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Correction to: Long-term spatio-temporal trends in atmospheric aerosols and trace gases over Pakistan using remote sensing

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Acta Geophysica 2023年第1期72卷 509-527页

作者： Javed, Muhammad Adil Mehmood, Usman Tariq, Salman Haq, Zia ul Department of Chemistry University of Management and Technology Lahore Pakistan Remote Sensing GIS and Climatic Research Lab (National Center of GIS and Space Applications) Centre for Remote Sensing University of the Punjab Lahore Pakistan Department Political Science University of Management and Technology Lahore Pakistan Department of Space Science University of the Punjab Lahore Pakistan

Air pollution is one of the most serious problems affecting atmosphere quality and human health worldwide. For the current study, various satellites and modeled datasets, i.e., PM2.5, SO2, AOD, CO, and Ozone were used over Pakistan between January 2005 and December 2021. According to the spatial distribution results, these characteristics have peak values throughout central Punjab, western Baluchistan, central Sindh, and Khyber Pakhtunkhwa due to wildfires, heavy traffics, and industrial activities. The seasonal variation of PM2.5, SO2, AOD, CO, and Ozone was calculated using monthly data. The highest value for PM2.5 is 8.7 × 10–8 kg/m3 during the monsoon season, while the highest value for SO2 is 1.4 × 10–5 kg/m2 during the winter. Over Punjab, Sindh, Baluchistan, KPK, and Gilgit, AOD was between 0.7 and 1.0, CO was 127.2 ppb, and Ozone was 330.7 DU. Furthermore, we create correlation maps of AOD, CO, SO2, PM2.5, and Ozone and evaluate their high and low values relationship across Pakistan. We investigated the strongest ever observed 0.99 correlation between AOD and PM2.5. Despite this, we look at time series graphs to show the rising and falling pattern of these parameters from January 2005 to December 2021. Tables were also utilized to calculate the relative change in a number of significant Pakistani cities, including Multan, Lahore, Karachi, Peshawar, Quetta, Rawalpindi, Faisalabad, Hyderabad, Gujranwala, and Abbottabad, between January 2005 and December 2021.

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Classification of Horticultural Crops in High Resolution Multispectral Imagery Using Deep Learning Approaches

Classification of Horticultural Crops in High Resolution Mul...

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Machine Intelligence for GeoAnalytics and remote sensing (MIGARS), International Conference on

作者： Anindya Palaparthi A.M. Ramiya Hebbar Ram Dr.Deepak Mishra Department of Earth and Space Sciences Indian Institute of Space Science and Technology Thiruvananthapuram India National Remote Sensing Center Regional Remote Sensing Center Bangalore India Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India

To match the ever increasing nutrition needs of growing population and dwindling agricultural labour force, agriculture sector has been progressively adapting farming practices with major technological interventions. Discrimination of different types of crops is a common requirement for mechanized crop management which includes tasks such as identifying pests, predicting crop yield etc. High resolution remote sensing data has been extensively used for various agricultural applications. Supervised classification has been the method of choice for crop discrimination in high resolution multispectral imagery. Considering the increasing complexity of agricultural lands wherein the co-occurrence of a host of crops and other urban landscape features is a common scenario evolved, classification of high resolution multispectral imagery using traditional supervised approaches is fraught with ambiguities and may lead to site-specific. We have explored the potential of high resolution multispectral satellite imagery (CARTOSAT-2D) for classifying horticultural crops under a complex landscape composition (rice, sugarcane, coconut, arecanut, forest, fallow land, urban, and water body) in Shivmogga region of Karnataka. Based on the selective ground truth data augmentation from implementing two traditional machine learning algorithms, we have adopted two popular deep learning (DL) network architectures: CNN based U-net and Seg net for the regional level classification of crops under complex agricultural settings. Validation of the results with independent ground truth data indicates fairly good accuracy of about 89 percent from the DL methods implemented. However, the accuracy of the results from the Random Forest algorithm is also competitive (about 80 percent) indicating the continued relevance of traditional ML models to crop classification at regional level.

关键词： Deep learning Image resolution Satellites Sociology Crops Network architecture Task analysis

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UAV and Deep Learning: Detection of selected riparian species along the Ganga River

UAV and Deep Learning: Detection of selected riparian specie...

