Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires ...understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.
•Spatiotemporal fusion framework is constructed for coal fire monitoring application.•The fusion of VIIRS with Landsat-8 OLI is achieved by weight-function based method.•The channel-specific ...retrieval of similar pixels is used in modified ESTARFM method.•The prediction error is reduced in modified ESTARFM method.
Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.
The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are ...extensively used for socio-economic studies. The improved NTL products from the Visible Infrared Imaging Radiometer Suite's Day/Night Band (VIIRS-DNB), on-board the Suomi National Polar-Orbiting Partnership spacecraft and National Oceanic and Atmospheric Administration - 20 (NOAA-20) spacecraft's, are now available since April 2012. This study investigates the potential of machine-learning algorithms for inter-calibrating them (i.e., DMSP-OLS and VIIRS-DNB) to produce time-series annual VIIRS-DNB-like NTL datasets for the time when VIIRS-DNB data did not exist, for long-term studies. Uttar Pradesh, one of the most populous and largest States of India, is selected as the study area. Two machine-learning algorithms are utilized: (1) Multi-Layer Perceptron (MLP), having deep neural networks (DNN) architecture, and (2) Random Forest (RF), a widely used method. The DMSP-OLS and VIIRS-DNB data of 2013 (common year of data availability) and ancillary data pertaining to land cover, topography, and road network are used to train the models. The qualitative and quantitative analysis of annual VIIRS-DNB-like NTL images simulated from annual DMSP-OLS composites of 2004-2012 indicates that RF captures better spatial details at the local-scale and is able to efficiently handle the saturation problem at urban centers; while MLP is found to be superior at regional-scale. Both MLP and RF models significantly reduce the blooming effect around settlements, a common problem observed in DMSP-OLS data. It is inferred that depending on the research objectives, both RF and MLP algorithms can be appropriately utilized for producing VIIRS-DNB-like NTL images from DMSP-OLS annual NTL composites. The research can be further expanded by using other DNN architecture-based algorithms and improved spatio-temporal ancillary datasets over areas with different socio-economic, physiographic, and climatic settings.
C-di-GMP, a bacterial second messenger plays a key role in survival and adaptation of bacteria under different environmental conditions. The level of c-di-GMP is regulated by two opposing activities, ...namely diguanylate cyclase (DGC) and phosphodiesterase (PDE-A) exhibited by GGDEF and EAL domain, respectively in the same protein. Previously, we reported a bifunctional GGDEF-EAL domain protein, MSDGC-1 from Mycobacterium smegmatis showing both these activities (Kumar and Chatterji, 2008). In this current report, we have identified and characterized the homologous protein from Mycobacterium tuberculosis (Rv 1354c) named as MtbDGC. MtbDGC is also a bifunctional protein, which can synthesize and degrade c-di-GMP in vitro. Further we expressed Mtbdgc in M. smegmatis and it was able to complement the MSDGC-1 knock out strain by restoring the long term survival of M. smegmatis. Another protein Rv 1357c, named as MtbPDE, is an EAL domain protein and degrades c-di-GMP to pGpG in vitro. Rv1354c and 1357c have seven cysteine amino acids in their sequence, distributed along the full length of the protein. Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. By proteolytic digestion and mass spectrometric analysis of MtbDGC, connectivity between cysteine pairs Cys94-Cys584, Cys2-Cys479 and Cys429-Cys614 was determined, whereas the third cysteine (Cys406) from N terminal was found to be free in MtbDGC protein, which was further confirmed by alkylation with iodoacetamide labeling. Bioinformatics modeling investigations also supported the pattern of disulfide connectivity obtained by Mass spectrometric analysis. Cys406 was mutated to serine by site directed mutagenesis and the mutant MtbC406S was not found to be active and was not able to synthesize or degrade c-di-GMP. The disulfide connectivity established here would help further in understanding the structure - function relationship in MtbDGC.
