Monitoring dynamic change of land cover based on SVM and satellite images in Hanoi, Vietnam

Abstract

An integral method, combining support vector machine (SVM) with remote-sensing analysis techniques, was explored to monitor Hanoi's dynamic change of land cover. The landsat thematic mapper (TM) image in 1993, the enhanced thematic mapper plus (ETM + ) image in 2000, and the image with the charge-coupled device camera (CCD) on the China-Brazil earth resources satellite (CBERS) in 2008 were used. Six land-cover types, including built-up areas, woodland, cropland, sand, water body and unused land, were identified. The detected results showed visually the rapid urban expansion as well as land-cover change of Hanoi from 1993 to 2008. There were 12 637.54 hm 2 cropland decreased between 1993 and 2000, and 8 227.6 hm 2 cropland decreased between 2000 and 2008. Compared with cropland, woodland firstly decreased and then increased, and the other types did not change significantly. The results indicate that CBERS dataset has the application potential in world resources researches.To a considerable degree, land-cover dynamic researches have been playing an important role for analyzing human activities [1] . As an advanced earth-observation means, remote sensing has a couple of advantages. In different scales, it can be used to establish land-cover datasets [2] . For land-cover change researches, changes are usually monitored by comparing multiple images of the same ground area acquired at different dates. As a result, dozens of pattern recognition methods were developed. In addition to commonly used supervised and unsupervised classification techniques, attention has been focused on machine-learning algorithms, such as artificial neural networks (ANNs), since they use all spectral channels and provide complete information about land-cover changes [3] . Recent researches have indicated that support vector machine (SVM)-based approaches have the considerable potential for the supervised classification of remotely sensed data. Comparative studies have shown that SVM-based change detection can be more accurate than popular contemporary techniques such as neural networks, decision trees as well as conventional probabilistic classifiers [4,5] .Since early 1990s, Hanoi's land cover has changed dramatically. A large area of agricultural land was converted to be used for other purposes. Consequently, it is quite necessary to monitor dynamic change of land cover for supporting the decision-makers to build an appropriate land-use plan. Remote sensing was firstly introduced into Vietnam in the 1980s; it has been applied in many fields since then. In these applications, due to the multi-temporal

A Remote Sensing Image Quality Interpretation Scale Characterization Method Based on the TTP Criterion

“…It can be seen that the prediction results of NORSIQE can show some validity for different tasks of interpreting remote sensing image information. Meanwhile, the accuracy ratio of the model proposed in this paper, along with the MTF-Nyquist model, MTF-50 model, and MTF-Area model proposed in reference [31], was statistically analyzed according to the ∆ NIIRS interval mentioned above, and the results are shown in Figure 8. From the figure, it can be seen that the MTF-50 model proposed in reference [31] has the best prediction accuracy of 50% for situations with ∆ NIIRS ≤ 0.1.…”

“…Meanwhile, the accuracy ratio of the model proposed in this paper, along with the MTF-Nyquist model, MTF-50 model, and MTF-Area model proposed in reference [31], was statistically analyzed according to the ∆ NIIRS interval mentioned above, and the results are shown in Figure 8. From the figure, it can be seen that the MTF-50 model proposed in reference [31] has the best prediction accuracy of 50% for situations with ∆ NIIRS ≤ 0.1. However, in comparison, the prediction accuracy of the NORSIQE model proposed in this paper exceeds this by 14%, which has certain advantages.…”

“…Lin L et al [30] establish a regression model of manual interpretation results and model prediction results, considering that the ground spatial resolution of the ZY-3 satellite is not within the applicable range of parameters of the GIQE model. Pengfei Zhao [31] improves the GIQE model by using three different Modulation Transfer Function (MTF) performance parameters.…”

Spatial and temporal variations of vegetation water use efficiency and its response to climate change and human activities in the West Liao River Plain, China

“…As this region is located in the monsoon fringe area, the transition zone between arid, semi-arid, and semi-humid regions, and at the edge of the Horqin Sandy Land, plant growth is particularly sensitive to climate change, environmental transition, and human activities. Currently, studies on the vegetation ecology and related climatic factors in this region focus mostly on the vegetation community and coverage, NPP, crop yields, biomass, and NEP (Huang et al, 2013;Feng et al, 2014;Gao et al, 2017;Zhao, 2017;Yan et al, 2018;Aruna, 2020;Gao W. D. et al, 2022;Zhu et al, 2022). Few studies have examined WUE and its response to meteorological factors and human activities.…”