scholarly journals Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

2016 ◽  
Vol XLI-B8 ◽  
pp. 1413-1416 ◽  
Author(s):  
R. A. Parekh ◽  
R. L. Mehta ◽  
A. Vyas
Keyword(s):  
Optical Sensors ◽  
Large Scale ◽  
Monsoon Season ◽  
Scanning System ◽  
Vegetation Density ◽  
Data Set ◽  
Multi Temporal ◽  
Sar Data ◽  
Rabi Crops ◽  
Color Composite

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9<sup>th</sup>Dec. 2012, 28<sup>th</sup>Jan. 2013 and 22<sup>nd</sup> Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

scholarly journals Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

2016 ◽  
Vol 13 (4) ◽  
pp. 961-973 ◽  
Author(s):  
W. Simonson ◽  
P. Ruiz-Benito ◽  
F. Valladares ◽  
D. Coomes
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Critical Role ◽  
Carbon Fluxes ◽  
Carbon Dynamics ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Low Carbon ◽  
Data Set ◽  
Multi Temporal

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 yr−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha−1 yr−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 yr−1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

scholarly journals EVALUATING UNMANNED AERIAL PLATFORMS FOR CULTURAL HERITAGE LARGE SCALE MAPPING

2016 ◽  
Vol XLI-B5 ◽  
pp. 355-362 ◽  
Author(s):  
A. Georgopoulos ◽  
C. Oikonomou ◽  
E. Adamopoulos ◽  
E. K. Stathopoulou
Keyword(s):  
Cultural Heritage ◽  
Optical Sensors ◽  
Large Scale ◽  
Point Clouds ◽  
Short Review ◽  
Data Set ◽  
Heritage Sites ◽  
3D Point Clouds ◽  
Aerial Platforms ◽  
3D Information

When it comes to large scale mapping of limited areas especially for cultural heritage sites, things become critical. Optical and non-optical sensors are developed to such sizes and weights that can be lifted by such platforms, like e.g. LiDAR units. At the same time there is an increase in emphasis on solutions that enable users to get access to 3D information faster and cheaper. Considering the multitude of platforms, cameras and the advancement of algorithms in conjunction with the increase of available computing power this challenge should and indeed is further investigated. In this paper a short review of the UAS technologies today is attempted. A discussion follows as to their applicability and advantages, depending on their specifications, which vary immensely. The on-board cameras available are also compared and evaluated for large scale mapping. Furthermore a thorough analysis, review and experimentation with different software implementations of Structure from Motion and Multiple View Stereo algorithms, able to process such dense and mostly unordered sequence of digital images is also conducted and presented. As test data set, we use a rich optical and thermal data set from both fixed wing and multi-rotor platforms over an archaeological excavation with adverse height variations and using different cameras. Dense 3D point clouds, digital terrain models and orthophotos have been produced and evaluated for their radiometric as well as metric qualities.

scholarly journals Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

2018 ◽  
Vol 7 (10) ◽  
pp. 389 ◽  
Author(s):  
Wei He ◽  
Naoto Yokoya
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Super Resolution ◽  
Optical Image ◽  
Optical Data ◽  
Image Simulation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Multi Temporal ◽  
Sar Data

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.

scholarly journals Mapping an Urban Boundary Based on Multi-Temporal Sentinel-2 and POI Data: A Case Study of Zhengzhou City

2020 ◽  
Vol 12 (24) ◽  
pp. 4103
Author(s):  
Zhe Wang ◽  
Haiying Wang ◽  
Fen Qin ◽  
Zhigang Han ◽  
Changhong Miao
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Reference Value ◽  
Training Sample ◽  
Data Set ◽  
Construction Land ◽  
Urban Construction ◽  
Multi Temporal ◽  
Extraction Model ◽  
Sentinel 2

Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries.

