Paras Negi Tulika Mondal Gagana H M Vinay M Divyashree T K Aparna Haridas Bhuwan Chandra Arya Gayatri Naik Srikanth Naik,Darshan Hegde, Niyam Dave
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Lake 2024: Wetlands for Human well-being (Abstract) ,ETR-212,SCR-140, 2024
Subject: Lake 2024: Wetlands for Human well-being
Keywords: None
Abstract: Lake 2024: Wetlands for Human well-being (Abstract)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:

ID: 66319
Title: Lake 2024: Wetlands for Human Well-being 14 th Biennial Conference, 17 th - 20 th October 2024
Author: T V Ramachandra, Tulika Mondal,Vinay S, Bharath Aithal
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Lake 2024: Wetlands for Human Well-being 14 th Biennial Conference, 17 th - 20 th October 2024 , ETR-211,SCR-139, 2024
Subject: Lake 2024: Wetlands for Human Well-being 14 th Biennial Conference, 17 th - 20 th October 2024
Keywords: None
Abstract: Lake 2024: Wetlands for Human Well-being 14 th Biennial Conference, 17 th - 20 th October 2024 (Posters)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:

ID: 66318
Title: Environmentally Sensitive Regions Delineation through Grid-based Geospatial Techniques in Bangalore Urban District, Karnataka
Author: TV Ramachandra Tulika Mondal Bharath Setturu Bharath Aithal
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Environmentally Sensitive Regions Delineation through Grid-based Geospatial Techniques in Bangalore Urban District, Karnataka, ETR-208, SCR-136, 2024
Subject: Environmentally Sensitive Regions Delineation through Grid-based Geospatial Techniques in Bangalore Urban District, Karnataka
Keywords: Urbanization, Land use and land cover (LULC), Machine Learning, Landscape Modelling, Fragmentation analysis, Environmentally Sensitive Regions (ESRs)
Abstract: Rapid urbanization and industrial growth have led to the degradation of natural resources, necessitating a quantitative analysis of landscape dynamics for sustainable planning. Urban green ecosystems and wetlands, crucial for ecological balance, are being fragmented due to haphazard development activities, impacting biodiversity and ecological services. The availability of spatiotemporal remote sensing data with advancements in artificial intelligence (AI) and machine learning (ML) algorithms has aided in assessing the ecological status in urban environments, markedly revealing complex patterns and interactions. The current study presents landscape dynamics in the Bengaluru Urban district from 1973 to 2022 using a supervised machine learning technique based on the Random Forest algorithm with temporal Landsat data, which showed a 51.86% increase in the built-up area and a 26.28% decrease in the green cover. Computation of fragmentation indices showed a decline of the native green cover by 177.2 sq. km. in the southern part of the district. In urban landscapes, this results in the loss of green spaces and increased urban heat island effects. Modelling and geo-visualization tools are essential for anticipating changes in land use and aiding decision-making. Likely land use changes are predicted using the Cellular Automata Markov model considering the base case scenario. The analyses revealed a further possible increase in built-up to 1536.08 sq. km, with a decrease in green cover by 14.32 sq. km by 2038, and the disappearance of water bodies, which highlights the need to mitigate the adverse impacts of land use changes through planned urbanization considering the environment and livelihood of local communities. Insights into land use dynamics helped to delineate Environmentally Sensitive Regions (ESRs) at disaggregated levels and the design of Decision support systems for implementing conservation strategies and mitigation of ecological impacts. Based on bio, geo, climatic, ecological, and social parameters there are 6.79 sq km and 1.75 sq km under ESR1 and ESR2 respectively, showing highly sensitive regions in Bangalore Urban district.
Location: T E 15 New Biology building
Literature cited 1: Adam, E.; Mutanga, O.; Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetl. Ecol. Manag. 2010, 18, 281–296. Ali U, Esau TJ, Farooque AA, Zaman QU, Abbas F, Bilodeau MF. Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms. ISPRS International Journal of Geo-Information. 2022. 11(6), 333. https://doi.org/10.3390/ijgi11060333
Literature cited 2: Anandhi A, Douglas-Mankin KR, Srivastava P, Aiken RM, Senay G, Leung LR et al. DPSIR-ESA vulnerability assessment (DEVA) framework: synthesis, foundational overview, and expert case studies. Transactions of the ASABE. 2020. 63(3), 741-752. https://doi.org/10.13031/trans.13516 Andersson, E.; Nykvist, B.; Malinga, R.; Jaramillo, F.; Lindborg, R. A social-ecological analysis of ecosystem services in two different farming systems. AMBIO 2015, 44 (Suppl. S1), 102–112

ID: 66317
Title: Geospatial Approaches of Sustainable Natural Resource Management in Yadgir district, Karnataka
Author: TV Ramachandra Paras Negi Bharath Setturu
Editor: T.V. Ramachandra
Year: 2025
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Geospatial Approaches of Sustainable Natural Resource Management in Yadgir district, Karnataka ,ETR-207, SCR-135, 2025
Subject: Geospatial Approaches of Sustainable Natural Resource Management in Yadgir district, Karnataka
Keywords: Climate change, Natural resources-rich regions, CA-Markov, Random Forest, Forest fragmentation, Semi-arid.
