A common phenomenon associated with alluvial rivers is their meander evolution, eventually forming cutoffs. Point bar deposits and ox-bow lakes are the products of lateral bend migration and meander ...cutoff. The present study focuses on identifying the meanders of River Manu and their cutoffs. Moreover, this study compares the temporal evolution and predicts the progress of selected meanders of River Manu. In the present research, the Survey of India topographical map, satellite imagery, and geographic information system (GIS) technique were used to examine the evolution of the Manu River meander. Subsequently, a field visit was done to the selected cutoffs and meanders of River Manu to ascertain the present status and collect data. It has been observed that many cutoffs have undergone temporal changes, and their sizes have decreased. Some have become dried or converted to agricultural fields. The width of River Manu has decreased in all the selected bends from 1932 to 2017. The sinuosity index has changed from 2.04 (1932) to 1.90 (2017), and the length of the river has decreased by 7 km in 85 years (1932–2017). The decrease in length is evident from lowering the number of meanders. Uniformity coefficient and coefficient of curvature of the bank soil samples were calculated, indicating that the soil is poorly graded and falls under the cohesionless category. Based on cross-section analysis, sediment discharge, grain-size analysis of the bank material, channel planform change, and radius of curvature, it can be stated that almost all the selected bends have the probability of future cutoff. The highest probabilities were observed in bend 3 (Jalai) and bend 4 (Chhontail). This work is aimed to provide planners with decisions regarding the construction of roads and bridges in areas that show the huge dynamicity of river meandering.
There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated ...field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available.
The present work examines the potential habitat distribution of Calamus floribundus using the Maxent model in Geographic Information System(GIS) in Assam. For the prediction, 19 bioclimatic variables ...downloaded from the worldclim website are used. Landuse (MODIS MCD12Q1 Version 6), aspect and slope data are also used for a better prediction. The model used a Jackknife test to identify the significant environmental variables for the prediction. The predictive model shows a highly significant AUC validation statistics of 0.96. Such predictions could be used to conserve the species in present and future climatic scenarios. The present communication also deals with ecology, local uses and the current distribution of the species. A taxonomic description is also added along with photographs for easy identification.
•Flood plain is prone to bankline migration along rivers.•The DSAS model is applied to the Brahmaputra River to evaluate bankline migration.•The probability for LULC to shift in the future is ...evaluated using a CA-Markov model.•Erosion-vulnerable zones of the Brahmaputra River were identified.•Results help in formulating comprehensive river hazard management plan for the region.
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River bankline migration is a frequent phenomenon in the river of the floodplain region. Nowadays, channel dynamics-related changes in land use and land cover (LULC) are becoming a risk to the life and property of people living in the vicinity of rivers. A comprehensive evaluation of the causes and consequences of such changes is essential for better policy and decision-making for disaster risk reduction and management. The present study assesses the changes in the Brahmaputra River planform using the GIS-based Digital Shoreline Analysis System (DSAS) and relates it with the changing LULC of the floodplain evaluated using the CA-Markov model. In this study, the future channel of the Brahmaputra River and its flood plain’s future LULC were forecasted to pinpoint the erosion-vulnerable zone. Forty-eight years (1973–2021) of remotely sensed data were applied to estimate the rate of bankline migration. It was observed that the river’s erosion-accretion rate was higher in early times than in more recent ones. The left and right banks’ average shifting rates between 1973 and 1988 were −55.44 m/y and −56.79 m/y, respectively, while they were −17.25 m/y and −48.49 m/y from 2011 to 2021. The left bank of the river Brahmaputra had more erosion than the right, which indicates that the river is shifting in the leftward direction (Southward). In this river course, zone A (Lower course) and zone B (Middle course) were more adversely affected than zone C (Upper course). According to the predicted result, the left bank is more susceptible to bank erosion than the right bank (where the average rate of erosion and deposition was −72.23 m/y and 79.50 m/y, respectively). The left bank’s average rate of erosion was −111.22 m/y. The research assesses the LULC study in conjunction with river channel dynamics in vulnerable areas where nearby infrastructure and settlements were at risk due to channel migration. The degree of accuracy was verified using the actual bankline and predicted bankline, as well as the actual LULC map and anticipated LULC map. In more than 90% of cases, the bankline’s position and shape generally remain the same as the actual bankline. The overall, and kappa accuracy of all the LULC maps was more than 85%, which was suitable for the forecast. Moreover, chi-square (x2) result values for classified classes denoted the accuracy and acceptability of the CA-Markov model for predicting the LULC map. The results of this work aim to understand better the efficient hazard management strategy for the Brahmaputra River for hazard managers of the region using an automated prediction approach.
