Geospatial approach for assessing spatiotemporal dynamics of urban green space distribution among neighbourhoods: A demonstration in Mumbai

Green spaces are an integral part of urban landscape and offer numerous benefits related to quality of urban life. However, due to various factors, the distribution of green spaces among city neighbourhoods is often skewed. Hence, urban planners require effective tools to routinely map and monitor the greening/ un-greening phenomena among the neighbourhoods. This study caters to this need by adopting a geospatial green space distribution assessment approach that encompasses green space quantity, quality and accessibility aspects. The green space distribution indicators were derived from remote sensing data, which facilitates cost-effective green space assessments at desired time scales. In this study, the approach is demonstrated in Mumbai, a typical city in a developing country undergoing urbanization transition accompanied by stark environmental challenges. The study results revealed that the green spaces in Mumbai had generally diminished, fragmented and disaggregated between 2001 and 2011. However, the level of degeneration of green spaces was found to vary significantly among the neighbourhoods. The results also unveiled that the verdant spaces in the city's western suburbs had experienced the worst degradation during the study period. These results would aid Mumbai's planners in formulating local greening strategies in a cost-effective manner. Further, the study has wider implications for green space planning, especially in the understudied, fast-urbanizing developing countries

Modeling potential glacial lake outburst flood process chains and effects from artificial lake‐level lowering at Gepang Gath lake, Indian Himalaya

Glacial lake outburst floods (GLOFs) are a severe threat to communities in the Himalayas; however, GLOF mitigation strategies have been implemented for only a few lakes, and future changes in hazard are rarely considered. Here, we present a comprehensive assessment of current and future GLOF hazard for Gepang Gath Lake, Western Himalaya, considering rock and/or ice avalanches cascading into the lake. We consider ground surface temperature and topography to define avalanche source zones located in areas of potentially degrading permafrost. GLOF process chains in current and future scenarios, also considering engineered lake lowering of 10 and 30 m, were evaluated. Here, varied avalanche impact waves, erosion patterns, debris flow hydraulics, and GLOF impacts at Sissu village, under 18 different scenarios were assessed. Authors demonstrated that a larger future lake does not necessarily produce larger GLOF events in Sissu, depending, among other factors, on the location from where the triggering avalanche initiates and strikes the lake. For the largest scenarios, 10 m of lowering reduces the high-intensity zone by 54% and 63% for the current and future scenarios, respectively, but has little effect on the medium-intensity flood zone. Even with 30 m of lake lowering, the Sissu helipad falls in the high-intensity zone under all moderate-to-large scenarios, with severe implications for evacuations and other emergency response actions. The approach can be extended to other glacial lakes to demonstrate the efficiency of lake lowering as an option for GLOF mitigation and enable a robust GLOF hazard and risk assessment

How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

© Author(s) 2017. Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably - locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24% of the mean ice thickness (1σ estimate). Models relying on multiple data sets - such as surface ice velocity fields, surface mass balance, or rates of ice thickness change - showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches

Improved streamflow simulations by coupling soil moisture analytical relationship in EnKF based hydrological data assimilation framework

The present study investigates the potential of coupled Soil Moisture Analytical Relationship (SMAR) and Ensemble Kalman Filter (EnKF) based surface soil moisture data assimilation for improving the streamflow simulations. For this purpose, synthetic and real data assimilation experiments were carried out using Soil and Water Assessment Tool (SWAT) hydrological model in two different sub-catchments lying in the Krishna River basin, India. Here, the satellite-based surface soil moisture estimates from Soil Moisture and Ocean Salinity (SMOS) and Advanced Scatterometer (ASCAT) are used for assimilation. Results of the synthetic experiment show that the use of physically based SMAR scheme coupled with EnkF for updating profile soil moisture has better ability to improve the surface flow, groundwater flow and consequently streamflow over the covariance-based updates using EnKF only. Likewise, the real data assimilation experiment also shows SMAR-EnKF assimilation strategy performs better than the EnKF only updates for simulating streamflow. However, in both the synthetic as well as real data experiment, the improvements are only moderate. This restricted success in improving streamflow simulations indicate that updating only the soil moisture through any updating scheme adopted here is not sufficient to reduce the effect of the errors in model forcing on subsequent simulation days

GIS-based modelling of soil erosion processes using the modified-MMF (MMMF) model in a large watershed having vast agro-climatological differences

The present study demonstrates a spatially distributed application of a field-scale annual soil loss model, the modified-MMF (MMMF), to a large watershed using hydrological routing techniques, remote sensing data and geospatial technologies. In this study, the MMMF model is implemented after incorporating the corrections suggested in recent literature along with appropriate modifications of the model to suit the agro-climatological conditions prevailing in most parts of India. Sensitivity analysis carried out through an Average Linear Sensitivity approach indicates that the model outputs are highly sensitive to soil moisture (MS), bulk density (BD), effective hydraulic depth (EHD), ground cover (GC) and settling velocity for clay (VSc). During calibration and validation, the performance evaluation statistics are mostly in the range of very good to satisfactory for both runoff and soil loss at the watershed outlet. Even spatial validation of the results of intermediate processes in the water phase and the sediment phase, although qualitative, seems to be reasonable and rational. Furthermore, the soil erosion severity analysis for different land-uses existing in the watershed indicates that about 90% of the watershed area, especially that occupied by agricultural lands, is vulnerable to the long-term effects of soil erosion. Copyright (c) 2018 John Wiley & Sons, Ltd. Highlights of the study Implements the MMMF model, a field scale soil erosion model to a large watershed. Provides a framework for implementation of the MMMF model at watershed scale. Improves the MMMF model to suit Indian climatic conditions. Point based quantitative tests show acceptable performance of the MMMF Model