Abstract: Changes in the climate have been posing a critical environmental and developmental challenges threatening the livelihood of people. Natural resource-rich regions are a foundation for human societies, shaping economic development, social organization, and cultural expression. Yadgir district consistently ranks last in the state for socio-economic development. This disparity is further exacerbated by declining agricultural productivity due to drought events. Assessment of landscape dynamics reveals that the spatial extent of the agricultural land had increased from 77.27 km 2 (1973) to 89.41 km 2 (2022), but the productivity has come down due to erratic rainfall patterns and pest attacks. Due to the population growth during the last five decades, the need for housing and industrial infrastructure has increased the built-up area from 12.89 km 2 (0.24%) in 1973 to 31.33 km 2 (0.60%) in 2022. With the increase in the agricultural area and built-up area, the forest and scrubland area has declined from 87.66 km 2 to 60.51 km 2 , and 632.21 km 2 to 276.98 km 2 respectively. The study has utilized the machine learning approach for the classification of land uses. Random forest is an ensemble learning algorithm that possesses the unique ability to handle high-dimensional datasets with thousands of input variables and estimate the relative importance of each variable in the classification process, aiding in understanding the model's decision-making. The study has predicted the likely land use using the CAMarkov method to understand the dynamics of semi-arid landscapes. The current study investigates the challenges faced in Yadgir district with land use changes and develops a framework for prioritizing the natural resource-rich regions based on bio-geo-climatic, hydrological, ecological, and social factors. The study identified that 8% of the district are NRRR 1 (6 grids), 7% are NRRR 2 (5 grids), 29% are NRRR 3 (27 grids), and the remaining 57% of geographical area are NRRR 4 (57 grids). The analysis emphasizes a multi-pronged approach that balances resource utilization with availability to ensure environmental sustainability. Strategies include adopting climate -resistant crops, lakes and tanks rejuvenation to enhance rainwater harvesting, sustainable water management practices, investments in education, women's empowerment, cluster-based development with the skill development of local youth.
Location: T E 15 New Biology building
Literature cited 1: Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., & Younis, I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539-42559. Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 120.
Literature cited 2: Adam, E., Mutanga, O., Odindi, J., & Abdel-Rahman, E. M. (2014). Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 35(10), 3440-3458. Ali, M. Z., Qazi, W., & Aslam, N. (2018). A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier. The Egyptian Journal of Remote Sensing and Space Science, 21, S29-S35.

ID: 66316
Title: Delineating Natural Resource-Rich Regions through Grid-based Geospatial Techniques in Kalaburagi District, Karnataka
Author: T V Ramachandra Paras Negi Bharath Setturu
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Delineating Natural Resource-Rich Regions through Grid-based Geospatial Techniques in Kalaburagi District, Karnataka ETR-206, SCR-134, 2024
Subject: Delineating Natural Resource-Rich Regions through Grid-based Geospatial Techniques in Kalaburagi District, Karnataka
Keywords: Land use dynamics, Machine learning, Random Forest, Modeling, Natural Resource-Rich Regions (NRRRs)
Abstract: Natural Resource-Rich Regions (NRRRs) are characterized at disaggregated levels by considering distinct bio-geo-climatic, ecological, hydrological, energy, and social factors. The current study examines the multifaceted concept of sustainable economic development within natural resource-rich regions. It extends beyond solely focusing on economic growth, recognizing the crucial role of ecological resilience (environmental protection) and human well-being. To achieve this, the study adopts an interdisciplinary approach, investigating the interplay of key factors. Anthropogenic activities have adversely affected the regime of ecology, biology, and hydrology resulting in the degradation of natural resources. Kalaburagi is located in the northern Karnataka state dominated by agricultural land and known as the “toor bowl” of Karnataka contributing 40% of the production of the state. The district showing immense growth in dal mills, cement, sugar industries, etc to promote industrial development under Industrial Policy 2014-19 announced by the state government. Supervised machine learning technique - Random Forest (RF) has been used to assess land use dynamics on a temporal scale. Spatio-temporal analysis of land use dynamic reveals that built-up had increased from 0.26% in 1973 to 1.22% in 2022 due to the greater emphasis on setting up of MSME units and cement industries in the district, the water body has increased from 0.12% in 1973 to 1.09% in 2022. Forest cover declined from 4.02% in 1973 to 1.52% in 2022. The NRRRs analysis reveals that 8 grids (5% area) of the Kalaburagi district fell under NRRR1 (highly sensitive), 15 grids (9% area) under NRRR2 (high sensitive), 52 grids (32%) under NRRR3 (moderately sensitive), and the rest under 103 grids (54%) representing NRRR4 (low sensitive). There is a decisive need to frame a systematic structure that would aid in the sustainability of natural resources and prevent the over-exploitation of natural resources.
Location: T E 15 New Biology building
Literature cited 1: Adam, E., Mutanga, O., Odindi, J., & Abdel-Rahman, E. M. (2014). Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 35(10), 3440-3458. Ali, A., Audi, M., & Roussel, Y. (2021). Natural resources depletion, renewable energy consumption and environmental degradation: A comparative analysis of developed and developing world. 4. Bajracharya, B., Shrestha, B. B., & Rajbhandari, L. (2016). Ecologically sensitive areas in the Hindu Kush Himalayan region: an overview. Journal of Mountain Science, 13(8), 1443-1456.Agarwal, C., & Reddy, V. R. (2016). Ecologically sensitive areas: a comprehensive review. International Journal of Environmental Science and Development, 7(10), 787-791.
