ID: 65928
Title: Emerging human–otter conflicts in the wetlands and rivers of Kerala, India
Author: K. K. Jayasooryan and P. K. Chandini
Editor: S.K.Satheesh
Year: 2024
Publisher: Current Science Association and Indian Academy of Sciences.
Source: ENVIS, CES & EWRG, CES
Reference: Current Science Vol. 127 (1) 13-13 10 July (2024)
Subject: Emerging human–otter conflicts in the wetlands and rivers of Kerala, India
Keywords: None
Abstract: A congregation of local daily wage earners,
house wives, fishermen and children was
held at the small town of Mukkam in Kozhikode district of Kerala on 2 October 2023.
The meeting was organized by the local
conservationist group to address the issue
related to the recently emerging human–
otter conflict in the Iruvazhinji river. Most
of the people who had gathered were victims of otter bites and a few fishermen
who lost fishing nets due to otter attacks.
Location: T E 15 New Biology building
Literature cited 1: Belanger, M. et al., IUCN Otter Spec. Group
Bull., 2011, 28(1), 11–16.
Anoop, K. R. and Hussain, S. A., J. Zool.,
2005, 266(1), 15–23.
Literature cited 2: Jayasooryan, K. K. and Sathian, A., Proceedings of the National Conference on Biodiversity Conservation, Kerala State Biodiversity
Board, Thiruvananthapuram, 2023.
Viswanathan, C. et al., Estuar. Coast. Shelf
Sci., 2020, 242 (online)
ID: 65927
Title: United Nations Climate Change Conference COP 28 and beyond
Author: S. K. Satheesh
Editor: S.K.Satheesh
Year: 2024
Publisher: Current Science Association and Indian Academy of Sciences.
Source: ENVIS, CES & EWRG, CES
Reference: Current Science Vol. 127 (1) 7-8 10 July (2024)
Subject: United Nations Climate Change Conference COP 28 and beyond
Keywords: None
Abstract: The United Nations Climate Change Conference COP 28
was held and concluded in Dubai. It is particularly important
as the first ‘global stocktake’, a process for countries and
stakeholders to examine the progress towards meeting the
goals of the Paris Agreement. The conference outcome
was a revelation of the slow progress in most areas of climate
action. Consequently, nations collectively made a pronouncement on the way forward to accelerate climate action. As
usual, transitioning from fossil fuels to renewable energy
sources was one of the focus areas.
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65926
Title: Lakes of Bangalore ( protect lakes to syustain water for your children bring water back to the city through lakes)
Author: T.V. Ramachandra , Asulabha K.S. , Sincy V. , Bhuwan Chandra Arya
Editor: None
Year: 2024
Publisher: None
Source: ENVIS, CES & EWRG, CES
Reference:
Subject: Lakes of Bangalore ( protect lakes to syustain water for your children bring water back to the city through lakes)
Keywords: None
Abstract: Lakes of Bangalore (protect lakes to sustain water for your children bring water back to the city through lakes) (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65925
Title: Western Ghats Spatial Decision Support System
Author: T V Ramachandra, Bharath Setturu , Vinay S, M.D.Subhashchandran , Bharath Aithal, Abhishek Baghel
Editor: None
Year: 2024
Publisher: None
Source: ENVIS, CES & EWRG, CES
Reference:
Subject: Western Ghats Spatial Decision Support System
Keywords: None
Abstract: Western Ghats Spatial Decision Support System (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65924
Title: Sahyadri ( visua,lise eco-sensitive villages in Wetern Ghats)
Author: T V Ramachandra, Bharath Setturu , Vinay S, M.D.Subhashchandran , Bharath Aithal, Abhishek Baghel
Editor: None
Year: 2024
Publisher: None
Source: ENVIS, CES & EWRG, CES
Reference:
Subject: Sahyadri ( visua,lise eco-sensitive villages in Wetern Ghats
Keywords: None
Abstract: Sahyadri ( visua,lise eco-sensitive villages in Wetern Ghats (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65923
Title: Karnataka: Natural capital accounting & valuation of ecosystem services
Author: T V Ramachandra, Bharath Setturu , Vinay S, , Bharath H. Aithal, Abhishek Baghel
Editor: None
Year: 2024
Publisher: None
Source: ENVIS, CES & EWRG, CES
Reference:
Subject: Karnataka: Natural capital accounting & valuation of ecosystem services
Keywords: None
Abstract: Karnataka: Natural capital accounting & valuation of ecosystem services (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65922
Title: Bangalore Urban Information System ( visualize the landscape dynamics of Bangalore city and Bangalore Urban district with other data set like administrative,environment, lakes and many more)
Author: T V Ramachandra, Bharath Aithal, Vinay S, Bharath Setturu ,Tulika Mondal, Abhishek Baghel
Editor: None
Year: 2024
Publisher: None
