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2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)最新文献

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Crop Yield Prediction Using Integration Of Polarimteric Synthetic Aperture Radar And Optical Data 基于偏振合成孔径雷达与光学数据集成的作物产量预测
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358978
M. Hosseini, I. Becker-Reshef, R. Sahajpal, Lucas Fontana, P. Lafluf, G. Leale, E. Puricelli, M. Varela, C. Justice
In this study, double-bounce parameter derived from Sentinel-1 was integrated with Difference vegetation index (DVI) derived from Landsat-8 for prediction of soybean yield at field level over central Argentina. Artificial Neural Network (ANN) model was trained using time series of Synthetic Aperture Radar (SAR) and optical features during the growing season. For comparison of SAR versus optical versus their integration for soybean yield prediction, the ANN model was trained and tested for three scenarios of SAR-only, optical-only and SAR-optical integration. Accuracies of yield prediction including correlation of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are 0.80, 0.589 t/ha, 0.445 t/ha for SAR-only; 0.65, 0.800 t/ha, 0.546 t/ha for optical-only; and 0.85, 0.554 t/ha, 0.389 t/ha for SAR-optical integration scenarios, respectively. These accuracies demonstrate of high potential of SAR and SAR-optical integration for soybean yield prediction at field level.
本研究将Sentinel-1卫星的双反弹参数与Landsat-8卫星的植被差异指数(DVI)相结合,用于预测阿根廷中部地区的大豆大田产量。利用合成孔径雷达(SAR)的时间序列和生长季节的光学特征训练人工神经网络(ANN)模型。为了比较合成孔径雷达与光学及其集成对大豆产量的预测效果,对人工神经网络模型进行了训练和测试,测试了三种场景:仅合成孔径雷达、仅光学和合成孔径雷达与光学集成。单用sar进行产量预测,包括决定相关性(R2)、均方根误差(RMSE)和平均绝对误差(MAE)的预测精度分别为0.80、0.589和0.445 t/ha;0.65、0.800 t/ha、纯光学0.546 t/ha;sar -光一体化场景下,分别为0.85、0.554、0.389 t/ha。这些精度表明,SAR和SAR-光学集成技术在大豆大田产量预测中具有很高的潜力。
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引用次数: 0
Analysis of Geochemical Data of Mica for the Development of Mineral Resources: Case of Southern Madagascar, Beraketa. 云母地球化学资料在矿产资源开发中的应用——以马达加斯加南部为例。
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358964
Miora Harivony Rakotondrabe, A. Mohandas, E. Rasolomanana
Beraketa is one of the localities in the South of Madagascar which has a strong potential in phlogopite mineral deposite. Many exploration operations are located in this area, the most important of which is the underground mine of Ampandrandava. The zone enters the shear axis of Ravintsara - Bongolava with a strong intensity of metamorphism. Various chemical and structural reactions have contributed to the deposits of numerous minerals in the region, namely: phlogopite, calcite, anhydrite, diopside, pyrite. Geochemical analysis has been carried out on samples collected, to assay the elements SiO2, TiO2, Al2O3, Cr2O3, FeO, MnO, MgO, BaO, K2O, NaO2, F and Cl in the phlogopites of the sector. These have been established with an aim of determining the structures and quality of these minerals and establishing an iso-content map for recovery for rational exploitation.
Beraketa是马达加斯加南部一个极具潜力的绢云母矿床。许多勘探作业都位于该地区,其中最重要的是Ampandrandava地下矿。该区进入Ravintsara - Bongolava剪切轴,具有强烈的变质作用。各种化学和结构反应促成了该地区许多矿物的沉积,即:云母、方解石、硬石膏、透辉石、黄铁矿。对采集的样品进行了地球化学分析,测定了该段云母中SiO2、TiO2、Al2O3、Cr2O3、FeO、MnO、MgO、BaO、K2O、NaO2、F和Cl等元素的含量。建立这些方法的目的是确定这些矿物的结构和质量,并建立一个等含量图,以便回收,以便合理开采。
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引用次数: 0
Sar And Optical Data Fusion Based On Anisotropic Diffusion With Pca And Classification Using Patch-Based Svm With Lbp 基于Pca的各向异性扩散Sar与光学数据融合与基于patch的Svm分类
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358949
Achala Shakya, M. Biswas, M. Pal
SAR (VV and VH polarization) and optical data are widely used in image fusion to use the complimentary information of each other and to obtain the better-quality image (in terms of spatial and spectral features) for the improved classification results. The optical data acquisition depends on whether conditions while SAR data can acquire the data in presence of clouds. This paper uses anisotropic diffusion with PCA for the fusion of SAR (Sentinel 1 (S1)) and Optical (Sentinel 2 (S2)) data for patch-based SVM Classification with LBP (LBP-PSVM). Fusion results with VV polarization performed better than VH polarization using considered fusion method. Classification results suggests that the LBP-PSVM classifier is more effective in comparison to SVM and PSVM classifiers for considered data.
