scholarly journals Fine scale optical remote sensing experiment of mixed stand over complex terrain (FOREST) in the Genhe Reserve Area: objective, observation and a case study

2021 ◽  
Vol 14 (10) ◽  
pp. 1411-1432 ◽  
Author(s):  
Biao Cao ◽  
Jianbo Qi ◽  
Erxue Chen ◽  
Qing Xiao ◽  
Qinhuo Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Complex Terrain ◽  
Mixed Stand ◽  
Optical Remote Sensing ◽  
Fine Scale ◽  
Reserve Area

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals A PAN-SHARPENING METHOD BASED ON GUIDED IMAGE FILTERING: A CASE STUDY OVER GF-2 IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1055-1060
Author(s):  
Y. Zheng ◽  
M. Guo ◽  
Q. Dai ◽  
L. Wang
Keyword(s):  
Remote Sensing ◽  
Quantitative Assessment ◽  
Spatial Information ◽  
State Of The Art ◽  
Image Filtering ◽  
Optical Remote Sensing ◽  
Low Resolution ◽  
Spectral Fidelity ◽  
Guided Image Filtering

The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art methods. Firstly, a simulated low-resolution panchromatic band is yielded; thereafter, the resampled multispectral image is taken as the guidance image to filter the simulated low resolution panchromatic Pan image, and extracting the spatial information from the original Pan image; finally, the pan-sharpened result is synthesized by injecting the spatial details into each band of the resampled MS image according to proper weights. Three groups of GF-2 images acquired from water body, urban and cropland areas have been selected for assessments. Four evaluation metrics are employed for quantitative assessment. The experimental results show that, for GF-2 imagery acquired over different scenes, the proposed method can not only achieve high spectral fidelity, but also enhance the spatial details

scholarly journals OBJECT BASED “DAYAS” CLASSIFICATION USING SENTINEL A-2 SATELLITE IMAGERY CASE STUDY CITY OF BENSLIMANE

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 33-39
Author(s):  
M. Benchelha ◽  
F. Benzha ◽  
H. Rhinane
Keyword(s):  
Remote Sensing ◽  
Object Oriented ◽  
Optical Remote Sensing ◽  
Kappa Index ◽  
Object Based ◽  
The Subject ◽  
Object Oriented Approach ◽  
Radiometric Characteristics ◽  
Sentinel 2

<p><strong>Abstract.</strong> The management of “DAYAS” is a major issue in the preservation and maintain of biodiversity and environmental balance, especially in a context where this fragile ecosystems face many degradation factors. The extraction of Dayas is a key component in the management process of this type of wetlands, and has been the subject of many researches related to remote sensing. The methods and instrumentation for optical remote sensing are used to improve the mapping of Dayas, based on the radiometric characteristics of local hydrosystems. The present paper studies the inputs of different methods for the delimitation and extraction of Dayas in the realm of Benslimane city, using Sentinel A-2 imagery for the mapping. The methodology for the application of the pixel-based and the object-oriented approaches requires many steps, starting from an image pre-processing with Sentinel-2 calibration, the calculation of NDWI index, to proceed to the extraction of Dayas from the very high resolution image segmentation, then the application of the object-oriented classification to validate the results. The cartographic results demonstrate the input of the applied methodology in the Dayas extraction in different situations and timing (winter/summer), and allow to measure the cartographic accuracy for each approach, finding 65% of accuracy for the pixel-based approach with Kappa index&amp;thinsp;=&amp;thinsp;0.40 versus 75% of accuracy for the object-oriented approach with Kappa index&amp;thinsp;=&amp;thinsp;0.72. The results achieved inform and orient about optimisation measures and regulations of the Kappa index to improve the Dayas extraction and mapping.</p>

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