Inland water plants, particularly those that thrive in shallow environments, are vital to the health of aquatic ecosystems. Water hyacinth is a typical example of inland species, an invasive aquatic ...plant that can drastically alter the natural plant community's floral diversity. The present study aims to assess the impact of water hyacinth biomass on the floristic characteristics of aquatic plants in the Merbil wetland of the Brahmaputra floodplain, NE, India. Using a systematic sampling technique, data were collected from the field at regular intervals for one year (2021) to estimate monthly water hyacinth biomass. The total estimate of the wetland's biomass was made using the Kriging interpolation technique. The Shannon-Wiener diversity index (
'), Simpson's diversity index (
), dominance and evenness or equitability index (
), density, and frequency were used to estimate the floristic characteristics of aquatic plants in the wetland. The result shows that the highest biomass was recorded in September (408.1 tons/ha), while the lowest was recorded in March (38 tons/ha). The floristic composition of aquatic plants was significantly influenced by water hyacinth biomass. A total of forty-one plant species from 23 different families were found in this tiny freshwater marsh during the floristic survey. Out of the total, 25 species were emergent, 11 were floating leaves, and the remaining five were free-floating habitats.
was the wetland's most dominant plant. A negative correlation was observed between water hyacinth biomass and the Shannon (
) index, Simpson diversity index, and evenness. We observed that water hyacinths had changed the plant community structure of freshwater habitats in the study area. Water hyacinth's rapid expansion blocked out sunlight, reducing the ecosystem's productivity and ultimately leading to species loss. The study will help devise plans for the sustainable management of natural resources and provide helpful guidance for maintaining the short- to the medium-term ecological balance in similar wetlands.
Riverbank erosion is one of the key geomorphological problems encountered in the floodplains of the alluvial rivers. Many recent studies on fluvial dynamics have indicated advantages of geospatial ...technology over traditional techniques in terms of time, cost, and practical usability by the end-users. This study aims to assess the riverbank erosion and erosion probability in a highly dynamic and unstable stretch of the Subansiri River in Assam (India) using geospatial approach along with the Graf’s model. Temporal Landsat datasets for a period of 29 years (1989 to 2017) in time step of 4–5 years are used for mapping the river channels (active floodplains) of the Subansiri River. These river channel datasets were then analyzed to spatially quantify the erosion/aggradation and identify the high riverbank erosion zones. Identification and analysis of the high riverbank erosion zones revealed a general westward shift of the Subansiri River during the studied period. The Graf’s model, used for estimating the riverbank erosion probability, is implemented in geographical information system (GIS). The transition probability matrices for riverbank erosion were generated for different time spans (1989–1994, 1994–1998, 1998–2002, 2002–2006, 2006–2010, and 2010–2014) using the distance to river channel and erosion/aggradation maps prepared using remote sensing data. Flood recurrence intervals of the annual floods from 1988 to 2017 were estimated using observed discharge data. The transition matrices and flood recurrence intervals were then used to calibrate the Graf’s model for estimating the probability of riverbank erosion of the Subansiri River. The results were validated with observed erosion/aggradation map of 2014–2017 time period. The study demonstrates the strength of geospatial approach for rapid assessment of riverbank erosion of alluvial channels. The calibrated Graf’s model developed in this study along with understanding of the migration behavior of the Subansiri River will be useful for taking mitigation measures and planning river management strategies.
