The Primary Productivity of Algae Plant of Ebinur Lake - Based on CBERS-2 Image Remote Sensing Research

2012 ◽  
Vol 433-440 ◽  
pp. 5396-5401
Author(s):  
Min Hou ◽  
Shu Jiang Chen ◽  
Tie Cheng Huang ◽  
Jun Feng Gao ◽  
Shuang Tang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Satellite Image ◽  
Spectral Information ◽  
Green Band ◽  
Mathematical Relationship ◽  
Ebinur Lake ◽  
Relationship Model ◽  
Satellite Image Processing ◽  
Total Primary Productivity

This paper use the technology to process satellite image, combined with sampling and analysis, to establish the mathematical relationship model between the algal primary productivity of Ebinur Lake with the spectral information of satellite imagery, inversion of algae plant primary productivity, then get the conclusion: Based on satellite image processing technology to study the algae plant primary productivity, measured during the growth of algae plant in the total primary productivity of 99.5 t; CBERS-2 was been found that is fit for measure the primary productivity of algae plant Ebinur Lake, the characteristic of the Green band is the accuracy band; the optimal inversion model is B = 3.945-0.033G-0.005 (GR) +0.128 (G / B).

scholarly journals THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 11-15
Author(s):  
A. H. Ahrari ◽  
M. Kiavarz ◽  
M. Hasanlou ◽  
M. Marofi
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Fusion ◽  
Satellite Image ◽  
Thermal Infrared ◽  
Qualitative Assessment ◽  
Spectral Information ◽  
Visible Image ◽  
Qualitative Approaches ◽  
Satellite Image Processing

Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

scholarly journals Optical Remote Sensing

2021 ◽  
pp. 315-344
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Urban Areas ◽  
Satellite Image ◽  
Background Information ◽  
Optical Remote Sensing ◽  
Basic Operation ◽  
City Development ◽  
Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

scholarly journals Classification of Landsat-8 Imagery Based On Pca And Ndvi Methods

2019 ◽  
Vol 8 (10) ◽  
pp. 4321-4325
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Region Of Interest ◽  
The Other ◽  
Spectral Information ◽  
Landsat 8 ◽  
Statistical Parameters ◽  
Sensing Applications

Remote sensing is an important issue in satellite image classification. In developing a significant sustainable system in agriculture farming, the major concern for remote sensing applications is the crop classification mechanism. The other important application in remote sensing is urban classification which gives the information about houses, roads, buildings, vegetation etc. A superior indicator for the presence of vegetation can be computed from the vegetation indices of a satellite image. This indicator supports in describing the health of vegetation through the image attributes like greenness and density. The other parameter in detecting objects or region of interest is an image is the texture. A satellite image contains spectral information and can be represented by more spectral bands and classification is very tough task. Generally, Classification of individual pixels in satellite images is based on the spectral information. In this research paper Principle component analysis and combination of PCA and NDVI classification methods are applied on Landsat-8 images. These images are acquired from USGS. The performance of these methods is compared in statistical parameters such as Kappa coefficient, overall accuracy, user’s accuracy, precision accuracy and F1 accuracy. In this work existing method is PCA and proposed method is PCA+NDVI. Experimental results shows that the proposed method has better statistical values compared to existing method.

Hydrogeological characterization and water use of a sector of sands, sandstones and gravel on the coast of Baixo Alentejo

2021 ◽  
Author(s):  
Thyago Anthony Soares Lima ◽  
José Paulo Patrício Geraldes Monteiro ◽  
Luis Ricardo Dias da Costa
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Ground Level ◽  
Balance Model ◽  
Aquifer System ◽  
Consumptive Use ◽  
Aquifer Systems ◽  
Satellite Image Processing ◽  
Agriculture Irrigation

<p>This reasearch discusses the necessary tasks to carry out the hydrogeological characterization of the sands, sandstones, and gravels of the Baixo Alentejo coast. Currently, this characterization has done in detail only in the areas where these formations constitute hydro-stratigraphic units of the aquifer systems of Sines and the Alvalade Basin. In addition to system hydrogeological characterization of the system, the volume of water used for irrigation in the study area was estimated, with the aim of characterizing its inter-annual evolution between 2000 and 2018 and intra-annual for the year 2018. To do so, remote sensing and satellite image processing methods were used (LANDSAT 5 and 8 and MODIS). A synthesis of the hydrogeological characterization is presented in an area of 195.8 km<sup>2</sup>, divided into two aquifer sectors, one located north of the Mira River with 94.12 km<sup>2</sup> and the other south with 101.75 km<sup>2</sup>. The first stage of the work consisted of the analysis of the studied aquifers recharge based on precipitation and the analysis of piezometry data in order to define the conceptual model of hydraulic functioning of the system. The available data were obtained from fieldwork and from the LIFE-Charcos Project (LIFE12NAT / PT / 997). In parallel, an analysis of land use and occupation performed, with emphasis on the identification of irrigation areas. Finally, the volume of water used in agriculture irrigation was determined using the method of estimating the consumptive use of water in irrigation at a local scale, based on the determination of evapotranspiration values, using the algorithm SEBAL, precipitation, and  irrigation efficiency. The results obtained were validated, with high precision, through the comparison with the irrigation volumes known during 2018, and the calibration of the monthly sequential water balance model at ground level.</p><p>Key words: aquifer system of sands, sandstones and gravels of the Baixo Alentejo coast; hydrogeology; Irrigation; Remote Sensing.</p>

scholarly journals A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

2017 ◽  
Vol 9 (10) ◽  
pp. 1048 ◽  
Author(s):  
Dimitris Stratoulias ◽  
Valentyn Tolpekin ◽  
Rolf de By ◽  
Raul Zurita-Milla ◽  
Vasilios Retsios ◽  
...  
Keyword(s):  
Image Processing ◽  
Satellite Image ◽  
Spectral Information ◽  
Smallholder Agriculture ◽  
Object Based ◽  
Satellite Image Processing

scholarly journals Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN

2019 ◽  
Vol 8 (4) ◽  
pp. 191 ◽  
Author(s):  
Philipp Schuegraf ◽  
Ksenia Bittner
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Early Stage ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Training Data ◽  
Spectral Information ◽  
Surface Model

Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, which is presently achievable due to the availability of high-resolution remote sensing data sources, makes it possible to improve the quality of the extracted building outlines. Recently, deep neural networks were extended from image-level to pixel-level labelling, allowing to densely predict semantic labels. Based on these advances, we propose an end-to-end U-shaped neural network, which efficiently merges depth and spectral information within two parallel networks combined at the late stage for binary building mask generation. Moreover, as satellites usually provide high-resolution panchromatic images, but only low-resolution multi-spectral images, we tackle this issue by using a residual neural network block. It fuses those images with different spatial resolution at the early stage, before passing the fused information to the Unet stream, responsible for processing spectral information. In a parallel stream, a stereo digital surface model (DSM) is also processed by the Unet. Additionally, we demonstrate that our method generalizes for use in cities which are not included in the training data.

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