Source: None
Reference:
Subject: Bangalore Urban Information System ( visualize the landscape dynamics of Bangalore city and Bangalore Urban district with other data set like administrative,environment, lakes and many more
Keywords: None
Abstract: Bangalore Urban Information System ( visualize the landscape dynamics of Bangalore city and Bangalore Urban district with other data set like administrative, environment, lakes and many more (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65921
Title: Bangalore Lakes Information System
Author: T V Ramachandra, Asulabha KS, Sincy Varhese, Abhishek Baghel
Editor: None
Year: 2024
Publisher: None
Source: None
Reference:
Subject: Bangalore Lakes Information System
Keywords: None
Abstract: Bangalore Lakes Information System (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65920
Title: Bangalore Urban Information System
Author: T V Ramachandra, Bharath Aithal, Vinay S, Bharath Setturu ,Tulika Mondal, Abhishek Baghel
Editor: None
Year: 2024
Publisher: None
Source: ENVIS, CES & EWRG, CES
Reference:
Subject: Bangalore Urban Information System
Keywords: None
Abstract: Bangalore Urban Information System (Poster)
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65919
Title: Ecologically Sensitive Regions in the Western Ghats, a Biodiversity Hotspot
Author: T V Ramachandra Bharath Setturu Vinay S M D Subash Chandran Bharath H Aithal
Editor: T.V. Ramachandra
Year: 2023
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Ecologically Sensitive Regions in the Western Ghats, a Biodiversity Hotspot Pg: 1-541, ETR: 200, SCS: 128 15 Aug 2023
Subject: Ecologically Sensitive Regions in the Western Ghats, a Biodiversity Hotspot
Keywords: Ecological fragility, spatial decision support system (SDSS), endemic taxa, energy. Ecology, bio-geo-climatic aspects
Abstract: Ecological sensitivity or fragility refers to the permanent and irreparable loss of extant life
forms or significant damage to the natural processes of evolution and speciation with the
alterations in the ecological integrity of a region. The comprehensive knowledge of the
ecological fragility of a region is quintessential for evolving strategies for conserving the area,
which entails identifying factors responsible for ecological sensitiveness, including landscape
dynamics, and visualizing future transitions to mitigate the problems of haphazard and
uncontrolled development approaches. The region witnessed large-scale land cover changes
during the past century due to unplanned developmental activities involving industrialization.
Globalisation and relaxing market norms led to rapid urbanisation with large-scale land cover
changes. This necessitates implementing mitigation measures involving stakeholders to
address the impacts through location-specific conservation measures. Framing conservation
and sustainable developmental policies entail delineating ecologically sensitive regions by
integrating bio-geo-climatic, ecological, and social factors representing the dynamics of socioecological
systems,
impacts,
and
drivers.
Location: T E 15 New Biology building
Literature cited 1: Aggarwal, A., Paul, V. and Das, S., 2009. Forest resources: Degradation, livelihoods, and
climate change. Looking back to change track, 219, pp.91-108.
Almeida, D.R., Stark, S.C., Schietti, J., Camargo, J.L., Amazonas, N.T., Gorgens, E.B., Rosa,
D.M., Smith, M.N., Valbuena, R., Saleska, S. and Andrade, A., 2019. Persistent effects of
fragmentation on tropical rainforest canopy structure after 20 yr of isolation. Ecological
Applications, 29(6), p.e01952.
Literature cited 2: Aldieri, L., Carlucci, F., Vinci, C.P. and Yigitcanlar, T., 2019. Environmental innovation,
knowledge spillovers and policy implications: A systematic review of the economic effects
literature. Journal of Cleaner Production, 239, p.118051.
Andronache, I., Marin, M., Fischer, R., Ahammer, H., Radulovic, M., Ciobotaru, A.M., Jelinek,
H.F., Di Ieva, A., Pintilii, R.D., Drăghici, C.C. and Herman, G.V., 2019. Dynamics of forest
fragmentation and connectivity using particle and fractal analysis. Scientific reports, 9(1),
pp.1-9.
ID: 65918
Title: Grid-based monitoring of natural resources at disaggregated levels in Raichur 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: Grid-based monitoring of natural resources at disaggregated levels in Raichur district, Karnataka Pg : 1-50 ETR: 201, SCS: 129
Subject: Grid-based monitoring of natural resources at disaggregated levels in Raichur district, Karnataka
Keywords: Natural Resource Rich Regions; Land use Land cover; Random Forest; Modeling; Sustainable Development; Supervised classification; Machine Learning.