Literature cited 2: Ali, A., Audi, M., & Roussel, Y. (2021). Natural resources depletion, renewable energy consumption and environmental degradation: A comparative analysis of developed and developing world. Bajracharya, B., Shrestha, B. B., & Rajbhandari, L. (2016). Ecologically sensitive areas in the Hindu Kush Himalayan region: an overview. Journal of Mountain Science, 13(8), 1443-1456.

ID: 66315
Title: Ecological Fragile Regions delineation at the disaggregated level through Grid-based monitoring of natural resources in Hassan district, Karnataka
Author: T V Ramachandra Tulika Mondal Bharath Setturu
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Ecological Fragile Regions delineation at the disaggregated level through Grid-based monitoring of natural resources in Hassan district, Karnataka, ETR-205, SCR-133, 2024
Subject: Ecological Fragile Regions delineation at the disaggregated level through Grid-based monitoring of natural resources in Hassan district, Karnataka
Keywords: landscape dynamics, LULC, forest fragmentation, CA-Markov, Random Forest, Machine Learning, Ecological Fragile Regions (EFRs)
Abstract: The landscape is a complex mosaic of environmental elements, each with its unique characteristics. However, the degradation of landscapes, often induced by changes in land use due to unplanned developmental activities, poses significant challenges, including exacerbating global warming, escalating greenhouse gas emissions, and compromising carbon sequestration capabilities through erosion. Hence, it is imperative to understand landscape dynamics for adopting sustainable measures through the prudent management of natural resources. The utilization of spatial data from spaceborne sensors, coupled with advancements in machine learning algorithms, presents a promising avenue for comprehensively analysing and monitoring landscapes. The current study highlights the application of machine learning techniques based on Random Forest (RF) algorithm, for mapping spatiotemporal changes in this region using temporal remote sensing data (Landsat) from 1973 to 2021 in Hassan district, Karnataka, a region characterized by diverse agroclimatic zones ranging from hot moist subhumid areas to hot, dry semi-arid plains. The study provides valuable insights that can contribute significantly for quantifying, monitoring, and understanding landscape dynamics, which are pivotal for framing stringent policy measures aimed at fostering sustainable development. The findings highlight that the eastern part of the district is predominantly covered by agriculture (34.24%) and horticulture (50.92%) in 2021. Urban expansion (0.53% to 2.11%) and agricultural activities have encroached upon forested areas (14.79% to 6.44%) over the past five decades. The Cellular Automata-Markov model, to forecast future land use patterns indicated a concerning decrease in forest cover (3.77%) coupled with an expansion of paved surfaces (5.49%) with agricultural (27.54%) and horticultural (55.15%) lands, driven primarily by increasing agricultural practices and the cultivation of cash crops like coffee and coconut. The study offers critical insights into of Ecological Fragile Regions (EFRs) based on a comprehensive assessment of bio-geo-climatic-social characteristics identifying 19% as EFR1 (most fragile), 38% EFR2 (fragile), 36% EFR3 (moderately fragile) and 7% EFR4 (less fragile). This enables policymakers to prioritize conservation efforts and implement targeted measures for ecological restoration and sustainable management of natural resources. The study emphasizes the importance of adopting sustainable development measures to mitigate the adverse impacts of land use changes and ensure the long-term ecological resilience of the landscape. By integrating spatial information of EFRs with policy interventions, stakeholders can collectively work towards conserving biodiversity, preserving ecosystem services, and promoting sustainable development in the Hassan district.
Location: T E 15 New Biology building
Literature cited 1: Aithal, B. H., & Ramachandra, T. V. (2016). Visualization of urban growth pattern in Chennai using geoinformatics and spatial metrics. Journal of the Indian Society of Remote Sensing, 44, 617-633. Aithal, B. H., Vinay, S., Durgappa, S., & Ramachandra, T. V. (2013, November). Modeling and simulation of urbanisation in greater Bangalore, India. In Proc. of National Spatial Data Infrastructure 2013 conference, IIT Bombay (pp. 34-50).
Literature cited 2: Alam, A., Bhat, M. S., & Maheen, M. (2020). Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal, 85, 1529-1543. Ali, U., Esau, T. J., Farooque, A. A., Zaman, Q. U., Abbas, F., & Bilodeau, M. F. (2022). Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms. ISPRS International Journal of Geo-Information, 11(6), 333. https://doi.org/10.3390/ijgi11060333.

