Browsing by Author "Milap Punia"

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Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics
(Elsevier B.V., 2021) Aman Arora; Manish Pandey; Varun Narayan Mishra; Ritesh Kumar; Praveen Kumar Rai; Romulus Costache; Milap Punia; Liping Di
Assessing the performance of land change simulation models is a critical step when predicting the future landscape scenario. The study was conducted in the district of Varanasi, Uttar Pradesh, India because the city being “the oldest living city in the world” attracts a vast population to reside here for short and long-term, leaving the city's ecosystem more exposed to fragility and less resilient. In this work, an approach based on landscape metrics is introduced for comparing the performance of the ensemble models designed to simulate the landscape changes. A set of landscape metrics were applied in this study that offered comprehensive information on the performance of scenario-based simulation models from the viewpoint of the spatial ordering of simulated results against the related reference maps. A supervised support vector machine classification technique was applied to derive the LULC maps using Landsat satellite images of the year 1988, 2001, and 2015. The LULC maps of 1988 and 2001 were used to simulate the LULC scenario for 2015 using three Markov chain-based simulation models namely, multi-layer perceptron-Markov chain (MLP_Markov), cellular automata-Markov chain (CA_Markov), and stochastic-Markov chain (ST_Markov) respectively. The mean relative error (MRE), as a measure of the success of simulation models, was calculated for metrics. The MRE values at both the class and landscape levels were accounted for 21.63 and 11.45% respectively using MLP_Markov simulation model. The MRE values at both the class and landscape levels were accounted for 39.61 and 28.31% respectively using CA_Markov simulation model. The MRE values at both the class and landscape levels were accounted for 55.36 and 45.75% respectively using ST_Markov simulation model. The MRE values considered at class and landscape levels are further evaluated qualitatively for comparing the performance of simulation models. The results indicate that the MLP_Markov performed excellently, followed by CA_Markov and ST_Markov simulation models. This work showed an ordered and multi-level spatial evaluation of the models’ performance into the decision-making process of selecting the optimum approach among them. Landscape metrics as a vital characteristic of the utilized method, employ the maximum potential of the reference and simulated layers for a performance evaluation process. It extends the insight into the main strengths and drawbacks of a specific model when simulating the spatio-temporal pattern. The quantified information of transition among landscape categories also provides land policy managers a better perception to build a sustainable city master plan. © 2021 The Author(s)
Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models
(Springer Verlag, 2018) Varun Narayan Mishra; Praveen Kumar Rai; Rajendra Prasad; Milap Punia; Mărgărit-Mircea Nistor
Land use/land cover changes (LULCC) are one of the foremost aspects of environmental changes caused by human-induced activities mainly in rapidly developing areas. This study endeavors to evaluate and compare three hybrid models: stochastic Markov chain (ST-MC), cellular automata-Markov chain (CA-MC), and multi-layer perceptron-Markov chain (MLP-MC) to predict future land use/land cover (LULC) scenario in Varanasi district. LULC information extracted for years 1988 and 2001 was first employed to predict LULC scenario for 2015 using three hybrid models. The predicted results were compared with the observed LULC information for the year 2015 to appraise the validity of models through kappa index statistics. The MLP-MC model yielded reliable and best results. Finally, based on this consequence, the prediction of future LULC scenarios for years 2030 and 2050 was performed. The findings of this study exhibited the constant but overall increase of built up area and a considerable reduction in agricultural land. The results also demonstrate the potentiality of MLP-MC hybrid model for better understanding of spatio-temporal dynamics and predicting future landsacpe scenario in Varanasi district of Uttar Pradesh, India. © 2018, Società Italiana di Fotogrammetria e Topografia (SIFET).
Using remote sensing technologies to enhance resource conservation and agricultural productivity in underutilized lands of South Asia
(2012) Parvesh Chandna; J.K. Ladha; U.P. Singh; Milap Punia; Raj Gupta; B.S. Sidhu
Satisfying the food demands of an ever-increasing population, preserving the natural resource base, and improving livelihoods are major challenges for South Asia. A large area of land in the Middle and Lower Gangetic Plains of South Asia remains either uncultivated or underused following the rice harvest in the kharif (wet) season. The area includes "rice-fallow," estimated at 6.7 million ha, flood-prone riversides (" diara lands," 2.4 million ha), waterlogged areas (4.9 million ha), and salt-affected soils (2.3 million ha). Bringing these lands under production could substantially improve the food supply and enhance livelihoods in the region. This paper describes a methodological case study that targeted resource-conserving technologies in underused lands of the Ballia District of eastern Uttar Pradesh (India) using multispectral remote-sensing images. Classification of temporal satellite data IRS-P6 in combination with Spot VGT 2 permitted the identification of all major categories of underused land during the post-rainy rabi/winter season, with an average accuracy of 89%. Based on three-year averages of field demonstrations, farmers gained an additional income of $63 ha-1 by introducing raised beds in salt-affected soils; $140 and $800 ha-1 by introducing deepwater rice varieties (monsoon) and boro rice (winter) in waterlogged areas; and $581 ha-1 by introducing zero-till lentil (winter) in rain-fed fallow lowland. Timely wheat planting through zero-tillage implies an additional income of $147 ha-1 and could increase wheat production by 35,000-65,000 tons in the district. The methodologies and technologies suggested in the study are applicable to more than 15 million ha of underutilized lands of the Indo-Gangetic Plains of South Asia. If the technologies are precisely applied, they can result in more than 3000 million US $ of additional income every year to these poverty prone areas. © 2011 Elsevier Ltd.