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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.

Disangmukh wetlands are an agglomeration of few wetlands beside the levee region of River Brahmaputra where the tributary Disang meets the Mighty Brahmaputra. The present study examines the physic-chemical properties of water and sees the relationship between water quality and the aquatic macrophytes in the Disangmukh wetland of Assam. The samples of plants and water were collected from four sites of Disangmukh wetland namely Ronga Garh, Rongmon, Kaziranga and Longsai beel. The plant’s samples were collected using the quadrat sampling method with 1 square meter and the diversity has been calculated using the Shannon Diversity Index (SDI) (H = -?s [(Pi) x ln (Pi)]). The results show the highest SDI at Longsai beel (2.58) and it was followed by Rongmon beel (2.2), Ronga Garh beel (2.13) and Kaziranga beel (2.11). DO, ORP and TDS shows a positive correlation with plant diversity.

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.

Guwahati, a city of 2 million residents is the largest city in northeastern part of India. Situated by the side of River Brahmaputra, the city is of an undulated topography with some hillocks and wetlands. The considerable area of the city is still forested and seven reserve forests are located within the city. The forested topography of the city offers a good habitat for the Common Indian Leopard (Panthera pardus fusca), a Scheduled 1 species under Indian Wildlife (Protection) Act 1972. Over the years, there have been growing casualties on human-leopard conflicts in the region. Indian common leopard (Panthera pardus fusca), is a highly adaptive species and do inhabit in human proximity, which creates conflicts resulting in a loss on both sides. The present study has found that there is a rise in the incidence of human-leopard conflict in Maligaon Gaushala farmland, located in 26o08′48.5″N and 91°41′39.2″E, covering land areas of 34.82 Hectare. The study has been conducted in this area from the last five years i.e. 2015-2019 and recorded 16 number of human-leopard conflicts. It is also found that the area is used for feeding and breeding ground of cattle, for keeping old unproductive cows and dumping ground for dead cattle’s carcass. The area is covered with thick elephant grasses (Pennisetum purpureum), and it is used as chief fodder for cows inside the Gaushala farmland, which is a good foraging for high yielding of milk production from Gaushala farmland and optimum hiding ground for leopard. Due to expansion and high escalations of encroachment to the habitat of leopard a total of 90 incidences of human-leopard conflicts occurs since 1995, which include 13 human causalities, 3 leopards’ mortality and 29 livestock predation. The present study investigates detail movements of the species, their habitat based on camera trapping, Scat analysis, GPS records and field interviews. The data thus gathered not only helped to record the nature of conflicts in the area but also to design a conservation plan for the species. The suggestions recorded in the study are intended to reduce the incidences human-leopard conflicts in the area.

The study examines the effects of urbanization on land surface temperature (LST) in Guwahati, a city in India using satellite data. Landsat images were utilized for LST retrieval, land-use land-cover (LULC) classification and the normalized difference built-up index mapping. Surface Energy Balance Algorithms for Land and support vector machine methods were used in the study. Results showed that the city has gone through massive changes in its LULC pattern with a high degree of urbanization during the period 1992–2015. The built-up area (BUA) increased to 87.8 km2 in 2015 from 11.6 km2 in 1992 while vegetation decreased from 143.3 to 76.6 km2. Open spaces and water bodies decreased from 14.5 to 5 km2 and to 6.6 km2 from 6.7 km2, respectively. Conversely, an increasing trend of LST was observed. The mean LST which was 18.5 °C in 1992 rose to 29.03 °C in 2015. Linear regression used in quantifying the relationship between urbanization and LST showed a positive relationship between LST and BUAs in the city.

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. Fortyeight 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.

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.

Satellite remote sensing and geographic information system (GIS) have revolutionalized the mapping, quantifying, and assessing the land surface processes, particularly analyzing the past and future land use-land cover (LULC) change patterns. Worldwide river basins have observed enormous changes in the land system dynamics as a result of anthropogenic factors such as population, urbanization, development, and agriculture. As is the scenario of various other river basins, the Brahmaputra basin, which falls in China, Bhutan, India, and Bangladesh, is also witnessing the same environmental issues. The present study has been conducted on the Brahmaputra Valley in Assam, India (a sub-basin of the larger Brahmaputra basin) and assessed its LULC changes using a maximum likelihood classification algorithm. The study also simulated the changing LULC pattern for the years 2030, 2040, and 2050 using the GIS-based cellular automata Markov model (CA-Markov) to understand the implications of the ongoing trends in the LULC change for future land system dynamics. The current rate of change of the LULC in the region was assessed using the 48 years of earth observation satellite data from 1973 to 2021. It was observed that from 1973 to 2021, the area under vegetation cover and water body decreased by 19.48 and 47.13%, respectively. In contrast, cultivated land, barren land, and built-up area increased by 7.60, 20.28, and 384.99%, respectively. It was found that the area covered by vegetation and water body has largely been transitioned to cultivated land and built-up classes. The research predicted that, by the end of 2050, the area covered by vegetation, cultivated land, and water would remain at 39.75, 32.31, and 4.91%, respectively, while the area covered by built-up areas will increase by up to 18.09%. Using the kappa index (ki) as an accuracy indicator of the simulated future LULCs, the predicted LULC of 2021 was validated against the observed LULC of 2021, and the very high ki observed validated the generated simulation LULC products. The research concludes that significant LULC changes are taking place in the study area with a decrease in vegetation cover and water body and an increase of area under built-up. Such trends will continue in the future and shall have disastrous environmental consequences unless necessary land resource management strategies are not implemented. The main factors responsible for the changing dynamics of LULC in the study area are urbanization, population growth, climate change, river bank erosion and sedimentation, and intensive agriculture. This study is aimed at providing the policy and decision-makers of the region with the necessary what-if scenarios for better decision-making. It shall also be useful in other countries of the Brahmaputra basin for transboundary integrated river basin management of the whole region.

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.

Land-use/land-cover change is an important agent of ecological degradation in tropical areas where forests are under threat from the pressure of human activities. This study assesses land-use change (1973–2007) in the Nameri Tiger Reserve (NTR) in Assam, India using Landsat imageries. Dense forests decreased sharply while open forest increased marginally. The increases in the degraded and open forest categories occurred at the expense of dense forests, which decreased at an average annual rate of 288 ha year−1, or at 0.56% annually. The number of patches in the NTR landscape recorded a fivefold increase indicating a high degree of fragmentation of the habitat. While the number of patches of the dense forests increased by 338% from 270 in 1973 to 1138 in 2007 at an annual rate of increase of 9.9% per annum, their mean patch area declined from 19.09 to 12.82 ha. Both class- and patch-level changes corroborate the trend of fragmentation with a consistent increase in the number of smaller patches. Encroachmen...

This chapter outlines how crowdsourcing and Google Earth have been used to create the first global crowdsourced map of human impact. Human impact in this context refers to the degree to which the landscape has been modified by humans as visible from satellite imagery on Google Earth. As human impact is measured on a continuum, it could be used to indicate the wildest areas on the Earth. This bottom-up approach to mapping using the crowd is in contrast to more traditional GIS-based wilderness mapping methods, which integrate proximity-based layers of remoteness and indicators of biophysical naturalness in a top-down manner. Data on human impact were collected via a number of different data collection campaigns using Geo-Wiki, a tool for visualization, crowdsourcing and validation of global land cover. An overview of the crowdsourced data is provided, along with the resulting map of human impact and a visual comparison with the map of human footprint (Sanderson EW, Jaiteh M, Levy MA, et al. BioSci 52:891–904. doi: 10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2, 2002).

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.