Abstract: 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, bio-geo-climatic,
and environmental variables would aid in the prioritization of natural resources-rich regions
(NRRRs). The current study assesses the LULC change in an agrarian district using temporal
remote sensing data through a supervised machine learning technique- Random Forest (RF). The
study reveals that built-up area had increased from 0.23% (1973) to 1.04% (2022), agricultural
area had increased from 84.55% (1973) to 93.43% (2022), water body had increased from 0.38%
(1973) to 0.92% (2022). There has been an increase in paddy cultivation spatial extent from 0.74%
(1973) to 18.41% in the region with the increase in the extent of water bodies due to the Krishna
and Tungabhadra River. The condition of forests assessed through fragmentation metrics indicates
the decline of intact forest cover from 4.19% (in 1973) to 3.08% (in 2022), and non-forest area
accounted for 95.87% in 2022 from 92.03 % in 1973. The likely land uses in 2022, 2030, and 2038
are predicted using Cellular Automata. The simulated LU for 2038 shows the likely increase in
built-up by 671 km
2
with a decline of agriculture land by 1159.33 km
2
and an expansion of the
road network and industrial area. This necessitates the identification of natural resources rich
regions (NRRRs) for formulating effective policies for prudent management of natural resources
to achieve sustainable development goals (SDG) by exploring all feasible dimensions and
analyzing the patterns and dynamics across various interdisciplinary themes such as social,
hydrological, ecological and bio-geo-climatic.
Location: T E 15 New Biology building
Literature cited 1: Barros, R. S., Barreto, M. R., & Falcão, A. X. (2020). Accurate classification of Brazilian Cerrado
vegetation using machine learning classifiers. Environmental Monitoring and Assessment, 192(3), 166.
Belgiu, M., Drǎguţ, L., & Strobl, J. (2014). Quantitative evaluation of variations in rule-based
classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of
Photogrammetry and Remote Sensing, 87, 205-215.
Literature cited 2: Bharath, S., Rajan, K. S., & Ramachandra, T. V. (2013). Land surface temperature responses to land
use land cover dynamics. Geoinfor Geostat: An Overview, 54, 50-78.
Bharath, S., Rajan, K. S., & Ramachandra, T. V. (2014). Status and future transition of rapid urbanizing
landscape in central Western Ghats-CA based approach. ISPRS Annals of the Photogrammetry, Remote
Sensing and Spatial Information Sciences, 2(8), 69.
ID: 65917
Title: Grid Based Mapping of Natural Resource-Rich Regions in Bidar district, Karnataka
Author: T V Ramachandra Paras Negi
Editor: T.V. Ramachandra
Year: 2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Grid Based Mapping of Natural Resource-Rich Regions in Bidar district, Karnataka Pg 1-46 , ETR: 202, SCS: 130
Subject: Grid Based Mapping of Natural Resource-Rich Regions in Bidar district, Karnataka
Keywords: LULC change, supervised learning, Machine learning, Random Forest, Natural Resource-Rich Regions, CA-Markov.
Abstract: Natural Resource Rich Regions (NRRRs) are ecologically and economically vital regions that
support the livelihood of people through the sustained ecosystem process involving the interaction
among biotic and abiotic elements. Identifying NRRRs, considering spatially ecological, geoclimatic,
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.
The anthropogenic activities have been the major driver for landscape dynamics with the largescale
land
use
and
land
cover
(LULC)
changes.
Unplanned
and
uncontrolled
exploitation
of
natural
resources
due
to
industrial
developmental
activities
has
escalated
rates
of
LULC
changes
that
led
to
ecosystem degradation. Spatio-temporal LULC change information provides insights into
affecting factors and their impacts on the landscape. Bidar district has witnessed drastic growth in
rural built-up areas and the expansions of the National Investment & Manufacturing Zone [NIMZ],
in response to the State’s industrial policy of 2014-19 for industrial development. Supervised
machine learning technique - Random Forest (RF) was used to assess land use dynamics. Random
forest is an ensemble of decision trees maintaining multi-variance and minimizes the correlation
among decision trees, in addition, it is less sensitive to noise and reduction of training. Modeling
of likely land use aided in the identification of ecologically fragile areas. CA-Markov model is a
dynamic model for predicting LULC changes and can simulate long-term predictions of spatial
variation of complex patterns. The current study suggests that there is a need to establish robust
systems to frame effective policy and make interventions for the conservation and restoration of
natural resources.
Location: T E 15 New Biology building
Literature cited 1: Abijith, D., & Saravanan, S. (2021). Assessment of land use and land cover change detection and
prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu,
India. Environmental Science and Pollution Research, 1-13.
