Comparison of satellite image-based vegetation indices for extraction and mapping of litchi (Litchi Chinensis) cultivation area in Muzaffarpur district, Bihar, India

The aim of present study was to evaluate the suitability of various vegetation indices (VIs) to ex- tract litchi cultivation area in Muzaffarpur district of Bihar, India. VIs computed from the multispectral bands of Landsat satellites have been used in delineating litchi cultivation areas from other land cover cate- gories. In this study, ten selected VIs have been applied and compared their effectiveness in litchi cultiva- tion area mapping for years 2016 and 2020 respectively. The results showed that the Normalized Green Blue Difference Index (NGBDI) was found to be most appropriate for extracting and mapping the litchi cultivation area. The area statistics of litchi cultivation was validated and are in closer correspondence with the data reported by the state horticulture department. It was found that the area of Litchi cultivation field is increased from 10272.79 ha to 10400.63 ha during the period of 4 years (2016-2020) in the area under in- vestigation. The spatial distribution maps of litchi fruit represent a vital reference suitable for developing a regional action plan to promote its cultivation and benefits to farmers

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

CRediT authorship contribution statement: Dr. Aman Arora and Dr. Alireza Arabameri have conceptualized the study, prepared the dataset, and optimized the models. Dr. Manish Pandey has helped in writing the manuscript. Prof. Masood A. Siddiqui, Prof. U.K. Shukla, Prof. Dieu Tien Bui, Dr. Varun Narayan Mishra, and Dr. Anshuman Bhardwaj have helped in improving the manuscript at different stages of this work.Peer reviewedPostprin

Changes of glacier lakes using multi-temporal remote sensing data: A case study from India

The present study used the potential of Landsat multispectral data and ASTER-DEM data to identify the changes of glacier lakes in part of Chandra basin and surrounding of Himachal Pradesh of India from 1989 to 2013. The Barashigri, Chotashigri, Hamtahand Parvati glacier are the major glaciers within the area. The Landsat data of TM (1989 and 2009), ETM+ (2001) and OLI-TIRS (2013) sensors having different band combinations were analysed to monitor variation in the glacier lakes and area of glaciers and terminus whereas ASTER-DEM data was used for relief information. Glaciers terminus and glacial lakes were identified and mapped using false-colour composites (FCC) with band combinations of red, near-infrared (NIR) and shortwave infrared (SWIR), and a true-colour composite of red, green and NIR, of Landsat TM/ETM+ images and normalized difference water index (NDWI) methods. It is observed that the number of lakes in the study area increased by 18.69% during the past 34 years while it was increased from 68 in 1989 to 89 in 2013. During the analysis, it is also found that the snow and glacier covered area within this period is also reduced from 1,317.39 to 1,125.59 km2

Geospatial Analysis of Land Use and Land Cover Dynamics and its Impact on Urban Wetland Ecosystems in Delhi NCR Region, India

783-795Urban wetlands are highly neglected and are encroached upon to accommodate more settlements or to dump waste materials. They are susceptible to encroachment and undergo Land Use/Land Cover (LULC) change thereby diminishing their value. This study aims to examine and assess the spatial-temporal LULC change for selected wetlands of Delhi NCR vis-à-vis ecosystem services. Landsat imageries for the years 1998, 2008 and 2018 are used to understand the change dynamics using supervised classification with overall accuracy of more than 80% for all years. Classification was done separately for Delhi NCR and 5 km buffer around the wetlands. In Delhi NCR the net percent change during the 20-year period was found to be +5.22% and +8.56% for built-up and cropland respectively. During the same period, the plantations/forest cover and water bodies changed by –8.30% and –0.50% respectively. Plantations/forest cover has shown a negative net percent change in six wetlands, with Najafgarh experiencing the highest (–10.75%), followed closely by Surajpur wetland (–10.68%), Bhalswa lake wetland (–9.93%), Yamuna Biodiversity Park (–6.77%), Pusa Hill Forest (–5.18%) and Asola Wildlife Sanctuary (–5.21%). The LULC change analysis has pointed to the loss of wetland area to built-up and/or cropland which is going to affect the ecosystem services provided by these wetlands. Geospatial tools are an important tool to understand the changing LULC in such sensitive ecosystems. It is needed to manage wetlands sustainably so that the corresponding ecosystem services could be preserved

Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India

Land use change models are tools to support the analysis of the causes and consequences of land use dynamics. Land use and land cover change (LUCC) has been recognized as an important driver of environmental change on all spatial and temporal scales. The primary objective of this paper is to predict and analyze the present and future growth of Muzaffarpur city and its surrounding, Bihar (India) using the Landsat satellite images of 1988 and 2010. These data are used for change prediction and for preparation of prediction map of year 2025 and 2035. IDRISI, Land Change Modeler (LCM) was used to analyze the land use and land cover changes between various classes during the period 1988-2008. Erdas Imagine software (ver-9.3) were also used to prepare land use/cover classification using image processing supervised classification method in a multi-temporal approach. The prediction of land use land cover change was done on neural network built-in module in the Selva version of IDRISI. The accuracy was obtained as 72.28% for all the conversion types

Mapping and yield prediction of castor bean (Ricinus communis) using Sentinel-2A satellite image in a semi-arid region of india

Castor bean (Ricinus communis) indigenous to the southeastern Mediterranean basin, eastern Africa and India is a crop having various industrial and medicinal applications. It is helpful in crop rotation and replenishing the soil nutrients due to less water consumption. The current study explores the utility of Sentinel-2A satellite image for mapping and yield prediction of castor beans. Several classification methods viz. migrating means clustering, maximum likelihood classifier, support vector machine and artificial neural network are used for the classification and mapping of different landscape categories. The overall classification accuracy was achieved to be highest for artificial neural network (85.81 %) subsequently support vector machine (80.12 %), maximum likelihood classifier (74.23 %) and migrating means clustering (73.03 %). The yield prediction is performed using Sentinel-2A-derived indices namely Normalized Difference Vegetation Index and Enhanced Vegetation Index-2. Further, the cumulative values of these two indices are investigated for castor bean yield prediction using linear regression from July 2017 to April 2018 in different seasons (pre-monsoon, post-monsoon, and winter). The regression model provided (adj R2=0.75) value using EVI-2 compared to (adj R2=0.55) using NDVI for yield prediction of Ricinus communis crop in the winter season. The methodology adopted in this study can serve as an effective tool to map and predict the productivity of Ricinus communis. The adopted methodology may also be extended to a wider spatial level and for other significant crops grown in semi-arid regions of world

Rule-based fuzzy inference system for landslide susceptibility mapping along national highway 7 in Garhwal Himalayas, India

The Mountainous terrain,in the Himalayas is experiencing rapid development in a bewildering manner, which makes it more susceptible to landslides. Management and mitigation of landslide hazard begin with its mapping by integrating numerous methods and Geographic Information System (GIS) tools. However, it is difficult to produce reliable landslide susceptibility maps (LSM) with traditional remote sensing and GIS methods due to complexity of the mountainous terrain environments and huge datasets. Therefore, the present study investigates the applicability of Mamdani’s fuzzy inference system (FIS) to produce LSM in Himalayan terrain in India. It is compared with commonly used frequency ratio (FR) and information value method (IVM) approaches. Several causative factors were extracted and used to prepare thematic layers, including slope, aspect, curvature, solar radiance, SPI, TWI, rainfall, soil depth and NDVI. Landslide inventory was also created using google earth images and previously published work. The accuracy estimates for FR, IVM and FIS were performed based on ROC curves. FIS was found to provide an accuracy of 77.7%, followed by IVM (72%) and FR (71%) for LSM. The current study is a prototype for further studies in the Garhwal Himalayas and similar terrains, based on the vigorous Mamdani’s techniques of fuzzy inference theory. The outcomes of this work propose that an expert’s knowledge-based FIS method can produce an accurate LSM in such a complex terrain. Planners and concerned authorities can use the results further for landslide management and mitigation

Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data

Abstract Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization

Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models

This work focuses on comparing results of flood susceptibility modelling in the part of Middle Ganga Plain, Ganga foreland basin. Following inclusivity rule, 12 major flood explanatory factors including a new one, geomorphology, have been utilized. Out of 1000 randomly generated flood-points from 2008 Landsat 5 TM image derived flood polygon, 70% have been utilized for the training purpose of Shannon’s entropy (SE) model and 30% for area under receiver operating characteristic (AUROC) method of validation of both, SE and frequency ratio (FR), models. Result from FR shows that the contributions of specific classes of different explanatory factors to flooding susceptibility vary whereas the SE model suggests that geomorphology is the most contributing factor. The AUROC curve for SE (0.90) was better than that for FR (0.85). Hence, the SE model predicts flood-susceptible areas more accurately than the multivariate statistical model FR in the study area