ID: 66314
Title: Ecological Sensitive Regions delineation at the disaggregated level through Grid-based monitoring of natural resources in Haveri district, Karnataka
Author: T V Ramachandra , Bharath Setturu
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Ecological Sensitive Regions delineation at the disaggregated level through Grid-based monitoring of natural resources in Haveri district, Karnataka ,ETR 204, SCR-132 ,2024
Subject: Ecological Sensitive Regions delineation at the disaggregated level through Grid-based monitoring of natural resources in Haveri district, Karnataka
Keywords: Landscape dynamics, Land use, land cover, forest fragmentation, land use modeling, CA Markov model
Abstract: Eco-sensitive regions (ESR) are the Livelihood Support Regions (LSR) with high sensitivity and environmental fragility, where unplanned developmental activities would cause large-scale disturbances in the natural structure of the biological communities and natural habitats, affecting the ecosystem processes. Integrated landscape assessment including biological, geological, climatic, ecological, environmental, and social characteristics is required to delineate ESRs which will aid in the conservation of ecological balance in the environment for planning interventions to ensure sustainable development. Landscape consists of interacting diverse ecosystem elements, governed by the natural processes, and the heterogeneity depends on the land use land cover (LULC) dynamics. Alterations in the LULC due to unplanned anthropogenic activities have induced changes in the landscape structure leading to ecological imbalance evident from alterations in the hydrologic regime, the decline in ecosystem services, etc. Accelerated unplanned developmental activities in recent times, have resulted in enhanced levels of pollutants, barren hilltops, loss of agriculture productivity, and conversion of perennial streams to intermittent or seasonal, etc. Impacts such as loss of life and property, reduction in the crop yield, the prevalence of vector borne diseases, acute water scarcity, recurring floods, and droughts, global mean surface temperatures, alteration in spatial patterns of rainfall and intensity, etc., due to changes in the climate with global warming has drawn the attention of decision-makers and researchers, which necessitated delineation of ecologically fragile regions at disaggregated levels for implementing location specific conservation measures. Land use and land cover (LULC) assessment using temporal remote sensing data provides insights into landscape status, which is crucial for the prudent management of natural resources. Integrated spatial analyses with LULC information with social, ecological, hydrological, biogeo-climatic, and environmental variables would aid in the prioritization of ecologically sensitive regions (ESRs). The current study investigates the spatial extent of ESRs with the help of landscape dynamics from 1989 to 2019 in Haveri district, Karnataka, India. Haveri district is known for its famous Blackbuck sanctuary, Bankapura Fort. Agriculture dominates the region with 85% land use. The district has shown an increase of built-up by 2%. Dairy and poultry activities are major activities. With the loss of forest cover by a value of 1.6%, the region has majorly dry deciduous species. The fragmentation analysis showed a decrease in interior forest by 1.3%, indicating the stress due to anthropogenic pressure on native forest cover. Modeling of LU in the Haveri district showed a reduction in agriculture class by 3.52%, an increase in built-up increase of 3.27 %, and a forest reduction of 0.13%. Ecologically sensitive region analysis showed the ESR1 region (8 grids) in the Ranebennur taluk of Blackbuck Sanctuary and the Shiggaonv taluk of the Bankapura fort. The conservation of these regions is the prime concern for protecting the diversity. ESR 2 (18 grids) covers the region of moderate sensitivity with extensive productive agriculture lands, producing Maize, paddy, jowar, and ragi, where large-scale development activity should be restricted with no encroachments and land conversion. Still, small-scale industries like agro-based and IT investments can be encouraged. ESR 3 & 4 (31 & 2 grids) cover a large portion of agricultural lands with scope for sustainable development.
Location: T E 15 New Biology building
Literature cited 1: the region with 85% land use. The district has shown an increase of built-up by 2%. Dairy and poultry activities are major activities. With the loss of forest cover by a value of 1.6%, the region has majorly dry deciduous species. The fragmentation analysis showed a decrease in interior forest by 1.3%, indicating the stress due to anthropogenic pressure on native forest cover. Modeling of LU in the Haveri district showed a reduction in agriculture class by 3.52%, an increase in built-up increase of 3.27 %, and a forest reduction of 0.13%. Ecologically sensitive region analysis showed the ESR1 region (8 grids) in the Ranebennur taluk of Blackbuck Sanctuary and the Shiggaonv taluk of the Bankapura fort. The conservation of these regions is the prime concern for protecting the diversity. ESR 2 (18 grids) covers the region of moderate sensitivity with extensive productive agriculture lands, producing Maize, paddy, jowar, and ragi, where large-scale development activity should be restricted with no encroachments and land conversion. Still, small-scale industries like agro-based and IT investments can be encouraged. ESR 3 & 4 (31 & 2 grids) cover a large portion of agricultural lands with scope for sustainable development.
Literature cited 2: Bharath, S. and Ramachandra, T.V., 2021. Modeling landscape dynamics of policy interventions in Karnataka State, India. Journal of Geovisualization and Spatial Analysis, 5(2), p.22. Chalise, D., Kumar, L. and Kristiansen, P., 2019. Land degradation by soil erosion in Nepal: A review. Soil systems, 3(1), p.12.

ID: 66313
Title: Ecological And Economic Worth Of Bangalore Wetlands
Author: Sincy V. , Asulabha K.S.Jaishanker R. and Ramachandra T.V
Editor: T.V. Ramachandra
Year: 2024
Publisher: EM International
Source: ENVIS, CES & EWRG, CES
Reference: Poll Res. 43 (1–2) : 164-170 (2024)
Subject: Ecological And Economic Worth Of Bangalore Wetlands
Keywords: TESV, NPV, Ecosystem valuation, Wetland resources, Ecosystem benefits
Abstract: Wetland ecosystems provide diverse services to sustain livelihoods, which include the provision of food, fish, water, etc. (provisioning services), moderation of microclimate, carbon sequestration, groundwater recharge, remediation (regulating services), and aesthetic, spiritual, recreational, and information (cultural services). Despite being one of the most productive ecosystems, wetlands are being mismanaged due to a lack of knowledge of ecosystem services and economic worth. This necessitates the valuation of ecosystem services for valuable insights into their economic and ecological worth, which would help evolve appropriate policy initiatives for sustainable management and conservation of fragile lifeline ecosystems at decentralised levels. In this context, an attempt has been made to value the ecological and economic worth of four wetlands in Bangalore City through standard protocol by computing the total ecosystem supply value (aggregation of provisioning, regulating, and cultural services: TESV) and the net present value (NPV). The Hebbal wetland has the highest amounts of TESV (INR 51.20 million/year) and NPV (INR 1317.48 million) compared to Nagavara, Sankey, and Mathikere. The major contribution is from the regulating services, and the economic worth assessment highlights the vital role played by wetlands in sustaining the livelihood of the local people and the urgent need for prudent management of wetland ecosystems, involving all stakeholders to ensure cooperation and active participation in the conservation endeavour.
