Post Classification Comparison Change Detection of GuangZhou Metropolis, China

2011 ◽  
Vol 467-469 ◽  
pp. 19-22 ◽  
Author(s):  
Xiao Feng Yang ◽  
Xing Ping Wen
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Southern China ◽  
Remote Sensing Data ◽  
Comparison Method ◽  
Global Scale ◽  
Likelihood Method ◽  
Unused Land ◽  
Remote Sensing Techniques ◽  
The One

Change detection is one of the most important applications of remote sensing techniques due to its capability of repetitive acquisition imageries with consistent image quality, at short intervals, on a global scale, and during complete seasonal cycles. This paper uses two Landsat ETM+ imageries acquired in 2000 and 2002 respectively to detect change of Guangzhou in southern China during two years using post classification comparison method. Firstly, two remote sensing data are precision geometrically corrected to UTM projection with a root mean square error (RMSE) of 0.3 pixels, and then they are classfied using Maximum Likelihood method respectively. Images are classified into four classes which are water, forest, grass or crop and building，soil or unused land. Sencondly, two classified images are calculated by band geometric algorithm pixel by pixel using programming. The class value of pixel in different year is the same, and then the processed pixel is zero, whereas the processed pixel is assigned to a certain value which represents change from the one land cover type to another during two years. Finally, statistic analyses of change information during two years are computed and the post classification comparison change detection image is outputted. It concludes that the largest change areas are exchanges of building, soil or unused land with grass land, and land covers in Baiyun district are changed mostly from 2000 to 2002.

scholarly journals Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H<sub>2</sub>O,<i>δ</i>D} pairs – a review

2016 ◽  
Vol 9 (7) ◽  
pp. 2845-2875 ◽  
Author(s):  
Matthias Schneider ◽  
Andreas Wiegele ◽  
Sabine Barthlott ◽  
Yenny González ◽  
Emanuel Christner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Measurement Techniques ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Horizontal Resolution ◽  
Atmospheric Composition ◽  
Remote Sensing Techniques ◽  
Middle Infrared

Abstract. In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products.

scholarly journals RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

2019 ◽  
Vol 50 (3) ◽  
Author(s):  
R. K. Abdullatiff
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Spectral Reflectivity ◽  
Tigris River ◽  
The Difference ◽  
Remote Sensing Techniques ◽  
The One ◽  
Relationship Of

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

scholarly journals CORAL REEF HABITAT CHANGING ASSESSMENT OF DERAWAN ISLANDS, EAST KALIMANTAN, USING REMOTE SENSING DATA

2010 ◽  
Vol 2 (-) ◽  
Author(s):  
MARLINA NURLIDIASARI ◽  
SYARIF BUDIMAN
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Coral Reefs ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Landsat Tm ◽  
East Kalimantan ◽  
Marine Diversity ◽  
Multi Temporal ◽  
Remote Sensing Techniques

Coral reefs in Dcrawan Islands are astonishingly rich in the marine diversity. However, these reefs are threatened by humans. Destructive fishing methods, such as trawl, blasting and cyanide fishing practise, are found to be the main cause of this degradation. The coral reefs habitat reduction is also caused by tourism activities due to trampling over the reef and charging organic and anorganic wastes. The capabilities of satellite remote sensing techniques combined with field data collection have been assessed for the coral reef mapping and the change detection of Derawan Island. Multi-temporal Landsat TM and ETM images (1991 and 2002) have been used. Comparison of the classified images of 1991 and 2002 shows spatial changes of the habitat. The changes were in accordance with the known changes in the reef conditions. The analysis shows the decrease of the coral reef and patchy seagrass percentage, while the increase of the algae composite and patchy reef percentage. Keywords : Coral Reef, Change Detection, Landsat-TM, Derawan

scholarly journals Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

2008 ◽  
Vol 46 (6) ◽  
pp. 1822-1835 ◽  
Author(s):  
G. Camps-Valls ◽  
L. Gomez-Chova ◽  
J. Munoz-Mari ◽  
J.L. Rojo-Alvarez ◽  
M. Martinez-Ramon
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Sensing Data

scholarly journals A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 999-1006
Author(s):  
Xiaodan Shi ◽  
Guorui Ma ◽  
Fenge Chen ◽  
Yanli Ma
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Comparison Method ◽  
Kernel Functions ◽  
Multidimensional Data ◽  
Remote Sensing Images ◽  
Model Free ◽  
Change Vector ◽  
Similarity Measuring ◽  
Better Than

This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi- temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9&amp;thinsp;%, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7&amp;thinsp;% and 95.9&amp;thinsp;% respectively.

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

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