scholarly journals Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images

2018 ◽  
Vol 10 (7) ◽  
pp. 1144 ◽  
Author(s):  
Wimala van Iersel ◽  
Menno Straatsma ◽  
Hans Middelkoop ◽  
Elisabeth Addink
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Accurate Solution ◽  
Vegetation Types ◽  
Time Step ◽  
Floodplain Vegetation ◽  
Herbaceous Vegetation ◽  
Single Time ◽  
Uav Images

The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The user’s accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

Classification of montane forest community types in the Cedar River drainage of western Washington, U.S.A.

1977 ◽  
Vol 7 (2) ◽  
pp. 217-225 ◽  
Author(s):  
Roger Del Moral ◽  
James N. Long
Keyword(s):  
Discriminant Analysis ◽  
Large Scale ◽  
Classification Scheme ◽  
Forest Community ◽  
Vegetation Types ◽  
Agglomerative Clustering ◽  
Lower Elevation ◽  
Community Types ◽  
Western Washington

Vegetation of the Cedar River watershed, located in the Cascade Mountains of western Washington, was analyzed by an agglomerative clustering method followed by discriminant analysis. Stepwise mutliple discriminant analysis provided a means to reallocate stands and assists in the production of a classification scheme and a key to the vegetation types. Ten types are recognized, six from upper-elevation older-growth stands, and four seral types from lower elevation stands logged since 1900. Each type can be identified in the field with a simple key based on cover percentage. The key provides a means for large-scale vegetation mapping with a limited amount of effort.

CFD Investigation of Gear Pump Mixing Using Deforming/Agglomerating Mesh

2006 ◽  
Vol 129 (4) ◽  
pp. 476-484 ◽  
Author(s):  
Wayne Strasser
Keyword(s):  
Large Scale ◽  
Accurate Solution ◽  
Progression Rate ◽  
Gear Pump ◽  
Feed Stream ◽  
Time Step ◽  
Pump Speed ◽  
Lower Viscosity ◽  
Fluid Properties ◽  
Process Fluid

A moving-deforming grid study was carried out using a commercial computational fluid dynamics (CFD) solver, FLUENT® 6.2.16. The goal was to quantify the level of mixing of a lower-viscosity additive (at a mass concentration below 10%) into a higher-viscosity process fluid for a large-scale metering gear pump configuration typical in plastics manufacturing. Second-order upwinding and bounded central differencing schemes were used to reduce numerical diffusion. A maximum solver progression rate of 0.0003 revolutions per time step was required for an accurate solution. Fluid properties, additive feed arrangement, pump scale, and pump speed were systematically studied for their effects on mixing. For each additive feed arrangement studied, the additive was fed in individual stream(s) into the pump-intake. Pump intake additive variability, in terms of coefficient of spatial variation (COV), was >300% for all cases. The model indicated that the pump discharge additive COV ranged from 45% for a single centerline additive feed stream to 5.5% for multiple additive feed streams. It was found that viscous heating and thermal/shear-thinning characteristics in the process fluid slightly improved mixing, reducing the outlet COV to 3.2% for the multiple feed-stream case. The outlet COV fell to 2.0% for a half-scale arrangement with similar physics. Lastly, it was found that if the smaller unit’s speed were halved, the outlet COV was reduced to 1.5%.

scholarly journals Time series analysis of the long-term hydrologic impacts of afforestation in the Águeda watershed of north-central Portugal

2015 ◽  
Vol 19 (7) ◽  
pp. 3033-3045 ◽  
Author(s):  
D. Hawtree ◽  
J. P. Nunes ◽  
J. J. Keizer ◽  
R. Jacinto ◽  
J. Santos ◽  
...  
Keyword(s):  
Land Cover ◽  
Soil Properties ◽  
Land Cover Change ◽  
Eucalyptus Globulus ◽  
Pinus Pinaster ◽  
Large Scale ◽  
Vegetation Types ◽  
North Central ◽  
Data Record ◽  
Hydrologic Impacts

Abstract. The north-central region of Portugal has undergone significant land cover change since the early 1900s, with large-scale replacement of natural vegetation types with plantation forests. This transition consisted of an initial conversion primarily to Pinus pinaster, followed by a secondary transition to Eucalyptus globulus. This land cover change is likely to have altered the hydrologic functioning of this region; however, these potential impacts are not fully understood. To contribute to a better understanding of the potential hydrologic impacts of this land cover change, this study examines the temporal trends in 75 years of data from the Águeda watershed (part of the Vouga Basin) over the period of 1936–2010. A number of hydrometeorological variables were analyzed using a combined Thiel–Sen/Mann–Kendall trend-testing approach, to assess the magnitude and significance of patterns in the observed data. These trend tests indicated that there have been no significant reductions in streamflow over either the entire test period, or during sub-record periods, despite the large-scale afforestation which has occurred. This lack of change in streamflow is attributed to the specific characteristics of the watershed and land cover change. By contrast, a number of significant trends were found for baseflow index, with positive trends in the early data record (primarily during Pinus pinaster afforestation), followed by negative trends later in the data record (primarily during Eucalyptus globulus afforestation). These trends are attributed to land use and vegetation impacts on streamflow generating processes, both due to species differences and to alterations in soil properties (i.e., infiltration capacity, soil water repellency). These results highlight the importance of considering both vegetation types/dynamics and watershed characteristic when assessing hydrologic impacts, in particular with respect to soil properties.