Location: T E 15 New Biology building
Literature cited 1: Akhtar, N., Syakir Ishak, M.I., Bhawani, S.A. and Umar, K. 2021. Various natural and anthropogenic factors responsible for water quality degradation: A review. Water. 13(19): 2660. https://doi.org/10.3390/ w13192660 Aryal, K., Ojha, B.R. and Maraseni, T. 2021. Perceived importance and economic valuation of ecosystem services in Ghodaghodi wetland of Nepal. Land Use Policy. 106: 105450.
Literature cited 2: Asselen, S.V., Verburg, P.H., Vermaat, J.E. and Janse, J.H. 2013. Drivers of wetland conversion: a global meta-analysis. PloS One. 8(11): e81292. https:// doi.org/10.1371/journal.pone.0081292 Asulabha, K.S., Jaishanker, R., Sincy, V. and Ramachandra, T.V. 2022. Diversity of phytoplankton in lakes of Bangalore, Karnataka, India. p. 147-178. 10 Chapter, In: Shashikanth Majige (Edited), Biodiversity Challenges: A Way th Forward , Daya Publishing House, New Delhi.

ID: 66312
Title: Wetlands for human well-being
Author: T.V. Ramachandra, K.S. Asulabha, V. Sincy, R. Jaishanker
Editor: Dr. Sumati Gaumat
Year: 2024
Publisher: Triveni Enterprises
Source: ENVIS, CES & EWRG, CES
Reference: Journal of Environmental Biology Volume 45 Issue 2 March 2024 pp. i-iv
Subject: Wetlands for human well-being
Keywords: None
Abstract: Wetlands, transitional lands bridging the gap between terrestrial and aquatic ecosystems, are among the most diverse and productive ecosystems, with biophysical interactions that provide numerous ecological, economic, and social benefits for human wellbeing. These vital ecosystems sustain ecological processes to provide services such as nutrient cycling, water purification, reducing pollution, carbon sequestration, groundwater recharge, flood reduction, erosion control, habitats for aquatic biota (Fig. 1), education opportunities, aesthetics, and recreation (Ramachandra et al., 2021; Ramachandra, 2022). In this context, World Wetlands Day is celebrated every year on 2 February to raise global awareness about the vital role of wetlands for human well-being and commemorate nd the adoption of the Convention on Wetlands on 2 February 1971 in the Iranian city of Ramsar. The Convention underscored sustainable and wise use of wetlands while advocating ecosystem approaches for the preservation of fragile ecosystems. Currently, 172 Ramsar Convention Contracting Parties and 2,500 Ramsar Sites totalling 2.5 million square kilometers are designated Ramsar Wetlands of International Importance. There are 80 wetlands of international significance in India (https://www.ramsar.org/) spanning 1,332,200 ha st nd 2 February 2024 – World Wetlands Day nd Wetlands for human well-being (which includes recently (on 31 January 2024) designated five wetlands - Ankasamudra Bird Conservation Reserve, Aghanashini Estuary (Ramachandra et al., 2018; Fig. 2) and Magadi Kere Conservation Reserve in Karnataka, and Karaivetti Bird Sanctuary and Longwood Shola Reserve Forest in Tamil Nadu). Pledged contracting parties advocate wise use of wetlands and water resources in the respective regions through national conservation plans, policies, legislation, management actions, and public education as per the tenets of 'seventeen Sustainable Development Goals (SDGs) of the United Nations to ensure sustainable water and land resource use, food and water security, biodiversity conservation, poverty alleviation, and climate change mitigation (https://sdgs.un.org/goals).
Location: T E 15 New Biology building
Literature cited 1: Clarkson, B. R., A. G. E. Ausseil and P. Gerbeaux: Wetland ecosystem services. Ecosystem services in New Zealand: conditions and trends. Manaaki Whenua Press, Lincoln. 1, 192-202 (2013). Costanza, R., R. d'Arge, R. De Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R.V. O'neill, J. Paruelo and R.G. Raskin: The value of the world's ecosystem services and natural capital. Nature, 387, 253-260 (1997).
Literature cited 2: De Groot, R., L. Brander, S. Van Der Ploeg, R. Costanza, F. Bernard, L. Braat, M. Christie, N. Crossman, A. Ghermandi, L. Hein and S. Hussain: Global estimates of the value of ecosystems and their services in monetary units. Ecosyst. Serv.,1, 50-61 (2012). MEA: Ecosystems and human well-being: A framework for assessment. Island Press: Washington, USA. pp. 1–266 (2005).

