scholarly journals Vegetation mapping using airborne hyperspectral sensor data measured in early summer

2007 ◽  
Vol 46 (5) ◽  
pp. 43-55 ◽  
Author(s):  
Hiroshi P. SATO ◽  
Mamoru KOARAI ◽  
Satoshi MIYASAKA ◽  
Hajime MAKITA ◽  
Hiroshi YAGI
Keyword(s):  
Vegetation Mapping ◽  
Early Summer ◽  
Sensor Data ◽  
Hyperspectral Sensor

scholarly journals Vegetation mapping in Tomari-no-tai area in Shirakami Mountains using airborne hyperspectral sensor data

2006 ◽  
Vol 45 (5) ◽  
pp. 32-45 ◽  
Author(s):  
Hiroshi P. SATO ◽  
Satoshi MIYASAKA ◽  
Hajime MAKITA ◽  
Hiroshi YAGI ◽  
Mamoru KOARAI
Keyword(s):  
Vegetation Mapping ◽  
Sensor Data ◽  
Hyperspectral Sensor

scholarly journals Estimation of Bacterial Pustule on Soybean Using Crane-Mounted Hyperspectral Sensor Data

2007 ◽  
Vol 46 (6) ◽  
pp. 16-24 ◽  
Author(s):  
Naoko KOSAKA ◽  
Yohei MINEKAWA ◽  
Kuniaki UTO ◽  
Yukio KOSUGI ◽  
Kunio ODA ◽  
...  
Keyword(s):  
Sensor Data ◽  
Hyperspectral Sensor

scholarly journals Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS

2019 ◽  
Vol 11 (15) ◽  
pp. 1814 ◽  
Author(s):  
Suo ◽  
McGovern ◽  
Gilmer
Keyword(s):  
High Resolution ◽  
Data Processing ◽  
Spatial Patterns ◽  
Near Infrared ◽  
Environmental Changes ◽  
Vegetation Mapping ◽  
Coastal Dune ◽  
Sensor Data ◽  
Surface Model ◽  
Dune Vegetation

Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.

scholarly journals Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Liu ◽  
Tingting Wang ◽  
Yulong Qiao
Keyword(s):  
Data Analysis ◽  
Network Architecture ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Kernel Learning ◽  
Discriminative Ability ◽  
Neural Network Learning ◽  
And Performance ◽  
Hyperspectral Sensor

Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the more discriminative ability. So, the deep kernel learning has the better performance compared with the multiple kernels learning. And it has the ability to adjust the network architecture for hyperspectral data space, with the optimization equation of the span bound. The experiments are implemented to testified the feasibility and performance of the algorithms on the hyperspectral data analysis, with the classification accuracy of hyperspectral data. The comprehensive analysis of the experiments shows that the proposed algorithm is feasible to hyperspectral sensor data analysis and its promising classification method in many areas data analysis.

scholarly journals VEGETATION MAPPING OF A COASTAL DUNE COMPLEX USING MULTISPECTRAL IMAGERY ACQUIRED FROM AN UNMANNED AERIAL SYSTEM

2018 ◽  
Vol XLII-1 ◽  
pp. 421-427
Author(s):  
C. Suo ◽  
E. McGovern ◽  
A. Gilmer
Keyword(s):  
High Resolution ◽  
Spatial Patterns ◽  
High Accuracy ◽  
Vegetation Mapping ◽  
Coastal Dune ◽  
Sensor Data ◽  
Surface Model ◽  
Unmanned Aerial System ◽  
Management Actions ◽  
Multispectral Camera

<p><strong>Abstract.</strong> Vegetation mapping, identifying the distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environment changes on vegetation, and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper represents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an Unmanned Aerial System (UAS) with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. UAS, also known as Unmanned Aerial Vehicles (UAV’s) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral camera used in this study has green, red, red-edge and near infrared wavebands, and a normal RGB camera, to capture both visible and NIR images of the land surface. The workflow of 3D vegetation mapping of the study site included establishing ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes include an orthomosiac model, a 3D surface model and multispectral images of the study site, in the Irish Transverse Mercator coordinate system, with a planimetric resolution of 0.024<span class="thinspace"></span>m and a georeferenced Root-Mean-Square (RMS) error of 0.111<span class="thinspace"></span>m. There were 235 sample area (1<span class="thinspace"></span>m<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>1<span class="thinspace"></span>m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using three different classification strategies to examine the efficiency of multispectral sensor data for vegetation mapping. Vegetation type classification accuracies ranged from 60<span class="thinspace"></span>% to 70<span class="thinspace"></span>%. This research illustrates the efficiency of data collection at Buckroney dune complex and the high-accuracy and high-resolution of the vegetation mapping of the site using a multispectral sensor mounted on UAS.</p>

scholarly journals Multiple Flights or Single Flight Instrument Fusion of Hyperspectral and ALS Data? A Comparison of their Performance for Vegetation Mapping

2019 ◽  
Vol 11 (8) ◽  
pp. 970 ◽  
Author(s):  
Łukasz Sławik ◽  
Jan Niedzielko ◽  
Adam Kania ◽  
Hubert Piórkowski ◽  
Dominik Kopeć
Keyword(s):  
Data Collection ◽  
Data Fusion ◽  
Laser System ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
Vegetation Mapping ◽  
Sensor Data ◽  
The Difference ◽  
Using Data ◽  
Instrument Fusion

Fusion of remote sensing data often improves vegetation mapping, compared to using data from only a single source. The effectiveness of this fusion is subject to many factors, including the type of data, collection method, and purpose of the analysis. In this study, we compare the usefulness of hyperspectral (HS) and Airborne Laser System (ALS) data fusion acquired in separate flights, Multiple Flights Data Fusion (MFDF), and during a single flight through Instrument Fusion (IF) for the classification of non-forest vegetation. An area of 6.75 km2 was selected, where hyperspectral and ALS data was collected during two flights in 2015 and one flight in 2017. This data was used to classify three non-forest Natura 2000 habitats i.e., Xeric sand calcareous grasslands (code 6120), alluvial meadows of river valleys of the Cnidion dubii (code 6440), species-rich Nardus grasslands (code 6230) using a Random Forest classifier. Our findings show that it is not possible to determine which sensor, HS, or ALS used independently leads to a higher classification accuracy for investigated Natura 2000 habitats. Concurrently, increased stability and consistency of classification results was confirmed, regardless of the type of fusion used; IF, MFDF and varied information relevance of single sensor data. The research shows that the manner of data collection, using MFDF or IF, does not determine the level of relevance of ALS or HS data. The analysis of fusion effectiveness, gauged as the accuracy of the classification result and time consumed for data collection, has shown a superiority of IF over MFDF. IF delivered classification results that are more accurate compared to MFDF. IF is always cheaper than MFDF and the difference in effectiveness of both methods becomes more pronounced when the area of aerial data collection becomes larger.

Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data

2014 ◽  
Vol 26 (2) ◽  
pp. 77-88 ◽  
Author(s):  
M. Rinaldi ◽  
A. Castrignanò ◽  
D. De Benedetto ◽  
D. Sollitto ◽  
S. Ruggieri ◽  
...  
Keyword(s):  
Sensor Data ◽  
Tomato Plants ◽  
Irrigation Regimes ◽  
Hyperspectral Sensor
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