Journal of the Indian Society of Remote Sensing最新文献_第10页

Modeling the Surface Thermal Discomfort Index (STDI) in a Tropical Environments using Multi Sensors: A Case Study of East Kalimantan, The Future New Capital City of Indonesia 利用多种传感器模拟热带环境中的地表热不舒适指数（STDI）：印度尼西亚未来新首都东加里曼丹案例研究

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-18 DOI: 10.1007/s12524-024-01919-w

Parwati Sofan, Khalifah Insan Nur Rahmi, Nurwita Mustika Sari, Jalu Tejo Nugroho, Trinah Wati, Anjar Dimara Sakti

{"title":"Modeling the Surface Thermal Discomfort Index (STDI) in a Tropical Environments using Multi Sensors: A Case Study of East Kalimantan, The Future New Capital City of Indonesia","authors":"Parwati Sofan, Khalifah Insan Nur Rahmi, Nurwita Mustika Sari, Jalu Tejo Nugroho, Trinah Wati, Anjar Dimara Sakti","doi":"10.1007/s12524-024-01919-w","DOIUrl":"https://doi.org/10.1007/s12524-024-01919-w","url":null,"abstract":"Thermal Discomfort Index has traditionally relied on parameters such as air temperature and relative humidity, obtained either from meteorological ground stations or through land-physical approaches estimated independently by satellites. These methods often fall short in adequately capturing both seasonal and detailed local spatial variations. This study addresses these limitations by establishing the Surface Thermal Discomfort Index (STDI), a composite of the Meteorological Discomfort Index (MDI) and the Discomfort Index over the land surface (DI-Land). Focused on Ibu Kota Negara Nusantara (IKN) in East Kalimantan and neighboring cities, MDI is derived from reanalysis data (ERA5-Land), validated with ground station data, while DI-Land is produced primarily from Landsat-8. An equal weighting factor was applied to MDI and DI-Land for estimating STDI. Results indicate that STDI captures both seasonal and spatial variations, reaching peak level in May and October, and hitting a low point in July. The spatial distribution of STDI is influenced by landuse types. In 2023, IKN experienced an STDI of 26.2 °C, while Balikpapan and Samarinda recorded at 26.5 and 26.4 °C, respectively. Compared to previous study in Jakarta, IKN and neighboring cities’s STDI are higher up to 0.2 °C, remaining within the partially comfortable range in the tropics. Projecting IKN’s development until 2045, an annual MDI increase of 0.01 °C is anticipated. Moreover, a 4% rise in built-up areas is expected to elevate STDI by 0.1–0.2 °C. This study provides insights into the thermal discomfort status in cities across East Kalimantan, anticipating a gradual increase in discomfort levels during the development of IKN.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516538","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}

引用次数: 0

Temporal and Spatial Changes and Driving Forces of Carbon Stocks and Net Ecosystem Productivity: A Case Study of Zoige County, Sichuan Province, China 碳储量和生态系统净生产力的时空变化及驱动力：中国四川省措勤县案例研究

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-18 DOI: 10.1007/s12524-024-01911-4

Xiyang Feng, Zhe Wang, Zhenlong Zhang, Jiaqian Zhang, Qiuping Zeng, Duan Tian, Chao Li, Li Jiang, Yong Wang, Bo Yuan, Yan Zhang, Jianmei Zhu

{"title":"Temporal and Spatial Changes and Driving Forces of Carbon Stocks and Net Ecosystem Productivity: A Case Study of Zoige County, Sichuan Province, China","authors":"Xiyang Feng, Zhe Wang, Zhenlong Zhang, Jiaqian Zhang, Qiuping Zeng, Duan Tian, Chao Li, Li Jiang, Yong Wang, Bo Yuan, Yan Zhang, Jianmei Zhu","doi":"10.1007/s12524-024-01911-4","DOIUrl":"https://doi.org/10.1007/s12524-024-01911-4","url":null,"abstract":"This study analysed the spatiotemporal changes in carbon stocks and Net Ecosystem Productivity (NEP) in Zoige County, Upper Yellow River, from 2000 to 2020 in response to China’s ecological civilization ideology and sustainable development. The carbon stock module of the InVEST model and carbon source/sink calculation formula were employed, and GeoDetector was used to analyze driving forces and spatial distributions. The findings were as follows: (1) The land use in Zoige County had undergone significant changes over the past two decades, characterized by a reduction in grassland area due to its conversion into woodland and peat wetland. (2) The carbon stock in Zoige County had consistently increased, accumulating 5.19 × 106 tons. (3) Zoige County had functioned as net ecosystem productivity (NEP) over the past two decades, with increasing trends, averaging 3.335 kg C/m2. (4) The primary driving force behind changes in carbon stock and NEP were identified as ‘biological abundance’.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505106","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}

引用次数: 0

Application of Efficient Channel Attention and Small-Scale Layer to YOLOv5s for Wheat Ears Detection 将高效通道关注和小规模层应用于 YOLOv5s 的麦穗检测

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-18 DOI: 10.1007/s12524-024-01913-2