Comprehensive inter-comparison of INSAT multispectral rainfall algorithm estimates and TMPA 3B42-RT V7 estimates across different climate regions of India during southwest monsoon period

The main objective of this study is to validate and inter-compare two Near-Real-Time Satellite Rainfall Estimates (NRT-SREs): INSAT Multispectral Rainfall Algorithm (IMSRA, simple blended product) and TMPA 3B42-RT V7 (3B42-RT, multisatellite product) across India. This study aims to provide some insight into the error characteristics of both the NRT-SREs to the algorithm developers and end users by inter-comparing the daily rainfall estimates during the southwest monsoon period of 2010-2013. This study utilizes various volumetric statistics and categorical statistics to understand and evaluate the performance of NRT-SREs in terms of both spatial and volumetric error characteristics (hit, miss, and false error) at different rainfall thresholds across different Koppen-Geiger climate regions of India using the gridded gauge data provided by Indian Meteorological Department as reference dataset. A detailed statistical evaluation shows that the 3B42-RT performs comparatively better than the IMSRA across India. The results indicate that both IMSRA and 3B42-RT have a general tendency of overestimating the low rainfall rates (0 - 2.5 mm/day) and underestimating the high rainfall rates (> 35.5 mm/day). At lower threshold values (0 and 2.5 mm/day), it is found that the miss error is dominant in IMSRA, whereas the false error is dominant in 3B42RT. As the threshold increases (7.5 and 35.5 mm/day), both the miss and false errors increase in both SREs. Additionally, the spatial analysis of the results clearly indicate that the performance of the tested NRT-SREs is not uniform across different climatic regions, an important aspect to be considered for development/ improvement of the tested NRT-SRE algorithms

Inter-comparison of remote sensing sensing-based shoreline mapping techniques at different coastal stretches of India

Many techniques are available for detection of shorelines from multispectral satellite imagery, but the choice of a certain technique for a particular study area can be tough. Hence, for the first time in literature, an inter-comparison of the most widely used shoreline mapping techniques such as Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Improved Band Ratio (IBR) Method, and Automatic Water Extraction Index (AWEI) has been done along four different coastal stretches of India using multitemporal Landsat data. The obtained results have been validated with the high-resolution images of Cartosat-2 (panchromatic) and multispectral images from Google Earth. Performance of the above indices has been analyzed based on the statistics, such as overall accuracy, kappa coefficient, user's accuracy, producer's accuracy, and the average deviation from the reference line. It is observed that the performance of NDWI and IBR techniques are dependent on the physical characteristics of the sites, and therefore, it varies from one site to another. Results indicate that unlike these two indices, the AWEI algorithm performs consistently well followed by MNDWI irrespective of the land cover types

Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data

Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple TIR threshold used in Global Precipitation Index (GPI) technique and Roca method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probabil ity of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new SVM based index performs much better than the earlier indices

Weakening of Indian Summer Monsoon Rainfall due to Changes in Land Use Land Cover

Weakening of Indian summer monsoon rainfall (ISMR) is traditionally linked with large-scale perturbations and circulations. However, the impacts of local changes in land use and land cover (LULC) on ISMR have yet to be explored. Here, we analyzed this topic using the regional Weather Research and Forecasting model with European Center for Medium range Weather Forecast (ECMWF) reanalysis data for the years 2000-2010 as a boundary condition and with LULC data from 1987 and 2005. The differences in LULC between 1987 and 2005 showed deforestation with conversion of forest land to crop land, though the magnitude of such conversion is uncertain because of the coarse resolution of satellite images and use of differential sources and methods for data extraction. We performed a sensitivity analysis to understand the impacts of large-scale deforestation in India on monsoon precipitation and found such impacts are similar to the observed changes in terms of spatial patterns and magnitude. We found that deforestation results in weakening of the ISMR because of the decrease in evapotranspiration and subsequent decrease in the recycled component of precipitation

Global Vipassana Pagoda: Exterior Geometry Envelope Extraction Using UAV Photogrammetry

The Global Vipassana Pagoda is the largest meditation hall in the world, located near Gorai, north west of Mumbai, Maharashtra, India. Although the monument's artistic integrity is still somewhat intact, material degradation and structural deformations are observed. The visual inspections of such monuments are the primary and most important practice subject to both natural hazards and deterioration over time. Therefore, the geometry envelope, dimension, and size of the monument have to be monitored and digitally captured in order to evaluate this structural deformation. In this paper, in order to acquire Global Vipassana Pagoda monument's efficient visual inspections and a 3D model of a historic masonry, advanced survey procedures UAV photogrammetry survey is used. But, due to its complex geometry and substantial dimensions, the exterior geometry envelope of the monument is difficult to generate. As a result, while conducting UAV surveys it's important to correctly design the flight plan, the photogrammetric parameters and the georeferencing configuration. The research aims to generate external geometry envelopes that serve as an input for an analyst to monitor the structural deformations of the monuments. The intended results were achieved after carrying out various trials of UAV surveys for Global Vipassana Pagoda monuments. Finally, the study enlists the factors and approach required to generate the external geometry envelope of complex monuments using UAV survey