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Evaluation and Comparison of Multi Resolution DEM Derived Through Cartosat-1 Stereo Pair – A Case Study of Damanganga Basin
Published in Journal of the Indian Society of Remote Sensing, 2012
The study evaluates and compares Digital Elevation Model (DEM) data of various grid spacing derived using high resolution Cartosat 1 stereo data for hydrologic applications. DEM is essential in modeling different environmental processes which depend on surface elevation. The accuracy of derived DEM varies with grid spacing and source. The CartoDEM is the photogrammetric DEM derived from stereo pairs. Damanganga basin lying in the Western Ghats was analysed using 11 Carto stereo pairs. The process of triangulation resulted in RMSE of 0.42. DEM was extracted at 10 m, 20 m, 30 m, 40 m, 50 m and 90 m grid spacing and compared with ASTER GDEM (30 m) and SRTM DEM (90 m). DEM accuracy was checked with Root Mean Square Error (RMSE) statistic for random points generated in different elevation zones. Extracted stream networks were compared based on Correctness Index and Figure of Merit index, calculated for all the Digital Elevation Models at varying cell sizes. In order to further evaluate the DEM’s, a simple flood simulation with no water movement and no consideration of real time precipitation data was carried out and relationship between heights of flood stage and inundation area for each Digital Elevation Model was also established.
An automated algorithm to detect timing of urban conversion of agricultural land with high temporal frequency MODIS NDVI data
Published in Personal Communication, 2013
Urban expansion is one of the major drivers of agricultural lands loss. However, current remote sensing-based efforts to monitor this process are limited to small scale case studies that require much user input. Given the rate and magnitude of contemporary urbanization, there is a need to develop a land change algorithm that can characterize the loss of agricultural land at large scales over long time periods. Moreover, characterizing agricultural land conversion trajectories from remote sensing images is complex due to farm size, climatic variability, changes in cropping patterns, and variations in the rate of development processes. Here we propose an econometric time series approach to identify agricultural land loss due to urban expansion, utilizing high temporal frequency MODIS NDVI data between 2000 and 2010. The algorithm is comprised of two main components: 1) detrending the time series, and 2) testing for the presence of a breakpoint in the detrended time series and estimating the date of the breakpoint. Evaluations of the algorithm with simulated and actual MODIS NDVI data confirm that the method can successfully detect when and where urban conversions of agricultural lands occur. The algorithm is simple, robust, and highly automated, thus is valuable for monitoring agricultural land loss at regional and even global scales.
Published in International Journal of Applied Earth Observation and Geoinformation, 2013
India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.
Published in Journal of Environmental Management, 2015
We examine the impacts of urbanization on agricultural land loss in India from 2001 to 2010. We combined a hierarchical classification approach with econometric time series analysis to reconstruct land-cover change histories using time series MODIS 250 m VI images composited at 16-day intervals and night time lights (NTL) data. We compared estimates of agricultural land loss using satellite data with agricultural census data. Our analysis highlights six key results. First, agricultural land loss is occurring around smaller cities more than around bigger cities. Second, from 2001 to 2010, each state lost less than 1% of its total geographical area due to agriculture to urban expansion. Third, the northeastern states experienced the least amount of agricultural land loss. Fourth, agricultural land loss is largely in states and districts which have a larger number of operational or approved SEZs. Fifth, urban conversion of agricultural land is concentrated in a few districts and states with high rates of economic growth. Sixth, agricultural land loss is predominantly in states with higher agricultural land suitability compared to other states. Although the total area of agricultural land lost to urban expansion has been relatively low, our results show that since 2006, the amount of agricultural land converted has been increasing steadily. Given that the preponderance of India’s urban population growth has yet to occur, the results suggest an increase in the conversion of agricultural land going into the future.
Numerical modelling spatial patterns of urban growth in Chandigarh and surrounding region (India) using multi-agent systems
Published in Modeling Earth Systems and Environment, 2015
This study explores application of multi agent system (MAS) to simulate spatial patterns of urban growth in Chandigarh and its surrounding region (India). A numerical simulation model is developed with MAS considering the dynamics of urban and rural population as the principal driver of urban growth. The model utilizes static and dynamic environment variables initialized using a logistic regression model. The logistic regression model uses pixel wise change/no-change information derived using Landsat TM data (1989–1999) as dependent variable and proximity, density, elevation and slope as independent variables. The optimum resolution of 90 m for modelling is decided using fractal analysis of series of transition probability surfaces generated using logistic regression from 30 to 240 m spatial resolution at 30 m interval. The model was finally calibrated using sensitivity analysis and behaviours space experiments with multiple simulation runs. A change to built-up area of 32.55 km2 is observed during 1989–1999 and 113.51 km2 in 1999–2009. The modelling shows a total 14.42 % disagreement between predicted map and reference map for the year 2009. The results were validated using ROC statistics and accuracy estimates with satellite data. The model was further used to predict urban growth for the year 2019. Diversity index was used to determine the potential of the model to capture overall spatial patterns of urban growth.
