Journal of the Indian Society of Remote Sensing最新文献

{"title":"Association of the Relationship Between Tectonic Lineaments and Natural Springs Around Nigde Massif, Central Anatolia, Turkey","authors":"Ramazan Demircioğlu","doi":"10.1007/s12524-024-01957-4","DOIUrl":"https://doi.org/10.1007/s12524-024-01957-4","url":null,"abstract":"<p>In this study, the aim was to determine the relationship between tectonic lineaments derived from satellite data and springs. The study area covers Gümüşler (Nigde) and its surroundings in the northern Nigde Massif. The study investigated the connection between this area’s tectonic lineaments and natural water resources. Remote sensing methods used in mineral exploration and the determination of geothermal fields have also been applied in this study, supported by intensive field studies. The high-grade metamorphic rocks of the massif exhibit faulted and fractured structures due to polyphase deformation, giving these rocks important aquifer characteristics. Numerous springs have formed due to the effects of faults and fractures. The study definitively established the relationship between 82 natural water resources (springs) and tectonic lineaments. Almost 87% of the identified natural water resources are located on lineaments. In addition, other springs were determined to have discharge due to discontinuities in formation boundaries.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"164 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System Design, Automatic Data Collection Framework and Embedded Software Development of Internet of Things (IoT) for Air Pollution Monitoring of Nagpur Metropolis","authors":"Anju Bajpai, T. P. Girish Kumar, G. Sreenivasan, S. K. Srivastav","doi":"10.1007/s12524-024-01943-w","DOIUrl":"https://doi.org/10.1007/s12524-024-01943-w","url":null,"abstract":"<p>In the era of smart computing, edge computing, and machine intelligence, the Internet of Things (IoT) is playing a greater role in establishing hyper connective, cost-effective infrastructure for monitoring the environment. With the increase in the level of urbanization and population density in very fast-growing cities like Nagpur, the increase in air pollution needs to be monitored. This requires a network of Pollution monitoring systems for carrying out spatio-temporal analysis of pollution in the city on a real-time basis. Such established networks can be a key to understand the sources of pollution under various city conditions. To monitor and manage air pollutants, it is essential to put in place monitoring stations at multiple places. Although commercial pollution monitoring stations exist, they are limited in number. In this study, an attempt has been made to develop and implement a network of IoT devices using cost effective Metal Oxide Semiconductor based gas sensors integrated with ATMEGA 328P Microcontroller. Commercial systems are found to be space, energy and cost expensive. The developed pollution monitoring system can be replicated easily since they are compact in size, cost-effective, network and energy independent. This study discusses the development and implementation of a network of 10 smart IoT sensors in the Nagpur metropolis. The developed smart air pollution monitoring system combines IoT technology with real-time pollution monitoring systems. It measures and monitors temperature, humidity and pollutant concentration of Carbon Monoxide, Ozone, Carbon Dioxide, Sulphur Dioxide and PM2.5 and Nitrous oxides simultaneously. The study envisages to support Sustainable Development Goals – SDG11 which aims to reduce the environmental impact of cities by improving air quality.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"80 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extraction of Lineaments Using Landsat Image and Digital Elevation Model: A Case Study of Zagros Orogenic Belt, West Iran","authors":"Shahriar Sadeghi, Ebrahim Sharifi Teshnizi, Rana Razavi Pash, Mohsen Golian","doi":"10.1007/s12524-024-01956-5","DOIUrl":"https://doi.org/10.1007/s12524-024-01956-5","url":null,"abstract":"<p>The extraction of structural lineaments was conducted on a portion of the Zagros orogenic belt in western Iran, using filters applied to Landsat satellite imagery (ETM) and a digital elevation model (DEM). The study area was divided into internal (Sanandaj-Sirjan) and external (Zagros) subzones by the Main Zagros Thrust. To extract lineaments, Edge Detector, Spectral Rationing, Principal Component Analysis (PCA) filters, and color combinations were applied to the ETM satellite imagery, while a directional filter (Sobel) was applied to the DEM for enhanced visual interpretation. The analysis identified 350 fault lineaments with a total length of 3689 km. The majority of these lineaments were shorter in length, with 188 lines measuring over 5 km, 110 lines between 5 and 10 km, 39 lines between 10 and 20 km, and 10 lines between 20 and 30 km. Only three lineaments exceeded 30 km in length. Statistical analysis of the lineaments, presented in Rose diagrams, revealed a predominance of NW and NE trends, with less frequent WNW, NNE, and E-W trends. The most dominant trend observed was NW. These findings suggest that the extracted lineaments are largely consistent with the faults in some inner subzones identified in previous studies of adjacent areas. However, differences in lineament orientations and densities, when considering subzones, were attributed to the likely reactivation of basement faults.