Majuli at the Crossroads: A Study of Cultural Geomorphology

Although well established elsewhere, cultural geomorphology has not yet been well-grounded in Northeast India where a perceived dearth of studies in this sub-branch of geography exists. The Brahmaputra valley, which has a long physical and cultural history, is a unique laboratory, which offers opportunities to study anthropo-geomorphologic, achaeo-geomorphologic and cultural landscapes. The Majuli river island, ostensibly the largest island in the world, houses traditional art crafts and dances, despite being continually under the siege of a plethora of physical obstacles such as flooding, bank erosion, etc..  The present study aims at studying how the physical processes that constantly reshape the map of the island exert their influence on the socio-economic and cultural milieu of the region. The paper further analyses why despite all odds Majuli thrives and continues to preserve and maintain its rich natural and cultural heritage, in ways that are perhaps unparalleled in the region or even elsewhere in the globe

Majuli at the Crossroads: A Study of Cultural Geomorphology

Although well established elsewhere, cultural geomorphology has not yet been well-grounded in Northeast India where a perceived dearth of studies in this sub-branch of geography exists. The Brahmaputra valley, which has a long physical and cultural history, is a unique laboratory, which offers opportunities to study anthropo-geomorphologic, achaeo-geomorphologic and cultural landscapes. The Majuli river island, ostensibly the largest island in the world, houses traditional art crafts and dances, despite being continually under the siege of a plethora of physical obstacles such as flooding, bank erosion, etc.  The present study aims at studying how the physical processes that constantly reshape the map of the island exert their influence on the socio-economic and cultural milieu of the region. The paper further analyses why despite all odds Majuli thrives and continues to preserve and maintain its rich natural and cultural heritage, in ways that are perhaps unparalleled in the region or even elsewhere in the globe

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas

Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services

Assessment of Ecosystem Service Value in Response to LULC Changes Using Geospatial Techniques: A Case Study in the Merbil Wetland of the Brahmaputra Valley, Assam, India

The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small freshwater wetland located in the Brahmaputra basin in Assam, India, is not exempt from this phenomenon. In the present study, we have estimated and shown a spatio-temporal variation of ecosystem service values in response to land use and land cover alteration for the years 1990, 2000, 2010, and 2021, and predicted the same for 2030 and 2040. Supervised classification and the CA-Markov model were used in this study for land-use and land-cover classification and future projection, respectively. The result showed a significant increase in built-up areas, agricultural land, and aquatic plants and a decrease in open water and vegetation during 1990–2040. The study area experienced a substantial rise in ecosystem service values during the observed period (1990–2021) due to the rapid expansion of built-up areas and agricultural and aquatic land. Although the rise of built-up and agricultural land is economically profitable and has increased the study site’s overall ecosystem service values, decreasing the area under open water and vegetation cover may have led to an ecological imbalance in the study site. Hence, we suggest that protecting the natural ecosystem should be a priority in future land-use planning. The study will aid in developing natural resource sustainability management plans and provide useful guidelines for preserving the local ecological balance in small wetlands over the short to medium term

Decoding Chambal River Shoreline Transformations: A Comprehensive Analysis Using Remote Sensing, GIS, and DSAS

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

Assessing the impacts of current and future changes of the planforms of river Brahmaputra on its land use-land cover

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

Land Use and Land Cover Change Monitoring and Prediction of a UNESCO World Heritage Site: Kaziranga Eco-Sensitive Zone Using Cellular Automata-Markov Model

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