The current study highlights the importance of a detailed representation of urban processes in numerical weather prediction models and emphasizes the need for accurate urban morphology data for ...improving the near‐surface weather prediction over Delhi, a tropical Indian city. The Met Office Reading Urban Surface Exchange Scheme (MORUSES), a two‐tile urban energy‐budget parameterization scheme, is introduced in a high‐resolution (330‐m) model of Delhi. A new empirical relationship is established for the MORUSES scheme from the local urban morphology of Delhi. The performance is evaluated using both the newly developed empirical relationships (MORUSES‐IND) and the existing empirical relationships for the MORUSES scheme (MORUSES‐LON) against the default one‐tile configuration (BEST‐1t) for clear and foggy events and validations are performed against ground observations. MORUSES‐IND exhibits a significant improvement in the diurnal evolution of the wind speed in terms of amplitude and phase, compared with the other two configurations. Screen temperature (Tscreen$$ {T}_{\mathrm{screen}} $$) simulations using MORUSES‐IND reduce the warm bias, especially during the evening and night hours. The root‐mean‐square error of Tscreen$$ {T}_{\mathrm{screen}} $$ is reduced up to 29% using MORUSES‐IND for both synoptic conditions. The diurnal cycle of surface‐energy fluxes is reproduced well using MORUSES‐IND. The net longwave fluxes are underestimated in the model and biases are more significant during foggy events, partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during clear‐sky conditions, but it is not evident on foggy days. Compared with BEST‐1t, MORUSES‐IND represents the impact of urbanization more realistically, which is reflected in the reduction of the urban heat island and UCI in both synoptic conditions. Future works would improve the coupling between the urban surface energy budget and anthropogenic aerosols by introducing MORUSES‐IND in a chemistry aerosol framework model.
Urban morphology parameters: (a) planar‐area index, (b) building height, and (c) frontal‐area index as a function of urban fraction derived from ISRO high‐resolution LULC data.
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•Indoor bifacial photovoltaics (i-BPVs) emerge as a promising solution for efficient and economical light energy harvesting.•Perovskite based i-BPVs show exceptional potential for ...indoor applications because of high-power conversion efficiency and design flexibility.•Developing perovskite-based BPV integrated IoT systems for indoor use is crucial in the current scenario, despite challenges such as stability, toxicity, and commercialization.
Bifacial photovoltaics (BPVs) have received tremendous attention as a potential contender for efficient and cost-effective light energy harvesting. Recently, advancements in BPV technologies have broadened their scope, allowing them to harness artificial indoor light energy efficiently from both top and bottom sides. This innovative approach has demonstrated its effectiveness in energy harvesting through a single-cell design. Among various available PV technologies, thin-film perovskite photovoltaics (PPVs) exhibit exceptional promise for indoor applications in low-light environments. They offer high-power conversion efficiency and ease of design, making them a superior choice when compared to other emerging indoor PV technologies such as kesterite and dye-sensitized PVs. To improve the performance of the indoor BPVs, it is necessary to further review several characteristics relating to their materials, architecture, processing, and indoor characterizations. Additionally, a comprehensive understanding of charge-transfer mechanisms, the variability in indoor lighting sources, the development of standardized indoor simulators, assessment of materials toxicity, and considerations of scalability are essential elements that must be addressed to advance this technology. In summary, this review examines recent developments, emerging trends, challenges, and provide suggestions for the further advancement of i-BPVs, also highlighting their potential as reliable and sustainable indoor energy-harvesting solution.
Improved DMSP nighttime light monthly products over India Jindal, Mehak; Gupta, Prasun Kumar; Srivastav, Sushil Kumar
International journal of remote sensing,
12/2022, Letnik:
ahead-of-print, Številka:
ahead-of-print
Journal Article
Recenzirano
Monthly night-time light (NTL) data can be very useful for studying intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines started providing monthly NTL imagery ...for free since 2021; based on Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) data. In the current study, an attempt has been made to produce an improved monthly DMSP product from 1992 to 2013. The monthly DMSP product suffers from several drawbacks such as spatial inconsistency, random fluctuations in the data for consecutive years, pixel saturation in bright cores, and blooming effect around settlements. As a result, geometric errors, background noise, and radiometric errors persist in the monthly DMSP images. This research tries to address each of these errors by applying various techniques to produce a vigorously tested and improved monthly DMSP dataset consisting of 216 images (1992-2013). Automatic intensity-based registration has been used to register the monthly images to their corresponding annual composite stable light image. Thresholding and land cover data have been used to remove the background noise. Finally, ridgeline regression is implemented considering F14 December 2003 as the reference image. Each technique has been verified by qualitative analysis of the intermediate outputs. The quantitative assessments of the improved monthly DMSP product reveal that it is a good proxy measure when split from 1992 to 2002 and 2003-2013 with an R
2
of 0.84 & 0.82 w.r.t GDP and 0.83 & 0.80 w.r.t population in contrast to the original DMSP data with R
2
of 0.70 & 0.75 w.r.t GDP and 0.73 and 0.80 w.r.t population. Transect analysis across six urban cities verifies that the improved product shows reduced saturation in urban areas and increased contrast in sub-urban regions. Finally, a consistent monthly DMSP product is delivered to the research community which will be useful for studying time-series intra-year changes in urbanization, economic growth, and other socio-economic dynamics.
