scholarly journals Analysis of Spatial and Temporal Distribution Characteristics of Land Desertification Based on GIS and Remote Sensing Images

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Li Cui
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Source Region ◽  
Temporal Distribution ◽  
Spatial And Temporal Distribution ◽  
Distribution Characteristics ◽  
Remote Sensing Images ◽  
Gis And Remote Sensing ◽  
Average Value ◽  
Land Desertification

Due to the complex geographical situation in China, in this paper, the area of land desertification is 98.5% of the total land desertification area in China. Based on the measured data of GIS and remote sensing images, we will discuss the spatial and temporal distribution characteristics of land desertification in China by calculating standardized precipitation evapotranspiration index (SPEI) and normalized vegetation index and establishing the CA model. The results show the following. (1) The trend of desertification in China has decreased as a whole, and the percentage of nondesertification has increased from 36.91% in 1991 to 44.46% in 2020, an increase of 7.55%. Extremely severe desertification increased from 21.72% to 24.25%, an increase of 2.53%. (2) The drought situation in the study area gradually improved, and the change trend of SPEI decreased by 74%. (3) In recent ten years (2011–2020), the vegetation grew well gradually, and it was in the best state in 2018. The NDVI index value increased by 5.9% compared with the average value in this decade. (4) The model designed by us works very well, and the results of simulating and testing the Three Rivers Source region are very little different from the actual situation, which meets our research requirements.

scholarly journals Spatio-temporal distribution and classification of utilization of urban bare lots in low-slope hilly regions

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246746
Author(s):  
Qi Cao ◽  
Manjiang Shi
Keyword(s):  
Remote Sensing ◽  
Temporal Distribution ◽  
Urban Landscapes ◽  
Distribution Characteristics ◽  
Remote Sensing Images ◽  
Landscape Index ◽  
Activity Intensity ◽  
Transportation Service ◽  
Spatio Temporal

Urban bare lots are persistent phenomena in urban landscapes in the course of urbanization. In the present study, we examined the spatio-temporal distribution of urban bare lots in low-slope hilly areas, and to assess the major pathways by which they are generated and later re-transformed for exploitation. We extracted land use and land cover (LULC) change information and analyzed spatio-temporal distribution characteristics of urban bare lots using Landsat TM/OLI series remote sensing images. Subsequently, we proposed an index system for their evaluation and classification, and identified five types of urban bare lots. Urban bare lot quantity and distribution are closely correlated with human activity intensity. Stakeholders should consider the multiple effects of location, topography, landscape index, transportation, service facilities, and urban planning in urban bare lot classification activities for renovation and re-transformation.

Spatial and temporal distribution characteristics of synthetic musk in Suzhou Creek

2010 ◽  
Vol 14 (4) ◽  
pp. 306-311 ◽  
Author(s):  
Feng-ling Wang ◽  
Ying Zhou ◽  
Ya-wen Guo ◽  
Li-yin Zou ◽  
Xiao-lan Zhang ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Spatial And Temporal Distribution ◽  
Distribution Characteristics ◽  
Synthetic Musk ◽  
Suzhou Creek

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

A case study on the detection of illegal dumps with GIS and remote sensing images

2018 ◽  
Author(s):  
Mariano Focareta ◽  
Giovanni Piacquadio ◽  
Giuseppe Meoli ◽  
Cesario Vincenzo Angelino ◽  
Luca Cicala ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Gis And Remote Sensing
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