scholarly journals Application of satellite images and VNREDSAT-1 images in study on marine environment in Truong Sa region

2019 ◽  
Vol 19 (3B) ◽  
pp. 149-162
Author(s):  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Xuan Tung ◽  
Nguyen The Luan ◽  
Le Dinh Nam ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Marine Environment ◽  
Surface Current ◽  
Environmental Parameters ◽  
Research Area ◽  
Real Field ◽  
Remote Sensing Images ◽  
Field Surveys ◽  
Different Depths

The remote sensing images, including images of MODIS, VNREDSAT-1 and altimeter, are applied for researching marine environment with the different resolutions. On the basis of different time remote sensing images, we concentrated on the assessment of several characteristics including the SST, chlorophyll-a concentration and sea surface current at the different depths in different monsoons as well. With the large areas, we used the images of MODIS and altimeter. The detailed research area focuses on the Nam Yet island, and the images of VNREDSAT-1 are used. The analysis method of environmental parameters of SST and chlorophyll-a used the regression functions based on the single and combined bands to enhance the accuracy of the analysis result. The marine parameters collected at different depths in the latest field surveys on Truong Sa archipelago in the years of 2015 and 2018 are presented in this paper. On the basis of these parameters, we can analyse the relationships and compare the real field survey data and corresponding results interpreted from remote sensing images.

scholarly journals Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

2018 ◽  
Vol 228 ◽  
pp. 02013
Author(s):  
Haibo Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Automatic Monitoring ◽  
Research Area ◽  
Decision Fusion ◽  
Land Change ◽  
Fuzzy Decision ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Gis Data

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

scholarly journals Petroleum Geological Analysis Based on Remote Sensing and laser scanning in Karamay Formation of Junggar Basin, China

2020 ◽  
Vol 206 ◽  
pp. 01023
Author(s):  
Qihong Zeng ◽  
Youyan Zhang ◽  
Linghua Kong ◽  
Yong Ye ◽  
Yan Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Precision ◽  
Laser Scanning ◽  
Laser Intensity ◽  
Junggar Basin ◽  
Research Area ◽  
Remote Sensing Images ◽  
Lithology Identification ◽  
Rock Lithology ◽  
Geological Analysis

This paper uses high-precision remote sensing and laser scanning to study petroleum geological analysis methods. The research area is Karamay Formation in Junggar Basin, China. Firstly, the outcrop lithologies are identified according to our clastic rock lithology identification pattern based on laser intensity, and the regional lithologies are identified based on high-precision remote sensing images. Furthermore, we analyze the horizontal and vertical distribution characteristics of the sandbodies. At last, we analyze the area sandbody connectivity and sandbody structure characteristics. These data can provide basic information for the analysis of underground reservoirs in Karamay Formation.

scholarly journals An Integrated Field and Hyperspectral Remote Sensing Method for the Estimation of Pigments Content of Stipa Purpurea in Shenzha, Tibet

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Bo Kong ◽  
Bing He ◽  
Huan Yu ◽  
Yu Liu
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Photosynthetic Pigment ◽  
Research Area ◽  
Water Area ◽  
Remote Sensing Images ◽  
Estimation Model ◽  
Stipa Purpurea ◽  
Pigment Contents ◽  
The Mean

Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remote sensing images. The results show that (1) there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728); (2) 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; (3) for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; (4) the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; (5) the distribution of Chla is opposite to Chlb and Chls content in water area.

scholarly journals Mapping of groundwater potentiality index parameters using remote sensing and GIS techniques in the southern mountain, Yogyakarta Special Region

2021 ◽  
Vol 930 (1) ◽  
pp. 012064
Author(s):  
H Hasibuan ◽  
A H Rafsanjani ◽  
D P E Putra ◽  
S S Surjono
Keyword(s):  
Remote Sensing ◽  
Research Area ◽  
Groundwater Potential ◽  
Remote Sensing Images ◽  
Digital Elevation ◽  
Distribution Parameters ◽  
Hydrogeological Map ◽  
Groundwater Potentiality ◽  
Elevation Model ◽  
Special Region

Abstract In the hydrogeological map sheet of the Special Region of Yogyakarta, the Mountain Zone is categorized as an area of scarce groundwater. This research is intended to determine the parameters of groundwater potential in the area of scarce groundwater according to the Groundwater Potentiality Index (GPI) methods, including; fractures, lithology, slope, topography, and rainfall. Fracture parameters, distribution, and topography were collected from the Indonesia Geospatial Portal and the Digital Elevation Model (DEM). The lithological parameters were obtained from data from the Geological Agency due to the Interpretation of Remote Sensing Images. Rainfall data for the last ten years was obtained from reports. Results show that most of the research area is a fairly massive rock area, and there are some local faults. The lithological parameters indicate that the research area is composed of breccias, sandstones, and tuffs. Distribution parameters obtained information that most distribution is notated river orders 1, 2, and 3 with several river orders notation 4, 5, and 6. The slope varies from <3% to> 65%, and the intensity of rainfall almost evenly ranges from 1600-2100 mm/year.