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.
Literature cited 2: Adhikari, S., & Southworth, J. (2012). Simulating forest cover changes of Bannerghatta National Park
based on a CA-Markov model: a remote sensing approach. Remote Sensing, 4(10), 3215-3243.
Ahmad, F., Goparaju, L., & Qayum, A. (2017). LULC analysis of urban spaces using Markov chain
predictive model at Ranchi in India. Spatial Information Research, 25(3), 351-359.
ID: 65916
Title: Grid-based Natural Environment Mapping to delineate Eco Sensitive Zones (ESZ) in Chikamagaluru district, Karnataka
Author: T V Ramachandra Tulika Mondal Bharath Setturu
Editor: T.V. Ramachandra
Year: 2023
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Grid-based Natural Environment Mapping to delineate Eco Sensitive Zones (ESZ) in Chikamagaluru district, Karnataka Pgno1-61 ETR: 203 , SCS: 131
Subject: Grid-based Natural Environment Mapping to delineate Eco Sensitive Zones (ESZ) in Chikamagaluru district, Karnataka
Keywords: Land use Land cover (LULC), Forest Fragmentation, Carbon Assessment, CA-Markov, Random Forest, Machine Learning, Eco sensitive Zones (ESZs)
Abstract: Eco sensitive zones (ESZ) or Livelihood Lifeline Regions (LLR) are the vulnerable areas with high
sensitivity and fragility based on the environmental aspect, where unplanned large-scale developmental
activities can 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 are required to
delineate ESZs which will aid in conservation of ecological balance in the environment for planning
interventions to ensure sustainable development. This study is focused on understanding the spatial
extent of ESZs with the help of landscape dynamics from 1973 to 2021 by implementing Machine
learning (ML) algorithms in Chikamagaluru district or the Coffee land of Karnataka, India. Temporal
land use land cover (LULC) analyses were carried out using Landsat series data from 1973 to 2021
through Random Forest classifier (supervised machine learning algorithm). The Land cover analysis
through NDVI showed decline in vegetation cover by 2.77% due to unplanned developmental activities
along with enhanced agriculture and horticultural activities. The land use analysis showed a decline in
spatial extent of forest cover of 32.77% in expense of agriculture (6.13%) and horticulture (43.14%) in
last five decades. Rapid urbanisation and infrastructural developments have also led to increase in builtup
areas
by
151.33 sq.
km.
Analysis
of
the
condition
of
forests
through
fragmentation
metrics
showed
that
spatial extent of interior forest has decreased from 55.72% to 28.05% in last five decades. As a
result, 3068.79 Gg of Carbon has been lost which was evident from carbon assessment of the forest
ecosystem through InVEST model. Simulation of the land use in business-as-usual scenario has also
been carried out to understand the impact of the current rate of land use transitions in the next two
decades, with the help of Markov and Cellular Automata techniques; which showed that the area under
agriculture and horticulture is likely to increase to 34.26% and 31.96%, respectively in 2038 and forest
would continue to decrease (2038). The weights of all bio-geo-climate-ecological-social variables were
aggregated and categorised Chikamagaluru district into 4 zones of ecological fragility where 309 and
410 villages are under ESZ1 and ESZ2 category (highest ecological fragility). The prioritization of
ecological sensitive zones or LLRs at disaggregated levels in Chikamagaluru district will aid in planning
for sustainable development with conservation of the fragile ecosystems to sustain livelihood of people
with the sustenance of natural resources and minimal disasters (floods, land slides, mud slides).
Location: T E 15 New Biology building
Literature cited 1: Anandhi, A., Douglas-Mankin, K. R., Srivastava, P., Aiken, R. M., Senay, G., Leung, L. R., &
Chaubey, I. (2020). DPSIR-ESA vulnerability assessment (DEVA) framework: synthesis,
foundational overview, and expert case studies. Transactions of the ASABE, 63(3), 741-752. 10. 2
https://doi.org/10.13031/trans.13516
Attri, P., Chaudhry, S., & Sharma, S. (2015). Remote sensing & GIS based approaches for LULC
change detection–a review. Int. J. Curr. Eng. Technol, 5, 3126-3137.
