scholarly journals A MULTI-SCALE SEGMENTATION APPROACH TO FILLING GAPS IN LANDSAT ETM+ SLC-OFF IMAGES THROUGH PIXEL WEIGHTING

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 79-84
Author(s):  
R. F. B. Marujo ◽  
L. M. G. Fonseca ◽  
T. S. Körting ◽  
H. N. Bendini
Keyword(s):  
Missing Values ◽  
Original Data ◽  
Image Texture ◽  
Gap Filling ◽  
Optical Images ◽  
Object Based ◽  
Small Streams ◽  
Segmentation Approach ◽  
Filling Method ◽  
Most Frequent Value

Abstract. Monitoring changes on Earth’s surface is a difficult task commonly performed using multi-spectral remote sensing images. The absence of surface information in optical images due to the presence of cloud, low temporal resolution and sensors defects interfere in analyses. In this context, we present an approach for filling gaps in imagery mainly caused by small clouds and sensor defects. Our method consists of an adaptation from an existing method that uses spatial context of close-in-time images through the use of the most frequent value obtained using multiscale segmentation. Our method uses the pixel proportion contained in each segment to fill missing values. We applied the gap-filling methodology on three dates containing simulated images from Landsat7 using Landsat8 images. We validated the method by introducing and filling artificial gaps, and comparing the original data with model predictions. The developed approach surpassed Maxwell et al. (2007) gap-filling method for all bands, presenting a minimal R2 of 0.78. Our method proved to enhance the Maxwell et al. (2007) gap-filling method, while also asymptotically maintaining the algorithm cost. It also allowed image texture to be conserved on reconstructed images. This characteristic enables narrow features, e.g., as roads, riparian areas, and small streams capable of being detected on the filled images. Based on that, further object-based approaches can be used on images filled using this methodology, demonstrating its capacity to estimate Earth’s surface data.

scholarly journals A Comparison of Three Gap Filling Techniques for Eddy Covariance Net Carbon Fluxes in Short Vegetation Ecosystems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosong Zhao ◽  
Yao Huang
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Diurnal Variation ◽  
Eddy Covariance ◽  
Nonlinear Regression ◽  
Missing Values ◽  
The Other ◽  
Gap Filling ◽  
Benchmark Datasets ◽  
Filling Method

Missing data is an inevitable problem when measuring CO2, water, and energy fluxes between biosphere and atmosphere by eddy covariance systems. To find the optimum gap-filling method for short vegetations, we review three-methods mean diurnal variation (MDV), look-up tables (LUT), and nonlinear regression (NLR) for estimating missing values of net ecosystem CO2exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on benchmark datasets from marsh and cropland sites in China. The cumulative errors for three methods have no consistent bias trends, which ranged between −30 and +30 mgCO2 m−2from May to October at three sites. To reduce sum bias in maximum, combined gap-filling methods were selected for short vegetation. The NLR or LUT method was selected after plant rapidly increasing in spring and before the end of plant growing, and MDV method was used to the other stage. The sum relative error (SRE) of optimum method ranged between −2 and +4% for four-gap level at three sites, except for 55% gaps at soybean site, which also obviously reduced standard deviation of error.

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

chapter 2 A Statistical Gap-Filling Method to Interpolate Global Monthly Surface Ocean Carbon Dioxide Data

2017 ◽  
pp. 15-62
Author(s):  
STEVE D. JONES ◽  
CORINNE LE QUÉRÉ ◽  
CHRISTIAN RÖDENBECK ◽  
ANDREW C. MANNING ◽  
ARE OLSEN
Keyword(s):  
Carbon Dioxide ◽  
Gap Filling ◽  
Surface Ocean ◽  
Filling Method ◽  
Ocean Carbon

Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil

2019 ◽  
Vol 99 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Rezvan Taki ◽  
Claudia Wagner-Riddle ◽  
Gary Parkin ◽  
Rob Gordon ◽  
Andrew VanderZaag
Keyword(s):  
Missing Values ◽  
Interpolation Method ◽  
Weather Conditions ◽  
Linear Interpolation ◽  
Bias Error ◽  
Research Station ◽  
Ann Model ◽  
Gap Filling ◽  
Adverse Weather ◽  
The Impact

Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.

scholarly journals New techniques for gap-filling and partitioning of H<sub>2</sub>O and CO<sub>2</sub> eddy fluxes measured over forests in complex mountainous terrain

