scholarly journals Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

2021 ◽  
Vol 11 (15) ◽  
pp. 6923
Author(s):  
Rui Zhang ◽  
Zhanzhong Tang ◽  
Dong Luo ◽  
Hongxia Luo ◽  
Shucheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Identification Accuracy ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Single Time ◽  
Growing Period ◽  
Sensing Technology ◽  
Sar Imagery ◽  
Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it

2014 ◽  
Vol 700 ◽  
pp. 394-399 ◽  
Author(s):  
Xin Ping Ma ◽  
Hong Ying Bai ◽  
Ying Na He ◽  
Shu Heng Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Phenology ◽  
Qinling Mountains ◽  
Threshold Method ◽  
Climate Change Research ◽  
Phenological Data ◽  
Growing Period ◽  
Macro Scale ◽  
Ndvi Time Series

The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .

Estimating ammonia and methane emissions from CAFOs using an open-path optical remote sensing technology

2007 ◽  
Author(s):  
Kyoung S Ro ◽  
Patrick G Hunt ◽  
Melvin H Johnson ◽  
Ariel A Szogi ◽  
Matias B Vanotti
Keyword(s):  
Remote Sensing ◽  
Methane Emissions ◽  
Optical Remote Sensing ◽  
Remote Sensing Technology ◽  
Open Path ◽  
Sensing Technology

scholarly journals Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3472
Author(s):  
Yuming Wei ◽  
Xiaojie Liu ◽  
Chaoying Zhao ◽  
Roberto Tomás ◽  
Zhuo Jiang
Keyword(s):  
Remote Sensing ◽  
Loess Plateau ◽  
High Precision ◽  
Large Scale ◽  
Surface Displacement ◽  
Optical Remote Sensing ◽  
Consolidation Process ◽  
Rapid Urbanization ◽  
Sar Imagery ◽  
The City

Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors” step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than −40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed.

Spatio-Temporal Segmentation Applied to Optical Remote Sensing Image Time Series

2018 ◽  
Vol 15 (8) ◽  
pp. 1299-1303 ◽  
Author(s):  
Wanderson Santos Costa ◽  
Leila Maria Garcia Fonseca ◽  
Thales Sehn Korting ◽  
Hugo do Nascimento Bendini ◽  
Ricardo Cartaxo Modesto de Souza
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Temporal Segmentation ◽  
Optical Remote Sensing Image ◽  
Spatio Temporal

Algorithm for Automatic Island Segmentation and Identification Based on Narrow Band Deformable Model in UAV Optical Remote Sensing Image

2015 ◽  
Vol 719-720 ◽  
pp. 1209-1216
Author(s):  
En Wei Zhao ◽  
He Meng Yang ◽  
Xiao Jie Wu ◽  
Zeng Zhang
Keyword(s):  
Remote Sensing ◽  
Narrow Band ◽  
Automatic Segmentation ◽  
Recognition Algorithm ◽  
Deformable Model ◽  
Optical Remote Sensing ◽  
Sensing Technology ◽  
Optical Remote Sensing Image ◽  
Initial Segmentation

The island survey is important in economic and strategic field, and in recent years the use of remote sensing technology becomes the mainstream in island investigation. As an effective way for improving the efficiency and accuracy of island survey, the automatic segmentation and recognition algorithm has greater significance. For the difficulty in application of deformed model to high-resolution remote sensing images, the segmentation framework of global initial segmentation and local extractive segmentation based on narrow band deformable model is proposed. Based on the sea and land extraction the island initial segmentation is accomplished, and then the narrow band deformable model is used to increase the accuracy of segmentation. Finally the double rings feature of island is used to improve the quality of the segmentation.

scholarly journals Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

2019 ◽  
Vol 26 (2) ◽  
pp. 63
Author(s):  
Desti Ayunda ◽  
Ketut Wikantika ◽  
Dandy A. Novresiandi ◽  
Agung B. Harto ◽  
Riantini Virtriana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Error Matrix ◽  
Tropical Peatland ◽  
Average Value ◽  
Sar Imagery ◽  
Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

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