Change vector analysis in relation to terrain parameters using temporal remote sensing satellite data

2021 ◽  
pp. 257-270
Author(s):  
Trilochana Basnett ◽  
Maya Kumari ◽  
Richa Sharma ◽  
Suresh Kumar
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Vector Analysis ◽  
Change Vector Analysis ◽  
Terrain Parameters ◽  
Change Vector ◽  
Remote Sensing Satellite

scholarly journals Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia

2021 ◽  
Vol 8 (3) ◽  
pp. 2731-2741
Author(s):  
Gatot Nugroho ◽  
Galdita Aruba Chulafak ◽  
Fajar Yulianto
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Remote Sensing Data ◽  
Vector Analysis ◽  
Tropical Savanna ◽  
Change Vector Analysis ◽  
Change Vector ◽  
Basin Area

In environmental management, land cover change is a crucial aspect. The area of tropical savanna environments is vulnerable to land degradation. This study aimed to rapidly detect land cover changes in a tropical savanna environment based on remote sensing data. Conditional change detection was performed using the Change Vector Analysis (CVA) with input parameters such as the Enhanced Vegetation Index (EVI) and Normalized Difference Soil Index (NDSI). The results showed that during the period 2015 to 2019, changes were detected in the Moyo watershed every year. From 2015 to 2016, the Moyo River Basin was dominated by changes with a change magnitude of less than 0.088, which was 63% of the Moyo River Basin area. From 2016 to 2017, the changes were dominated by the change magnitude value of 0.063, which was 58.6% of the Moyo River Basin area. From 2017 to 2018, changes were dominated by the change magnitude value of 0.084 of 55.26% of the Moyo watershed area. From 2018 to 2019, the change was dominated by the change magnitude value of 0.057, which was 47.57% of the Moyo watershed area. The direction of land cover change was dominated by Q2 in 2016, Q4 in 2017 and 2018, and Q2 and Q4 in 2019. These changes generally occurred in the Moyo watershed middle and downstream parts, which are grasslands. The use of the Conditional Change Vector Analysis (CCVA) approach in a tropical savanna environment can detect changes and the direction of change with an accuracy of about 70%.

scholarly journals MIDDLE RESOLUTION REMOTE SENSING IMAGE CHANGE DETECTION BASED ON VECTOR ANALYSIS OF MIDLINE CHANGE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 775-782
Author(s):  
Q. Ye ◽  
X. Zhang ◽  
X. Jiang ◽  
Q. Huang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Change Detection ◽  
Direction Cosine ◽  
Remote Sensing Image ◽  
Comparison Method ◽  
Vector Analysis ◽  
Threshold Method ◽  
Change Vector Analysis ◽  
Change Vector

Abstract. The extraction and timely updating of land use /cover information is a key issue in remote sensing change detection. The change vector analysis (CVA) is a better method of change detection. However, the CVA method is the blindness of artificial choice of threshold. Moreover, the direction cosine of CVA cannot represent the unique point in change vector space and it can’t distinguish the change category effectively. In order to avoid this defect, the midline vector is added to CVA method. In this paper, we use the midline change vector analysis (MCVA) method to detect the land use /cover change in multi temporal remote sensing images. We proposed the two-step threshold method to get the optimal threshold and determine the change and the unchanged region of the difference remote sensing image. We chose Hefei city of Anhui Province as the study area, and adopted two Landsat5 TM images in 2000 and 2008 year as experiment data. We use the MCVA and two-step threshold method to achieve remote sensing change detection. In order to compare the detection accuracy between MCVA method and the traditional post classification comparison method, the paper choose the same area (178 pixels × 180 pixels) in the two images to analyse the accuracy, and compare the accuracy of MCVA method with that of the traditional post classification comparison method based on SVM. The experiment results show that the MCVA method has higher overall accuracy, lower allocation disagreement and quantity disagreement. What’s more, the overall accuracy of MCVA method can reach nearly 60%, much higher than the traditional post classification comparison method (less than 40%). And the MCVA method can effectively avoid the problem of change vector direction cosine values are not unique, and the result is much better than the traditional post classification (SVM) comparison method. It indicates that MCVA is a more effective method in land use / cover change detection for middle resolution multispectral images.

scholarly journals Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis

2019 ◽  
Vol 11 (20) ◽  
pp. 2345 ◽  
Author(s):  
Hanqiu Xu ◽  
Yifan Wang ◽  
Huade Guan ◽  
Tingting Shi ◽  
Xisheng Hu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Change Detection ◽  
Ecological Status ◽  
Vector Analysis ◽  
Ecological Index ◽  
Ecological Conditions ◽  
Change Vector Analysis ◽  
Ecological Changes ◽  
Change Vector

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

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