2017 ◽  
Author(s):  
Minseok Kang ◽  
Joon Kim ◽  
Bindu Malla Thakuri ◽  
Junghwa Chun ◽  
Chunho Cho
Keyword(s):  
Environmental Changes ◽  
Water Cycle ◽  
Co2 Flux ◽  
Test Method ◽  
Co2 Fluxes ◽  
Gap Filling ◽  
New Techniques ◽  
Diameter Increment ◽  
Filling Method ◽  
Rain Events

Abstract. The continuous measurement of H2O and CO2 fluxes using the eddy covariance (EC) technique is still challenging for forests in complex terrain because of large amounts of wet canopy evaporation (EWC), which occur during and following rain events when the EC systems rarely work correctly, and the horizontal advection of CO2 generated at night. We propose new techniques for gap-filling and partitioning of the H2O and CO2 fluxes: (1) a model-stats hybrid method (MSH) and (2) a modified moving point test method (MPTm). The former enables the recovery of the missing EWC in the traditional gap-filling method and the partitioning of the evapotranspiration (ET) into transpiration and (wet canopy) evaporation. The latter determines the friction velocity (u*) threshold based on an iterative approach using moving windows for both time and u*, thereby allowing not only the nighttime CO2 flux correction and partitioning but also the assessment of the significance of the CO2 drainage. We tested and validated these new methods using the datasets from two flux towers, which are located at forests in hilly and complex terrains. The MSH reasonably recovered the missing EWC of 16 ~ 41 mm year−1 and separated it from the ET (14 ~ 23 % of the annual ET). The MPTm produced consistent carbon budgets using those from the previous research and diameter increment, while it has improved applicability. Additionally, we illustrated certain advantages of the proposed techniques, which enables us to understand better how ET responses to environmental changes and how the water cycle is connected to the carbon cycle in a forest ecosystem.

scholarly journals Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

2020 ◽  
Vol 12 (3) ◽  
pp. 487 ◽  
Author(s):  
Biwei Wang ◽  
Zengxiang Zhang ◽  
Xiao Wang ◽  
Xiaoli Zhao ◽  
Ling Yi ◽  
...  
Keyword(s):  
Regional Scale ◽  
Black Soil ◽  
Google Earth ◽  
Gully Erosion ◽  
Image Feature ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Optical Images ◽  
Black Soil Region ◽  
Object Based

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

scholarly journals Gap-filling method for suppressing grating lobes in ultrasound imaging: Theory and simulation results

2019 ◽  
Vol 145 (3) ◽  
pp. EL236-EL242
Author(s):  
Bae-Hyung Kim ◽  
Viksit Kumar ◽  
Azra Alizad ◽  
Mostafa Fatemi
Keyword(s):  
Ultrasound Imaging ◽  
Gap Filling ◽  
Filling Method ◽  
Imaging Theory ◽  
Simulation Results

scholarly journals A comparative assessment of the uncertainties of global surface­-ocean CO<sub>2</sub> estimates using a machine learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?

2019 ◽  
Author(s):  
Luke Gregor ◽  
Alice D. Lebehot ◽  
Schalk Kok ◽  
Pedro M. Scheel Monteiro
Keyword(s):  
Machine Learning ◽  
Decadal Variability ◽  
Comparative Assessment ◽  
Gap Filling ◽  
Test Dataset ◽  
Surface Ocean ◽  
Southeastern Pacific ◽  
Data Gaps ◽  
Regression Approach ◽  
Filling Method

Abstract. Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO2 measurements (Rödenbeck et al. 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea-air CO2 fluxes and the drivers of these changes (Landschützer et al. 2015, 2016, Gregor et al. 2018). However, it is also becoming clear that these methods are converging towards a common bias and RMSE boundary: the wall, which suggests that pCO2 estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble of six machine learning models (CSIR-ML6 version 2019a), where each model is constructed with a two-step clustering-regression approach. The ensemble is then statistically compared to well-established methods. The ensemble, CSIR-ML6, has an RMSE of 17.16 µatm and bias of 0.89 µatm when compared to a test-dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of pCO2. We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean are too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean pCO2 estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution. Larger improvements will only be realised with an increase in CO2 observational coverage, particularly in today's poorly sampled areas.

scholarly journals Effects of a gap-filling method on p-mode parameters

2001 ◽  
Vol 324 (4) ◽  
pp. 1159-1168 ◽  
Author(s):  
D. Fierry Fraillon ◽  
T. Appourchaux
Keyword(s):  
Gap Filling ◽  
Filling Method
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