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

Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification

1993 ◽  
Vol 44 (2) ◽  
pp. 235 ◽  
Author(s):  
RM Johnston ◽  
MM Barson
Keyword(s):  
Remote Sensing ◽  
Water Regime ◽  
Landsat Tm ◽  
Classification Methods ◽  
Vegetation Types ◽  
Infrared Band ◽  
South Wales ◽  
Multi Temporal ◽  
Density Slicing ◽  
Remote Sensing Techniques

This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales.

scholarly journals A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data

2014 ◽  
Vol XL-7 ◽  
pp. 15-18 ◽  
Author(s):  
U. H. Atasever ◽  
P. Civicioglu ◽  
E. Besdok ◽  
C. Ozkan
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Backtracking Search ◽  
Search Optimization ◽  
Detection Approach ◽  
Multi Temporal ◽  
Backtracking Search Optimization Algorithm

Change detection is one of the most important subjects of remote sensing discipline. In this paper, a new unsupervised change detection approach is proposed for multi-temporal remotely sensed optic imagery. This approach does not require any prior information about changed and unchanged pixels. The approach is based on Discrete Wavelet Transform (DWT) based image fusion and Backtracking Search Optimization Algorithm (BSA). In the first step of the approach, absolute-valued difference image and absolute-valued log-ratio image is calculated from co-registered and radiometrically corrected multi-temporal images. Then, these difference images are fused using DWT. The fused image is filtered by median filter for edge information preservation and by wiener filter for image smoothing. Then, a min-max normalization is applied to the filtered data. The normalized data is clustered into two groups with BSA as changed and unchanged pixels by minimizing an objective function, unlike classical methods using CVA, PCA, FCM or K-means techniques. To show effectiveness of proposed approach, two remote sensing data sets, Sardinia and Mexico, are used. False Alarm, Missed Alarm, Total Alarm and Total Error Rate are selected as performance criteria to evaluate the effectiveness of new approach using ground truth images. Experimental results show that proposed approach is effective for unsupervised change detection of optical remote sensing data.

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 FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 243-249
Author(s):  
P. Ebel ◽  
S. Saha ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Optical Observations ◽  
Detection Methods ◽  
Data Set ◽  
Modal Data ◽  
Multi Temporal ◽  
Modal Change

Abstract. With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible. Different sensors often provide complementary information and thus a more detailed and accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous data, combining multi-sensor multi-temporal data with different characteristics and resolution may provide a more robust interpretation of spatio-temporal evolution. However, integration of multi-temporal information from disparate sensory sources is challenging. Moreover, research in this direction is often hindered by a lack of available multi-modal data sets. To resolve these current shortcomings we curate a novel data set for multi-modal change detection. We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data. An experimental validation on the aforementioned data set demonstrates the potentials of the proposed model, which outperforms common mono-modal methods compared against.

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