ID: 66311
Title: Geoinformatics‑based prioritisation of natural resources rich regions at disaggregated levels for sustainable management
Author: T. V. Ramachandra, Paras Negi
Editor: T.V. Ramachandra
Year: 2025
Publisher: Discover
Source: ENVIS, CES & EWRG, CES
Reference: Discover Sustainability (2025) 6:195 Pg No. 1-27
Subject: Geoinformatics‑based prioritisation of natural resources rich regions at disaggregated levels for sustainable management
Keywords: LULC change · Supervised learning · Machine learning · Random Forest · CA-Markov · Natural Resource Rich Regions (NRRRs)
Abstract: Natural Resource Rich Regions (NRRRs) are ecologically and economically vital regions that support the livelihood of people through the sustained ecosystem process involving interaction among biotic and abiotic elements. Identifying NRRRs, considering spatially ecological, geo-climatic, biological, and social dimensions, would help in conservation planning and prudent management of natural resources as per the Biodiversity Act 2002, Government of India. Changes in the landscape structure would lead to alterations in the composition and health of these regions with irreversible changes in the ecosystem process, impacting the sustenance of natural resources. Landscape dynamics is assessed by classifying temporal remote sensing data using the supervised machine learning (ML) technique based on the Random Forest (RF) algorithm. Additionally, predicting likely land use changes in ecologically fragile areas would help formulate appropriate location-speciic mitigation measures. Modeling likely land uses through the simulation of long-term spatial variations of complex patterns has been done through the CA–Markov model. Prioritization of NRRRs at disaggregated levels highlights that 12% of the total geographical area of the district is under NRRR 1 and NRRR 2, 54% of the total geographical area under NRRR 3, and the rest of the region under NRRR 4. The current study emphasizes the need for robust decision support systems to aid in efective policy formulation for conserving and restoring natural resources. Clinical trial number: Not applicable
Location: T E 15 New Biology building
Literature cited 1: Forman RT. Some general principles of landscape and regional ecology. Landsc Ecol. 1995;10(3):133–42. https://doi.org/10.1007/BF001 33027. Ramachandra TV, Setturu B, Bhatta V. Landscape ecological modeling to identify ecologically significant regions in Tumkur district, Karnataka. Phys Sci Rev. 2022. https://doi.org/10.1515/psr-2022-0154.
Literature cited 2: Matlhodi B, Kenabatho PK, Parida BP, Maphanyane JG. Evaluating land use and land cover change in the Gaborone dam catchment, Botswana, from 1984–2015 using GIS and remote sensing. Sustainability. 2019;11(19):5174. https://doi.org/10.3390/su11195174. Spruce J, Bolten J, Mohammed IN, Srinivasan R, Lakshmi V. Mapping land use land cover change in the Lower Mekong Basin from 1997 to 2010. Front Environ Sci. 2020;8:21. https://doi.org/10.3390/rs10121910.

ID: 66310
Title: Biomonitoring of urban lakes through microalgae
Author: Asulabha K.S. , Sincy V. , Jaishanker R. and Ramachandra T.V.
Editor: T.V. Ramachandra
Year: 2025
Publisher: EM International
Source: ENVIS, CES & EWRG, CES
Reference: Eco. Env. & Cons. 31 (1) : 2025; pp. (9-18)
Subject: Biomonitoring of urban lakes through microalgae
Keywords: Lake, Microalgae, Water quality, Pollution, CCA
Abstract: Biomonitoring entails monitoring the quality of an ecosystem through representative biota, which responds to environmental changes through alterations in morphological, physiological, biochemical, molecular, and genetic traits. The sustained inflow of untreated wastewater due to point and non-point sources has been putting significant strain on aquatic ecosystems, leading to a decline in aquatic biodiversity, the loss of vital habitats for sensitive biota, and consequent erosion in ecosystem services. Microalgae constitute the primary producers in aquatic ecosystems and serve as pollution indicators as they respond to changes in water quality. This necessitates an understanding of microalgae dynamics in relation to environmental factors for prudent management of aquatic resources. The study examines microalgal composition and water quality in Sankey and Mathikere lakes, Bangalore, revealing pollution from untreated wastewater in Mathikere Lake, which exhibits high physicochemical parameters. The microalgae composition in both lakes varied in response to the water quality and across seasons. Multivariate analyses through nonparametric canonical correspondence analysis (CCA) demonstrate linkages between microalgal composition and water quality parameters in both lakes. Nutrient enrichment leading to eutrophic conditions with the profuse growth of invasive exotic macrophytes has declined microalgal diversity, suggesting immediate interventions to mitigate pollutants to improve the chemical integrity of waterbodies.
Location: T E 15 New Biology building
Literature cited 1: Algae Base. 2021. Available at: https:// www.algaebase.org/ Amaro, H.M., Sousa, J.F., Salgado, E.M., Pires, J.C. and Nunes, O.C. 2023. Microalgal systems, a green solution for wastewater conventional pollutants removal, disinfection, and reduction of antibiotic resistance genes prevalence? Applied Sciences. 13(7): 4266.
Literature cited 2: APHA, 2012. Standard Methods for the Examination of Water and Wastewater, 22nd ed. American Public Health Association/American Water Works Association/Water Environment Federation: Washington, DC, USA Asulabha, K.S., Jaishanker, R., Sincy, V. and Ramachandra, T.V. 2022. Diversity of phytoplankton in lakes of Bangalore, Karnataka, India, p. 147-178. In: Shashikanth Majige (Edited), Biodiversity Challenges: A way forward, Daya Publishing House, New Delhi, India.