scholarly journals Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

2020 ◽  
Vol 12 (6) ◽  
pp. 943
Author(s):  
Andreas Schmitt ◽  
Anna Wendleder ◽  
Rüdiger Kleynmans ◽  
Maximilian Hell ◽  
Achim Roth ◽  
...  
Keyword(s):  
Image Fusion ◽  
Optical Sensors ◽  
Remote Sensing Data ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Alos Palsar ◽  
Data Set ◽  
Multi Temporal ◽  
The Stability ◽  
Sentinel 2

This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.

scholarly journals APPROACHES TO DISTINGUISH ‘REAL’ CHANGES FROM ‘UNREAL’ CHANGES BASED ON MULTI-TEMPORAL 2D BUILDING FOOTPRINT DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 559-563 ◽  
Author(s):  
M. Schorcht ◽  
R. Hecht ◽  
G. Meinel
Keyword(s):  
Change Detection ◽  
Large Scale ◽  
Geospatial Data ◽  
Data Sets ◽  
Building Stock ◽  
Data Set ◽  
Detection Techniques ◽  
European Settlement ◽  
Multi Temporal ◽  
Building Footprint

<p><strong>Abstract.</strong> Building footprint data from National Mapping and Cadastral Agencies are available in Germany for 7 years as a uniform, nation-wide geospatial data set and are updated annually. These multi-temporal building data sets can form the basis for the application of change detection techniques to derive national figures on dynamics in the building stock. Since these building data sets have only been built up in recent years, it is necessary to distinguish real changes from false changes. This is done by applying vector geometry-based operations and statistical analyses, which are presented in this article. Furthermore, by the additional use of the raster dataset Copernicus &amp;ndash; European Settlement Map (classified, resolution 2.5<span class="thinspace"></span>m) it is approximately possible to estimate whether it is a correct change or not. The advantage of this approach is that large-scale comparable results can be derived simply and quickly based on uniform basic data.</p>

Large-Scale Rice Mapping of Thailand using Sentinel-1 Multi-Temporal SAR Data

2019 ◽  
Author(s):  
Sisi Wei ◽  
Hong Zhang ◽  
Chao Wang ◽  
Lu Xu ◽  
Fan Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Multi Temporal ◽  
Sar Data

scholarly journals RICE (KHARIF) PRODUCTION ESTIMATION USING SAR DATA OF DIFFERENT SATELLITES AND YIELD MODELS: A COMPARATIVE ANALYSIS OF THE ESTIMATES GENERATED UNDER FASAL PROJECT

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 99-107
Author(s):  
V. Jain ◽  
S. Saxena ◽  
S. Dubey ◽  
K. Choudhary ◽  
S. Sehgal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Monsoon Season ◽  
Ground Truth ◽  
Coefficient Of Determination ◽  
Optical Remote Sensing ◽  
Acreage Estimation ◽  
Statistical Measures ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> Rice is the most important food crop of India. Majority of Rice is sown in kharif season in the country. This is monsoon season for the country where cloud cover poses a major problem for optical remote sensing. Therefore, for these states rice acreage estimation is being done using Synthetic Aperture Radar (SAR) data operationally in India since 1998. A case study is presented in this paper for analysis of past 6 years’ (2012&amp;ndash;13 to 2017&amp;ndash;18) estimations. Multi temporal Radarsat-2 (HH), RISAT-1 ScanSAR (HH) and Sentinel-1 (VV) data was used in years 2012, 2013&amp;ndash;2016, and 2017, respectively for paddy identification. Hierarchal Decision Rule based classification (HDRC) approach was used to identify rice areas under sample segments. Extensive ground truth collected by state remote sensing departments and agriculture departments was utilized in setting the limits of HDRC models and accuracy assessment. Yield was estimated using weather based and remote sensing-based models. Area, production and yield estimates were made and compared with those given by DES. RMSE and R<sup>2</sup> were used as statistical measures to assess the accuracy of results. The RMSE % ranged from 2.3 to 4.3; 0.84 to 1.35; 0.24 to 0.27 for area, production and yield respectively. The coefficient of determination (R<sup>2</sup>) ranged from 0.62 to 0.92; 0.75 to 0.91; 0.5 to 0.83 for area, production and yield respectively. The study showed that use of multi temporal SAR data (both HH and VV) is quite useful for paddy acreage estimation, especially during monsoon.</p>