Feijie Dai, Yongan Xue, Linsheng Huang, Wenjiang Huang, Jinling Zhao

{"title":"Application of Efficient Channel Attention and Small-Scale Layer to YOLOv5s for Wheat Ears Detection","authors":"Feijie Dai, Yongan Xue, Linsheng Huang, Wenjiang Huang, Jinling Zhao","doi":"10.1007/s12524-024-01913-2","DOIUrl":"https://doi.org/10.1007/s12524-024-01913-2","url":null,"abstract":"Wheat is a crucial global grain crop that plays a vital role in ensuring food security worldwide. The automatic and accurate counting of wheat ears is essential for assessing wheat yield. However, the detection accuracy is greatly affected by the complex background and small target size. To address these challenges and improve the performance, we propose an enhanced YOLOv5s method. In the backbone, we introduce the efficient channel attention (ECA) to enhance the feature extraction capability of the original C3 module. Additionally, we incorporate a small-scale detection layer in the neck and prediction stages. This modification expands the original three-scale feature detection (20 × 20, 40 × 40, and 80 × 80) to a four-scale feature detection (20 × 20, 40 × 40, 80 × 80, and 160 × 160), thereby enhancing the recognition accuracy of small targets. Experimental results demonstrate that our method achieves an Accuracy (Acc) of 93.97%, which represents a 2.94% improvement over the YOLOv5s. Additionally, our method has a mean absolute error (MAE) of 0.57, a reduction of 0.6 from the YOLOv5s. The Acc of the improved YOLOv5s approaches that of YOLOv7; however, the giga floating-point operations per second (GFLOPs) and inference speed of the enhanced YOLOv5s are significantly lower than those of YOLOv7. Across various phases of the wheat test dataset, the enhanced model demonstrated superior performance. As a result, the enhanced YOLOv5s enhances its suitability for challenging field conditions and offers a dependable technical framework for ear detection and wheat yield estimation.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516541","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}

引用次数: 0

Development of Fog Detection Algorithm Using AWiFS Data: A Case Study Over Indo-Gangetic Plains 利用 AWiFS 数据开发雾探测算法：印度-甘地平原案例研究

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-18 DOI: 10.1007/s12524-024-01907-0

Sasmita Chaurasia

引用次数: 0

Deep Learning Techniques for Crater Detection on Lunar Surface Images from Chandrayaan-2 Satellite 利用深度学习技术检测 Chandrayaan-2 卫星拍摄的月球表面图像中的陨石坑

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-17 DOI: 10.1007/s12524-024-01909-y

Sanjay Raju, S. Nandakishor, Sreerag K. Vivek, S. Don

引用次数: 0

CSDUNet: Automatic Cloud and Shadow Detection from Satellite Images Based on Modified U-Net CSDUNet：基于修正 U-Net 的卫星图像云影自动检测技术

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-16 DOI: 10.1007/s12524-024-01903-4

S. R. Surya, M. Abdul Rahiman

{"title":"CSDUNet: Automatic Cloud and Shadow Detection from Satellite Images Based on Modified U-Net","authors":"S. R. Surya, M. Abdul Rahiman","doi":"10.1007/s12524-024-01903-4","DOIUrl":"https://doi.org/10.1007/s12524-024-01903-4","url":null,"abstract":"Detection of clouds and shadows in remote sensing imagery is important due to its wide range of applications. There are a lot of applications in remote sensing images such as monitoring of the environment, change detection etc. It is an important and booming research area. Ineffective and inaccurate cloud and cloud shadow masking will cause undesirable effects on different task that can be performed by using remote sensing images. Because of high spectral conglomeration and the spectral and temperature discrepancy of the underlying surface the detection of clouds and associated shadows is not candid. In this paper, we propose CSDUNet a modified U-Net network for precise pixel-wise semantic segmentation of cloud and its associated shadow from optical remote sensing images. It uses an encoder network and a decoder network. This method concatenated feature maps at different scales. We have proposed a novel network for cloud detection, which extract features corresponding cloud and shadow at different scales from multilevel layers to generate sharp boundaries. Which will help to detect clouds in heterogeneous landscape, under complex underlying surfaces with varying geometry. Experimental analysis on the Landsat satellite dataset proves that the proposed CSDUNet achieves a dice coefficient of 95.05%. Our method got 95.93% precision, recall of 94.71% and Jaccard index of 97.29%. CSDUNet achieves accurate detection accuracy and surpass several traditional methods.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516543","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}

引用次数: 0

Study on Hyperspectral Remote Sensing Based Rapid Determination of Coal Quality Parameters 基于高光谱遥感技术的煤质参数快速测定研究

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-15 DOI: 10.1007/s12524-024-01893-3

Chinmay Mondal, Aditya Pandey, Samir Kumar Pal, Biswajit Samanta, Dibyendu Dutta

引用次数: 0

A Novel Deep Learning Approach for High-Resolution Satellite-Based DEM Filtering 基于卫星的高分辨率 DEM 滤波的新型深度学习方法

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-15 DOI: 10.1007/s12524-024-01902-5

J. G. Singla, Hinal B. Patel, Darshan K. Patel

引用次数: 0

AUXG: Deep Feature Extraction and Classification of Remote Sensing Image Scene Using Attention Unet and XGBoost AUXG：利用注意力 Unet 和 XGBoost 对遥感图像场景进行深度特征提取和分类

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-15 DOI: 10.1007/s12524-024-01908-z

Diksha Gautam Kumar, Sangita Chaudhari

引用次数: 0

Using Selective Principal Component Analysis (SPCA) for Lithologic Mapping of Different Granitic Phases in South Sinai, Egypt 利用选择性主成分分析 (SPCA) 绘制埃及西奈半岛南部不同花岗岩岩相的岩性图

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-11 DOI: 10.1007/s12524-024-01892-4

Kholoud M. AbdelMaksoud, Reda A. El-Arafy

引用次数: 0