Pub Date : 2020-12-01DOI: 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.
{"title":"Crop Yield Prediction Using Integration Of Polarimteric Synthetic Aperture Radar And Optical Data","authors":"M. Hosseini, I. Becker-Reshef, R. Sahajpal, Lucas Fontana, P. Lafluf, G. Leale, E. Puricelli, M. Varela, C. Justice","doi":"10.1109/InGARSS48198.2020.9358978","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358978","url":null,"abstract":"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.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"35 1","pages":"17-20"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80632464","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}
Pub Date : 2020-12-01DOI: 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.
{"title":"Analysis of Geochemical Data of Mica for the Development of Mineral Resources: Case of Southern Madagascar, Beraketa.","authors":"Miora Harivony Rakotondrabe, A. Mohandas, E. Rasolomanana","doi":"10.1109/InGARSS48198.2020.9358964","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358964","url":null,"abstract":"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.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"25 1","pages":"110-113"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77431943","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}
Pub Date : 2020-12-01DOI: 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。
{"title":"Sar And Optical Data Fusion Based On Anisotropic Diffusion With Pca And Classification Using Patch-Based Svm With Lbp","authors":"Achala Shakya, M. Biswas, M. Pal","doi":"10.1109/InGARSS48198.2020.9358949","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358949","url":null,"abstract":"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.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"21 1","pages":"25-28"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81712347","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}
Pub Date : 2020-12-01DOI: 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.
{"title":"Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing","authors":"A. Kumar, Shreeshan S, Tejasri N, P. Rajalakshmi, W. Guo, B. Naik, B. Marathi, U. Desai","doi":"10.1109/InGARSS48198.2020.9358930","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358930","url":null,"abstract":"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.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"24 1","pages":"146-149"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90107983","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}
Pub Date : 2020-12-01DOI: 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.
{"title":"Land Use Changes and Their Effects on Urban Ecosystem Services Value: A Study of Khulna City, Bangladesh","authors":"M. Patwary, Sadia Ashraf, F. Shuvo","doi":"10.1109/InGARSS48198.2020.9358927","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358927","url":null,"abstract":"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.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"24 1","pages":"62-65"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75835832","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}
Pub Date : 2020-12-01DOI: 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.
{"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}
Pub Date : 2020-12-01DOI: 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.
{"title":"Long Term Trend of Aerosol Optical Depth (AOD) over Ahmedabad and Gandhinagar: A Satellite Approach","authors":"Khushi Chanllawala, Tejas Turakhia, Rajesh C. Iyer","doi":"10.1109/InGARSS48198.2020.9358958","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358958","url":null,"abstract":"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.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"18 1","pages":"189-192"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82498926","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}
Pub Date : 2020-12-01DOI: 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.
{"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}