SAR (VV和VH极化)和光学数据广泛用于图像融合,利用彼此的互补信息,获得质量更好的图像(在空间和光谱特征方面),以改进分类结果。光学数据的获取取决于条件,而SAR数据能否在有云的情况下获取数据。本文利用各向异性扩散与PCA融合SAR (Sentinel 1 (S1))和Optical (Sentinel 2 (S2))数据,利用LBP (LBP- psvm)进行基于patch的SVM分类。VV极化融合效果优于VH极化融合。分类结果表明,对于考虑的数据,LBP-PSVM分类器比SVM和PSVM分类器更有效。
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引用次数: 1
InGARSS 2020 Committees
Pub Date : 2020-12-01 DOI: 10.1109/ingarss48198.2020.9358914
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引用次数: 0
Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing 基于无人机的玉米作物缺水区遥感识别
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358930
A. Kumar, Shreeshan S, Tejasri N, P. Rajalakshmi, W. Guo, B. Naik, B. Marathi, U. Desai
Agronomic inputs such as water , nutrients and fertilisers play a vital role in the health, growth and yield of crops. The lack of each of these inputs induces biotic and abiotic stress in the crop. Farmers are relying on groundwater because of decreased rainfall. The irrigation method can be improved by acquiring awareness of the health of crops and soils. In general, crop and soil quality is controlled by means of manual observation, which is time-consuming, labour-intensive and contributes to incorrect choices and substantial waste of resources. There is also an immediate need to automate the inspection process that will finally benefit farmers and agricultural scientists. In this paper, the identification of the water-stressed areas in the crop(maize) field has been studied, and an Unmanned Aerial Vehicle (UAV) based remote sensing is used to automate the crop health-monitoring process. We proposed a framework (model) based on Convolutional Neural Networks (CNN) to identify the stressed and normal/healthy areas in the maize crop field. The performance of the proposed framework has been compared with different models of CNN, such as ResNet50, VGG-19, and Inception-v3. The results show that the proposed model outperforms the baseline models and successfully classify stressed and normal areas with 95 % accuracy on train data and 93 % accuracy with 0.9370 precision and 0.9403 F1 score on test data.
水、养分和肥料等农艺投入对作物的健康、生长和产量起着至关重要的作用。每一种投入的缺乏都会引起作物的生物和非生物胁迫。由于降雨量减少,农民依赖地下水。通过了解作物和土壤的健康状况,可以改进灌溉方法。一般来说,作物和土壤质量是通过人工观察来控制的,这是耗时的,劳动密集型的,并导致错误的选择和大量的资源浪费。目前还迫切需要自动化检验过程,这最终将使农民和农业科学家受益。本文研究了作物(玉米)田缺水区域的识别,并利用无人机(UAV)遥感技术实现作物健康监测过程的自动化。提出了一种基于卷积神经网络(CNN)的框架(模型)来识别玉米作物田间的胁迫区和正常/健康区。本文将该框架的性能与不同的CNN模型(如ResNet50、VGG-19和Inception-v3)进行了比较。结果表明,本文提出的模型优于基线模型,在列车数据和测试数据上分别以95%和93%的精度和0.9370的精度和0.9403的F1分数对应力区和法向区进行了分类。
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引用次数: 7
InGARSS 2020 Cover Page InGARSS 2020封面
Pub Date : 2020-12-01 DOI: 10.1109/ingarss48198.2020.9358937
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引用次数: 0
Land Use Changes and Their Effects on Urban Ecosystem Services Value: A Study of Khulna City, Bangladesh 土地利用变化及其对城市生态系统服务价值的影响——以孟加拉国库尔纳市为例
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358927
M. Patwary, Sadia Ashraf, F. Shuvo
The rapid growth of urbanization has altered the urban surfaces posing serious threats to natural ecosystems in the urban area worldwide. Landscape changes have a negative impact on urban ecosystem services. In this paper, we estimated the changes in ecosystem services value (ESV) in terms of land use land cover (LULC) for the 2014-2019 period in the Khulna City of Bangladesh. Landsat-8 images were used to estimate land use land cover changes over the study periods. The changes of ESV were calculated by following the previously published global value coefficient. The results show that the water body and vegetation significantly decreased in the study area. The net decline of ESV from 2014-2019 was US$ 2.4 million. The highest contribution of change in total ESV was the loss to the water body. We suggest formulating a sustainable land use policy to ensure ecological balance with urban growth.