Satellite-based nighttime lights (NTL) are often used as indicators of the socioeconomic dynamics of an area. The Earth Observation Group at Colorado School of Mines provides monthly NTL products ...from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992–December 2013). VIIRS-DNB (hereinafter referred to as VIIRS) and DMSP-OLS (hereinafter referred to as DMSP) NTL images have 15-arc second and 30-arc second spatial resolution, respectively. In the current study, an attempt has been made to generate synthetic (fine-resolution) monthly VIIRS-like products of 1992–2012 from (coarse-resolution) DMSP products, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992–2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done by thresholding. Improved monthly NTL images of DMSP-OLS and VIIRS from April 2012–December 2013 were used in a conditional generative adversarial network (cGAN) along with the land cover, as auxiliary input, to generate VIIRS-like monthly imagery from 1992 to 2012. The modelled imagery was aggregated annually, which showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products (2000–2020) over India; R2 of 0.85 w.r.t GDP; and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis is also performed on both country and State scales. Visual analysis over 1992–2013 confirms a gradual increase in the brightness of the nighttime lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, synthetically generated monthly VIIRS-like products are delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.
Assessment of urban growth is considered an important study for urban areas. The growth patterns and dynamics play a pivotal role in defining urban areas. Spatial metrics can prove to be instrumental ...in analysing these dynamics. The study focuses on categorizing urban growth and quantifying the growth compactness and speed of Bengaluru and Karnataka using built-up obtained from Global Human Settlement Layer (GHSL) and Nighttime Lights (NTLs) data, respectively. The evolution of Bengaluru and the skewed development in Karnataka are also observed. Landscape Expansion Index (LEI) is used to identify growth categories showing that 37.75% of Bengaluru's growth was as edge expansion. Area-weighted mean Expansion Index (AWMEI), Urban Expansion Intensity Index (UEII) and Shannon's entropy (SE) are used to quantify growth compactness, speed and entropy, respectively. The city has more growth compactness and speed compared to any other settlement in Karnataka. These urban growth dynamics shall help decision-makers in making data-driven decisions for urbanscape.
Paucity of information on Urban Canopy Parameters (UCPs) is considered as one of the major reason behind limited urban climate research and modelling in developing region of Asia, Africa, and Latin ...America. UCPs define those characteristics of urban built form which have direct or indirect influence on urban climate. Most of the studies in developed world have utilized 3-Dimension (3D) Geographic Information System (GIS) database either developed from ground survey or Remotely Sensed (RS) data such as Aerial Photographs, Airborne Light Detection and Ranging (LiDAR) and high-resolution Interferometric Synthetic Aperture Radar (InSAR) data for retrieval of UCPs. However, non-existence of 3D GIS database and limited availability of above RS datasets in developing region necessitates to employ widely available low-cost alternative datasets for retrieval of UCPs. Hence, this study focuses on retrieval of UCPs by employing Very High Resolution Satellite (VHRS) optical stereo data, which has repeat availability, low cost and extensive coverage, in a highly dense and complex urban environment with a challenging composite climate like Delhi, India. A novel methodology for extraction of gridded UCPs from VHRS optical stereo data has been developed in this study to overcome the limitation of extraction of individual building footprint in dense and compact urban built-up. The validation of key UCPs such as building height (Mean error, Root Mean Square Error (RMSE) and Mean Absolute Error <1 m in all height groups), Building Surface Area (Accuracy 84.27%) and Sky View Factor (RMSE = 0.046 and correlation = 0.94) with ground measurements has displayed reasonable accuracies. Hence, the study demonstrates successfully the use of VHRS optical satellite stereo data for generation of gridded UCPs in a highly heterogeneous and dense urban built-up environment. The developed approach is globally replicable in any city of any country.
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