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ISPRS Technical Commission I Midterm Symposium on Intelligent sensing and remote sensing Application

作者： Tripathi, Ravindra Nath Ramachandran, Aishwarya Agarwal, Karan Tripathi, Vikas Badola, Ruchi Hussain, Syed Ainul Ganga Aqualife Conservation Monitoring Center Wildlife Institute of India Dehradun248001 India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun248002 India Dept. of Remote sensing Birla Institute of Technology Mesra Ranchi835215 India

Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and wellbeing. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 x 3200 resolution. Additionally, our

关键词： Object detection

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Application of remote sensing in Coral Reef Mapping and Assessment of Climatic and Environmental Conditions

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Journal of Geoscience and Environment Protection 2024年第11期12卷 284-306页

作者： Amina Said Mark Boitt Michael Hahn Faculty of Geomatics Computer Science and Mathematics Hochschule fuer Technik Stuttgart Stuttgart Germany Institute of Geomatics GIS and Remote Sensing Dedan Kimathi University of Technology Nyeri Kenya

Coral reefs have tremendous benefits to both humans and marine biodiversity but are globally endangered. Traditionally, data collection of coral reefs was fieldwork based, which is time-consuming, costly and lacking in full coverage. This study shows that optical imageries can be utilised to map coral reefs, by fusing visible bands and depth invariant index (DII) to classify benthic features. Overall accuracy of at least 68 percent is achieved. However, to overcome the challenge of cloud cover, which is common in coastal areas, microwave imagery is used to map the emergent coral reefs. Again, remote sensing proves to be reliable in estimation of sea surface temperature (SST) and extraction of water quality parameters through Case-2 Region CoastColour (C2RCC) processor. Thereafter, regression analysis is performed to evaluate the correlation of SST and water quality. It is concluded that higher spatial resolution can improve coral mapping and that there is a need for more research on correlation to its environment.

关键词： Coral Reef DII SST C2RCC Water Quality

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Unsupervised Deep Adaptive Learning Spatial Reconstruction Network Based on Hyperspectral Data Fusion

Unsupervised Deep Adaptive Learning Spatial Reconstruction N...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Haoyang Yu Jinbei Zhao Xueteng Wang Zhixin Jiang Yao Liu Enyu Zhao Chunyan Yu Center of Hyperspectral Imaging in Remote Sensing (CHIRS) Information Science and Technology College Dalian Maritime University Dalian China Land Satellite Remote Sensing Application Center Ministry of Natural Resources of China Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Due to limitations of satellite imaging systems, hyperspectral image (HSI) often suffers from incomplete coverage, with certain regions of the study area missing. Data fusion and reconstruction are effective approaches to resolve the contradiction in spatial and spectral domains, where related theories have intensively developed in recent years. However, existing fusion methods are mostly applicable to simulated data and are challenging to apply to real data. In this paper, we propose an unsupervised fusion spatial reconstruction network namely UFSRnet, which not only reconstructs the missing regions of HSI but also learns the differences between heterogeneous data adaptively. Specifically, a sensor radiation deviation correction (SRDC) module is designed to tackle the disparities between heterogeneous data adaptively. The model demonstrates commendable performance across both simulated and real data sets.

关键词： Adaptation models Soft sensors Satellite broadcasting Data integration Data models Task analysis Spatial resolution

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InSAR Joint Modeling and Deformation Estimation for Highway Network in Soft Soil Areas

IEEE Journal on Miniaturization for Air and Space Systems

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IEEE Journal on Miniaturization for Air and Space Systems 2025年

作者： Long, Jingjian Xing, Xuemin Zheng, Guanfeng Wang, Liang Yao, Xiangjun Yang, Xiongwei Changsha University of Science and Technology School of Traffic and Transportation Engineering Changsha410114 China Changsha University of Science and Technology Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping Changsha410114 China Hunan Tianxianghe Information Technology Company Changsha410114 China

Objectives: Long-term deformation monitoring for highway network constructed in soft soil areas is essential. The plastic creep characteristic plays an important role in soft soil deformation. However, it has been neglected in most traditional InSAR time-series deformation models. To address this limitation, a joint modeling and deformation estimation method is proposed for highway networks in soft soil areas. technology or Method: The processing of deformation modeling and parameter estimation are performed separately for subgrades and bridges. For the roadbed objects constructed in soft soil areas, a Non-linear Visco-plastic Body Periodical Precipitation (NVPBPP) model, which combines the Non-linear Visco-plastic Body model with periodical and precipitation models to consider the plastic creep effects on temporal deformation for soft soil clay areas;For the bridge region, a Thermal Expansion Linear (TEL) model and the traditional linear velocity model are incorporated, which characterizes the thermal expansion properties for bridge material. Results: The experiment is conducted on a highway network including roads and three bridges in a soft soil area in Beijing. The time series settlement from 22 January 2012 to 6 February 2015 are generated, with the maximum cumulative settlement estimated as 135mm. The modeling accuracy of NVPBPP model is estimated as ± 6.5mm, with 61.3% improvement compared to traditional InSAR linear rate model;The external deformation cross-validation shows that our work has a high correlation coefficient of 0.97 with existed published results. Clinical or Biological Impact: Our method can provide data support and a reference for monitoring long-term health and ensuring transportation safety especially in poor soil regions. © 2019 IEEE.