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"26 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and Spectral Analysis of Resampling Algorithms in Image Fusion of Optical and Microwave Satellite Images: A Case Study Over Western Himalayas","authors":"Rajinder Kaur, Sartajvir Singh, Ganesh Kumar Sethi","doi":"10.1007/s12524-024-01912-3","DOIUrl":"https://doi.org/10.1007/s12524-024-01912-3","url":null,"abstract":"<p>The Western Himalayas receive significant snowfall, and it's essential to monitor the snow cover for various purposes like managing water resources, conducting hydrological research, and predicting avalanches using remote sensing techniques. With the pan-sharpening of different satellite sensor images, the essential features can be obtained to understand the snow cover dynamics. The resampling algorithms play a very important role in the pan-sharpening procedure to highlight the important features of the pan-sharpened dataset. However, the performance of various resampling algorithms for pan-sharpening is very rarely validated with optical and microwave datasets. In this article, the spatial and spectral analysis of different resampling algorithms, i.e., Nearest Neighbour (NN), Bilinear (BI), and Cubic Convolution (CC) resampling, have been performed using the fusion of optical and microwave satellite images. In this study, two datasets, i.e., MODIS (optical) and SCATSAT-1 (microwave) were used over the western Himalayas (i.e., Ladakh, Jammu and Kashmir, Himachal Pradesh, and Uttrakhand). To evaluate the performance of each resampled pan-sharpened dataset, the output is classified using a Support Vector Machine (SVM) classifier and a Spectral Angle Mapper (SAM) classifier. Root Mean Square Error values were computed to quantify the level of agreement between the pan-sharpened datasets and the ground truth data. The result of statistical analysis showed that NN-based pan-sharpened performed better than BI-based and CC-based pan-sharpened classified images with both classifiers i.e., SVM and SAM. This study is important in terms of the effective utilization of the resampling techniques along with pan-sharpening algorithms.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"32 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEALB: A Post-classification Framework for Regionalizing Local Climate Zones in the Urban Environment","authors":"Mrunali Vaidya, Ravindra Keskar, Rajashree Kotharkar","doi":"10.1007/s12524-024-01950-x","DOIUrl":"https://doi.org/10.1007/s12524-024-01950-x","url":null,"abstract":"<p>Local climate zone (LCZ) map, an outcome of a supervised classification procedure using satellite imagery, can be generated at different landscape resolutions. Because of the large spatial extent, huge-sized satellite imagery, and fine granularity, it is difficult to analyze supervised LCZ (pixel-classified satellite image) outcome creating a scope for some post-classification tasks. In this paper, we have proposed an entropy-based directional edge algorithm for locating LCZ boundaries, named as DEALB, which creates homogeneous LCZ regions and delineates their boundaries. In DEALB, an image is initially partitioned into <i>superpixels</i> using directional edges considered at different angles (0°, 90°, 45°, and 135°) within a specified spatial scale. Next, similar but spatially cohesive superpixels are clustered to form large homogeneous regions. Spatial cohesiveness, which is a crucial characteristic to be considered in landscape clustering, is implemented by using the breadth-first search and deque data structure. Further, to validate the correctness and pureness of boundaries in the absence of any ground truth image, we have proposed the concept of boundary purity index focusing on spatial contrast inside and outside of LCZ regions. We have demonstrated the algorithm on LCZ classified results for heterogeneous landscape of the city Nagpur in India that has been found useful by domain experts.