scholarly journals Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information

Silva Fennica ◽  
2020 ◽  
Vol 54 (2) ◽  
Author(s):  
Agnese Marcelli ◽  
Walter Mattioli ◽  
Nicola Puletti ◽  
Francesco Chianucci ◽  
Damiano Gianelle ◽  
...  
Keyword(s):  
Large Scale ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Monte Carlo Integration ◽  
Regression Estimator ◽  
Two Phase ◽  
Wood Volume ◽  
Poplar Plantations ◽  
Sentinel 2 ◽  
Tessellation Stratified Sampling

Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed.

New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

2013 ◽  
Vol 43 (11) ◽  
pp. 1023-1031 ◽  
Author(s):  
Daniel Mandallaz ◽  
Jochen Breschan ◽  
Andreas Hill
Keyword(s):  
Auxiliary Information ◽  
Cluster Sampling ◽  
Lidar Data ◽  
Second Phase ◽  
Regression Estimator ◽  
Two Phase ◽  
Sampling Schemes ◽  
Forest Inventories ◽  
Phase Sampling ◽  
Two Phase Sampling

We consider two-phase sampling schemes where one component of the auxiliary information is known in every point (“wall-to-wall”) and a second component is available only in the large sample of the first phase, whereas the second phase yields a subsample with the terrestrial inventory. This setup is of growing interest in forest inventory thanks to the recent advances in remote sensing, in particular, the availability of LiDAR data. We propose a new two-phase regression estimator for global and local estimation and derive its asymptotic design-based variance. The new estimator performs better than the classical regression estimator. Furthermore, it can be generalized to cluster sampling and two-stage tree sampling within plots. Simulations and a case study with LiDAR data illustrate the theory.

scholarly journals SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 TIME SERIES FOR POPLAR PLANTATIONS MONITORING AT LARGE SCALE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1457-1461
Author(s):  
Y. Hamrouni ◽  
É. Paillassa ◽  
V. Chéret ◽  
C. Monteil ◽  
D. Sheeren
Keyword(s):  
Time Series ◽  
Active Learning ◽  
Supervised Classification ◽  
Large Scale ◽  
Training Data ◽  
Passive Learning ◽  
Training Samples ◽  
Poplar Plantations ◽  
Annual Means ◽  
Sentinel 2

Abstract. The current context of availability of Earth Observation satellite data at high spatial and temporal resolutions makes it possible to map large areas. Although supervised classification is the most widely adopted approach, its performance is highly dependent on the availability and the quality of training data. However, gathering samples from field surveys or through photo interpretation is often expensive and time-consuming especially when the area to be classified is large. In this paper we propose the use of an active learning-based technique to address this issue by reducing the labelling effort required for supervised classification while increasing the generalisation capabilities of the classifier across space. Experiments were conducted to identify poplar plantations in three different sites in France using Sentinel-2 time series. In order to characterise the age of the identified poplar stands, temporal means of Sentinel-1 backscatter coefficients were computed. The results are promising and show the good capacities of the active learning-based approach to achieve similar performance (Poplar F-score ≥ 90%) to traditional passive learning (i.e. with random selection of samples) with up to 50% fewer training samples. Sentinel-1 annual means have demonstrated their potential to differentiate two stand ages with an overall accuracy of 83% regardless of the cultivar considered.

scholarly journals Thermoelectric Properties of Thin Films from Sorted Single-Walled Carbon Nanotubes

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3808 ◽  
Author(s):  
Blazej Podlesny ◽  
Bogumila Kumanek ◽  
Angana Borah ◽  
Ryohei Yamaguchi ◽  
Tomohiro Shiraki ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Thermoelectric Properties ◽  
Large Scale ◽  
Single Walled Carbon Nanotubes ◽  
Parent Material ◽  
Free Standing ◽  
Two Phase ◽  
Single Walled Carbon ◽  
Walled Carbon Nanotubes ◽  
Aqueous Two Phase Extraction

Single-walled carbon nanotubes (SWCNTs) remain one of the most promising materials of our times. One of the goals is to implement semiconducting and metallic SWCNTs in photonics and microelectronics, respectively. In this work, we demonstrated how such materials could be obtained from the parent material by using the aqueous two-phase extraction method (ATPE) at a large scale. We also developed a dedicated process on how to harvest the SWCNTs from the polymer matrices used to form the biphasic system. The technique is beneficial as it isolates SWCNTs with high purity while simultaneously maintaining their surface intact. To validate the utility of the metallic and semiconducting SWCNTs obtained this way, we transformed them into thin free-standing films and characterized their thermoelectric properties.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

2021 ◽  
Vol 13 (11) ◽  
pp. 2220
Author(s):  
Yanbing Bai ◽  
Wenqi Wu ◽  
Zhengxin Yang ◽  
Jinze Yu ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Flood Events ◽  
Test Set ◽  
Source Data ◽  
Flood Disasters ◽  
Temporary Water ◽  
Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

scholarly journals Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
José P. Granadeiro ◽  
João Belo ◽  
Mohamed Henriques ◽  
João Catalão ◽  
Teresa Catry
Keyword(s):  
Large Scale ◽  
Exposure Period ◽  
Intertidal Area ◽  
Digital Elevation ◽  
Geographical Bias ◽  
Novel Method ◽  
Tidal Stage ◽  
Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

scholarly journals Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

2021 ◽  
Vol 13 (9) ◽  
pp. 1837
Author(s):  
Eve Laroche-Pinel ◽  
Sylvie Duthoit ◽  
Mohanad Albughdadi ◽  
Anne D. Costard ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Potential ◽  
Large Scale ◽  
Water Status ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Severe Drought ◽  
Stem Water Potential ◽  
Stem Water ◽  
Sentinel 2

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).

scholarly journals Time-resolved PIV measurements of a deflected submerged jet interacting with liquid-gas and liquid-liquid interfaces

2021 ◽  
Author(s):  
Stefan Puttinger ◽  
Mahdi Saeedipour
Keyword(s):  
Liquid Layer ◽  
Large Scale ◽  
Free Surface Flow ◽  
Industrial Applications ◽  
Liquid Pool ◽  
Surface Flow ◽  
Two Phase ◽  
Time Resolved ◽  
Submerged Jet ◽  
Major Interest

AbstractThis paper presents an experimental investigation on the interactions of a deflected submerged jet into a liquid pool with its above interface in the absence and presence of an additional lighter liquid. Whereas the former is a free surface flow, the latter mimics a situation of two stratified liquids where the liquid-liquid interface is disturbed by large-scale motions in the liquid pool. Such configurations are encountered in various industrial applications and, in most cases, it is of major interest to avoid the entrainment of droplets from the lighter liquid into the main flow. Therefore, it is important to understand the fluid dynamics in such configurations and to analyze the differences between the cases with and without the additional liquid layer. To study this problem, we applied time-resolved particle image velocimetry experiments with high spatial resolution. A detailed data analysis of a small layer beneath the interface shows that although the presence of an additional liquid layer stabilizes the oscillations of the submerged jet significantly, the amount of kinetic energy, enstrophy, and velocity fluctuations concentrated in the proximity of the interface is higher when the oil layer is present. In addition, we analyze the energy distribution across the eigenmodes of a proper orthogonal distribution and the distribution of strain and vortex dominated regions. As the main objective of this study, these high-resolution time-resolved experimental data provide a validation platform for the development of new models in the context of the volume of fluid-based large eddy simulation of turbulent two-phase flows.

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