Literature cited 2: Batar, A. K., Watanabe, T., & Kumar, A. (2017). Assessment of land-use/land-cover change and
forest fragmentation in the Garhwal Himalayan Region of India. Environments, 4(2), 34.
https://doi.org/10.3390/environments4020034
Beinat E (1997). Value functions for environmental management. Kluwer Academic, Boston, p 241
ID: 65915
Title: IISc_EIACP: Environmental Information, Awareness, Capacity Building and Livelihood Programme (EIACP)
Author: T V Ramachandra
Editor: T.V. Ramachandra
Year: 2023-2024
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: IISc_EIACP: Environmental Information, Awareness, Capacity Building and Livelihood Programme (EIACP) 2023-24 Pg 1-300
Subject: ISc_EIACP: Environmental Information, Awareness, Capacity Building and Livelihood Programme (EIACP)
Keywords: None
Abstract: ENVIS_IISC [RP] has conducted the series of webinars under MISSION LIFE and Ek Bharat
Shreshtha Bharat scheme. The webinar details were shared across the various social media
platforms such as Facebook, Twitter, and WhatsApp. The webinars received large participation
from students, reserachers, faculties and others across the globe. The webinars are archived
under YouTube platform for the benefit of global community. The webinars were focused on
the topics such as “Mangroves, Climate Change, Integrated Costal Management, Diatoms,
Geospatial technology, Great lake ecosystem”.
Location: T E 15 New Biology building
Literature cited 1:
Literature cited 2:
ID: 65914
Title: Inventorying, Mapping, and Monitoring of Natural Resources at Grid levels using Temporal Remote Sensing Data in Bellary district, Karnataka
Author: T V Ramachandra Paras Negi Bharath Setturu
Editor: T.V. Ramachandra
Year: 2023
Publisher: Energy &Wetlands Research Group
Source: ENVIS, CES & EWRG, CES
Reference: Inventorying, Mapping, and Monitoring of Natural Resources at Grid levels using Temporal Remote Sensing Data in Bellary district, Karnataka Pg No: 1-56 ETR: 199 SCS: 199 15 Nov 2023
Subject: Inventorying, Mapping, and Monitoring of Natural Resources at Grid levels using Temporal Remote Sensing Data in Bellary district, Karnataka
Keywords: LULC, Forest Fragmentation, CA-Markov, Random Forest (RF), Natural Resource Rich Regions (NRRRs).
Abstract: Temporal land use and land cover (LULC) information of a landscape provide comprehensive knowledge
about the factors affecting the landscape dynamic and their impacts on the ecosystem (biotic and abiotic
elements). Accelerating anthropogenic activities leads to the over-exploitation of natural resources and
changes in the climate regime. Bellary district in the southern Indian state of Karnataka is known for its
rich mineral resources and diverse landscapes. The region has witnessed significant land use changes,
primarily due to urbanization, industrialization, and mining activities. These changes have adverse effects
on the environment, including loss of biodiversity, soil degradation, and water pollution. The study
highlights the significance of using supervised machine learning techniques for the classification of land
use namely random forest (RF), support vector machine (SVM), and parametric maximum likelihood
classifier (MLC). The performance of these algorithms was evaluated through accuracy assessments.
Results reveal that RF has the highest overall accuracy (88.94%) and Kappa value (0.76) compared to
overall accuracy, and Kappa of MLC (85.51%, 0.74) and SVM (85.47%, 0.63). Based on this, RF was
considered for temporal data analyses, which highlight the decline of forest cover from 2.61% (1973) to
0.74% (2022). The built-up has increased from 0.27% (1973) to 2.43% (2022), and agriculture from 68.21%
(1973) to 84.95% (2022). Fragmentation of contiguous forests is evident from the decline in the interior or
intact forests from 6.73% (1973) to 2.41% (2022) and the increase in the non-forest areas such as built-up,
agriculture, etc. amounting now to 89.81%. The current study highlights the importance of sustainable land
use management practices to protect the environment and the need for efficient tools such as supervised
machine learning techniques for land use classification and analysis. Simulation and prediction have been
performed through the hybrid CA-Markov method for 2030 and 2038. The predicted results show that the
built-up area will increase considering the current rate of land use change. NRRRs are prioritized in the
study region based on the biological, ecological, geo-climatic, hydrological, and social aspects. A
weightage matrix has been used for the classification of NRRRs into four regions NRRR 1 and NRRR 2
(highly sensitive and any alterations are not permitted), NRRR 3 (moderate sensitive and developmental
activities are allowed with stringent environmental norms), and NRRR 4 (less sensitive and suitable for the
development activities) based on conservation aspects. Prioritization of NRRRs helps in a systematic
framework for the sustainability of natural resources with the appropriate conservation strategies through the involvement of decision-makers and local stakeholders.
Location: T E 15 New Biology building
Literature cited 1: Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms
in a boreal landscape using Sentinel-2 data. GIScience and Remote Sensing, 57(1), 1-20.
Adam, E., Mutanga, O., Odindi, J., and 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), 34403458.
Literature cited 2: 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).