城市化的快速发展改变了城市地表,对世界范围内城市地区的自然生态系统构成了严重威胁。景观变化对城市生态系统服务产生负面影响。本文以孟加拉国库尔纳市为研究对象,估算了2014-2019年土地利用和土地覆盖下生态系统服务价值(ESV)的变化。使用Landsat-8图像来估计研究期间的土地利用和土地覆盖变化。根据先前公布的全球价值系数计算ESV的变化。结果表明:研究区水体和植被明显减少;2014-2019年,ESV净下降240万美元。总ESV变化中贡献最大的是水体损失。我们建议制定可持续的土地利用政策,以确保生态平衡与城市发展。
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引用次数: 1
UAV-Thermal Imaging: A Robust Technology to Evaluate in-field Crop Water Stress and Yield Variation of Wheat Genotypes 无人机热成像:一种评估小麦基因型田间作物水分胁迫和产量变化的可靠技术
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358955
Sumanta Das, J. Christopher, A. Apan, Malini Roy Choudhury, S. Chapman, N. Menzies, Y. Dang
In recent years, unmanned aerial vehicle (UAV) - based thermal imaging techniques have become increasingly popular in precision agriculture, especially in monitoring crop biotic and abiotic stresses, and soil water, irrigation scheduling, and residue mapping. However, studies are limited on thermal imaging techniques in yield estimation and in-field variability assessment. Here we evaluate the potential of UAV thermal imaging techniques to assess crop water stress and predict grain yield of 18 contrasting wheat genotypes. We conducted an airborne campaign close to crop flowering to capture thermal imagery for a rain fed wheat experimental field in southern Queensland, Australia. Plot wise canopy temperatures (°C) (Tcanopy) were extracted from thermal imagery to determine crop water stress index (CWSI). Wheat grain yield was significantly correlated with CWSI (R2= 0.63; RMSE= 0.34 t/ha). The results suggest potential for UAV thermal imaging techniques to measure crop water status and predict yield under water-limited environments.
近年来,基于无人机(UAV)的热成像技术在精准农业中越来越受欢迎,特别是在作物生物和非生物胁迫监测、土壤水分、灌溉调度和残留物测绘等方面。然而,热成像技术在产量估算和田间变异性评估方面的研究有限。在此,我们评估了无人机热成像技术在评估作物水分胁迫和预测18种不同小麦基因型籽粒产量方面的潜力。我们在澳大利亚昆士兰州南部的一个雨养小麦试验田进行了一次接近作物开花的空降活动,以捕捉热图像。从热图像中提取逐图冠层温度(°C) (Tcanopy)来确定作物水分胁迫指数(CWSI)。小麦籽粒产量与CWSI显著相关(R2= 0.63;RMSE= 0.34 t/ha)。研究结果表明,在缺水环境下,无人机热成像技术在测量作物水分状况和预测产量方面具有潜力。
{"title":"UAV-Thermal Imaging: A Robust Technology to Evaluate in-field Crop Water Stress and Yield Variation of Wheat Genotypes","authors":"Sumanta Das, J. Christopher, A. Apan, Malini Roy Choudhury, S. Chapman, N. Menzies, Y. Dang","doi":"10.1109/InGARSS48198.2020.9358955","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358955","url":null,"abstract":"In recent years, unmanned aerial vehicle (UAV) - based thermal imaging techniques have become increasingly popular in precision agriculture, especially in monitoring crop biotic and abiotic stresses, and soil water, irrigation scheduling, and residue mapping. However, studies are limited on thermal imaging techniques in yield estimation and in-field variability assessment. Here we evaluate the potential of UAV thermal imaging techniques to assess crop water stress and predict grain yield of 18 contrasting wheat genotypes. We conducted an airborne campaign close to crop flowering to capture thermal imagery for a rain fed wheat experimental field in southern Queensland, Australia. Plot wise canopy temperatures (°C) (Tcanopy) were extracted from thermal imagery to determine crop water stress index (CWSI). Wheat grain yield was significantly correlated with CWSI (R2= 0.63; RMSE= 0.34 t/ha). The results suggest potential for UAV thermal imaging techniques to measure crop water status and predict yield under water-limited environments.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"150 6 1","pages":"138-141"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83150101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Long Term Trend of Aerosol Optical Depth (AOD) over Ahmedabad and Gandhinagar: A Satellite Approach 艾哈迈达巴德和甘地那加尔气溶胶光学深度(AOD)的长期趋势:卫星方法
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358958
Khushi Chanllawala, Tejas Turakhia, Rajesh C. Iyer
Satellite based observations can provide detailed knowledge in this regard on a long timescale covering a large spatial area. In this study Aerosol Optical Depth (AOD) data based on MODIS (Terra and Aqua), MISR satellite along with MERRA reanalysis product was analyzed to find out changes in the trend of AOD on urban area of Ahmedabad and Gandhinagar city from 2000 to 2018. The results strongly suggest that on this region there has been increased in AOD over the past years even though it shows a decrease during certain time period. The seasonal analysis of these cities show that the AOD is maximum in the months of summer and is minimum in the months of winter. Ahmedabad comparatively to Gandhinagar can be said a fairly polluted city. These results in general agree with the recently reported global increase in pollution- "global warming" and also the trend in some of the anthropogenic emissions.
