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 Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

2021 ◽  
Vol 13 (22) ◽  
pp. 4671
Author(s):  
Bing Lu ◽  
Yuhong He
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Chlorophyll Content ◽  
Spatial Resolution ◽  
Ecosystem Management ◽  
Remote Sensing Images ◽  
Species Classification ◽  
Growth Status ◽  
Species Specific ◽  
Estimation Models

Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.

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 Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

2020 ◽  
Vol 9 (10) ◽  
pp. 571
Author(s):  
Jinglun Li ◽  
Jiapeng Xiu ◽  
Zhengqiu Yang ◽  
Chen Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Augmentation Strategies ◽  
Rich Information ◽  
The Mean ◽  
The Rich

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.

scholarly journals Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

2020 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Liguo Weng ◽  
Yiming Xu ◽  
Min Xia ◽  
Yonghong Zhang ◽  
Jia Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Spatial Information ◽  
Global Climate ◽  
Semantic Segmentation ◽  
Water Area ◽  
Remote Sensing Images ◽  
The One ◽  
High Level ◽  
Almost All

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

scholarly journals Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement

2020 ◽  
Vol 37 (6) ◽  
pp. 1037-1043
Author(s):  
Jie Zhang ◽  
Minquan Feng ◽  
Yu Wang
Keyword(s):  
Remote Sensing ◽  
Automatic Segmentation ◽  
Water Bodies ◽  
Target Area ◽  
Remote Sensing Images ◽  
Segmentation Accuracy ◽  
Enhancement Method ◽  
The Mean ◽  
Adaptive Morphology ◽  
Gray Values

By virtue of high-resolution remote sensing satellites, there is a possibility to analyze remote sensing images on water bodies through digital image processing (DIP). In many remote sensing images, however, the water bodies have similar gray values as other ground objects. To effectively distinguish water bodies from other ground objects in these images, this paper proposes a logarithmic enhancement method for remote sensing images on water bodies based on adaptive morphology. The proposed method can filter the noise of non-target area, and enhance the water body in the original image. On this basis, a morphology-based segmentation method was designed for remote sensing images on water bodies. Experimental results show that our method achieved a high segmentation accuracy, controlling the mean segmentation error at below 1.32%.

scholarly journals VaryBlock: A Novel Approach for Object Detection in Remote Sensed Images

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5284 ◽  
Author(s):  
Heng Zhang ◽  
Jiayu Wu ◽  
Yanli Liu ◽  
Jia Yu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Poor Performance ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Novel Approach ◽  
Challenging Tasks ◽  
The Mean ◽  
General Object

In recent years, the research on optical remote sensing images has received greater and greater attention. Object detection, as one of the most challenging tasks in the area of remote sensing, has been remarkably promoted by convolutional neural network (CNN)-based methods like You Only Look Once (YOLO) and Faster R-CNN. However, due to the complexity of backgrounds and the distinctive object distribution, directly applying these general object detection methods to the remote sensing object detection usually renders poor performance. To tackle this problem, a highly efficient and robust framework based on YOLO is proposed. We devise and integrate VaryBlock to the architecture which effectively offsets some of the information loss caused by downsampling. In addition, some techniques are utilized to facilitate the performance and to avoid overfitting. Experimental results show that our proposed method can enormously improve the mean average precision by a large margin on the NWPU VHR-10 dataset.

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 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 Narrow band spectral indexes for chlorophyll determination in soybean canopies [Glycine max (L.) Merril]

2004 ◽  
Vol 16 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Clotilde Pinheiro Ferri ◽  
Antonio Roberto Formaggio ◽  
Marlene Aparecida Schiavinato
Keyword(s):  
Remote Sensing ◽  
Glycine Max ◽  
Chlorophyll Content ◽  
Narrow Band ◽  
Photosynthetic Pigment ◽  
Wide Band ◽  
Accurate Estimation ◽  
Reliable Estimate ◽  
Species Discrimination ◽  
Glycine Max L

Photosynthetic pigments are essential for plant development. Quantifying these pigments in great extensions of agricultural crops is an important objective in remote sensing for agricultural purposes. This information can be used to produce a more accurate estimation of the physiological state of the vegetation, for species discrimination and productivity estimation. The aim of the present study was to (a) evaluate the potential for estimating chlorophyll content of crop canopies, using narrow band spectral indexes, and (b) in this respect compare the performances of NDVI (a multispectral wide band index) and two narrow band vegetation indexes (R750/700 and R750/550). Experiments were carried out under greenhouse conditions whereby soybean [Glycine max (L.), Merril] was monitored with a high-resolution spectroradiometer (10 nm at 365-1,126 nm range) during the phenological cycle of the crop. Chlorophyll (a, b and total) contents were determined weekly in the laboratory. A statistical correlation analysis was performed between narrow band spectral indexes against chlorophyll content and r² coefficients near 0.84 were obtained. For NDVI r² was around 0.51. These analyses showed that R750/700 and R750/550 ratios are very useful indexes for chlorophyll determination and very effective compared with NDVI (one of the wide band indexes widely used). Thus, it can be stated that hyperspectral remote sensing has great potential for providing a reliable estimate of photosynthetic pigment content at the canopy level through evaluated indexes and other such indexes that might arise. Thus, further studies should be carried out for evaluating other indexes at the canopy level, both in the laboratory and under field conditions, using spectroradiometers and hyperspectral images, aimed at providing information for agricultural purposes.

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