scholarly journals Correlation of Environmental Parameters and the Water Saturation Induced Deterioration of Earthen Archaeological Sites: The Case of World Heritage Liangzhu City, China

Heritage ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 387-400
Author(s):  
Mei Dong ◽  
Hui Hu ◽  
Qingling Guo ◽  
Xiaonan Gong ◽  
Rafig Azzam ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Saturation ◽  
Environmental Parameters ◽  
High Water ◽  
Research Area ◽  
Atmospheric Environment ◽  
High Water Content ◽  
Humid Environment ◽  
Different Depths ◽  
Moisture Conditions

This paper proposes a combined methodology for the quantitative analysis of the correlations between the monitored influencing environmental factors and the water saturation induced deterioration of earthen relics in a humid area. The Archaeological Ruins of Liangzhu City that have been exposed and severely damaged in a humid environment with high water content and dry–wet cycles are chosen as examples. A monitoring system including atmospheric, groundwater, soil moisture conditions, and images of the surface was installed. Based on the proposed methodology, 11 key influencing indexes involving groundwater, soil moisture and temperature at different depths, atmospheric radiation, and rainfall for the water saturation induced deterioration are investigated, and their correlation is described by a regression model. The weight rankings of influencing factors to the deterioration of the research area are calculated. The results can help quantitatively control the atmospheric environment where the earthen relics are located and can promote the conservation of the archaeological ruins in the humid environment.

scholarly journals WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

2020 ◽  
Vol 12 (12) ◽  
pp. 1966 ◽  
Author(s):  
Muhammad Aldila Syariz ◽  
Chao-Hung Lin ◽  
Manh Van Nguyen ◽  
Lalu Muhamad Jaelani ◽  
Ariel C. Blanco
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Remote Sensing Images ◽  
Two Stage ◽  
Chl A ◽  
The Neural Network

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

scholarly journals Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2734
Author(s):  
Su-Jin Shin ◽  
Seyeob Kim ◽  
Youngjung Kim ◽  
Sungho Kim
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Complex Structure ◽  
Hierarchical Classification ◽  
Research Area ◽  
Visual Features ◽  
Remote Sensing Images ◽  
Efficient Detection ◽  
Fundamental Research ◽  
Label Object

Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images. Despite the advance, using the object detection datasets with a more complex structure, i.e., datasets with hierarchically multi-labeled objects, is limited to the existing detection models. Especially in remote sensing images, since objects are obtained from bird’s-eye view, the objects are captured with restricted visual features and not always guaranteed to be labeled up to fine categories. We propose a hierarchical multi-label object detection framework applicable to hierarchically partial-annotated datasets. In the framework, an object detection pipeline called Decoupled Hierarchical Classification Refinement (DHCR) fuses the results of two networks: (1) an object detection network with multiple classifiers, and (2) a hierarchical sibling classification network for supporting hierarchical multi-label classification. Our framework additionally introduces a region proposal method for efficient detection on vain areas of the remote sensing images, called clustering-guided cropping strategy. Thorough experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from WorldView-3 and SkySat satellites. Under our proposed framework, DHCR-based detections significantly improve the performance of respective baseline models and we achieve state-of-the-art results on the datasets.

scholarly journals Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection

2019 ◽  
Vol 9 (17) ◽  
pp. 3583
Author(s):  
Fen Cai ◽  
Miao-Xia Guo ◽  
Li-Fang Hong ◽  
Ying-Yi Huang
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Research Area ◽  
Reconstruction Error ◽  
Remote Sensing Images ◽  
Discriminative Ability ◽  
Structure Information ◽  
Label Information ◽  
Sparsity Preserving Projection ◽  
Hyperspectral Remote Sensing Images

Dimensionality reduction is an important research area for hyperspectral remote sensing images due to the redundancy of spectral information. Sparsity preserving projection (SPP) is a dimensionality reduction (DR) algorithm based on the l1-graph, which establishes the relations of samples by sparse representation. However, SPP is an unsupervised algorithm that ignores the label information of samples and the objective function of SPP; instead, it only considers the reconstruction error, which means that the classification effect is constrained. In order to solve this problem, this paper proposes a dimensionality reduction algorithm called the supervised sparse embedded preserving projection (SSEPP) algorithm. SSEPP considers the manifold structure information of samples and makes full use of the label information available in order to enhance the discriminative ability of the projection subspace. While maintaining the sparse reconstruction error, the algorithm also minimizes the error between samples of the same class. Experiments were performed on an Indian Pines hyperspectral dataset and HJ1A-HSI remote sensing images from the Zhangjiang estuary in Southeastern China, respectively. The results show that the proposed method effectively improves its classification accuracy.

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