基于卫星的观测可以在覆盖大空间区域的长时间尺度上提供这方面的详细知识。本研究利用MODIS (Terra和Aqua)、MISR卫星以及MERRA再分析产品的气溶胶光学深度(AOD)数据,分析了2000 - 2018年艾哈迈达巴德市区和甘地那格尔市AOD的变化趋势。结果强烈表明,在过去的几年中,该地区的AOD有所增加,尽管在某些时间段出现了下降。季节分析表明,AOD在夏季最大,冬季最小。艾哈迈达巴德与甘地那加尔相比,可以说是一个污染相当严重的城市。这些结果大体上与最近报道的全球污染增加——“全球变暖”——以及一些人为排放的趋势一致。
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引用次数: 0
Surface Deformation of The 2019 Mirpur Earthquake Estimated from Sentinel-1 Insar Data 从Sentinel-1 Insar数据估计2019年Mirpur地震的地表变形
Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358915
Divya Sekhar Vaka, Y. S. Rao, T. Singh
The coseismic surface displacement of the 2019 Mw 5.4 Mirpur earthquake is derived using Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. Two Sentinel-1 radar images before and after the earthquake acquired in interferometric wide swath mode are used for displacement map generation. Two definite lobes of deformation corresponding to subsidence and uplift are observed from the displacement map. The results indicate an uplift of 9.5 cm and subsidence of −6.2 cm in the earthquake epicentral region. Using a forward elastic dislocation model the causative source parameters of the earthquake are randomly searched using an iterative approach, which minimizes the error between the InSAR data and the modeled results. The inversion results indicate a rectangular fault of length ~10 km and width ~5 km is responsible for the earthquake. Other source parameters such as strike, dip, depth, and the slip of the earthquake are also calculated during the coseismic inversion.
利用差分合成孔径雷达干涉测量(DInSAR)技术推导了2019年Mw 5.4 Mirpur地震的同震地表位移。采用干涉宽幅方式获取的两幅Sentinel-1地震前后雷达图像进行位移图生成。在位移图上观测到沉降和隆升对应的两个明确的变形裂片。结果表明,地震震中地区隆起9.5 cm,沉降−6.2 cm。采用正演弹性位错模型,采用迭代法随机搜索震源参数,使InSAR数据与模型结果之间的误差最小化。反演结果表明,长10 km、宽5 km的矩形断层是此次地震的主因。在同震反演过程中,还计算了其他震源参数,如走向、倾角、深度和地震滑动。
{"title":"Surface Deformation of The 2019 Mirpur Earthquake Estimated from Sentinel-1 Insar Data","authors":"Divya Sekhar Vaka, Y. S. Rao, T. Singh","doi":"10.1109/InGARSS48198.2020.9358915","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358915","url":null,"abstract":"The coseismic surface displacement of the 2019 Mw 5.4 Mirpur earthquake is derived using Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. Two Sentinel-1 radar images before and after the earthquake acquired in interferometric wide swath mode are used for displacement map generation. Two definite lobes of deformation corresponding to subsidence and uplift are observed from the displacement map. The results indicate an uplift of 9.5 cm and subsidence of −6.2 cm in the earthquake epicentral region. Using a forward elastic dislocation model the causative source parameters of the earthquake are randomly searched using an iterative approach, which minimizes the error between the InSAR data and the modeled results. The inversion results indicate a rectangular fault of length ~10 km and width ~5 km is responsible for the earthquake. Other source parameters such as strike, dip, depth, and the slip of the earthquake are also calculated during the coseismic inversion.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"26 1","pages":"130-133"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76128821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
期刊
2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)
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