ID: 66309
Title: Biomonitoring of urban lakes through microalgae
Author: Asulabha K.S. , Sincy V. , Jaishanker R and Ramachandra T.V
Editor: T.V. Ramachandra
Year: 2025
Publisher: EM International
Source: ENVIS, CES & EWRG, CES
Reference: Eco. Env. & Cons. 31 (1) : 2025; pp. (286-295)
Subject: Biomonitoring of urban lakes through microalgae
Keywords: Lake, Microalgae, Water quality, Pollution, CCA
Abstract: Biomonitoring entails monitoring the quality of an ecosystem through representative biota, which responds to environmental changes through alterations in morphological, physiological, biochemical, molecular, and genetic traits. The sustained inflow of untreated wastewater due to point and non-point sources has been putting significant strain on aquatic ecosystems, leading to a decline in aquatic biodiversity, the loss of vital habitats for sensitive biota, and consequent erosion in ecosystem services. Microalgae constitute the primary producers in aquatic ecosystems and serve as pollution indicators as they respond to changes in water quality. This necessitates an understanding of microalgae dynamics in relation to environmental factors for prudent management of aquatic resources. The study examines microalgal composition and water quality in Sankey and Mathikere lakes, Bangalore, revealing pollution from untreated wastewater in Mathikere Lake, which exhibits high physicochemical parameters. The microalgae composition in both lakes varied in response to the water quality and across seasons. Multivariate analyses through nonparametric canonical correspondence analysis (CCA) demonstrate linkages between microalgal composition and water quality parameters in both lakes. Nutrient enrichment leading to eutrophic conditions with the profuse growth of invasive exotic macrophytes has declined microalgal diversity, suggesting immediate interventions to mitigate pollutants to improve the chemical integrity of waterbodies.
Location: T E 15 New Biology building
Literature cited 1: Algae Base. 2021. Available at: https:// www.algaebase.org/ Amaro, H.M., Sousa, J.F., Salgado, E.M., Pires, J.C. and Nunes, O.C. 2023. Microalgal systems, a green solution for wastewater conventional pollutants removal, disinfection, and reduction of antibiotic resistance genes prevalence? Applied Sciences. 13(7): 4266.
Literature cited 2: APHA, 2012. Standard Methods for the Examination of Water and Wastewater, 22nd ed. American Public Health Association/American Water Works Association/ Water Environment Federation: Washington, DC, USA Asulabha, K.S., Jaishanker, R., Sincy, V. and Ramachandra, T.V. 2022. Diversity of phytoplankton in lakes of Bangalore, Karnataka, India, p. 147-178. In: Shashikanth Majige (Edited), Biodiversity Challenges: A Way Forward, Daya Publishing House, New Delhi, India.

ID: 66308
Title: Assessmentof Climate Trends and Carbon Sequestrationin a Forest Ecosystemthrough lnVEST
Author: T.V. Ramachandra, Tulika Mondal, Paras Negi And Bharath Setturu
Editor: T.V. Ramachandra
Year: 2024
Publisher: None
Source: ENVIS, CES & EWRG, CES
Reference: Productivity Vol. 64, No. 4, January - March, 2024 pG nO. 1-13
Subject: Assessmentof Climate Trends and Carbon Sequestrationin a Forest Ecosystemthrough lnVEST
Keywords: None
Abstract: Carbon sequestration constitutes a vital ecological function executed by ecosystems to mitigate global warming due to the burgeoning sustained anthropogenic activities which release greenhouse gases (carbon dioxide, methane, carbon monoxide, etc.). The current study evaluates carbon dynamics in forest ecosystems through the lnVest model with temporal land use analyses using remote sensing data from 1973 to 2021 in the Chikamagaluru district of Kamataka. Land use dynamics were assessed using temporal remote sensing data through a machine• /earning-basedsupervised Random Forest algorithm, which shows a decline In forest cover of 28.98%, an increase in agricultural area by 5.31%, and horticulture by 42.52% in the last five decades, which has led to the depletion of carbon stock by 30683.81 Gg. Land use changes have a long-term effect on climatic variables, leading to changes in local temperature, annual rainfall, and number of rainy days in the study area.
Location: T E 15 New Biology building
Literature cited 1: Babbar, D., Areendran, G., Sahana, M., Sarma, K., Raj, K., & Sivadas, A. (2021). Assessment and prediction of carbon sequestration using Markov chain and InVEST model in Sariska Tiger Reserve, India. Journal of Cleaner Production, Volume 278, 123333. Bharath, S., Rajan, K. S., & Ramachandra, T. V. (2014). Status and future transition of rapid urbanizing landscape in central Westem Ghats - CA-based approach. ISPRS Annals of Photogrammetry, Remote Sensing• & Spatial Information Sciences, 2(8), 69-75.