The Kaziranga Eco-Sensitive Zone is located on the edge of the Eastern Himalayan biodiversity hotspot region. In 1985, the Kaziranga National Park (KNP) was declared a World Heritage Site by UNESCO. ...Nowadays, anthropogenic interference has created a significant negative impact on this national park. As a result, the area under natural habitat is gradually decreasing. The current study attempted to analyze the land use land cover (LULC) change in the Kaziranga Eco-Sensitive Zone using remote sensing data with CA-Markov models. Satellite remote sensing and the geographic information system (GIS) are widely used for monitoring, mapping, and change detection of LULC change dynamics. The changing rate was assessed using thirty years (1990–2020) of Landsat data. The study analyses the significant change in LULC, with the decrease in the waterbody, grassland and agricultural land, and the increase of sand or dry river beds, forest, and built-up areas. Between 1990 and 2020, waterbody, grassland, and agricultural land decreased by 18.4, 9.96, and 64.88%, respectively, while sand or dry river beds, forest, and built-up areas increased by 103.72, 6.96, and 89.03%, respectively. The result shows that the area covered with waterbodies, grassland, and agricultural land is mostly converted into built-up areas and sand or dry river bed areas. According to this study, by 2050, waterbodies, sand or dry river beds, and forests will decrease by 3.67, 3.91, and 7.11%, respectively; while grassland and agriculture will increase by up to 16.67% and 0.37%, respectively. The built-up areas are expected to slightly decrease during this period (up to 2.4%). The outcome of this study is expected to be useful for the long-term management of the Kaziranga Eco-Sensitive Zone.
Illegal sand mining has been identified as a significant cause of harm to riverbanks, as it leads to excessive removal of sand from rivers and negatively impacts river shorelines. This investigation ...aimed to identify instances of shoreline erosion and accretion at illegal sand mining sites along the Chambal River. These sites were selected based on a report submitted by the Director of the National Chambal Sanctuary (NCS) to the National Green Tribunal (NGT) of India. The digital shoreline analysis system (DSAS v5.1) was used during the elapsed period from 1990 to 2020. Three statistical parameters used in DSAS—the shoreline change envelope (SCE), endpoint rate (EPR), and net shoreline movement (NSM)—quantify the rates of shoreline changes in the form of erosion and accretion patterns. To carry out this study, Landsat imagery data (T.M., ETM+, and OLI) and Sentinel-2A/MSI from 1990 to 2020 were used to analyze river shoreline erosion and accretion. The normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to detect riverbanks in satellite images. The investigation results indicated that erosion was observed at all illegal mining sites, with the highest erosion rate of 1.26 m/year at the Sewarpali site. On the other hand, the highest accretion was identified at the Chandilpura site, with a rate of 0.63 m/year. We observed significant changes in river shorelines at illegal mining and unmined sites. Erosion and accretion at unmined sites are recorded at −0.18 m/year and 0.19 m/year, respectively, which are minor compared to mining sites. This study’s findings on the effects of illegal sand mining on river shorelines will be helpful in the sustainable management and conservation of river ecosystems. These results can also help to develop and implement river sand mining policies that protect river ecosystems from the long-term effects of illegal sand mining.
This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal ...Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.
Bank erosion hazard is a frequent occurrence that poses threats to floodplain ecosystems. This analysis examined changes to the Jia Bharali River channel in India using the GIS-based Digital ...Shoreline Analysis System DSAS. The Jia Bharali’s future channel was predicted so as to identify the most erosion-susceptible zones. The rate of bankline movement was calculated using remotely sensed data collected over a period of 45 years (1976–2021). The results show that the river’s erosion and deposition rates were higher in the early years than towards the later part of the period under analysis. On the right and left banks of the river, the average shift rate was −9.22 and 5.8 m/y, respectively, which is comparatively high. The chosen portion of the river was evenly divided into three zones, A, B, and C. The most positively affected zone was zone A. The left bank of zone B exhibited a higher rate of erosion than the right bank, indicating that the river was moving to the left eastward in this zone. At the same time, the right bank was being eroded faster than the left, indicating a westward thrust at zone C. The predicted result demonstrates that the left bank of zone B and the right bank of zone C would have a higher average migration rate. Therefore, these banks were identified as being the most susceptible to bank erosion. The study evaluates the spatio-temporal change of the river in sensitive regions where neighboring settlements and infrastructure were at risk of changing channel dynamics. Using the actual and forecasted bankline, the degree of accuracy was confirmed. The results of the automated prediction approach could be useful for river hazard management in the Jia Bharali and in similar environmental settings with tropical high precipitation zones.
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