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"111 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Sentinel-5P TROPOMI Data for Air Quality Assessment in the City of Oran, Western Algeria","authors":"Abdelhalim Bendib, Mohamed Lamine Boutrid","doi":"10.1007/s12524-024-01951-w","DOIUrl":"https://doi.org/10.1007/s12524-024-01951-w","url":null,"abstract":"<p>With economic development, the emergence of cities, and the growth of transportation, air quality has become a significant concern. This leads to ecological imbalance and threatens the health of millions of people. The city of Oran, due to its importance and economic growth in recent decades, is no exception. The primary objective of this study is to conduct a comprehensive analysis of pollutant trends for the period 2019–2022 and their relationships with surface temperatures. The data for four pollutants from Sentinel-5P, namely ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂), were obtained and processed using the Google Earth Engine (GEE) platform. Given the variability in emissions from year to year, the results from the Air Pollution Index (API) indicate a trend toward moderate to high pollutant concentrations (&gt; 1.54e-2 mol/m²), with the city being the primary source of pollutants. In 2019, 61% of the study area was heavily polluted, with concentrations exceeding 1.54e-2 mol/m². This percentage was 45% in 2020, 58% in 2021, and 44% in 2022, while concentrations below 1.54e-2 mol/m² represented 15%, 26%, 17%, and 30%, respectively. Except for SO₂ (&gt; 40 µg/m³), the levels of NO₂ (~ 10 µg/m³) and CO (&lt; 4 mg/m³) align with the levels recommended by the World Health Organization (WHO). Furthermore, a comparison of the results with surface temperatures shows that, except for O₃, no significant correlation exists. In the context of sustainable development, these findings represent a proactive strategy for understanding pollutant concentrations and formulating effective policies to improve air quality in the city of Oran.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"24 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Promoting Agricultural Sustainability in Semi-arid Regions: An Integrated GIS–AHP Assessment of Land Suitability for Encouraging Crop Diversification","authors":"B. Kalaiselvi, M. Lalitha, Ranabir Chakraborty, S. Dharumarajan, R. Srinivasan, V. Ramamurthy, K. Karunya Lakshmi, Rajendra Hegde, K. V. Archana","doi":"10.1007/s12524-024-01937-8","DOIUrl":"https://doi.org/10.1007/s12524-024-01937-8","url":null,"abstract":"<p>Informed decision regarding cultivating the right crop in the right land will guarantee maximum production, which is the need of the hour to meet the world’s burgeoning food demand and to ensure the sustainability of agriculture. The present study aimed to identify the land suitability for major crops in the semi-arid ecosystem of Palani block in Tamil Nadu by integrating the analytical hierarchy process (AHP) and geographic information system (GIS). Soil slope and various soil characteristics influencing crop growth such as soil depth, texture, drainage, gravelliness, pH and organic carbon were considered for assessing the land suitability. Weights and scores were assigned to the selected criteria and their respective sub-criteria based on their relative significance in influencing crop growth. It was found that soil drainage and texture were the most influencing factors for paddy cultivation, with weights of 0.49 and 0.27, respectively. For field beans, coconut, and guava, texture and depth were identified as the major influencing factors with high weightages ranging from 0.26 to 0.40. Results indicate that about 22% (8627 ha) of the study area was highly suitable for field beans, followed by paddy (18%). In contrast, paddy and coconut registered the largest land area under the marginally suitable class and were deemed unsuitable for about 19% and 21% of the land, respectively. For guava and field beans, respectively 37% and 44% of the land were found moderately suitable while 77% and 76.6% of the land were found suitable. Soil texture, soil depth, and drainage were identified as the major impediments to coconut and paddy suitability. An error matrix was generated by comparing the land suitability derived through the AHP–GIS method with the farmers’ opinions on land suitability for different crops. It indicated a high agreement between the suitability classes and farmers’ opinion for field beans, followed by coconut, guava and rice with kappa indices of 0.64, 0.51, 0.49 and 0.40 and overall accuracy of 75%, 65%, 62% and 60%, respectively. The present study not only helps in identifying suitable areas for crop cultivation, but also recommends land management strategies to each land parcel to improve land productivity and sustainability.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"80 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Effect of Relative Spectral Response on the Estimation of Atmospheric Parameters: A Case Study of Landsat 8 (OLI) and Sentinel 2 (MSI)","authors":"Akhilesh Kumar, Manu Mehta","doi":"10.1007/s12524-024-01945-8","DOIUrl":"https://doi.org/10.1007/s12524-024-01945-8","url":null,"abstract":"<p>Accurate estimation of atmospheric parameters is crucial for accurate atmospheric correction as well as aerosol retrieval. While spectral responsivity within a spectral band is much higher at central wavelength, the sensor is sensitive across the entire bandwidth, thereby increasing the possibility of differences in atmospheric parameters estimated using only the central