Published in The Routledge Handbook of Urbanization and Global Environmental Change, 2015
Published in IEEE Transactions on Geoscience and Remote Sensing, 2016
The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time.
Published in Remote Sensing of Environment, 2017
With over two decades of historical observations, DMSP/OLS nighttime lights (NTL) are an invaluable asset for monitoring, characterizing, and understanding human activity. Due to the lack of on-board calibration, there are systematic biases in NTL data. Consequently, a key deterrent to the use of the entire NTL archive is the difficulty in generating a consistent NTL time series. Currently, there are a number of methods to calibrate NTL data in order to generate a consistent time series. However, there is no systematic evaluation of the performances of these calibration efforts. The purpose of this paper is to compare and evaluate existing calibration methods and to provide guidance to NTL end users on their relative strengths and weaknesses. We apply two widely adopted criteria to assess the performance of the methods: a) systematic bias minimization, and b) relation with ancillary socio-economic data on economic activity and electricity consumption. Our results show that global-scale calibration methods outperform regionally-based calibration methods. Furthermore, inconsistencies, in the form of differences in pixel values from two satellites of the same region and year, continue to exist in NTL data even after the calibration methods are applied, both across regions and across scales. We find that dimly lit regions are more difficult to calibrate compared to brightly lit regions and that inconsistencies persist in higher northern and lower southern latitudes. We conclude that significant improvements in calibration results will require a shift either towards absolute correction methods using in-situ data or relative calibration methods using NTL from VIIRS.
Mapping floods and assessing flood vulnerability for disaster decision-making: a case study remote sensing application in Senegal
Published in Earth Observation Open Science and Innovation, 2018
Published in Remote Sensing of Environment, 2018
NASA’s Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA’s Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.
Monitoring Annual Vegetated Land Loss to Urbanization with Landsat Archive: A Case Study in Shanghai, China
Published in Remote Sensing Time Series Image Processing, 2018
Urban expansion (urbanization) often causes significant disturbance to ecosystems surrounding cities, sometimes resulting in the removal of large amounts of biomass and in turn putting the human-nature systems at risk. Landsat imagery has long been utilized to monitor urbanization and ecosystem change at regional and local scales. However, few studies use Landsat time series to monitor urbanization at higher temporal frequencies, especially for applications focusing on large geographic areas, mainly due to the lack of efficient algorithms and computation facilities to handle large data volume. Here we extract annual vegetated land loss to urbanization information with Landsat time series and implement it on the Google Earth Engine (GEE), a cloud-computing platform, for large area applications. We first generate annual Landsat cloud/shadow free NDVI mosaics and then NDVI time series spanning the period from 2000 to 2010. We then develop change and stable models to identify change time points in the time series. We evaluate the performance of the proposed method in Shanghai, China, which has experienced rapid urbanization during the past few decades. Results show annual ecosystem disturbance caused by urban expansion is well captured, with a change detection accuracy higher than 80%. Our method is fast, simple, and can be easily extended to large areas on the Google Earth Engine cloud-computing platform.
Time series analysis of satellite data to characterize multiple land use transitions: a case study of urban growth and agricultural land loss in India
Published in Journal of Land Use Science, 2018
Urban land use change is one of the most impactful land transitions on the biosphere, resulting in land conversion, habitat loss, and changes in biogeochemical cycling, climate, and hydrology. Thus, understanding it is essential for global change research. Most land change detection algorithms assume linear changes. However, urban land-use changes are often nonlinear, i.e., follow multiple transitions over time. We propose a new methodology to identify multiple transitions due to urbanization with high frequency remote sensing time series. We design, implement, and evaluate a time series approach to detect the timing of urban conversion of agricultural land in India. Results show an overall accuracy of 82.11% in detecting change timing when the algorithm is applied to MODIS normalized difference vegetation index (NDVI) time series. The proposed algorithm yields better results with raw time series than filtered time series. We discuss the usefulness of our algorithm to understand nonlinear land transitions.
Published in Applications and Challenges of Geospatial Technology, 2018
Explaining urban growth patterns is a fundamental need to understand the recent rapid urbanization globally. This study identifies geographic features explaining the spatial patterns of urban land expansion (ULE) in the rapidly urbanizing Pune metropolitan region (India). ULE maps were derived from Landsat Thematic Mapper and Operational Land Imager images using support vector machine (SVM) classification. Relation between geographic features and spatial patterns of ULE was analyzed using statistical modelling including ordinary least squares (OLS) regression, spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). SEM specification best modeled ULE patterns. High density of existing urban areas is identified to negatively affect ULE, suggesting dominant dispersed urban growth. In addition, proximity to special economic zones and transportation infrastructure explains multicentric growth in the region. GWR model was identified inappropriate due to the presence of high local collinearity. Models accounting for spatial dependencies are recommended while studying ULE patterns.
Published in One Earth, 2019
Urban land use has impacts spread across time and place. How urban areas are built and configured in the coming decades will have long-term implications on the environment and the lives of billions of people. A systems approach that emphasizes the importance of science and the relevance of policy to understanding urban land use is vital to building a sustainable future.
Understanding urban growth inequalities in India using satellite measurements and socio-economic statistics