Analysis of Land Use Change in Binhai New Area Based on Remote Sensing Images

2014 ◽  
Vol 933 ◽  
pp. 1014-1018
Author(s):  
Chun Zhe Xia ◽  
Xiao Shen Zheng ◽  
Meng Yin Zhao
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Economic Development ◽  
Land Use Change ◽  
Land Reclamation ◽  
Ecological Environment ◽  
Remote Sensing Images ◽  
Construction Sites ◽  
Salt Works ◽  
Unused Land

Based on the TM remote sensing images in autumn 1992, 2001 and 2009, the land use change of Binhai New Area is analyzed through the ENVI software. During remote sensing images processed, Binhai New Area is collected according to the administrative zoning maps. The results of land use change are vegetation cover and water changing little, which show the ecological environment remained stable in overall Binhai New Area. At that time, the area of unused land and salt works area is reduced, and the area of land reclamation and construction sites is increased, which indicates the rapid economic development of Binhai new area in past 20 years.

Land Use Change Detection Based on GF-1 Satellite Remote Sensing Images

2020 ◽  
Vol 57 (16) ◽  
pp. 162802
Author(s):  
付青 Fu Qing ◽  
郭晨 Guo Chen ◽  
罗文浪 Luo Wenlang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Change Detection ◽  
Satellite Remote Sensing ◽  
Remote Sensing Images

scholarly journals A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (12) ◽  
pp. 1933 ◽  
Author(s):  
Mingchang Wang ◽  
Haiming Zhang ◽  
Weiwei Sun ◽  
Sheng Li ◽  
Fengyan Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
High Resolution ◽  
Land Use Change ◽  
Change Detection ◽  
Detection Method ◽  
Remote Sensing Images ◽  
High Quality ◽  
Coarse To Fine

In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovatively considered the adaptation relationship between the convolutional neural network (CNN) and the scene complexity by selecting the CNN that best fit the scene complexity. The CNN trained by NS-55 was used to detect the category of the scene, define the final category of the scene according to the majority voting method, and obtain the changed scene by comparison to obtain the so-called coarse change result. Then, we created a multi-scale threshold (MST) method, which is a new method for obtaining high-quality training samples. We used the high-quality samples selected by MST to train the deep belief network to obtain the pixel-level range change detection results. By mapping coarse scene changes to range changes, we could obtain fine multi-type land-use change detection results. Experiments were conducted on the Multi-temporal Scene Wuhan dataset and aerial images of a particular area of Dapeng New District, Shenzhen, where promising results were achieved by the proposed method. This demonstrates that the proposed method is practical, easy-to-implement, and the NS-55 dataset is physically justified. The proposed method has the potential to be applied in the large scale land use fine change detection problem and qualitative and quantitative research on land use/cover change based on HR remote sensing data.

scholarly journals ANALYSIS OF LAND USE CHANGE IN INNER MONGOLIA REGION FROM 1978 TO 2018 BASED ON REMOTE SENSING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 745-749
Author(s):  
W. Qu ◽  
Y. Yao ◽  
Z. Pang ◽  
J. Lu ◽  
K. Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Inner Mongolia ◽  
Arable Land ◽  
Human Interaction ◽  
Eastern Region ◽  
Forest Land ◽  
Unused Land ◽  
The Impact

Abstract. Land use change is an important theme of the research on the impact of human interaction on global change. In this paper, two phases of land use data were interpretated from remote sensing images of 1978 and 2018, and the spatial-temporal characteristics of land use change in China's Inner Mongolia Region from 1978 to 2018 were analyzed. The results indicated that grasslands and arable land are mainly distributed in the central and eastern region of Inner Mongolia, forest land are mainly distributed in the eastern region, and unused land is mainly distributed in the western region. From 1978 to 2018, the area of arable land in Inner Mongolia decreased by 9,000 km2, forest land increased by 900 km2, and the area of grassland decreased by 1,400 km2. Urban and rural, industrial mines, and residential land continued to increase with an area of 7,800 km2; and unused land increased by an area of 11,500 km2. It was indicated that after 40 years of development, land use in urban and rural areas, industrial mines, and residential areas caused by human activities in the Inner Mongolia Region has increased significantly. At the same time, the policy of returning farmland to forests to protect the environment has achieved significant results.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

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