Literature cited 2: Bonan, G. B. (2008). Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320(5882), 1444-1449. Department of Civll and Environmental Engineering, Princeton University. (2006). Global Meteorological Forcing Dataset for Land Surface Modeling. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/JV89-AH11

ID: 66307
Title: Bangalore Lakes Information System (BLIS) for Sustainable Management of Lakes
Author: T. V. Ramachandra , K. S. Asulabha , V. Sincy , Abhishek Baghel and S Vinay
Editor: T.V. Ramachandra
Year: 2025
Publisher: Springer
Source: ENVIS, CES & EWRG, CES
Reference: Journal of Indian Institute of Science , 2024 Pg No.1-20
Subject: Bangalore Lakes Information System (BLIS) for Sustainable Management of Lakes
Keywords: BLIS, Lakes, Urbanization, Biodiversity, Restoration, Ecosystem services
Abstract: Wetlands (lakes, tanks, ponds, etc.), transitional lands linking hydrologically the terrestrial ecosystem with aquatic ecosystems with biophysical interactions, are the most productive and diverse ecosystems and provide numerous ecological, economic, and social beneits for human well-being. These vital ecosystems sustain ecological processes to provide services such as nutrient cycling, water puriication, reducing pollution, carbon sequestration, groundwater recharge, provision of ish, fodder, fuel, and water, lood reduction, erosion control, aquatic biota habitats, education opportunities, aesthetics, and recreation. However, due to globalization, these fragile ecosystems are vulnerable to unplanned developmental activities and rapid urbanization, leading to large-scale land cover changes and hydrologic regimes. The sustained inlow of untreated wastewater (from the industrial and domestic sectors) into wetlands has altered the chemical integrity, which necessitates inventorying, mapping, and regular wetland monitoring to evolve conservation strategies. Integrating spatial and non-spatial data, analysis, and visualization with decision models through decision support systems enables informed decisions. In this context, the Bangalore Lake Information System (BLIS) is designed with information on water quality, biodiversity (microalgae, zooplankton, ichthyofauna, macrophytes, and birds), threats (encroachments, inlow of untreated sewage, etc.) and ecosystem services of lakes in Bangalore, Karnataka State, India. Rapid large-scale land use changes have resulted in an alteration in the hydrologic regime, the loss of habitats, and the disappearance of native species. BLIS empowers decision-making through knowledge of lake distribution in terms of the physical, chemical, and biological aspects and the value of ecosystem services, which is crucial for evolving strategies for prudent management of water bodies in Greater Bangalore.
Location: T E 15 New Biology building
Literature cited 1: Ramachandra TV, Sincy V, Asulabha KS (2021) Accounting of ecosystem services of wetlands in Karnataka State, India. JRED 18(1–2):1–26 Ramachandra TV, Sudarshan PB, Mahesh MK, Vinay S (2018) Spatial patterns of heavy metal accumulation in sediments and macrophytes of Bellandur wetland, Bangalore. J Environ Manag 206:1204–1210. https://doi.org/ 10.1016/j.jenvman.2017.10.014
Literature cited 2: Näschen K, Diekkrüger B, Evers M, Höllermann B, Steinbach S, Thonfeld F (2019) The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios. Sustainability 11(24):7083. https://doi.org/10. 3390/su11247083 Bera T, Kumar V, Sarkar DJ, Devi MS, Behera BK, Das BK (2022) Pollution assessment and mapping of potentially toxic elements (PTE) distribution in urban wastewater fed natural wetland, Kolkata, India. Environ Sci Pollut Res 29:67801–67820. https://doi.org/10.1007/ s11356-022-20573-8

ID: 66306
Title: Trophic composition linkages with the environmnetal quality of Urban Lakes
Author: Sincy V. Asulabha KS, Jaishanker R and Ramachandra T V
Editor: T.V. Ramachandra
Year: 2024
Publisher: Kalpana Corporation
Source: ENVIS, CES & EWRG, CES
Reference: Indian Journal of Environmental Protection vol.44 (12) Dec 2024 Pg No.1-8 (2024)
Subject: Trophic composition linkages with the environmnetal quality of Urban Lakes
Keywords: Diversity Index, Fish Lake,Primary Producers, Primary Consumers
Abstract: Freshwater ecosystems are one of the world's richest sources of biological diversity. Environmental quality and biodiversity in freshwater ecosystems are interrelated, as the interaction helps to perform diverse functions, which are valuable and essential for the sustainability of biotic communities. However, freshwater ecosystems in urban landscapes are undergoing stress due to overexploitation of biotic species, introduction of exotic species, sustained inflow of point sources of pollution and alterations in the ecological niche. Conservation of fragile eco• systems in urban landscapes to sustain native biodiversity requires comprehensive studies through regular moni• toring to understand variations in abiotic (physico-chemical) characteristics with composition of biotic elements at trophic levels. The current study investigates the trophic composition and water quality status in freshwater lakes in Bangalore. The study reveals that the diversity of fish, zooplankton and microalgae varied with the physico• chemical characteristics of lakes. The water quality results showed that Hebbal lake was more polluted than Nagavara lake. Multivariate analyses reveal that the density of primary consumers, ionic parameters and dissolved oxygen are the main factors influencing the fish population. These findings provide insights for adopting sustain• able management approachesthrough a deeper comprehension of the dynamics of a lake ecosystem, which strength• ens the conservation of fragile ecosystems.
Location: T E 15 New Biology building
Literature cited 1: Ramachandra, T.V., V. Sincy and K.S. Asulabha. 2021 a. Accounting of ecosystem services of wet• lands in Karnataka state, India. TIDEE (Teri lnfor. Digest Energy Env.). 18(1-2): 1-26. Ramachandra, T.V., V. Sincy and K.S. Asulabha. 2020. Efficacy of rejuvenation of lakes in Bengaluru, India. Green Chem. Tech. Letters. 6( 1): 14-26.
Literature cited 2: Shuvo, A., et al. 2021. Total phosphorus and cli• mate are equally important predictors of water qual• ity in lakes. Aquat. Sci., 83( 1): 1 6. Singh, N. and S.K. Patidar. 2020. Status of water quality in various ponds and lakes in India. In Ad• vances in renewable energy and sustainable envi• ronment. Ed L. Dewan, R.C. Bansal and U.K. Kalla. Springer, Singapore. pp 417-425.