scholarly journals Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations

2018 ◽  
Vol 10 (8) ◽  
pp. 1300 ◽  
Author(s):  
Raphaël d’Andrimont ◽  
Guido Lemoine ◽  
Marijn van der Velde
Keyword(s):  
High Resolution ◽  
Collection Efficiency ◽  
Ground Truth ◽  
Bare Soil ◽  
Agricultural Policies ◽  
High Definition ◽  
Street Level ◽  
In Situ Data ◽  
Crop Type ◽  
Efficiency And Effectiveness

The introduction of high-resolution Sentinels combined with the use of high-quality digital agricultural parcel registration systems is driving the move towards at-parcel agricultural monitoring. The European Union’s Common Agricultural Policy (CAP) has introduced the concept of CAP monitoring to help simplify the management and control of farmers’ parcel declarations for area support measures. This study proposes a proof of concept of this monitoring approach introducing and applying the concept of ‘markers’. Using Sentinel-1- and -2-derived (S1 and S2) markers, we evaluate parcels declared as grassland in the Gelderse Vallei in the Netherlands covering more than 15,000 parcels. The satellite markers—respectively based on crop-type deep learning classification using S1 backscattering and coherence data and on detecting bare soil with S2 during the growing season—aim to identify grassland-declared parcels for which (1) the marker suggests another crop type or (2) which appear to have been ploughed during the year. Subsequently, a field-survey was carried out in October 2017 to target the parcels identified and to build a relevant ground-truth sample of the area. For the latter purpose, we used a high-definition camera mounted on the roof of a car to continuously sample geo-tagged digital imagery, as well as an app-based approach to identify the targeted fields. Depending on which satellite-based marker or combination of markers is used, the number of parcels identified ranged from 2.57% (marked by both the S1 and S2 markers) to 17.12% of the total of 11,773 parcels declared as grassland. After confirming with the ground-truth, parcels flagged by the combined S1 and S2 marker were robustly detected as non-grassland parcels (F-score = 0.9). In addition, the study demonstrated that street-level imagery collection could improve collection efficiency by a factor seven compared to field visits (1411 parcels/day vs. 217 parcels/day) while keeping an overall accuracy of about 90% compared to the ground-truth. This proposed way of collecting in situ data is suitable for the training and validating of high resolution remote sensing approaches for agricultural monitoring. Timely country-wide wall-to-wall parcel-level monitoring and targeted in-season parcel surveying will increase the efficiency and effectiveness of monitoring and implementing agricultural policies.

scholarly journals VERY HIGH RESOLUTION LAND COVER MAPPING OF URBAN AREAS AT GLOBAL SCALE WITH CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 201-208
Author(s):  
T. Tilak ◽  
A. Braun ◽  
D. Chandler ◽  
N. David ◽  
S. Galopin ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Ground Truth ◽  
Bare Soil ◽  
Aerial Images ◽  
Training Dataset ◽  
Large Area ◽  
Land Cover Map ◽  
Very High

Abstract. This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model.We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class.A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions.The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.

scholarly journals Identification of Crop Type in Crowdsourced Road View Photos with Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1165
Author(s):  
Fangming Wu ◽  
Bingfang Wu ◽  
Miao Zhang ◽  
Hongwei Zeng ◽  
Fuyou Tian
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Decision Fusion ◽  
Identification Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Ground Truth Data ◽  
In Situ Data ◽  
Crop Type ◽  
Crop Types

In situ ground truth data are an important requirement for producing accurate cropland type map, and this is precisely what is lacking at vast scales. Although volunteered geographic information (VGI) has been proven as a possible solution for in situ data acquisition, processing and extracting valuable information from millions of pictures remains challenging. This paper targets the detection of specific crop types from crowdsourced road view photos. A first large, public, multiclass road view crop photo dataset named iCrop was established for the development of crop type detection with deep learning. Five state-of-the-art deep convolutional neural networks including InceptionV4, DenseNet121, ResNet50, MobileNetV2, and ShuffleNetV2 were employed to compare the baseline performance. ResNet50 outperformed the others according to the overall accuracy (87.9%), and ShuffleNetV2 outperformed the others according to the efficiency (13 FPS). The decision fusion schemes major voting was used to further improve crop identification accuracy. The results clearly demonstrate the superior accuracy of the proposed decision fusion over the other non-fusion-based methods in crop type detection of imbalanced road view photos dataset. The voting method achieved higher mean accuracy (90.6–91.1%) and can be leveraged to classify crop type in crowdsourced road view photos.

To What Extent are Inelastically Scattered Electrons Useful in the Stem?