scholarly journals EVALUATING UNMANNED AERIAL PLATFORMS FOR CULTURAL HERITAGE LARGE SCALE MAPPING

2016 ◽  
Vol XLI-B5 ◽  
pp. 355-362
Author(s):  
A. Georgopoulos ◽  
C. Oikonomou ◽  
E. Adamopoulos ◽  
E. K. Stathopoulou
Keyword(s):  
Cultural Heritage ◽  
Optical Sensors ◽  
Large Scale ◽  
Point Clouds ◽  
Short Review ◽  
Data Set ◽  
Heritage Sites ◽  
3D Point Clouds ◽  
Aerial Platforms ◽  
3D Information

When it comes to large scale mapping of limited areas especially for cultural heritage sites, things become critical. Optical and non-optical sensors are developed to such sizes and weights that can be lifted by such platforms, like e.g. LiDAR units. At the same time there is an increase in emphasis on solutions that enable users to get access to 3D information faster and cheaper. Considering the multitude of platforms, cameras and the advancement of algorithms in conjunction with the increase of available computing power this challenge should and indeed is further investigated. In this paper a short review of the UAS technologies today is attempted. A discussion follows as to their applicability and advantages, depending on their specifications, which vary immensely. The on-board cameras available are also compared and evaluated for large scale mapping. Furthermore a thorough analysis, review and experimentation with different software implementations of Structure from Motion and Multiple View Stereo algorithms, able to process such dense and mostly unordered sequence of digital images is also conducted and presented. As test data set, we use a rich optical and thermal data set from both fixed wing and multi-rotor platforms over an archaeological excavation with adverse height variations and using different cameras. Dense 3D point clouds, digital terrain models and orthophotos have been produced and evaluated for their radiometric as well as metric qualities.

scholarly journals Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model

2019 ◽  
Vol 11 (1) ◽  
pp. 68 ◽  
Author(s):  
Sisi Wei ◽  
Hong Zhang ◽  
Chao Wang ◽  
Yuanyuan Wang ◽  
Lu Xu
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Mapping Method ◽  
Support Vector ◽  
Smallholder Farming ◽  
Convolutional Network ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Sar Data ◽  
Crop Planting

Due to the unique advantages of microwave detection, such as its low restriction from the atmosphere and its capability to obtain structural information about ground targets, synthetic aperture radar (SAR) is increasingly used in agricultural observations. However, while SAR data has shown great potential for large-scale crop mapping, there have been few studies on the use of SAR images for large-scale multispecies crop classification at present. In this paper, a large-scale crop mapping method using multi-temporal dual-polarization SAR data was proposed. To reduce multi-temporal SAR data redundancy, a multi-temporal images optimization method based on analysis of variance (ANOVA) and Jeffries–Matusita (J–M) distance was applied to the time series of images after preprocessing to select the optimal images. Facing the challenges from smallholder farming modes, which caused the complex crop planting patterns in the study area, U-Net, an improved fully convolutional network (FCN), was used to predict the different crop types. In addition, the batch normalization (BN) algorithm was introduced to the U-Net model to solve the problem of a large number of crops and unbalanced sample numbers, which had greatly improved the efficiency of network training. Finally, we conducted experiments using multi-temporal Sentinel-1 data from Fuyu City, Jilin Province, China in 2017, and we obtained crop mapping results with an overall accuracy of 85% as well as a Kappa coefficient of 0.82. Compared with the traditional machine learning methods (e.g., random forest (RF) and support vector machine (SVM)), the proposed method can still achieve better classification performance under the condition of a complex crop planting structure.

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