scholarly journals Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

2020 ◽  
Author(s):  
Zhongling Huang
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Transfer Learning ◽  
Large Scale ◽  
Target Area ◽  
Sar Images ◽  
Label Noise ◽  
Deep Model ◽  
Imbalanced Classes

<div> <div> <div> <p>The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What’s more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%. </p> </div> </div> </div>

Large-Scale Classification of Land Cover Using Retrospective Satellite Data

2016 ◽  
Vol 52 (1) ◽  
pp. 127-138 ◽  
Author(s):  
M. S. Lavreniuk ◽  
S. V. Skakun ◽  
A. Ju. Shelestov ◽  
B. Ya. Yalimov ◽  
S. L. Yanchevskii ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Large Scale ◽  
Scale Classification

An integrated explicit–implicit algorithm for vehicle–rail–bridge dynamic simulations

2018 ◽  
Vol 232 (6) ◽  
pp. 1895-1913 ◽  
Author(s):  
Zhibin Jin ◽  
Chuanchuan Hu ◽  
Shiling Pei ◽  
Hongyan Liu
Keyword(s):  
Large Scale ◽  
Large Time ◽  
Dynamic Interaction ◽  
Degree Of Freedom ◽  
Time Step ◽  
Single Time ◽  
Large Time Step ◽  
Step Algorithm ◽  
Bridge Responses ◽  
Two Degree Of Freedom

The dynamic interaction between the vehicle, rail, and bridges presents a huge computational challenge, especially for reliability analysis based on Monte Carlo simulations. In this study, an integrated algorithm is proposed for the vehicle–rail–bridge dynamic interaction problem. This algorithm divides the system into two subdomains, i.e. the vehicle–rail subdomain and the bridge subdomain. The vehicle–rail subdomain and the bridge subdomain are integrated by the Zhai algorithm and the Newmark-β algorithm, respectively. The integrated algorithm allows different time steps (or multitime steps) to be used for the two domains: a large time step for the bridge subdomain and a smaller one for the vehicle–rail subdomain. The stability region of the proposed algorithm was found through the two-degree-of-freedom model problem, when a single time step is used in both subdomains. The accuracy of the algorithm was numerically investigated through the two-degree-of-freedom model. The vehicle–rail–bridge vibration excited by rail irregularities and earthquakes was simulated using the multitime step algorithm. The effect of the time step ratio (ratio of the large time step to the small time step) on the accuracy of the vehicle–rail–bridge responses was investigated. It has been shown that the time step ratio of less than 50 produces vehicle–rail–bridge responses in an accurate manner for engineering purposes. The multitime step algorithm can solve the vehicle–rail–bridge problem 20 times faster than the single time step algorithms that are conventionally used in the vehicle–rail–bridge simulations. This multitime step algorithm provides an efficient alternative for solving the dynamic interaction between vehicle–rail and large-scale civil structures.

scholarly journals Analysis of Land use and Land cover Characteristics of Kanke, Pankshin, and Langtang Local Government Areas, Plateau State, Nigeria

2020 ◽  
Vol 3 (3a) ◽  
pp. 53-67
Author(s):  
YA Andesikuteb ◽  
WW Musa ◽  
LV Ezra ◽  
MT Obasi ◽  
RG Rogers ◽  
...  
Keyword(s):  
Local Government ◽  
Land Cover ◽  
Large Scale ◽  
Survey Design ◽  
Adaptation Strategy ◽  
Primary Data ◽  
Rock Outcrops ◽  
Plateau State ◽  
Productive Land

This study assessed landuse and landcover changes and how they affect the agrarian production in Kanke, Pankshin and Langtang North Local Government Areas of plateau state, Nigeria. The study adopted the survey design. The primary data was obtained through field observations, interview of stakeholders, satellite (landsat8 and shuttle radar terrain mission) and questionnaire administration which asked questions on socioeconomic status of respondents, constraints to farmers and the respondents’ perception on the existing adaptation strategies in place. ArcGIS10.6version software was employed for the classification of land cover types while supervised classification method was adopted using maximum likelihood algorithm for the classification of feature types. Data generated by Landsat8 and ArcGIS10.6 version software were subjected to Pan-Sharpening processing for clarity of terrain features. The study findings revealed that 66.13% of the earth’s surface in the study area is covered by rock outcrops while water body; one of man’s most precious resources occupied less than 1% (0.15%). The distribution of farmers based on constraints to farming indicated that poor soils and small farm land sizes constituted the most severe challenges to farming activities in the study area. As an adaptation strategy to inadequate farm lands, terrace farming practice and dry season farming, large scale quarrying activities is recommended to serve as a source of employment and income to authorities and a means of surface leveling to convert the dominant rock outcrops to productive land and prioritizing farming in the limited plains.

scholarly journals Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

2020 ◽  
Author(s):  
Zhongling Huang
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Transfer Learning ◽  
Large Scale ◽  
Target Area ◽  
Sar Images ◽  
Label Noise ◽  
Deep Model ◽  
Imbalanced Classes

<div> <div> <div> <p>The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What’s more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%. </p> </div> </div> </div>

scholarly journals Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

2021 ◽  
Vol 13 (23) ◽  
pp. 4859
Author(s):  
Yonglei Shi ◽  
Zhihui Wang ◽  
Liangyun Liu ◽  
Chunyi Li ◽  
Dailiang Peng ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Mixed Forest ◽  
Vegetation Coverage ◽  
Woody Vegetation ◽  
Support Vector ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Near Infrared Reflectance ◽  
Herbaceous Vegetation ◽  
Uav Images

Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.

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