关键词： Creep

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A Saliency-Aware Deep Network for Narrow Road Extraction of High-Resolution remote sensing Imagery

A Saliency-Aware Deep Network for Narrow Road Extraction of ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Ningjing Wang Yang Pan Xinyu Wang Wanqiang Yao Yanfei Zhong College of Geomatics Xi'an University of Science and Technology China School of Remote Sensing and Information Engineering Wuhan University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Road extraction from high-resolution remote sensing imagery is important and efficacious due to deep learning. However, most methods face challenges in capturing narrow roads, i.e. rural roads. In this paper, we propose a saliency-aware deep network (SAN) for narrow road extraction. Specifically, a multi-scale context module is employed to extract contextual features in different scales, and a global context module is followed to aggregate the above multi-scale context features, to improve the connectivity of narrow roads. In addition, motivated by visual saliency, a saliency-aware module is proposed to highlight roads during skip connections, to further separate narrow roads from complex backgrounds. In the experiments, SAN is compared with some state-of-the-art methods by using the DeepGlobe road dataset and achieves better performances, especially for narrow roads, which proves its superiority.

关键词： Deep learning Visualization Roads Face recognition Architecture Aggregates Neural networks

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Impacts of climate change and land cover factor on runoff in the Coastal Chinese Mainland region

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Geography and Sustainability 2024年第4期5卷 526-537页

作者： Song Song Ziqiang Ye Zhijie Zhou Xiaowei Chuai Rui Zhou Jinwei Zou Yi Chen School of Geography and Remote Sensing Guangzhou UniversityGuangzhou 510006China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511453China School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Jiangsu Provincial Academy of Social Sciences Nanjing 210004China Bao’an Institute of Education Sciences Shenzhen 518101China Zhejiang Design Institute of Water Conservancy and Hydroelectric Power Co. Ltd.Hangzhou 310002China

The increasingly frequent storms pose significant threats to the sustainable development of coastal regions,particularly in densely populated and economically vibrant *** the dynamics and intricate mechanisms underlying runoff generation is crucial in the context of climate change and anthropogenic *** on hydro-meteorological and land-use data from 1980 to 2018,this study investigates the runoff variation and its driving factors in the Coastal Chinese Mainland(CCM).The aims of this study are to reveal the temporal and spatial trends of runoff yield,to clarify the sensitivity of runoff in coastal cities to the integrated and individual parameters of climate change and anthropogenic interference,including precipitation(P),potential evapotranspiration(E_(0)),and land cover factor(n),and to support the establishment of spatially tailored adaptation *** results show that:(1)runoff has generally increased over the study period,particularly in regions such as the Yangtze River Delta,Shandong,and Guangxi,while it has decreased in western Liaoning and eastern Guangdong;(2)in the northern CCM with larger aridity index,the land cover factor plays a dominant role in runoff production,while in the wetter southern CCM,precipitation is more influential,and potential evapotranspiration mainly hinders runoff generation all over CCM;(3)urban expansion tends to negatively impact n,while the loss of grasslands and shrinkage of croplands tend to undermine the value of *** facilitate the achievement of sustainable development goals in the CCM,it is imperative to introduce a more comprehensive and theoretical framework that encompasses the natural,technical,and social dimensions of human-water systems into traditional flood regulation and water resource *** framework should promote interdisciplinary collaboration from an integrated perspective,to bridge the administrative and watershed boundaries,to effectively address the complex cha

关键词： Coastal Chinese Mainland Runoff production Elastic analysis Climate change Anthropogenic interference

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Estimation of winter wheat chlorophyll content by combing canopy spectrum red edge parameters with random forest machine learning