1973 ◽  
Vol 31 ◽  
pp. 286-287 ◽  
Author(s):  
H. Rose
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Electron Correlation ◽  
Quantum Noise ◽  
Collection Efficiency ◽  
Scanning Transmission Electron Microscope ◽  
Electron Cloud ◽  
Scanning Time ◽  
Transmission Electron ◽  
Scanning Transmission

The scanning transmission electron microscope offers the possibility of utilizing inelastically scattered electrons. Use of these electrons in addition to the elastically scattered electrons should reduce the scanning time (dose) Which is necessary to keep the quantum noise below a certain level. Hence it should lower the radiation damage. For high resolution, Where the collection efficiency of elastically scattered electrons is small, the use of Inelastically scattered electrons should become more and more favorable because they can all be detected by means of a spectrometer. Unfortunately, the Inelastic scattering Is a non-localized interaction due to the electron-electron correlation, occurring predominantly at the circumference of the atomic electron cloud.

Low-workfunction field-emission source for high-resolution EELS

1987 ◽  
Vol 45 ◽  
pp. 132-133
Author(s):  
P. E. Batson
Keyword(s):  
High Resolution ◽  
Energy Loss ◽  
Electron Probe ◽  
Collection Efficiency ◽  
Electron Sources ◽  
Wien Filter ◽  
Half Angle ◽  
Thermal Emitter ◽  
Electron Energy Loss ◽  
Intrinsic Energy

In recent years,instrumentation for electron energy loss spectroscopy (EELS) has been steadily improved to increase energy resolution and collection efficiency. At present 0.40eV at 10mR collection half angle is available with commercial magnetic sectors (e.g. Gatan, Inc. and VG Microscopes, Ltd.), and 70meV at 10mR has been demonstrated by use of a Wien filter within a large deceleration field. When these high resolution spectrometers are coupled to the modern small electron probe instrument, we obtain a tool which promises to reveal local changes in bandstructure and bonding near defects and interfaces in heterogeneous materials.Unfortunately, typical electron sources have intrinsic energy widths which limit attainable spectroscopic resolution in the absence of some monochromation system. For instance, the W thermal emitter has a half width of about 1eV.

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

scholarly journals OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

2021 ◽  
Vol 10 (12) ◽  
pp. 805
Author(s):  
Xuan Fang ◽  
Jincheng Li ◽  
Ying Zhu ◽  
Jianjun Cao ◽  
Jiaming Na ◽  
...  
Keyword(s):  
High Resolution ◽  
Loess Plateau ◽  
Water Conservation ◽  
Ground Truth ◽  
Field Investigation ◽  
The Loess Plateau ◽  
Flow Process ◽  
Local Flow ◽  
Object Based ◽  
Damage Mapping

Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China.

scholarly journals Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

2020 ◽  
Vol 12 (4) ◽  
pp. 650
Author(s):  
Pablo Sánchez-Gámez ◽  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Marcos Portabella
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
Brightness Temperature ◽  
European Space Agency ◽  
Ground Truth ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
In Situ Data ◽  
L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

scholarly journals High-resolution and high-definition anorectal manometry: rediscovering anorectal function

2018 ◽  
Vol 110 ◽  
Author(s):  
Constanza Ciriza de los Ríos ◽  
Miguel Mínguez ◽  
Jose Mariaí Remes-Troche ◽  
Gl�ria Lacima
Keyword(s):  
High Resolution ◽  
Anorectal Manometry ◽  
Anorectal Function ◽  
High Definition

scholarly journals A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mehdi Khoshboresh-Masouleh ◽  
Reza Shah-Hosseini
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computational Cost ◽  
High Definition ◽  
Satellite Mission ◽  
Real Time Processing ◽  
National Space ◽  
Spatial Features ◽  
Efficiency And Effectiveness ◽  
Earth Observation Satellite

In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.

scholarly journals Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China

2018 ◽  
Vol 10 (11) ◽  
pp. 1852 ◽  
Author(s):  
Lei Lu ◽  
Tingjun Zhang ◽  
Tiejun Wang ◽  
Xiaoming Zhou
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northwest China ◽  
Bare Soil ◽  
Vegetation Monitoring ◽  
Modis Land Surface Temperature ◽  
Global Circulation Models ◽  
In Situ Data

Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are widely used in ecology, hydrology, vegetation monitoring, and global circulation models. Compared to the collection-5 (C5) LST products, the newly released collection-6 (C6) LST products have been refined over bare soil pixels. This study aims to evaluate the C6 MODIS 1-km LST product using multi-year in situ data covering barren surfaces. Evaluation using all in situ data shows that the MODIS C6 LSTs are underestimated with a root-mean-square error (RMSE) of 2.59 K for the site in the Gobi area, 3.05 K for the site in the sand desert area, and 2.86 K for the site in the desert steppe area at daytime. For nighttime LSTs, the RMSEs are 2.01 K, 2.88 K, and 1.80 K for the three sites, respectively. Both biases and RMSEs also show strong seasonal signals. Compared to the error of C5 1-km LSTs, the RMSE of C6 1-km LST product is smaller, especially for daytime LSTs, with a value of 2.24 K compared to 3.51 K. The large errors in the sand desert region are presumably due to the lack of global representativeness of the magnitude of emissivity adjustment and misclassification for the barren surface causing error in emissivities. It indicates that the accuracy of the MODIS C6 LST product might be further improved through emissivity adjustment with globally representative magnitude and accurate land cover classification. From this study, the MODIS C6 1-km LST product is recommended for applications.

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