wavelength as compared to that using the relative spectral response (RSR). In the present study, an attempt has been made to investigate the differences in the values of different atmospheric parameters and the consequent impact it has on surface reflectance (SR) estimation and aerosol optical depth (AOD) retrieval from Operational Land Imager (OLI) sensor onboard Landsat 8 and Multispectral Imager (MSI) onboard Sentinel 2. The SR has been estimated from the top-of-atmosphere signals using the Simplified and Robust Surface Reflectance Estimation Method (SREM). AOD has been thereafter, retrieved using a simplistic physics-based approach in single scattering approximation. The results suggest that though the difference in RSR and central wavelength derived parameters is quite small in absolute terms, the effect of RSR is more evident in case of green spectral band for Landsat 8 and in blue band for Sentinel 2. The difference in retrieved AOD is more pronounced in case of OLI as compared to MSI, with difference being more in green band for the former and in blue band for the latter.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"6 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-Based Detection of Deciduous Tree Species Using Structural and Spectral Characteristics","authors":"Mohammad Hassan Naseri, Shaban Shataee Jouibary","doi":"10.1007/s12524-024-01944-9","DOIUrl":"https://doi.org/10.1007/s12524-024-01944-9","url":null,"abstract":"<p>The use of remote sensing technology is essential for identifying and mapping tree species. In species management, remote sensing tools like Unmanned Aerial Vehicles (UAVs) are used because of their short-cycle replication, high-resolution images, and 3D capabilities. The main objectives of this research were to evaluate the ability to use UAV images and the reliability of three Nearest Neighbor (NN), Random Forest (RF), and Decision Tree (DT) algorithms, as well as the ability to differentiate deciduous tree species based on their spectral and structural characteristics. UAV images were obtained and processed, and 3D canopy crown structure features, i.e. DSM, DTM, CHM, and mean slope of the canopy crown, were prepared for object-based classification. The results showed that adding the structural feature of CHM, DSM, and slope, as combined with multispectral bands, could improve the results compared to using only multispectral bands for NN and RF algorithms. However, the DT algorithm provided the highest classification accuracy with an overall accuracy of 69.04% and a Kappa coefficient of 0.595, using spectral characteristics of the main bands, vegetation indices, and texture analysis. In contrast to the DT algorithm, which does not improve classification results by using tree structural properties, CHM shape properties in combination with their spectral properties can improve classification results. Overall, in dense deciduous forests where all trees have normal spectral reflections during the growing season, UAV images and structural features such as mean slope provide valuable information.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"17 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban Water Security: Geospatial Insights into the Water Scarcity of Bengaluru City during 2023–2024","authors":"Saksham Joshi, Bharath Kumar Reddy Kadapala, Nidhi Misra, B Simhadri Rao, K Chandrasekar, Abdul Hakeem, K Sreenivas, P V Raju, K Sreenivas, Prakash Chauhan","doi":"10.1007/s12524-024-01939-6","DOIUrl":"https://doi.org/10.1007/s12524-024-01939-6","url":null,"abstract":"<p>With a teeming population and rapid urban expansion, water demands in large cities are increasing multi-fold. Coping up with such large demands, especially during the post-monsoon season, poses a significant challenge to the line departments. The problem is exacerbated by the depleting ground water levels resulting from high withdrawals exceeding the recharge by large proportions. Bengaluru city, India, is experiencing a shortage of water during the post-monsoon of 2023-24. The present study employed the satellite data based geospatial products and hydrological modelling to analyse the water scarcity manifested in Bengaluru and southern districts of Karnataka. Rainfall and runoff analysis revealed the deviations from long-term average conditions, during 2023-24, leading to decreased surface water runoff, lower inflows into reservoirs/water bodies &amp; reduced water availability during 2023-24. Assessment of inflow patterns into key reservoirs underscores the impact of reduced runoff on water storage. Analysis of water spread dynamics and crop water stress using satellite data highlights the anomalies &amp; severity of the scarcity. The findings underscore the need to use in-season satellite data &amp; geospatial inputs for improved water management strategies and sustainable development practices to ensure urban water security.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"36 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}