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2024年第4期40卷 166-176页

作者： Wang, Zhen Li, Yingxue Wu, Fang Zou, Xiaochen School of Remote Sensing and Surveying Engineering Nanjing University of Information Science and Technology Nanjing210044 China Technology Innovation Center of Integration Applicaitons in Remote Sensing and Navigation Ministry of Natural Resources P.R. China Nanjing210044 China Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications Nanjing210044 China School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing210044 China Xinghua Meteorological Bureau Xinghua225700 China

The sudden increase in vegetation canopy reflectance from low reflectance in red band to near infrared band forms the red-edge spectral characteristics, which is unique to healthy vegetation. Many parameters that can describe this characteristic have been designed and developed as important indicators of crop growth and nutrition status. However, few studies have systematically compared and evaluated the applicability of these red-edge parameters to estimate winter wheat leaf CHL values at different growth stages and nitrogen application levels. In this study, canopy spectral reflectance and leaf CHL of winter wheat at 4 key growth stages (jointing stage, heading stage, flowering stage and filling stage) and 3 nitrogen application levels were obtained through a 4-year field experiment. The sensitivity of 47 spectral red-edge parameters to CHL was evaluated, and the relative importance of spectral red-edge parameters was used to optimize the random forest machine learning model to estimate winter wheat CHL. The results showed that the sensitivity of spectral red edge parameter to CHL was affected by the growth period and nitrogen application level of winter wheat, and the correlation R2 between the best red edge parameter and CHL in a single growth period was between 0.39 and 0.89. The best red-edge parameter in the whole growth period was NDDRmid, and the coefficient of determination between it and CHL was 0.76. The sensitivity was the highest in filling stage, and the coefficient of determination between the best red edge parameter RVI5 and CHL was 0.89 and the R2 between red edge parameters REPRpi, NDDRmid, RVI2, RVI4, RVI5, RVI6, NDRE, RVI12 and RVI13 with CHL were all higher than 0.80. Nitrogen application level increased the sensitivity of red-edge parameter to CHL. At single nitrogen application level, the coefficient of determination between best red edge parameter and CHL was between 0.75 and 0.81. At N1 and N2 conditions the best red-edge parameter is NDDRm

关键词： Nitrogen

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Accelerated glacier mass loss in the mid-latitude Eurasia from 2019 to 2022 revealed by ICESat-2

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Advances in Climate Change Research 2024年第5期15卷 815-829页

作者： Gen-Yu WANG Chang-Qing KE Yu-Bin FAN Xiao-Yi SHEN Yu CAI Vahid NOURANI Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Collaborative Inovation Centerof Novel Sofiware Technology and Industrialization Nanjing 20023China Collaborative Innovation Center of South China Sea Studies Nanjing 210023China School of Earth Sciences and Engineering Hohai UniversityNanjing 210024.China Center of Excellence in Hydroinformatics and Faculty of Civil Engineering University of TabrizTabriz 51368Iran Faculty of Civil and Environmental Engineering Near East UniversityNicosia 99138Turkey

The dynamics of glaciers serve as one of the most important indicators of climate *** current research has primarily concentrated on long-term interannual glacier mass balance and its response to climate change,glaciers may respond more rapidly to climate change,highlighting the urgent need for intra-annual mass balance *** seasonal or short-term variations in glacier mass balance not only enhances our understanding of the interactions between glaciers and the climate system but also provides crucial data for water resource management and ecological *** ICESat-2 and NASADEM datasets were used to estimate the inter-and intra-annual glacier mass balance changes in the mid-latitude Eurasia from 2019 to ***,the response of glacier mass balance to regional air temperature and precipitation values was analysed using ERA5-Land data and multiple regression analysis,*** 2019 to 2022,glacier mass loss in mid-latitude Eurasia reached-45.02±34.21 Gt per year,contributing to a global sea-level rise of 0.12±0.09 mm per *** glacier melt rate in the study area from 2019 to 2022 was 2.33 times higher than that from 2000 to *** the exception of the Western Kunlun region,which experienced a weak accumulation rate of 0.04±0.35 m *** year,all other areas experienced ablation *** mass balance responds differently to temperature and precipitation variations across seasons:higher temperatures in different seasons lead to more negative mass balances,while increased winter and spring precipitation can slow down glacier *** temperature dominates the glacier mass balance changes in the study *** intense heat in 2022 raised average glacier temperatures by 1.04°C compared to 2019-2021,resulting in a more negative mass balance and an increased ice loss of-0.34±1.01 m *** year(-35.07±103.22 Gt per year).This analysis indicates that glacier mass balance is highly sensitive to climate chan

关键词： Glacier mass balance ICESat-2 NASADEM The mid-latitude Eurasia Temperature

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